| » 2024 IEEE Fellows Elevation and Recognition |
Abstract:
Presents a listing of MMTS members who were elevated to the status of IEEE Fellow in 2024. |
| Autors: Ali Darwish;Robert H. Caverly; |
| Appeared in: IEEE Microwave Magazine |
| Publication date: May 2024, volume: 25, issue:6, pages: 94 - 127 |
| Publisher: IEEE |
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| » 3-D Inversion of Semi-Airborne Transient Electromagnetic Data Based on Decoupled Mesh |
Abstract:
Semi-airborne transient electromagnetic (SATEM) method is an effective exploration tool in complex environments due to its utilization of unmanned aerial platforms for data collection. With the escalating demands of detection accuracy, the conventional 1-D inversion is no longer sufficient. Over the last two decades, there has been rapid development in 3-D electromagnetic (EM) inversions. SATEM method usually has a substantial number of receiver stations. To obtain accurate 3-D solutions, one has to refine the grids near the receiver points, which causes a huge number of grids and reduces computational efficiency. Thus, computational complexity is one of the primary factors restricting the practicality of 3-D inversions. In this article, we have developed an approach using decoupled meshes. This method uses a series of meshes to parallelly calculate the forward modeling and Jacobian information, with one mesh containing only a subset of receiver points. This scheme aims to decrease the number of grids and improve the efficiency. After that, we map the results to an overarching inversion mesh using the node cloud technique and renew the model parameters. We check our algorithm via both synthetic and field data. Numerical experiments show that the decoupled mesh inversion method can effectively recover the location and resistivities of the anomalies under the complex topography. When compared with the conventional inversion method, the decoupled mesh inversion method can significantly reduce the calculation time. |
| Autors: Zhejian Hui;Xuben Wang;Changchun Yin;Yunhe Liu; |
| Appeared in: IEEE Transactions on Geoscience and Remote Sensing |
| Publication date: May 2024, volume: 62, issue:null, pages: 1 - 11 |
| Publisher: IEEE |
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|
| » 5 Questions for David Frankel: One Man's Quixotic Quest to Stop Robocalls |
Abstract:
At some point, our phone habits changed. It used to be that if the phone rang, you answered it. With the advent of caller ID, you'd pick up only if it was someone you recognized. And now, with spoofing and robocalls, it can seem like a gamble to pick up the phone, period. In 2023, robocall blocking service Youmail estimates there were more than 55 billion robocalls in the United States. How did robocalls proliferate so much that now they seem to be dominating phone networks? And can any of this be undone? IEEE Spectrum spoke with David Frankel of ZipDX, who's been fighting robocalls for over a decade, to find out. |
| Autors: Michael Koziol; |
| Appeared in: IEEE Spectrum |
| Publication date: May 2024, volume: 61, issue:5, pages: 21 - 21 |
| Publisher: IEEE |
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|
| » 50 & 25 Years Ago |
Abstract:
Summary form only: Summaries of articles presented in this issue of the publication. |
| Autors: Erich Neuhold; |
| Appeared in: Computer |
| Publication date: May 2024, volume: 57, issue:5, pages: 7 - 11 |
| Publisher: IEEE |
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|
| » A 3D Hybrid Optical-Electrical NoC Using Novel Mapping Strategy Based DCNN Dataflow Acceleration |
Abstract:
A large number of multiply-accumulate operations and memory accesses required in deep convolutional neural networks (DCNN) leads to high latency and energy consumption (EC), that hinder their further applications. Dataflow-based acceleration schemes reduce memory accesses by leveraging reusable data in DCNNs. Row Stationary (RS) dataflow is a more advanced dataflow. In the convolutional layer acceleration of RS dataflow, the flexibility of mapping from logical processing element (LPE) sets to physical PE sets is relatively poor. The utilization of processing elements (PEs) is low. In this article, a novel mapping strategy based on genetic algorithm (GAMS) with the goal of optimizing EC is proposed. GAMS is designed to address the energy inefficiencies faced when mapping RS dataflow. A 3D hybrid optical-electrical Network-on-Chip (3DHOENoC) is proposed to further improve the communication efficiency, energy efficiency and the processing speed of DCNN. Simulation and evaluation results show that GAMS can achieve better mapping flexibility, higher PEs utilization and 15.9% improvement of execution speed on average. In addition, the execution time (ET) performance of processing the DCNN can be further improved by adopting the 3DHOENoC architecture with better communication parallelism. |
| Autors: Bowen Zhang;Huaxi Gu;Grace Li Zhang;Yintang Yang;Ziteng Ma;Ulf Schlichtmann; |
| Appeared in: IEEE Transactions on Parallel and Distributed Systems |
| Publication date: May 2024, volume: 35, issue:7, pages: 1139 - 1154 |
| Publisher: IEEE |
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|
| » A 56-GBaud Linear Optical Receiver Hybrid Integrated with InGaAs PIN PDs |
Abstract:
This letter presents a 56-GBaud linear optical receiver (ORX) hybrid integrated with InGaAs PIN photodiodes (PDs) and fully-differential SiGe BiCMOS transimpedance amplifier (TIA) for high-speed optical links. The ORX achieves peaking-free transmission from the intrinsic PD current to the TIA input by employing a two-stage transimpedance (TI) topology of a common-base (CB) input stage and a shunt-feedback (SFB) amplification stage. The topology also generates a high-frequency peaking and effectively expands the bandwidth (BW) of the overall ORX despite the large PD capacitance. The optical/electrical (O/E) measurement results show that the proposed ORX achieves 41-GHz –3-dB BW at the maximum gain setting. With the pseudo-random bit sequence (PRBS) length of 231–1, the proposed ORX exhibits –10-dBm optical modulation amplitude (OMA) sensitivity at 1E−6 bit error rate (BER) for 56-Gb/s NRZ transmission, –2.9-dBm OMA sensitivity at 2.4E−4 BER (KP4-FEC) and 4-dBm OMA overload below KP4-FEC for 56-GBaud (112-Gb/s) PAM4 transmission, resulting in an input OMA dynamic range >6.9 dB. The ORX dissipates 330 mW from a 3.3-V voltage supply and occupies 1.5-mm2 die area. |
| Autors: Zhiyuan Cao;Yunhao Zeng;Xi Xiao;Jin He; |
| Appeared in: IEEE Photonics Technology Letters |
| Publication date: May 2024, volume: 36, issue:11, pages: 737 - 740 |
| Publisher: IEEE |
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|
| » A Case Study of the Public Sector Digital Ecosystem in Estonia |
Abstract:
A universal electronic identification system ensures that all citizens have default access to digital services. We discuss the core features of the Estonian digital ecosystem that have led to an exponential growth of in public digital service usage and started a move into the so-called postdigital society. |
| Autors: Mihkel Solvak;Ave Lauringson; |
| Appeared in: Computer |
| Publication date: May 2024, volume: 57, issue:5, pages: 44 - 49 |
| Publisher: IEEE |
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|
| » A CNN-LSTM Phase Compensation Method for Unidirectional Two-way Radio Frequency Transmission System |
Abstract:
A convolutional neural network combined with long short-term memory (CNN-LSTM) phase compensation method (PCM) is proposed and demonstrated, where CNN is employed to extract spatial features, and LSTM is used to capture temporal features and realize the long-term predictions of residual phase fluctuations. This is the first-time machine learning (ML) has been used to mitigate the effects of optical path asymmetry caused by temperature variations on radio frequency (RF) transmission systems. The performance is verified by experiments on a unidirectional two-way RF transmission system, in which both the two 259-km-long separate fibers are coupled into one optical cable. The results demonstrate the CNN-LSTM model presents better prediction performance than the other eight previously proposed ML models. When the prediction duration is 40,000 s and the ambient temperature variation range is 14.38 °C, the coefficient of determination (R Squared, R2) between the predicted value and the actual value is higher than 0.99. In addition, compared to the phase locked loop (PLL) PCM, the proposed CNN-LSTM PCM can reduce the root-mean-square (RMS) phase jitter of the received signal from 219 ps to 19.72 ps, and improve the frequency stability of the system at 10,000 s by 84.5%. Overall, the proposed CNN-LSTM PCM can effectively compensate for residual phase fluctuations generated by the optical path asymmetry, providing a potential option for achieving stable RF transmission in telecommunication networks. |
| Autors: Jiahui Cheng;Zhengkang Wang;Yaojun Qiao;Hao Gao;Chenxia Liu;Zhuoze Zhao;Jie Zhang;Baodong Zhao;Bin Luo;Song Yu; |
| Appeared in: IEEE Photonics Journal |
| Publication date: May 2024, volume: 16, issue:3, pages: 1 - 8 |
| Publisher: IEEE |
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|
| » A Data-Driven Classification Framework for Cybersecurity Breaches |
Abstract:
Unauthorized access to sensitive or confidential data results in a data breach, which can cause significant harm to an organization. Reporting breaches and reviewing prior records can help reduce damages. To aid in preparation, antivirus and security companies have published data breach reports, but they can be difficult to comprehend and require substantial effort to study. This article proposes a data breach incident classification framework using machine learning algorithms (naive Bayes, logistic regression, support vector machine, and random forest) on a dataset from the Privacy Rights Clearinghouse. The framework’s performance is evaluated using various metrics, including accuracy, F1 score, and confusion matrix. The article also employs topic modeling with latent Dirichlet allocation to enhance the classification’s accuracy. |
| Autors: Priyanka Rani;Abhijit Kumar Nag;Rifat Shahriyar; |
| Appeared in: IT Professional |
| Publication date: May 2024, volume: 26, issue:2, pages: 39 - 48 |
| Publisher: IEEE |
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|
| » A Data-Driven Method for Enhancing Spatial Resolution in Estimating Terrestrial Water Storage Changes From Satellite Gravimetry |
Abstract:
Understanding terrestrial water storage (TWS) changes is crucial for water management and hydrological applications. TWS changes are accurately observed by the Gravity Recovery and Climate Experiment and its Follow-On (GRACE/-FO) mission. However, the low spatial resolution limits the knowledge of water storage distribution. This study proposes a novel constrained point-mass modeling (CPM) approach, introducing a data-driven regularization matrix to improve spatial resolution. We derived point-mass solutions over the Amazon River basin from April 2002 to December 2019. Then, we evaluated our method using the WaterGAP global hydrology model (WGHM). Compared to results from traditional point-mass method (TPM) and three state-of-the-art GRACE/-FO solutions, the annual amplitudes in TWS changes from our method agree better with that from WGHM (slope =0.80 and $R^{2} =0.69$ ). Besides, the 179-month TWS changes also show a better consistency between our results and WGHM, indicating that our method effectively improves spatial resolution over the Amazon River basin. Moreover, benefiting from the improved spatial resolution of our method, our results, based solely on GRACE/-FO data, reveal spatial patterns in TWS changes that generally correspond with the major river channels of the basin. |
| Autors: Wei Wang;Yunzhong Shen;Qiujie Chen;Fengwei Wang; |
| Appeared in: IEEE Geoscience and Remote Sensing Letters |
| Publication date: May 2024, volume: 21, issue:null, pages: 1 - 5 |
| Publisher: IEEE |
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|
| » A Density Clustering-Based CFAR Algorithm for Ship Detection in SAR Images |
Abstract:
The clutter selection strategy based on sliding window in the conventional constant false alarm rate (CFAR) algorithm leads to different clutter qualities between pixels of the same target in a complex environment. To solve the problem, this letter proposes an improved CFAR algorithm based on density clustering. First, a two-parameter CFAR is used to detect ship targets. Then, density clustering is performed on each detected target pixel based on spatial distance and detection threshold to improve the target detection accuracy. Finally, false alarms caused by speckle noise are eliminated by using the number of times a pixel is clustered. The experimental results show that compared with the conventional CFAR algorithm and the superpixel-level CFAR detectors for ship detection in synthetic aperture radar (SAR) imagery (SP-CFAR), the proposed algorithm achieves a detection accuracy improvement of over 14.8% in heterogeneous clutter scenarios and dense target scenarios, while maintaining a low false alarm rate no higher than 0.13% in strong noise environments. |
| Autors: Yang Li;Zeyu Wang;Hongmeng Chen;Yachao Li; |
| Appeared in: IEEE Geoscience and Remote Sensing Letters |
| Publication date: May 2024, volume: 21, issue:null, pages: 1 - 5 |
| Publisher: IEEE |
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|
| » A Different View of Sigma-Delta Modulators Under the Lens of Pulse Frequency Modulation [Feature] |
Abstract:
The fact that VCO-ADCs produce noise-shaped quantization noise suggests that a link between frequency modulation and Sigma-Delta modulation should exist. The connection between a VCO-ADC and a first-order Sigma-Delta modulator has been already explained using Pulse Frequency Modulation. In this article, we attempt to extend this explanation to a generic Sigma-Delta modulator. We show that the link between Sigma-Delta modulation and Pulse Frequency Modulation relies in a sampling invariance property that defines the equivalence between both entities. This equivalence property, allows to go beyond the white quantization noise model of a Sigma-Delta modulator, revealing the origin of some nonlinear phenomena. We first predict spurious tones which cannot be explained by circuit non linearity. Multi-bit and single-bit modulators are shown to belong to a same generic class of systems. Finally, quantizer overload is analyzed using our model. The results are applied to Continuous-Time Sigma-Delta modulators of orders one, two and three and then extended to a generic case. |
| Autors: Victor Medina;Pieter Rombouts;Luis Hernandez-Corporales; |
| Appeared in: IEEE Circuits and Systems Magazine |
| Publication date: May 2024, volume: 24, issue:2, pages: 80 - 97 |
| Publisher: IEEE |
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|
| » A Dynamic Initialization Method for LEO/INS Integrated Positioning |
Abstract:
In the context of Global Navigation Satellite System (GNSS) denial environments, positioning can be achieved using low Earth orbit (LEO) satellite signals of opportunity (SOPs). When the instantaneous visibility of LEO satellites is insufficient, independent dynamic positioning cannot be realized, necessitating the combination with inertial navigation systems (INSs) for dynamic positioning. However, in current research, LEO/INS integrated positioning either requires the initialization of states using GNSS/INS or demands a prolonged stationary period to utilize SOP for providing initial states. Both of these scenarios severely limit practical applications. Addressing this issue, this article proposes an initialization method based on dynamic multiepoch (D-ME), enabling independent dynamic initialization of Iridium satellites and INS with a very minor loss of positioning accuracy. Due to the low orbital altitude of LEO satellites, arbitrary selection of initial iteration values for position is not feasible. To ensure the strong convergence of the initialization model, an algorithm is proposed based on Iridium signal pseudorange differences and Doppler differences (PDs-DDs) for determining initial position iteration values. Vehicular experiments were conducted using actual Iridium signals, and the results indicate that, compared with GNSS initialization and the 15-min static initialization, the 15-min dynamic initialization based on the proposed method can achieve comparable positioning performance after filter convergence. |
| Autors: Honglei Qin;Yansong Du;Jiatong Li;Zhenbo Xu;Huaiyuan Liang; |
| Appeared in: IEEE Transactions on Instrumentation and Measurement |
| Publication date: May 2024, volume: 73, issue:null, pages: 1 - 12 |
| Publisher: IEEE |
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|
| » A Gamification Method for Improving the Onboarding Process of Software Engineers |
Abstract:
In software engineering, the onboarding process of new software engineers is crucial. The primary goal of this process is to ensure that professionals gain the necessary knowledge and skills to comprehend the company’s culture, integrate into the organization, and perform their activities effectively. Enhancing corporate learning processes and addressing human aspects, such as motivation, engagement, and commitment, are among the key factors influencing the success of the onboarding process. Gamification—the use of game elements in nongame contexts—has been widely employed in different contexts to promote positive changes in people’s behavior and increase their engagement and performance. We consider the integration of gamification into the onboarding process an interesting field of study and research. In this work, we propose a novel method for gamifying the onboarding process and introduce an application case within an organizational context. |
| Autors: Mercedes Ruiz;Elena Orta;Javier Gutiérrez; |
| Appeared in: IT Professional |
| Publication date: May 2024, volume: 26, issue:2, pages: 67 - 75 |
| Publisher: IEEE |
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|
| » A GNSS Multiantenna Fast Millimeter-Level Positioning Method for Rail Track Deformation Monitoring |
Abstract:
Accurate rail track deformation and regularity monitoring is the basic condition to ensure the safe operation of high-speed railways. The technical challenge that needs to be addressed is to achieve positioning accuracy better than 3 mm within the shortest possible measurement time due to the limitation of railway skylight time. Among the existing Global Navigation Satellite System (GNSS) high-precision positioning technologies, real-time kinematic (RTK) can achieve centimeter-level accuracy in a single epoch, while 3-mm positioning accuracy by static baseline processing requires at least 1 h of observation time. This article presents a GNSS multiantenna fast millimeter-level positioning (MA-FMP) method, which uses multiantenna, multibase, and multiepoch observations to achieve the effect of improving positioning accuracy and shortening observation time simultaneously. The innovation of the proposed method is using the integration of RTK, multibaseline adjustment, and inertial measurement unit (IMU) attitude angle calibration to achieve a 3-mm positioning accuracy within a few minutes of static observation time. First, multiantenna, multibase, and multiepoch measurements are used to perform multiple static single-baseline RTK solutions. Then, multibaseline adjustment including baseline vector geometric closure constraint and coordinate adjustment is used to improve the positioning accuracy. Finally, high-precision IMU attitude angles are used to calibrate the weighted average of the multiantenna result. A comprehensive experiment was conducted with real-world data on the Lunan high-speed railway in China, and results show that the proposed MA-FMP method using 3 min of static observation time and a distance up to 5 km from the base station can achieve a positioning accuracy of (1.5 mm +0.3 ppm) and (3.0 mm +0.5 ppm) in horizontal and vertical components, respectively. The MA-FMP method enables horizontal positioning accuracy of better than 3 mm within 3 min of static observation time, which can meet the requirements of rail track deformation monitoring within the skylight of high-speed railway. |
| Autors: Rong Yuan;Xiaowei Cui;Mingquan Lu;Zhengdong Bai; |
| Appeared in: IEEE Transactions on Instrumentation and Measurement |
| Publication date: May 2024, volume: 73, issue:null, pages: 1 - 8 |
| Publisher: IEEE |
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|
| » A Multimodal Feature Fusion Network for Building Extraction With Very High-Resolution Remote Sensing Image and LiDAR Data |
Abstract:
Building extraction from remote sensing images is extremely important for urban planning, land-cover change analysis, disaster monitoring, and so on. With the growing diversity in building features, shape, and texture, coupled with frequent occurrences of shadowing and occlusion, the use of high-resolution remote sensing image (HRI) alone has limitations in building extraction. Therefore, feature fusion using multisource data has gradually become one of the most popular. However, the unique characteristics and noise issues make it difficult to achieve effective fusion and utilization. Thus, it is very challenging to realize the full fusion of multisource data to achieve complementary advantages. In this article, we propose an end-to-end multimodal feature fusion building extraction network based on segformer, which utilizes the fusion of HRI and light detection and ranging (LiDAR) data to realize the building extraction. First, we utilize the segformer encoder to break through the limitations of the traditional convolutional neural network (CNN) with the restricted receptive field, so as to achieve effective feature extraction of complex buildings. In addition, we propose a cross-modal feature fusion (CMFF) method utilizing the self-attention mechanism to ensure the fusion of multisource data. In the decoder part, we propose a multiscale upsampling decoder (MSUD) strategy to achieve a full fusion of multilevel features. As demonstrated by experiments on three datasets, our model shows better performance than several multisource building extraction and semantic segmentation models. The intersection over union (IoU) for buildings on the three datasets reaches 91.80%, 93.03%, and 84.59%. Subsequent ablation experiments further validate the effectiveness of each strategy. |
| Autors: Hui Luo;Xibo Feng;Bo Du;Yuxiang Zhang; |
| Appeared in: IEEE Transactions on Geoscience and Remote Sensing |
| Publication date: May 2024, volume: 62, issue:null, pages: 1 - 19 |
| Publisher: IEEE |
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|
| » A New Method of Security Bug Reports Analysis |
Abstract:
The investigation develops a method for improving the quality of security bug report (SBR) prediction during the software development and application processes. The research includes three stages. The first stage is preparing the source data. The second stage is constructing an original SBR prediction method using a machine learning algorithm [random forest (RF)]. The third stage is evaluating our method with well-established methods like filtering and ranking for security bug report prediction (FARSEC) and Keywords Matrix. It was shown that the values of such indicators as accuracy, precision, recall, and F-score when using the RF algorithm are, on average, 0.2–1% higher than when using the FARSEC and Keywords Matrix methods. The more initial number of reports the database contains, the higher the value of accuracy, precision, recall, and F-score that can be obtained. A new method can be used to predict SBRs during the software development and application processes. |
| Autors: Yunwu Xu;Yan Li; |
| Appeared in: IT Professional |
| Publication date: May 2024, volume: 26, issue:2, pages: 49 - 56 |
| Publisher: IEEE |
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|
| » A Path to 100 Percent Renewable Energy: Grid-Forming Inverters will Give Us the Grid We Need Now |
Abstract:
This is a more urgent problem than it might sound. The westernmost Hawaiian island of significant size, Kauai is home to around 70,000 residents and 30,000 tourists at any given time. Renewable energy accounts for 70 percent of the energy produced in a typical year—a proportion that's among the highest in the world and that can be hard to sustain for such a small and isolated grid. During the day, the local system operator, the Kauai Island Utility Cooperative, sometimes reaches levels of 90 percent from solar alone. But on 2 April, the 26-MW generator was running near its peak output, to compensate for the drop in solar output as the sun set. At the moment when it failed, that single generator had been supplying 60 percent of the load for the entire island, with the rest being met by a mix of smaller generators and several utility-scale solar-and-battery systems. |
| Autors: Benjamin Kroposki;Andy Hoke; |
| Appeared in: IEEE Spectrum |
| Publication date: May 2024, volume: 61, issue:5, pages: 50 - 57 |
| Publisher: IEEE |
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|
| » A Robust Approach for Geo-Electromagnetic Sounding Data Inversion Using l1-Norm Misfit and Adaptive Moment Estimation |
Abstract:
The choice of data misfit measure has a great impact on the convergence of electromagnetic (EM) inversion. The conventional measure based on the $l_{2}$ -norm tends to excessively amplify the weights of a larger misfit, inadvertently neglecting data with a smaller misfit during the inversion process, thereby diminishing the resolution to a certain degree. To solve this problem, we propose a robust inversion strategy based on $l_{1}$ -norm data misfit and adaptive moment estimation (Adam). In this scheme, we use the Ekblom-type $l_{1}$ -norm to simplify the derivative computation of the absolute value function. The Adam algorithm is further applied to optimize this type of non-smooth objective function, which incorporates momentum terms and adaptive steps, allowing it to better adapt to the irregularities in gradient changes. The inversion results obtained from both synthetic models and field measurements demonstrate that the Adam method performs considerably better than the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method for optimizing the $l_{1}$ - $l_{2}$ norm of the objective function. Compared with the conventional ${l} _{2}$ -norm data misfit, the $l_{1}$ -norm data misfit can effectively avoid excessive optimization of data with large misfits and achieve high-resolution inversion results. |
| Autors: Yunhe Liu;Xinpeng Ma;Luyuan Wang;Changchun Yin;Xiuyan Ren;Bo Zhang;Yang Su; |
| Appeared in: IEEE Transactions on Geoscience and Remote Sensing |
| Publication date: May 2024, volume: 62, issue:null, pages: 1 - 12 |
| Publisher: IEEE |
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|
| » A Separation-Based Localization Method Between Rotating and Static Sources |
Abstract:
Traditional sound source localization methods encounter significant challenges in simultaneously locating rotating and static sources. These challenges arise from the different motion patterns of these two types of sound sources, and they are typically not situated on the same plane. To address this issue, a method based on Modal Composition Beamforming (MCB) and the equivalent source method is proposed for separating rotating and static sound sources, which fully utilizes the prior knowledge of the spatiotemporal properties of these sources. The proposed approach involves establishing a Rotating-Static Sources Power Propagation (R-S2P) model, utilizing the relationship between the equivalent source strength and the actual beamforming output. By employing this forward model and applying an appropriate inversion method, it is possible to separate the components of rotating and static sources. Simulations for three cases with different source strengths are presented, and the R-S2P inversion problem is resolved using the Least Absolute Shrinkage and Selection Operator (LASSO) method. We showed that this method enables accurate separation and localization of rotating and static sources on different planes with varying relative intensities, even if the background noise is strong. |
| Autors: Keyu Hu;Ning Chu;Liang Yu;Hanbo Jiang;Ali Mohammad-Djafari; |
| Appeared in: IEEE Signal Processing Letters |
| Publication date: May 2024, volume: 31, issue:null, pages: 1359 - 1363 |
| Publisher: IEEE |
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|
| » A Simple Numerical Model to Estimate the Temperature Distributions Over Photodetectors in Steady-State |
Abstract:
This research introduces an approximation method for computing the temperature distribution in photodetectors operating in a steady state under optical excitation. The derived temperature profile is utilized to assess the impact of temperature variations on crucial performance metrics of photodetectors, encompassing quantum efficiency, bandwidth, and phase noise. Our methodology, grounded in simplified heat transport equations, yields significant insights into the intricate relationship between temperature and photodetector performance. Our findings reveal that assuming constant room temperature operation leads to an overestimate of the output current and quantum efficiency and an underestimate of bandwidth, by contrast, a model in which the temperature varies produces estimates that closely align with experimentally-measured values for quantum efficiency and bandwidth. The low thermal conductivity of InGaAs hampers heat dissipation, resulting in temperature accumulation. Varying the reverse bias voltage while keeping the output current constant by changing the input optical power leads to nonlinear variations in the bandwidth, phase noise, and quantum efficiency. These insights contribute to the understanding and optimization of thermal management in photodetectors under strong optical excitations. |
| Autors: Ergun Simsek;Alexander S. Hastings;David A. Tulchinsky;Keith J. Williams;Curtis R. Menyuk; |
| Appeared in: IEEE Photonics Journal |
| Publication date: May 2024, volume: 16, issue:3, pages: 1 - 6 |
| Publisher: IEEE |
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|
| » A Zero-Shot NAS Method for SAR Ship Detection Under Polynomial Search Complexity |
Abstract:
One-shot neural architecture search (NAS) has achieved impressive results in the field of synthetic aperture radar (SAR) ship detection. However, it is a challenge to balance resource consumption and search speed. To address this issue, we propose a zero-shot NAS method for searching the backbone of SAR ship detection model, named as ZeroSARNas, which is implemented via a multi-characterization proxy and an integer linear programming (ILP) search algorithm. Specifically, we first design the multi-characterization proxy for network capacity prediction, which takes advantage of information entropy and local intrinsic dimensionality (LID) of feature maps, named as ELID proxy, to obtain a more comprehensive understanding of each candidate module in the search space. We then formulate the NAS problem as a ‘0–1’ ILP problem which maximizes the ELID value under the different constraints such as parameters to quickly identify the optimal network. The experimental results show that the detection accuracy of the networks found by ZeroSARNas on the SSDD, HRSID, and LS-SSDD-v1.0 datasets can reach 98.59%, 91.30%, and 75.11% in mean average precision (mAP) with only 1.23 M, 1.75 M, and 1.29 M parameters, respectively. The proposed method reduces the search time from several GPU days or hours to 10.0 seconds, achieving competitive search efficiency. |
| Autors: Hang Wei;Zulin Wang;Gengxin Hua;Yuanhan Ni; |
| Appeared in: IEEE Signal Processing Letters |
| Publication date: May 2024, volume: 31, issue:null, pages: 1329 - 1333 |
| Publisher: IEEE |
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|
| » AAFormer: Attention-Attended Transformer for Semantic Segmentation of Remote Sensing Images |
Abstract:
The rapid advancements in remote sensing technology have enabled the widespread availability of fine-resolution remote sensing images (RSIs), offering rich spatial details and semantics. Despite the applicability and scalability of transformers in semantic segmentation of RSIs by learning pairwise contextual affinity, they inevitably introduce irrelevant context, hindering accurate inference of patch semantics. To address this, we propose a novel multihead attention-attended module (AAM) that refines the multihead self-attention mechanism (AM). The AAM filters out irrelevant context while highlighting informative ones by considering the relevance between self-attention maps and the query vector. The AAM generates an attention gate to complement contextual affinity and emphasize the useful ones with a higher weight simultaneously. Leveraging multihead AAM as the core unit, we construct a lightweight attention-attended transformer block (ATB). Subsequently, we devise AAFormer, a pure transformer with a mask transformer decoder, for achieving semantic segmentation of RSIs. We extensively evaluate our approach on the ISPRS Potsdam and LoveDA datasets, demonstrating compelling performance compared to mainstream methods. Additionally, we conduct evaluations to analyze the effects of AAM. |
| Autors: Xin Li;Feng Xu;Linyang Li;Nan Xu;Fan Liu;Chi Yuan;Ziqi Chen;Xin Lyu; |
| Appeared in: IEEE Geoscience and Remote Sensing Letters |
| Publication date: May 2024, volume: 21, issue:null, pages: 1 - 5 |
| Publisher: IEEE |
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|
| » Accuracy of Video-Based Hand Tracking for People With Upper-Body Disabilities |
Abstract:
Utilization of hand-tracking cameras, such as Leap, for hand rehabilitation and functional assessments is an innovative approach to providing affordable alternatives for people with disabilities. However, prior to deploying these commercially-available tools, a thorough evaluation of their performance for disabled populations is necessary. In this study, we provide an in-depth analysis of the accuracy of Leap’s hand-tracking feature for both individuals with and without upper-body disabilities for common dynamic tasks used in rehabilitation. Leap is compared against motion capture with conventional techniques such as signal correlations, mean absolute errors, and digit segment length estimation. We also propose the use of dimensionality reduction techniques, such as Principal Component Analysis (PCA), to capture the complex, high-dimensional signal spaces of the hand. We found that Leap’s hand-tracking performance did not differ between individuals with and without disabilities, yielding average signal correlations between 0.7-0.9. Both low and high mean absolute errors (between 10-80mm) were observed across participants. Overall, Leap did well with general hand posture tracking, with the largest errors associated with the tracking of the index finger. Leap’s hand model was found to be most inaccurate in the proximal digit segment, underestimating digit lengths with errors as high as 18mm. Using PCA to quantify differences between the high-dimensional spaces of Leap and motion capture showed that high correlations between latent space projections were associated with high accuracy in the original signal space. These results point to the potential of low-dimensional representations of complex hand movements to support hand rehabilitation and assessment. |
| Autors: Alexandra A. Portnova-Fahreeva;Momona Yamagami;Adrià Robert-Gonzalez;Jennifer Mankoff;Heather Feldner;Katherine M. Steele; |
| Appeared in: IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Publication date: May 2024, volume: 32, issue:null, pages: 1863 - 1872 |
| Publisher: IEEE |
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| » Adaptive Coherent Detection for Maritime Radar Range-Spread Targets in Correlated Heavy-Tailed Sea Clutter With Lognormal Texture |
Abstract:
This letter addresses the problem of adaptive coherent detection of maritime high-resolution radar range-spread targets in correlated heavy-tailed sea clutter. We first model radar sea clutter by the compound Gaussian model with lognormal texture and unknown speckle covariance matrix. The lognormal-distributed texture can capture the tail level of sea clutter, and the speckle covariance matrix contains the pulse-to-pulse correlation of sea clutter. Then, based on the two-step generalized likelihood ratio test and the maximum a posteriori (MAP) estimation of unknown parameters, an adaptive coherent generalized likelihood ratio test with a lognormal texture detector is proposed to detect radar range-spread targets. The proposed detector can be adaptive to clutter power mean, non-Gaussianity, and pulse-to-pulse correlation. The performance evaluation experiments on simulated and measured data show that the proposed detector outperforms conventional adaptive detectors. More specifically, the detection results on measured data indicate that when the number of target range cells is 3 and the probability of detection reaches 0.8, the proposed detector has a signal-to-clutter ratio gain of about 1 dB over its competitors. |
| Autors: Jian Xue;Zhen Fan;Shuwen Xu; |
| Appeared in: IEEE Geoscience and Remote Sensing Letters |
| Publication date: May 2024, volume: 21, issue:null, pages: 1 - 5 |
| Publisher: IEEE |
|
| |
|
| » Addressing the Perceived Skills Gap |
Abstract:
While not without detractors, the perception of a skills gap remains prevalent among business leaders. After analysis of some salient trends, this article offers some modest near-term initiatives to address immediate skills gap concerns. |
| Autors: George Hurlburt; |
| Appeared in: Computer |
| Publication date: May 2024, volume: 57, issue:5, pages: 91 - 96 |
| Publisher: IEEE |
|
| |
|
| » Advanced Methods and Algorithms for Selected Connected Autonomous Vehicles (CAVs) Benefits |
Abstract:
Autonomous vehicles offer many benefits to driving, including safety, comfort, and efficiency. The significant extended benefit of autonomy in traffic comes from the connectivity through communications among vehicles, vehicle and infrastructure, and vehicle and other road users (also known as V2V, V2I, and V2X.) Connected Autonomous Vehicles or CAVs connectivity allows automated trajectories, plans, and coordinated speed adaptations that are safer, enhance traffic throughput and reduce energy consumption. CAVs will have more comprehensive situational awareness in the vehicle’s vicinity for safety assurance. Driving tasks like lane changing, merging, navigating automated intersections, and various collision avoidance scenarios benefit significantly from the connectivity and the enhanced collective perception of the surrounding areas. Appropriate algorithms based on CAVs’ connectivity data can also improve driving task decision-making and navigation plans within the traffic. This talk presents a few examples of advanced methods and algorithms that demonstrate various benefits and challenges of CAVS through simulation and laboratory experiments. It serves as an overview referring to reference co-authored articles by the presenter. |
| Autors: Azim Eskandarian; |
| Appeared in: IEEE Transactions on Intelligent Transportation Systems |
| Publication date: May 2024, volume: 25, issue:4, pages: 405 - 442 |
| Publisher: IEEE |
|
| |
|
| » Also in this Issue |
Abstract:
Summaries of articles presented in this issue of the publication. |
| Autors: Jeffrey Voas; |
| Appeared in: Computer |
| Publication date: May 2024, volume: 57, issue:5, pages: 13 - 13 |
| Publisher: IEEE |
|
| |
|
| » An All-Fiber Multimode Interference Based 1×4 Power Splitter Using Square Core Multimode Fiber |
Abstract:
We investigate the performance of a highly compact ( $\leq 1$ cm) all-fiber multimode interference (MMI) device for splitting optical power from a standard single mode fiber (SMF) input port to four (4) SMF output ports. In C-band, the best and worst case uniformity among the output ports are obtained as 0.14 dB and 1 dB, respectively. This compact device provides a low-cost, high-performing solution for potential usage in optical networks. |
| Autors: Kritarth Srivastava;Nitin Bhatia; |
| Appeared in: IEEE Photonics Technology Letters |
| Publication date: May 2024, volume: 36, issue:11, pages: 729 - 732 |
| Publisher: IEEE |
|
| |
|
| » An Ethical Trio—AI, Cybersecurity, and Coding at Scale |
Abstract:
IT professionals must lead a necessary bottom-up initiative to ensure their constituents are sufficiently protected in this age of rampant and still maturing AI. |
| Autors: George Hurlburt; |
| Appeared in: IT Professional |
| Publication date: May 2024, volume: 26, issue:2, pages: 4 - 9 |
| Publisher: IEEE |
|
| |
|
| » An Introduction to the Purdue Digital Twin Lab [ITS Research Lab] |
Abstract:
Please send your proposal on profiling research activities of your or other intelligent transportation systems research groups and labs for the “ITS Research Labs” column to Yisheng Lv at yisheng.lv@ ia.ac.cn. |
| Autors: Ziran Wang;Yisheng Lv; |
| Appeared in: IEEE Intelligent Transportation Systems Magazine |
| Publication date: May 2024, volume: 16, issue:3, pages: 125 - 128 |
| Publisher: IEEE |
|
| |
|
| » Assessing Plasma-Etched InP Laser Facet Quality |
Abstract:
This work presents an approach to assess the quality of etched laser facets, considering factors such as roughness, inclination, and non-uniform light emission. Broad area InP lasers, using plasma etched facets, operating at 1550 nm are manufactured with varying facet quality on five 100 mm wafers. Comparison of the threshold current density of lasers of different length was used to derive relative facet reflectivity and demonstrated the relationship between the reflectivity and the optical mode weighted facet roughness and facet inclination. |
| Autors: Tristan T. Burman;Zhongming Cao;Craig Allford;Jack Baker;Jash Patel;Huma Ashraf;Samuel Shutts;Peter M. Smowton; |
| Appeared in: IEEE Photonics Technology Letters |
| Publication date: May 2024, volume: 36, issue:11, pages: 745 - 748 |
| Publisher: IEEE |
|
| |
|
| » Association Between Sleep Quality and Deep Learning-Based Sleep Onset Latency Distribution Using an Electroencephalogram |
Abstract:
To evaluate sleep quality, it is necessary to monitor overnight sleep duration. However, sleep monitoring typically requires more than 7 hours, which can be inefficient in termxs of data size and analysis. Therefore, we proposed to develop a deep learning-based model using a 30 sec sleep electroencephalogram (EEG) early in the sleep cycle to predict sleep onset latency (SOL) distribution and explore associations with sleep quality (SQ). We propose a deep learning model composed of a structure that decomposes and restores the signal in epoch units and a structure that predicts the SOL distribution. We used the Sleep Heart Health Study public dataset, which includes a large number of study subjects, to estimate and evaluate the proposed model. The proposed model estimated the SOL distribution and divided it into four clusters. The advantage of the proposed model is that it shows the process of falling asleep for individual participants as a probability graph over time. Furthermore, we compared the baseline of good SQ and SOL and showed that less than 10 minutes SOL correlated better with good SQ. Moreover, it was the most suitable sleep feature that could be predicted using early EEG, compared with the total sleep time, sleep efficiency, and actual sleep time. Our study showed the feasibility of estimating SOL distribution using deep learning with an early EEG and showed that SOL distribution within 10 minutes was associated with good SQ. |
| Autors: Seungwon Oh;Young-Seok Kweon;Gi-Hwan Shin;Seong-Whan Lee; |
| Appeared in: IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Publication date: May 2024, volume: 32, issue:null, pages: 1806 - 1816 |
| Publisher: IEEE |
|
| |
|
| » Assured Autonomy Through Combinatorial Methods |
Abstract:
Many conventional software engineering methods for high-trust software are not well suited to assured autonomy, but concepts from combinatorial testing can add confidence by providing a quantitative measure of the usefulness of a dataset. |
| Autors: D. Richard Kuhn;M. S. Raunak;Raghu N. Kacker;Jaganmohan Chandrasekaran;Erin Lanus;Tyler Cody;Laura Freeman; |
| Appeared in: Computer |
| Publication date: May 2024, volume: 57, issue:5, pages: 86 - 90 |
| Publisher: IEEE |
|
| |
|
| » Automated fiber switch with path verification enabled by an AI-powered multi-task mobile robot |
Abstract:
As the capacity of optical transport networks undergoes significant growth, there is an ongoing discussion on how to effectively leverage both spectral and spatial degrees of freedom to scale future network capacity. This paper presents an artificial intelligence (AI)-powered multi-task robot comprising a collaborative robotic arm and a mobile robotic base designed for optical network automation. The robot demonstrates the capability of direct fiber switching, establishing static fiber links that consume zero power and have minimal insertion loss from fiber connectors. As a precautionary measure before physically switching fiber cables, the robot performs path verification by detecting robot-driven events using real-time coherent receivers, aiming to avoid accidental unplugging. Additionally, the robot showcases its mobility by efficiently navigating between different network racks and rooms while executing various tasks. Implementing the automation of network operations using robots has the potential to reduce both capital and operational expenditures. |
| Autors: Xiaonan Xu;Haoshuo Chen;Michael Scheutzow;Jesse E. Simsarian;Roland Ryf;Gin Qua;Amey Hande;Rob Dinoff;Mijail Szczerban;Mikael Mazur;Lauren Dallachiesa;Nicolas K. Fontaine;Jim Sandoz;Mike Coss;David T. Neilson; |
| Appeared in: IEEE/OSA Journal of Optical Communications and Networking |
| Publication date: May 2024, volume: 16, issue:6, pages: 624 - 630 |
| Publisher: IEEE |
|
| |
|
| » Backscatter Modulation for Wearable RFID Tags: RFID Tag Optimization for Maximum Backscatter Energy |
Abstract:
The interest in wearable antenna design has increased significantly due to its potential applications for humans, especially in the context of RF identification (RFID) technology. RFID transponders can wirelessly transmit identification numbers or sensor data over short distances. RFID systems comprise essential components, including a reader or interrogator, an RFID tag or transponder (receive and respond), as well as a comprehensive set of coding and decoding protocols [1], [2]. |
| Autors: Niloufar Bateni;Michael Meuleners;Christoph Degen; |
| Appeared in: IEEE Microwave Magazine |
| Publication date: May 2024, volume: 25, issue:6, pages: 58 - 64 |
| Publisher: IEEE |
|
| |
|
| » Be a PES Leader!: And Make a Difference [Leader’s Corner] |
Abstract:
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers. |
| Autors: Chan Wong; |
| Appeared in: IEEE Power and Energy Magazine |
| Publication date: May 2024, volume: 22, issue:3, pages: 15 - 17 |
| Publisher: IEEE |
|
| |
|
| » Calendar [Calendar] |
Abstract:
null |
| Autors: Martin Lauer; |
| Appeared in: IEEE Intelligent Transportation Systems Magazine |
| Publication date: May 2024, volume: 16, issue:3, pages: C3 - C3 |
| Publisher: IEEE |
|
| |
|
| » Candidates Announced for Board of Governors Election |
Abstract:
null |
| Autors: Vincent Chan; |
| Appeared in: IEEE Communications Magazine |
| Publication date: May 2024, volume: 62, issue:5, pages: 6 - 11 |
| Publisher: IEEE |
|
| |
|
| » Careers: Susana Contrera: A Network Engineer who Keeps Meta's AI Infrastructure Humming |
Abstract:
Making breakthroughs in artificial intelligence these days requires huge amounts of computing power. In January, Meta CEO Mark Zuckerberg announced that by the end of this year, the company will have installed 350,000 Nvidia GPUs—the specialized computer chips used to train AI models—to power its AI research. |
| Autors: Edd Gent; |
| Appeared in: IEEE Spectrum |
| Publication date: May 2024, volume: 61, issue:5, pages: 19 - 20 |
| Publisher: IEEE |
|
| |
|
| » CD-SLAM: A Real-Time Stereo Visual–Inertial SLAM for Complex Dynamic Environments With Semantic and Geometric Information |
Abstract:
The most commonly used simultaneous localization and mapping (SLAM) scheme often assumes a static environment, leading to significant errors in pose estimation when operating in highly dynamic scenes. To address this limitation and improve the robustness and accuracy of positioning in dynamic environments, this study proposes CD-SLAM, a real-time stereo vision inertial SLAM system specifically designed for complex dynamic environments, based on ORB-SLAM3. CD-SLAM enhances the tracking thread and introduces a new parallel thread that utilizes YOLOv5 to detect objects in each input frame and extract semantic information. This semantic information, combined with prior information from the inertial measurement unit (IMU), is used for pose estimation, eliminating the pose information of dynamic objects and consequently improving the accuracy and robustness of positioning. Furthermore, CD-SLAM employs scene flow to calculate the distance between adjacent frames and determine the spatial velocity between them, compensating for potential static information through a velocity filtering algorithm. To enhance positioning accuracy in challenging environments with weak textures, CD-SLAM integrates an IMU for motion prediction and coherence detection. Finally, appeal information is integrated to determine the motion status of objects in the scene and filter out dynamic feature points. Experimental tests conducted on the VIODE dataset demonstrate that CD-SLAM outperforms the existing algorithms in terms of accuracy and robustness. |
| Autors: Shuhuan Wen;Sheng Tao;Xin Liu;Artur Babiarz;F. Richard Yu; |
| Appeared in: IEEE Transactions on Instrumentation and Measurement |
| Publication date: May 2024, volume: 73, issue:null, pages: 1 - 8 |
| Publisher: IEEE |
|
| |
|
| » Centralized Protection and Control for Transmission System Operations: Practical Applications and Perspectives |
Abstract:
Since substation automations systems were introduced in the 1990s to protect, control, and automate high-voltage and medium voltage substations, several new technologies have been applied to improve the functionality, performance, and efficiency of the design and architecture. Digital substations are beginning to replace substation automations systems, benefitting from new architectures that are based on digital interfaces provided by intelligent primary devices and related sensors. This transition was prompted by the intensive work in standardization of communication protocols and data models dedicated for power system automation, as defined in the International Electrotechnical Commission (IEC) 61850 series of standards. The evolution of technologies and performances driven by microelectronics enabled the functional integration of previously separated devices onto common hardware over the last three decades. Still, the functional integration was limited by requirements concerning reliability, resilience, testability, and life-cycle perspectives. In a substation, any device, such as a protection relay, was allocated to a bay, a feeder, or a zone. Applying concepts of IEC 61850 concerning the separation of physical signals, their conversion into digital information, and allocation of application functions using this information from any physical device, makes it possible to think about the next generation of digital substations. Based on virtualization technologies developed in the world of information technology (IT) to dramatically improve the design, operation, and maintenance of IT systems, centralized protection and control systems (CPCs) would allow an even more flexible and economic way to protect, control, and automate substations. |
| Autors: Christian Guibout;Aurélien Wataré;Florent Carli;Arnaud Carbonne;Karine Mourier;Thomas Rudolph; |
| Appeared in: IEEE Power and Energy Magazine |
| Publication date: May 2024, volume: 22, issue:3, pages: 67 - 78 |
| Publisher: IEEE |
|
| |
|
| » Charging Support Communication System Based on Vehicle-to-Vehicle Communication for Electric Vehicles |
Abstract:
The goal of this study is to support a charging management system for electric vehicles that efficiently selects the charging station based on vehicle-to-vehicle communication while preventing broadcast storm problems. The simulation results show that the proposed system outperforms the centralized scheme. |
| Autors: Ajmal Khan;Amer Saeed;Farman Ullah;Muhammad Bilal;Yasir Muhammad;Hesham El-Sayed; |
| Appeared in: Computer |
| Publication date: May 2024, volume: 57, issue:5, pages: 67 - 77 |
| Publisher: IEEE |
|
| |
|
| » Class-Φ2 Power Amplifier With Resonant Gate Driver: High-Efficiency Power Amplifier for 50 MHz |
Abstract:
RF power amplifiers (PAs) serve an important role for various applications, such as communication, induction heating, plasma generation, and wireless power transfer [1], [2], [3]. With the current demands for energy conservation, the importance of high-efficiency PAs has been ever increasing. Efficient power delivery alleviates the burden of thermal management, improving the system stability and increasing the power density. In this article, we present our first-place design for the Student Design Competition “High-Efficiency Power Amplifier for 50 MHz” in the 2023 IEEE International Microwave Symposium (IMS) in San Diego, CA, USA. |
| Autors: Zhechi Ye;Calvin H Lin;Juan Rivas-Davila; |
| Appeared in: IEEE Microwave Magazine |
| Publication date: May 2024, volume: 25, issue:6, pages: 88 - 92 |
| Publisher: IEEE |
|
| |
|
| » Classification of Sugar Samples From PCA-Reduced Impedimetric Data Using QD Model |
Abstract:
This article presents single-model identification algorithm for the classification of sugar sample suitable for designing a portable meter for in-home application. The classification algorithm is based on the impedimetric data of a typical hydrogel taken over a frequency range from 20 Hz to 200 kHz. Principal component analysis (PCA) is used for data reduction and selection of dominating principal components (PCs). Five suitable impedance features are identified to attain maximum accuracy. The work identifies quadratic discriminant (QD) as the most accurate model among 32 different types of machine learning (ML) models for this classification problem. The QD model is trained using 14 904 data points obtained from 414 sugar samples of different concentrations and later tested with 107 sugar samples. It is found that the proposed model can identify sucrose, glucose, galactose, and fructose with true positive rate (TPR) values 100%, 95.8%, 58.3%, and 33.3%, respectively. It clearly indicates the proposed QD-based algorithm and sensing technique is suitable for accurate identification of sucrose and glucose in hexose samples. |
| Autors: Dibakar Roy;Akbar Ali;Suchetan Pal;Avishek Adhikary; |
| Appeared in: IEEE Transactions on Instrumentation and Measurement |
| Publication date: May 2024, volume: 73, issue:null, pages: 1 - 8 |
| Publisher: IEEE |
|
| |
|
| » Client-Side Embedding of Screen-Shooting Resilient Image Watermarking |
Abstract:
The proliferation of portable camera devices, represented by smartphones, is increasing the risk of sensitive internal data being leaked by screen shooting. To trace the leak source, a lot of research has been done on screen-shooting resilient watermarking technique, which is capable of extracting the previously embedded watermark from the screen-shot image. However, all existing screen-shooting resilient watermarking schemes follow the owner-side embedding mode. In this mode, the management center will suffer heavy computational and communication burden in the case of numerous screens, which hinders the system scalability. As another embedding mode of digital watermarking, client-side embedding can solve the above scalability problem by migrating the watermark embedding operation to the same time when the screen decrypts the image. By designing a pair of image encryption and personalized decryption algorithms based on matrix operation, this paper is the first to realize the client-side embedding of screen-shooting resilient watermarking. In this implementation, challenges are overcome and the following key achievements are attained. First, our scheme embeds watermark using the algorithm of Fang et al. without modification, and thus fully inherits its robustness against screen shooting. Second, the original image is securely encrypted and the watermarked image can be directly retrieved through decryption. Third, the secrecy of the screen watermark is ensured by concealing the embedding pattern. Finally, our scheme is validated by experiments, which shows that the efficiency advantage of client-side embedding is realized while maintaining robustness. |
| Autors: Xiangli Xiao;Yushu Zhang;Zhongyun Hua;Zhihua Xia;Jian Weng; |
| Appeared in: IEEE Transactions on Information Forensics and Security |
| Publication date: May 2024, volume: 19, issue:null, pages: 5357 - 5372 |
| Publisher: IEEE |
|
| |
|
| » Co-Benefits and Tradeoffs Between Safety, Mobility, and Environmental Impacts for Connected and Automated Vehicles |
Abstract:
A large number of Connected and Automated Vehicle (CAV) applications are being designed, developed, and deployed in order to greatly improve our transportation systems in terms of safety, mobility, and reducing environmental impacts. These benefits can be quantified by a variety of performance measures that are often cited in the literature. However, most of these CAV applications are typically designed to improve transportation systems only in a particular dimension, usually focusing on either safety, mobility, or the environment. Very few research papers have considered a wider range or combination of performance measures across multiple dimensions, examining potential co-benefits or tradeoffs between these measures. For example, you can design a CAV application that greatly improves safety, but it might come at the cost of reducing traffic throughput. Further, the design of the CAV applications is often static and limited to specific traffic scenarios and conditions. CAVs that can adapt to different conditions, and be “tunable” for different societal needs will have much greater impact and versatility. In this presentation, we examine various co-benefits and tradeoffs of current CAV applications and consider how we can design these systems to have greater flexibility when it comes to deployment. We cite not only different CAV applications evaluated in simulation, but also real-world CAV deployments that operate on various testbeds, such as the Innovation Corridor located in Riverside, California. Based on this analysis, we can consider several new research directions for future CAV deployments. |
| Autors: Matthew Barth; |
| Appeared in: IEEE Transactions on Intelligent Transportation Systems |
| Publication date: May 2024, volume: 25, issue:4, pages: 184 - 213 |
| Publisher: IEEE |
|
| |
|
| » Coherence Matrix Power Model for Scattering Variation Representation in Multi-Temporal PolSAR Crop Classification |
Abstract:
The multitemporal polarimetric SAR (PolSAR) data contains the scattering change information during the growth of crops. However, the current classification methods usually directly use the addition of features extracted at single-temporal or use the temporal and spatial variations of certain features, not really exploring the complete scattering variation information. The specific data representation models for multitemporal PolSAR data should combine time with polarimetry to characterize the scattering variations. However, the characterization and utilization of such kind of models are inadequate. In this article, we construct data representation model based on the power form of coherence matrix to comprehensively represent all kinds of scattering mechanism variation, which is full-rank positive semidefinite Hermitian matrix. We extract new time-variant scattering features and design vision transformer classifier accordingly for crop classification. Experiment results on RADARSAT-2 datasets show that the proposed power representation model outperforms other models. |
| Autors: Qiang Yin;Li Gao;Yongsheng Zhou;Yang Li;Fan Zhang;Carlos López-Martínez;Wen Hong; |
| Appeared in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Publication date: May 2024, volume: 17, issue:null, pages: 9797 - 9810 |
| Publisher: IEEE |
|
| |
|
| » Company Name Matching Using Job Market Data Enrichment |
Abstract:
This article contributes to the field of matching techniques by introducing a new algorithm based on labor market data enrichment. This approach is able to collect and balance the training and test samples for data integration purposes. By setting thresholds for textual matching and geographic proximity, it simplifies the process of finding suitable company matches. Based on insufficiently studied datasets, the experimental findings show that the performance evaluation of proposed models differs depending on the similarity thresholds used. |
| Autors: Andrei A. Ternikov; |
| Appeared in: IT Professional |
| Publication date: May 2024, volume: 26, issue:2, pages: 76 - 82 |
| Publisher: IEEE |
|
| |
|
| » Comparison of BPNN and Dual-Branch CNN for Significant Wave Height Estimation From Polarimetric Gaofen-3 SAR Wave Mode Data |
Abstract:
The present study utilizes the backward propagation neural network (BPNN) and the dual-branch convolutional neural network (DB-CNN) algorithms to construct models for estimating significant wave height (SWH) from polarimetric Gaofen-3 SAR wave mode data, using a dataset of 11 164 images that are collocated with SWH from the European Centre for Medium-Range Weather Forecasts fifth generation reanalysis (ERA5). The models are assessed and compared across nine polarizations [vertical–vertical (VV), horizontal–horizontal (HH), RL, vertical–horizontal (VH), horizontal-vertical (HV), RR, 45° linear, RV, and RH] and various sea states using the SAR-ERA5 test samples as well as buoy and altimeter SWH observations. The results demonstrate the robust performance of BPNN models, with RMSEs around 0.30–0.32 m on SAR-ERA5 test data, 0.32–0.48 m on buoy data, 0.40–0.48 m on Jason-3 data, and 0.36–0.42 m on SARAL data. By comparison, the DB-CNN models, which additionally include two-dimensional (2-D) image spectra as input, only exhibit improved performance at VV, 45° linear, and RL polarizations, while showing negligible improvement at HH, RV, and RH polarizations and a notable degradation at VH, HV, and RR polarizations. Furthermore, the DB-CNN models generally fail to improve the overestimation (underestimation) in low (high) seas, and they even aggravate the overestimation (underestimation) under most polarizations. Additionally considering the heightened complexity, increased vulnerability to overfitting, and training times that are more than 200 times longer, the use of complex deep learning network structures to incorporate 2-D spectral information appears to be operationally limited for SAR SWH retrieval. |
| Autors: Qiushuang Yan;Chenqing Fan;Junmin Meng;Tianran Song;Jie Zhang;Weifu Sun; |
| Appeared in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Publication date: May 2024, volume: 17, issue:null, pages: 9582 - 9594 |
| Publisher: IEEE |
|
| |
|
| » Computing Through Time: Venture Scientists |
Abstract:
null |
| Autors: Ergun Akleman; |
| Appeared in: Computer |
| Publication date: May 2024, volume: 57, issue:5, pages: 8 - 8 |
| Publisher: IEEE |
|
| |
|
| » ComSoc Publications |
Abstract:
ComSoc's publications represent a shining star of the society, playing an essential role in the fast-evolving realm of communications and networking, and representing a great service to our members. In this month's column of the IEEE Communications Magazine, I am delighted to introduce Octavia A. Dobre, Vice-President for Publications. Octavia is a Professor and Canada Research Chair Tier 1 with Memorial University, Canada. Her research interests encompass wireless communication and networking technologies, as well as optical and underwater communications. She has (co-)authored over 500 refereed papers in these areas. Octavia was the inaugural Editor-in-Chief (EiC) of the IEEE Open Journal of the Communications Society and the EiC of the IEEE Communications Letters. She was the recipient of the 2022 IEEE Communications Society Joseph LoCicero Award for Exemplary Service to Publications. Dr. Dobre is a Fulbright Scholar, Royal Society Scholar, and Distinguished Lecturer of the IEEE Communications Society. She obtained Best Paper Awards at various conferences, including IEEE ICC, IEEE Globecom, IEEE WCNC, and IEEE PIMRC. Octavia is an elected member of the European Academy of Sciences and Arts, a Fellow of the Engineering Institute of Canada, a Fellow of the Canadian Academy of Engineering, and a Fellow of the IEEE. |
| Autors: Robert Schober;Octavia A. Dobre; |
| Appeared in: IEEE Communications Magazine |
| Publication date: May 2024, volume: 62, issue:5, pages: 4 - 4 |
| Publisher: IEEE |
|
| |
|
| » Conceptual Framework for Software Change |
Abstract:
Ever since the invention of software, change has been a destabilizing factor. Although many new software changes are being applied, the terminologies used to describe them are often inconsistent. This restricts practitioners to designing and evaluating their changes. This article aims to develop a conceptual framework of software change based on six main dimensions regarding the source, essence, and consequences of software change. To evaluate the proposed framework, benchmarking is applied against selected 11 previous studies. |
| Autors: Mohamed Saied;Ahmed Mohammed Elfatatry;Shawkat Kamal Guirguis; |
| Appeared in: IT Professional |
| Publication date: May 2024, volume: 26, issue:2, pages: 57 - 66 |
| Publisher: IEEE |
|
| |
|
| » Connectivity, Automation and Safety in Transportation of the Future |
Abstract:
Advances in technologies such as sensors, communications, robotics, computer software and hardware are paving the way of imagining the future of transportation system as well as the roadblocks and challenges that need to be overcome. While there have been considerable technology advancements on the vehicle level the objectives of these successes is driver comfort and marketing rather than solving the problem of congestion and mobility on the system level in general. Vehicle automation is attractive to a small group of users, it opens the way to re-imagine the ownership and use of vehicles but also raises safety concerns and doubts as to whether it will have any benefit to alleviating congestion. On the infrastructure level the traffic network system operates as an open loop system with limited feedback and control leading to a highly unbalanced system in space and time. As a result hidden capacities cannot be utilized by planning in a centralized manner. In order to achieve effective management and control of the transportation system information and data are necessary and can only be generated by connecting the infrastructure with transportation users. In this talk we present examples how this connectivity can be achieved, what is the benefit of making decisions in a centralized coordinated manner versus decentralized approaches involving individual greedy users. These examples will identify the potential benefits in moving from the current system to a more coordinated system where connectivity can be used to improve mobility, reduce costs and the impact on the environment. |
| Autors: Petros Ioannou; |
| Appeared in: IEEE Transactions on Intelligent Transportation Systems |
| Publication date: May 2024, volume: 25, issue:4, pages: 94 - 123 |
| Publisher: IEEE |
|
| |
|
| » Consistency of Stackelberg and Nash Equilibria in Three-Player Leader-Follower Games |
Abstract:
There has been significant recent interest in a class of three-player leader-follower game models in many important cybersecurity scenarios. In such a tri-level hierarchical structure, a defender usually serves as a leader, dominating the decision process by the Stackelberg equilibrium (SE) strategy. However, such a leader-follower scheme may not always work, and the Nash equilibrium (NE) strategy may provide an alternative choice. Thus, we need to reveal the consistency between SE and NE in the three-player model to help the leader evaluate its strategy impact and avoid a choice dilemma. To this end, we first provide a necessary and sufficient condition such that each SE is an NE, which not only provides access to seek a satisfactory SE but also makes a criterion for an obtained SE. Then, we apply the results for case studies with a unique SE or with at least one SE being an NE. Moreover, when the consistency condition falls short, we give an upper bound of the deviation between SE and NE to help the leader tolerably adopt an SE strategy. Finally, we apply our consistency analysis to practical scenarios, including secure wireless transmission and advanced persistent threat defense. |
| Autors: Gehui Xu;Guanpu Chen;Zhaoyang Cheng;Yiguang Hong;Hongsheng Qi; |
| Appeared in: IEEE Transactions on Information Forensics and Security |
| Publication date: May 2024, volume: 19, issue:null, pages: 5330 - 5344 |
| Publisher: IEEE |
|
| |
|
| » Continual Learning for SAR Target Incremental Detection via Predicted Location Probability Representation and Proposal Selection |
Abstract:
The gradual increase of synthetic aperture radar (SAR) imagery often accompanies the appearance of new targets, but traditional detection frameworks can only detect existing target classes and cannot detect new ones. Typically, we must update the model using both new and old data, which puts a strain on storage and computation, but if we only update with new data, the detection performance on old classes will suffer dramatically. For this reason, this article proposes to use the continual learning (CL) method to solve the problem of SAR target incremental detection. Mainstream CL methods generally consider localization to be a class-irrelevant function, however, this strategy is unsuitable for SAR imagery with significant background changes, leading to poor detection performance. Addressing the above issues, this article proposes a CL object detection (CLOD) method, with an overall framework based on knowledge distillation. The focus of the methodology consists of two parts: first, we introduce the predicted location probability representation (PLPR) method, by using spatial discretization and segmented probabilistic statistics, to transform the localization results into probability distributions, thus allowing the localization function to participate in the CL process; second, we design a proposal selection strategy according to the background characteristics of SAR images, which improves the quality of the proposals during the knowledge review to further optimize the learning effect. Experiments on the latest multiclass SAR target detection dataset MSAR-1.0, show that our method is able to learn new knowledge with less performance penalty for old classes than other methods. In multiple data incremental settings, our method provides a 2%–11% performance improvement over numerous common methods. |
| Autors: Yu Tian;Zongyong Cui;Jizhen Ma;Zheng Zhou;Zongjie Cao; |
| Appeared in: IEEE Transactions on Geoscience and Remote Sensing |
| Publication date: May 2024, volume: 62, issue:null, pages: 1 - 15 |
| Publisher: IEEE |
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|
| » Contrastive Semi-Supervised Learning for Image Highlight Removal |
Abstract:
Image highlight removal is a fundamental and challenging visual task. Although fully supervised deep learning-based methods have achieved remarkable results, their performance is limited by the diversity and quantity of paired highlight images. To address this issue, we propose a student-teacher semi-supervised deep learning method based on contrastive learning for highlight removal, which integrates paired and unpaired data for boosting image highlight removal. Specifically, our semi-supervised network consists of the parallel student and teacher sub-networks with the same U-shaped CTransformer. The CTransformer integrates a dual multiscale convolution module and a parallel multiaxial self-attention module to promote local feature representation and global contextual semantic comprehension of the network. The dual multiscale convolution module realizes the representation of multiple perceptual fields by the internal and external multiscales. The parallel multiaxial self-attention module implements multidimensional autocorrelation attention with the selective fusion mechanism. Quantitative and qualitative results show that our method takes SOAT results on different datasets. |
| Autors: Pengyue Li;Xiaolan Li;Wentao Li;Xinying Xu; |
| Appeared in: IEEE Signal Processing Letters |
| Publication date: May 2024, volume: 31, issue:null, pages: 1334 - 1338 |
| Publisher: IEEE |
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|
| » Cooperative and Automated Road Transport - European Perspective |
Abstract:
Deployment of cooperative systems has been less rapid than of autonomous systems. But certainly they are a prelude and an indispensable component of an advanced technology, which has come to rapid development in recent years, and is receiving widespread attention. This presentation presents recent results of the development and deployment of cooperative systems for road transport. These systems use communication between vehicles, as well as between vehicles and infrastructure, other road users and network, for exchange of information, enabling various applications for safety, efficiency and comfort. Cooperative Intelligent Transport Systems (C-ITS), also referred to as connected vehicles, are a prelude to, and pave the way towards road transport automation. Vehicle connectivity and information exchange will be an important asset for future highly-automated driving The presentation provides an insight in the state of the art of C-ITS, especially addresses the important role of ICT infrastructure, and presents the main R&D achievements in recent European projects, EU R&D funding scheme and international cooperation, as well as related standardisation activities. |
| Autors: Meng Lu; |
| Appeared in: IEEE Transactions on Intelligent Transportation Systems |
| Publication date: May 2024, volume: 25, issue:4, pages: 332 - 365 |
| Publisher: IEEE |
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|
| » Correction to “A New Construction Method for Keystream Generators” |
Abstract:
The authors would like to extend their apologies for the inadvertent inclusion of an erroneous index of the matrix ${M}$ for DIZY-80 in [1]. We sincerely regret any inconvenience caused by this typographical error and appreciate the chance to rectify it. |
| Autors: Çaǧdaş Gül;Orhun Kara; |
| Appeared in: IEEE Transactions on Information Forensics and Security |
| Publication date: May 2024, volume: 19, issue:null, pages: 4198 - 4198 |
| Publisher: IEEE |
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|
| » Corrections to “Static and Dynamic Performance of z-LiNbO₃ Optical Temperature Sensor” |
Abstract:
In the above article [1], we have found that the contents of Figs. 7 and 9 were the same, while the detailed explanations for the two figures were correct. After carefully checking the final accepted file uploaded to the submission system and the journal publication file, the above-mentioned mix-up may be triggered by the incautious manuscript proof procedure. More specifically, the mix-up figures were only related to Fig. 9 and did not affect other contents in the manuscript. The correct Fig. 9 is provided as follows. |
| Autors: Xinbing Jiao;Zixuan Guo;Xueping Gong; |
| Appeared in: IEEE Sensors Journal |
| Publication date: May 2024, volume: 24, issue:9, pages: 15690 - 15690 |
| Publisher: IEEE |
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|
| » Coupled Dual-Frequency Phase-Shifting Coder for Precise Rotated Angle Representation in Oriented Object Detection |
Abstract:
The classically oriented object detection method often suffers boundary discontinuity and square-like problems, hindering the model’s ability to predict orientation accurately. Therefore, in this letter, we introduce a novel angle representation scheme named coupled dual-frequency phase-shifting coder (CDFP) which draws inspiration from optical measurement technology to address the aforementioned issues. Specifically, in the angle encoding stage, we represent the ground-truth (GT) rotation angle as a combination of two five-step phase-shifting at different frequencies and use this representation to supervise the learning of the model’s angle branch. In the angle decoding stage, alongside utilizing the corresponding dual-frequency phase-shifting decoding and unwrapping method, we impose additional constraints on the decoding angle range for predicted square-like objects. Extensive experiments on three challenging aerial image datasets using different detectors prove the effectiveness of our approach. Specifically, our RetinaNet-CDFP achieves an average improvement of 2.16% AP50 and 6.83% AP75 on DOTA, and when combined with RTMDet, our RTMDet-R-m-CDFP achieves state-of-the-art (SOTA) detection performance on DIOR-R and DOTA, with 70.11% and 78.77% AP50, respectively. Our codes will be released at https://github.com/liufeinuaa/aisodet.git. |
| Autors: Fei Liu;Renwen Chen;Junyi Zhang;Shanshan Ding;Hao Liu;Shaofei Ma;Kailing Xing; |
| Appeared in: IEEE Geoscience and Remote Sensing Letters |
| Publication date: May 2024, volume: 21, issue:null, pages: 1 - 5 |
| Publisher: IEEE |
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|
| » Cross-City Semantic Segmentation (C2Seg) in Multimodal Remote Sensing: Outcome of the 2023 IEEE WHISPERS C2Seg Challenge |
Abstract:
Given the ever-growing availability of remote sensing data (e.g., Gaofen in China, Sentinel in the EU, and Landsat in the USA), multimodal remote sensing techniques have been garnering increasing attention and have made extraordinary progress in various Earth observation (EO)-related tasks. The data acquired by different platforms can provide diverse and complementary information. The joint exploitation of multimodal remote sensing has been proven effective in improving the existing methods of land-use/land-cover segmentation in urban environments. To boost technical breakthroughs and accelerate the development of EO applications across cities and regions, one important task is to build novel cross-city semantic segmentation models based on modern artificial intelligence technologies and emerging multimodal remote sensing data. This leads to the development of better semantic segmentation models with high transferability among different cities and regions. The Cross-City Semantic Segmentation contest is organized in conjunction with the 13th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). |
| Autors: Yuheng Liu;Ye Wang;Yifan Zhang;Shaohui Mei;Jiaqi Zou;Zhuohong Li;Fangxiao Lu;Wei He;Hongyan Zhang;Huilin Zhao;Chuan Chen;Cong Xia;Hao Li;Gemine Vivone;Ronny Hänsch;Gulsen Taskin;Jing Yao;A. K. Qin;Bing Zhang;Jocelyn Chanussot;Danfeng Hong; |
| Appeared in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Publication date: May 2024, volume: 17, issue:null, pages: 8851 - 8862 |
| Publisher: IEEE |
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|
| » Cryogenic CMOS Design for Qubit Control: Present Status, Challenges, and Future Directions [Feature] |
Abstract:
This article will review recent progress in cryogenic CMOS designs for future scaled quantum computing applications. After introducing the scaling challenges associated with qubit control and readout electronics operating at room temperature, approaches taken to date to cryogenic control electronics design will be discussed, focusing on the most recent relevant publications. Elements of ultra-low power circuit and system design approaches for cryogenic controllers in scaled CMOS nodes (40nm to 14nm) will be reviewed, including a discussion of current state-of-the art cryogenic controller performance and power efficiency. Note that leading designs, when operated as transmon qubit state controllers, have achieved gate error rates in the range of 10-4 to 10-3 achieving spurious free dynamic range (SFDR) of ~40dB while consuming 4-23mW of power per qubit under active control, with power efficiency strongly driven by the complexity of the digital processor integrated in the controller design. These demonstrations, while significant, are just the first steps toward achieving the performance, efficiency, and scalability that will be required for future systems. This review article will discuss fundamental tradeoffs in CMOS cryogenic designs in order to address the needs of future scaled quantum computing systems. |
| Autors: Sudipto Chakraborty;Rajiv V. Joshi; |
| Appeared in: IEEE Circuits and Systems Magazine |
| Publication date: May 2024, volume: 24, issue:2, pages: 34 - 46 |
| Publisher: IEEE |
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|
| » CSI-RFF: Leveraging Micro-Signals on CSI for RF Fingerprinting of Commodity WiFi |
Abstract:
This paper introduces CSI-RFF, a new framework that leverages micro-signals embedded within Channel State Information (CSI) curves to realize Radio-Frequency Fingerprinting of commodity off-the-shelf (COTS) WiFi devices for open-set authentication. The micro-signals that serve as RF fingerprints are termed “micro-CSI”. Through experimentation, we have found that the presence of micro-CSI can primarily be attributed to imperfections in the RF circuitry. Furthermore, this characteristic signal is detectable in WiFi 4/5/6 network interface cards (NICs). We have conducted further experiments to determine the most effective CSI collection configurations to stabilize micro-CSI. Yet, extracting micro-CSI for authentication purposes poses a significant challenge. This complexity arises from the fact that CSI measurements inherently include both micro-CSI and the distortions introduced by wireless channels. These two elements are intricately intertwined, making their separation non-trivial. To tackle this challenge, we have developed a signal space-based extraction technique for line-of-sight (LoS) scenarios, which can effectively separate the distortions caused by wireless channels and micro-CSI. Over the course of our comprehensive CSI data collection period extending beyond one year, we found that the extracted micro-CSI displays unique characteristics specific to each WiFi device and remains invariant over time. This establishes micro-CSI as a suitable candidate for device fingerprinting. Finally, we conduct a case study focusing on area access control for mobile robots. In particular, we applied our CSI-RFF framework to identify mobile robots operating in real-world indoor LoS environments based on their transmitted WiFi signals. To accomplish this, we have compared and employed anomaly detection algorithms for the authentication of 15 COTS WiFi 4/5/6 NICs that were carried by a mobile robot under both static and mobile conditions, maintaining an average signal-to-noise ratio (SNR) of 34 dB. Our experimental results demonstrate that the micro-CSI-based authentication algorithm can achieve an average attack detection rate close to 99% with a false alarm rate of 0% in both static and mobile conditions when using 20 CSI measurements to construct one fingerprint. |
| Autors: Ruiqi Kong;He Chen; |
| Appeared in: IEEE Transactions on Information Forensics and Security |
| Publication date: May 2024, volume: 19, issue:null, pages: 5301 - 5315 |
| Publisher: IEEE |
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|
| » Current Estimation and Optimal Control in Multiphase DC–DC Converters With Single Current Sensor |
Abstract:
The interleaved parallel topology of the dc–dc converter can increase the load capacity and reduce the ripple. However, the multiphase control system requires many current sensors, which increases the software/hardware cost. To solve this problem, this article proposes a dual time-scale model predictive control (DMPC) algorithm based on a single current sensor. First, the current estimator replaces current sensors to measure the unbalanced branch currents and reduce hardware complexity. Then, a DMPC algorithm is proposed to reduce the parallel computational complexity of the controller. Furthermore, a parameter identification module dynamically updates the current estimator and controller to further increase the system’s accuracy. This method reduces the software/hardware complexity while ensuring control effectiveness. The validity of the proposed algorithm is verified on a three-phase buck converter. |
| Autors: Junyi Li;Liyan Zhang;Xixiu Wu;Ze Zhou;Yuchen Dai;Qihong Chen; |
| Appeared in: IEEE Transactions on Instrumentation and Measurement |
| Publication date: May 2024, volume: 73, issue:null, pages: 1 - 14 |
| Publisher: IEEE |
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|
| » Cybercrime: Understanding the Current State of Literature and Issues Facing CISOs |
Abstract:
The meteoric rise in cybercrime in recent years has resulted in renewed efforts to stem the potential negative effects of these nefarious activities. In this context, the role of the chief information security officer (CISO) has become one of strategic importance, safeguarding the integrity of the organization’s digital assets. Given the economic impact of cybercrime, it has become critically important to understand the cybercrime-related issues that organizations face. We sought to identify these issues by conducting a bibliographic analysis of cybercrime research. The results identified the most prolific and impactful authors, journals, and countries of publication, the most influential articles, and trends in the literature on cybercrime. The research suggests that interest in the field is wide-reaching with the growth in publications stemming from diverse academic disciplines and geographies. The identified trends represent critical knowledge areas for the CISO that are likely to continue the expansion of the field. |
| Autors: Caitlin Ferreira;Andrew Park;Jan Kietzmann;Dionysios Demetis;Andrew Flostrand;Ian McCarthy;Leyland Pitt;Amir Dabirian; |
| Appeared in: IT Professional |
| Publication date: May 2024, volume: 26, issue:2, pages: 83 - 89 |
| Publisher: IEEE |
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|
| » Cybersecurity-Enabling Technologies: Digital Applications in the Energy Transition |
Abstract:
Data exchanges and cybersecurity obligations are mandated by energy regulations and cybersecurity legislation. This article examines Italy’s experience with the implementation of International Electrotechnical Commission (IEC) 62351 standards for securing distribution system operator (DSO)–distributed energy resource (DER) communications. |
| Autors: Giovanna Dondossola;Roberta Terruggia;Mauro G. Todeschini;Gianpatrizio Bianco;Luca Delli Carpini;Marco Modica; |
| Appeared in: IEEE Power and Energy Magazine |
| Publication date: May 2024, volume: 22, issue:3, pages: 42 - 49 |
| Publisher: IEEE |
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|
| » DC Survival: Myth of the War of the Currents [History] |
Abstract:
Popular history of electric power promotes an erroneous legend that a titanic 1890 “War of the Currents” immediately obliterated dc installations in favor of ac systems. Some authors may mention that dc has specialized railway and industrial applications. While there was a legal and public relations competition among proponents of each system, the actual competition was very different. There was no immediate change, as ac systems took time to perfect and dc retained its place, as it was superior for certain applications. Electric power at that time had three primary markets: local distribution of power to customers, electric railways, and long-distance transmission. Only in the latter did ac gain immediate acceptance, though not without competition from dc schemes. |
| Autors: John Paserba;Joseph J. Cunningham; |
| Appeared in: IEEE Power and Energy Magazine |
| Publication date: May 2024, volume: 22, issue:3, pages: 104 - 109 |
| Publisher: IEEE |
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|
| » Deep Boosting Learning: A Brand-New Cooperative Approach for Image-Text Matching |
Abstract:
Image-text matching remains a challenging task due to heterogeneous semantic diversity across modalities and insufficient distance separability within triplets. Different from previous approaches focusing on enhancing multi-modal representations or exploiting cross-modal correspondence for more accurate retrieval, in this paper we aim to leverage the knowledge transfer between peer branches in a boosting manner to seek a more powerful matching model. Specifically, we propose a brand-new Deep Boosting Learning (DBL) algorithm, where an anchor branch is first trained to provide insights into the data properties, with a target branch gaining more advanced knowledge to develop optimal features and distance metrics. Concretely, an anchor branch initially learns the absolute or relative distance between positive and negative pairs, providing a foundational understanding of the particular network and data distribution. Building upon this knowledge, a target branch is concurrently tasked with more adaptive margin constraints to further enlarge the relative distance between matched and unmatched samples. Extensive experiments validate that our DBL can achieve impressive and consistent improvements based on various recent state-of-the-art models in the image-text matching field, and outperform related popular cooperative strategies, e.g., Conventional Distillation, Mutual Learning, and Contrastive Learning. Beyond the above, we confirm that DBL can be seamlessly integrated into their training scenarios and achieve superior performance under the same computational costs, demonstrating the flexibility and broad applicability of our proposed method. |
| Autors: Haiwen Diao;Ying Zhang;Shang Gao;Xiang Ruan;Huchuan Lu; |
| Appeared in: IEEE Transactions on Image Processing |
| Publication date: May 2024, volume: 33, issue:null, pages: 3341 - 3352 |
| Publisher: IEEE |
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|
| » Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment |
Abstract:
The statistical regularities of natural images, referred to as natural scene statistics, play an important role in no-reference image quality assessment. However, it has been widely acknowledged that screen content images (SCIs), which are typically computer generated, do not hold such statistics. Here we make the first attempt to learn the statistics of SCIs, based upon which the quality of SCIs can be effectively determined. The underlying mechanism of the proposed approach is based upon the mild assumption that the SCIs, which are not physically acquired, still obey certain statistics that could be understood in a learning fashion. We empirically show that the statistics deviation could be effectively leveraged in quality assessment, and the proposed method is superior when evaluated in different settings. Extensive experimental results demonstrate the Deep Feature Statistics based SCI Quality Assessment (DFSS-IQA) model delivers promising performance compared with existing NR-IQA models and shows a high generalization capability in the cross-dataset settings. The implementation of our method is publicly available at https://github.com/Baoliang93/DFSS-IQA. |
| Autors: Baoliang Chen;Hanwei Zhu;Lingyu Zhu;Shiqi Wang;Sam Kwong; |
| Appeared in: IEEE Transactions on Image Processing |
| Publication date: May 2024, volume: 33, issue:null, pages: 3227 - 3241 |
| Publisher: IEEE |
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|
| » Deep-reinforcement-learning-based RMSCA for space division multiplexing networks with multi-core fibers [Invited Tutorial] |
Abstract:
The escalating demands for network capacities catalyze the adoption of space division multiplexing (SDM) technologies. With continuous advances in multi-core fiber (MCF) fabrication, MCF-based SDM networks are positioned as a viable and promising solution to achieve higher transmission capacities in multi-dimensional optical networks. However, with the extensive network resources offered by MCF-based SDM networks comes the challenge of traditional routing, modulation, spectrum, and core allocation (RMSCA) methods to achieve appropriate performance. This paper proposes an RMSCA approach based on deep reinforcement learning (DRL) for MCF-based elastic optical networks (MCF-EONs). Within the solution, a novel state representation with essential network information and a fragmentation-aware reward function were designed to direct the agent in learning effective RMSCA policies. Additionally, we adopted a proximal policy optimization algorithm featuring an action mask to enhance the sampling efficiency of the DRL agent and speed up the training process. The performance of the proposed algorithm was evaluated with two different network topologies with varying traffic loads and fibers with different numbers of cores. The results confirmed that the proposed algorithm outperforms the heuristics and the state-of-the-art DRL-based RMSCA algorithm in reducing the service blocking probability by around 83% and 51%, respectively. Moreover, the proposed algorithm can be applied to networks with and without core switching capability and has an inference complexity compatible with real-world deployment requirements. |
| Autors: Yiran Teng;Carlos Natalino;Haiyuan Li;Ruizhi Yang;Jassim Majeed;Sen Shen;Paolo Monti;Reza Nejabati;Shuangyi Yan;Dimitra Simeonidou; |
| Appeared in: IEEE/OSA Journal of Optical Communications and Networking |
| Publication date: May 2024, volume: 16, issue:7, pages: C76 - C87 |
| Publisher: IEEE |
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|
| » Deformation Field Formation Algorithm Based on Modified Kriging Interpolator in GNSS-Based InBSAR |
Abstract:
Global navigation satellite system-based bistatic synthetic aperture radar interferometry (GNSS-based InBSAR) is a radar remote sensing technique for measuring surface deformation with mm-level accuracy. However, it suffers from a low signal-to-noise ratio (SNR) and low image resolution, resulting in few persistent scatterers (PS) extracted. Thus, deformation measurements are more sparsely compared with other InSAR and an accurate interpolator is needed to obtain the deformation field. In this letter, a deformation field formation algorithm based on a modified Kriging interpolator is proposed to obtain the deformation field over the whole scene in GNSS-based InBSAR. First, the deformation measurements on the PS are divided into two sets by an adaptive threshold, and then the linear optimal matching model is constructed to overcome the invalidity of fitting parameters. Second, the spherical model re-fitting is used to obtain the final Kriging weights. The raw data of Beidou navigation satellites are used to validate the effectiveness of the proposed algorithm. |
| Autors: Feifeng Liu;Xiyue Zeng;Jian Gao;Zhanze Wang; |
| Appeared in: IEEE Geoscience and Remote Sensing Letters |
| Publication date: May 2024, volume: 21, issue:null, pages: 1 - 5 |
| Publisher: IEEE |
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|
| » Demonstration of Watt Level 375 nm Short Cavity Laser Diode With Etched Facets |
Abstract:
III-Nitride Laser diodes (LDs) emitting in the Ultra-Violet A (UVA) range with various cavity lengths, down to $100~\mu $ m, were demonstrated by implementing etched facets. Operating without mirror coating or packaging, the LD with a cavity length of $500~\mu $ m and a ridge width of $10~\mu $ m exhibited a threshold current (I $_{\mathrm {th}}$ ) of 300 mA and a slope efficiency (SE) exceeding 1W/A under pulse conditions. The highest power of 2.16 W was obtained under an injection current of 3 A. As the cavity length further decreased to $100~\mu $ m, the lowest Ith of 160 mA was obtained. Our results demonstrated the potential of optimizing SE and Ith for LDs in the UV A range with a careful design of the cavity lengths, especially toward the short cavities side, facilitated by etched facets. Such optimization can be particularly useful for applications prioritizing low power dissipation such as photonic integrated circuits. |
| Autors: Qinchen Lin;Guangying Wang;Cheng Liu;Surjava Sanyal;Swarnav Mukhopadhyay;Matthew Dwyer;Matthew Seitz;Tom Earles;Nelson Tansu;Jing Zhang;Luke Mawst;Shubhra S. Pasayat;Chirag Gupta; |
| Appeared in: IEEE Photonics Technology Letters |
| Publication date: May 2024, volume: 36, issue:11, pages: 741 - 744 |
| Publisher: IEEE |
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|
| » Density Prediction From Full Waveform Inversion With Gravity Gradient Constraints |
Abstract:
Adequate density information is essential for geophysical reservoir prediction and lithological interpretation. Achieving optimal density results through full waveform inversion (FWI) poses a consistent challenge. The low wavenumber components essential for density, easily derived from gravity data, are often unavailable in seismic inversion. Integrating gravity information into the seismic inversion can provide long wavelength density information and reduce the feasible solution space. Hence, we propose a joint gravity-seismic inversion method and develop a joint inversion objective function. We start by inverting the low wavenumber components for density using gravity data, followed by FWI with gravity gradient constraints for the high wavenumber information. Numerical examples show that our method can improve numerical accuracy and recover an accurate density model, providing petrophysical guidance for resource exploration. |
| Autors: Hongying Liu;Guochen Wu;Qingyang Li;Junzhen Shan;Sen Yang; |
| Appeared in: IEEE Geoscience and Remote Sensing Letters |
| Publication date: May 2024, volume: 21, issue:null, pages: 1 - 5 |
| Publisher: IEEE |
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|
| » Development of Dual-Band UV Photodetector Utilizing Nanostructured ZnO-ZnCr₂O₄ |
Abstract:
The use of multi-band photo-detection plays a crucial role in advancing several technologies, including light-wave communication, optical navigation technology, and environmental monitoring. In recent times, there has been a significant surge in the interest around the advancement of multiband ultraviolet (UV) photodetectors. Here, a high-performance dual-band UV photodetector based on ZnO-ZnCr2O4 nanostructures has been realized using a simple solution-process. The device has demonstrated dominant responses in UV-C (210 nm) and UV-A (350) regions. The associated photo-responsivity of 2.71 A/W and 1.91 A/W was calculated for UV-C and UV-A regions, respectively. The d-d transitions of Cr3+ in deep-UV region and band-to-band transitions of ZnO in UV-A region were found to be the prime reason for dual-band detection. This work paves the way for development of next-generation multi-band UV photodetector. |
| Autors: Tejendra Dixit;Jitesh Agrawal;Kolla Lakshmi Ganapathi;Vipul Singh; |
| Appeared in: IEEE Photonics Technology Letters |
| Publication date: May 2024, volume: 36, issue:11, pages: 733 - 736 |
| Publisher: IEEE |
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|
| » Development, Modeling and Assessment of Connected Automated Vehicle Applications |
Abstract:
The transportation system has evolved into a complex Cyber Physical System (CPS) with the introduction of wireless communication and the emergence of connected travelers and Connected Automated Vehicles (CAVs). The talk will discuss the challenges associated with multi-modal transportation system optimization and modeling, large-scale integrated modeling of the transportation and communication systems, some research in the area of multi-objective CAV optimization, some research in CAV-enabled traffic signal control, and the modeling and optimization of battery electric vehicles and hybrid electric vehicles. |
| Autors: Hesham A. Rakha; |
| Appeared in: IEEE Transactions on Intelligent Transportation Systems |
| Publication date: May 2024, volume: 25, issue:4, pages: 306 - 331 |
| Publisher: IEEE |
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|
| » Dilated Deep MPFormer Network for Hyperspectral Image Classification |
Abstract:
Hyperspectral image (HSI) possesses distinctive advantages in the classification of materials due to its rich spectral information. Convolutional neural network (CNN) and vision transformer (ViT), as mainstream methodologies, have demonstrated significant success. However, they often ignore subtle spectral differences, leading to the inadequate utilization of the spectral information. In this letter, we present a novel feature extraction and classification method, i.e., dilated deep MPFormer network (DDMN), which takes the inherent advantages of CNN and ViT while enhancing the exploitation of spectral information. First, the dilated depthwise separable convolution (DDSC) is proposed to expand the channel dimension, enabling the capture of subtle spectral differences among similar materials. Second, a sequence of improved transformers, i.e., MPFormer, are adopted to effectively extract spatial-spectral features, in which a new multiscale pooling mixer (MPMixer) is designed to replace the attention module in ViT, resulting in reduced parameter numbers and accelerated training speed. Finally, an adaptive weighted fusion module (AWFM) is developed to improve the interaction between specific texture features in shallow layers and abstract semantic features in deep layers. Extensive experiments demonstrate that the proposed DDMN method achieves improvements in OA of 0.8%, 1.04%, and 1.99% when compared to SOTA methods on three HSI datasets of LK, PU, and HS, respectively. |
| Autors: Qinggang Wu;Mengkun He;Wei Huang;Fubao Zhu; |
| Appeared in: IEEE Geoscience and Remote Sensing Letters |
| Publication date: May 2024, volume: 21, issue:null, pages: 1 - 5 |
| Publisher: IEEE |
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|
| » Direction-Aware and Foreground-Guided Remote Sensing Road Detection |
Abstract:
Road detection in remote sensing images holds significant application value in urban planning and autonomous driving. However, due to the complex background and the strip-like distribution of roads, road detection remains a challenging task. In this letter, we propose an innovative method for remote sensing road detection called direction-aware and foreground-guided (DAFG). First, direction-aware trans bridge (DATB) is formulated, which uses a dual-branch structure encompassing vertical and horizontal dimensions to capture the global–local context, effectively mitigating interference from nonroad area features. Next, foreground guidance module (FGM) is constructed, with the help of prediction maps derived from high-level semantic features, enhancing the encoding of low-level road features and suppressing background and noise. Finally, a dense aggregation module (DAM) is introduced to enhance feature representation and augment the capacity of classic decoding blocks (several cascaded convolutions). Experiments show that DAFG achieves higher intersection over union (IoU) than other methods on the Munich Road dataset (89.22%) and the WHU Road dataset (70.78%). |
| Autors: Ying Zhang;Zican Hu;Rui Lin;Xue Zhu;Xueyun Chen; |
| Appeared in: IEEE Geoscience and Remote Sensing Letters |
| Publication date: May 2024, volume: 21, issue:null, pages: 1 - 5 |
| Publisher: IEEE |
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|
| » Disinfecting AI: Mitigating Generative AI’s Top Risks |
Abstract:
Generative artificial intelligence (GenAI) is poised to become a cornerstone of tomorrow’s enterprise architecture, driving innovation and efficiency across industries. But, as organizations embrace this technology, they must mitigate key risks to ensure responsible implementation and thwart AI cyberattacks. |
| Autors: Mark Campbell;Mlađan Jovanović; |
| Appeared in: Computer |
| Publication date: May 2024, volume: 57, issue:5, pages: 111 - 116 |
| Publisher: IEEE |
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|
| » Dissertation, Inc. |
Abstract:
This “EIC’s Message” column offers a few key takeaway thoughts for anyone interested in trying to turn a Ph.D. dissertation into a business. |
| Autors: Jeffrey Voas; |
| Appeared in: Computer |
| Publication date: May 2024, volume: 57, issue:5, pages: 12 - 14 |
| Publisher: IEEE |
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|
| » Distortion and Compensation of Doppler Shift Characteristics of Vortex Beam Superposition Due to Rotating Rough Surfaces and Occlusion |
Abstract:
We employed the signal-to-maximum sideband ratio (SMSR) to investigate the sideband interference and compensation of the rough surface target-derived scattering distortions and occlusion on the frequency shift characteristics of the vortex beams superposition in the rotational Doppler effect (RDE). The critical roughness, particularly the critical occlusion ratio hindering rotational Doppler shift (RDS) peak discrimination subject to phase distortion, are discussed in detail. Finally, the phase retrieval algorithm is employed to compensate the distortion induced by atmospheric turbulence, aiming to enhance the optical field purity, RDS peak amplitude and SMSR indicators. The simulations demonstrate that the ±2 order beam has a stronger capability to resist the dispersion effect of occlusion on the RDS peak discrimination, which is 57% higher than the lowest one. The average enhancement factor of SMSR for higher-order beam is 13.53-folds higher than that of lower orders. $C_n^2$= 2 × 10−16 m−2/3 is the critical turbulence intensity that achieves a relative enhancement of the RDS peak amplitude. This research provides valuable insights for optimizing the precise measurement of rotational velocity in free-space RDE applications. |
| Autors: Hongyang Wang;Zijing Zhang;Chengshuai Cui;Yuan Zhao; |
| Appeared in: IEEE Photonics Journal |
| Publication date: May 2024, volume: 16, issue:3, pages: 1 - 8 |
| Publisher: IEEE |
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|
| » Distributed Kalman Filter Through Trace Proximity and Covariance Intersection |
Abstract:
Distributed filtering algorithms have gained wide-spread application in wireless sensor networks attributed to their advantages of low communication overhead and strong robustness. We propose a novel distributed Kalman filter. The core idea is that each node selects an adjacent node with the minimum cost function, which is followed by data fusion between the two nodes through the covariance intersection method. The specific selection process significantly reduces the complexity of the algorithm, and the unique fusion method ensures data reliability while improving estimation accuracy. We demonstrate that the designed distributed Kalman filtering algorithm is unbiased and consistent. Simulation results are provided to verify the correctness and performance of our algorithm. |
| Autors: Xiaoxi Yan;Longhu Jin; |
| Appeared in: IEEE Signal Processing Letters |
| Publication date: May 2024, volume: 31, issue:null, pages: 1299 - 1303 |
| Publisher: IEEE |
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|
| » Distributed Stochastic Model Predictive Control for Heterogeneous Vehicle Platooning Under Uncertainty |
Abstract:
Vehicle platooning for connected and automated vehicles (CAVs) has many potential benefits, such as lowering fuel consumption, improving traffic safety, and reducing traffic congestion. However, challenges remain toward safe and efficient vehicle platooning since its performance could be degraded due to uncertainty from vehicle dynamics, environmental disturbances, and communication delays, among others. In this talk, I will introduce one recent work on a new control method that combines distributed stochastic model predictive control (DSMPC) with Taguchi’s robustness (TR-DSMPC) for vehicle platooning. The proposed method inherits the advantages of both Taguchi’s robustness (maximizing the mean performance and minimizing the performance variation due to uncertainty) and stochastic model predictive control (ensuring a specific reliability level). The proposed method was compared with two other MPC-based methods in terms of safety (spacing error) and efficiency (relative velocity). The results indicate that the proposed method can effectively reduce the performance variation and maintain the mean performance compared to other methods. |
| Autors: Lingxi Li; |
| Appeared in: IEEE Transactions on Intelligent Transportation Systems |
| Publication date: May 2024, volume: 25, issue:4, pages: 124 - 148 |
| Publisher: IEEE |
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|
| » Don't Start a Career as an AI Prompt Engineer AI will Take Your Job |
Abstract:
Since ChatGPT dropped in the fall of 2022, everyone and their donkey has tried their hand at prompt engineering—finding a clever way to phrase their query to a large language model (LLM) or AI art or video generator to get the best results (or sidestep protections). The Internet is replete with prompt-engineering guides, cheat sheets, and advice threads to help you get the most out of an LLM. |
| Autors: Dina Genkina; |
| Appeared in: IEEE Spectrum |
| Publication date: May 2024, volume: 61, issue:5, pages: 30 - 34 |
| Publisher: IEEE |
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|
| » Drawing the Boundaries Between Blockchain and Blockchain-Like Systems: A Comprehensive Survey on Distributed Ledger Technologies |
Abstract:
Bitcoin’s success as a global cryptocurrency has paved the way for the emergence of blockchain, a revolutionary category of distributed systems. However, the growing popularity of blockchain has led to a significant divergence from its core principles in many systems labeled as “blockchain.” This divergence has introduced complexity into the blockchain ecosystem, exacerbated by a lack of comprehensive reviews on blockchain and its variants. Consequently, gaining a clear and updated understanding of the diverse spectrum of current blockchain and blockchain-like systems has become challenging. This situation underscores the necessity for an extensive literature review and the development of thematic taxonomies. This survey seeks to offer a comprehensive and current assessment of existing blockchains and their variations while delineating the boundaries between blockchain and blockchain-like systems. To achieve this objective, we propose a holistic reference model for conceptualizing and analyzing these systems. Our layer-wise framework envisions all distributed ledger technologies (DLTs) as composed of four principal layers: data, consensus, execution, and application (DCEA). In addition, we introduce a new taxonomy that enhances the classification of blockchain and blockchain-like systems, offering a more useful perspective than existing works. Furthermore, we conduct a state-of-the-art review from a layered perspective, employing 23 evaluative criteria predefined by our framework. We perform a qualitative and quantitative comparative analysis of 44 DLT solutions and 26 consensus mechanisms while discussing differences and boundaries between blockchain and blockchain-like systems. We emphasize the significant challenges and tradeoffs encountered by distributed ledger designers, decision-makers, and project managers during the design or adoption of a DLT solution. Finally, we outline crucial research challenges and directions in the field of DLTs. |
| Autors: Badr Bellaj;Aafaf Ouaddah;Emmanuel Bertin;Noel Crespi;Abdellatif Mezrioui; |
| Appeared in: Proceedings of the IEEE |
| Publication date: May 2024, volume: 112, issue:3, pages: 247 - 299 |
| Publisher: IEEE |
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|
| » Drift Detection for Black-Box Deep Learning Models |
Abstract:
Dataset drift is a common challenge in machine learning, especially for models trained on unstructured data, such as images. In this article, we propose a new approach for the detection of data drift in black-box models, which is based on Hellinger distance and feature extraction methods. The proposed approach is aimed at detecting data drift without knowing the architecture of the model to monitor, the dataset on which it was trained, or both. The article analyzes three different use cases to evaluate the effectiveness of the proposed approach, encompassing a variety of tasks including document segmentation, classification, and handwriting recognition. The use cases considered for the drift are adversarial assaults, domain shifts, and dataset biases. The experimental results show the efficacy of our drift detection approach in identifying changes in distribution under various training settings. |
| Autors: Luca Piano;Fabio Garcea;Andrea Cavallone;Ignacio Aparicio Vazquez;Lia Morra;Fabrizio Lamberti; |
| Appeared in: IT Professional |
| Publication date: May 2024, volume: 26, issue:2, pages: 24 - 31 |
| Publisher: IEEE |
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|
| » DTST: A Dual-Aspect Time Series Transformer Model for Fault Diagnosis of Space Power System |
Abstract:
Fault diagnosis is one of the key technologies for maintaining the reliability and safety of space power systems. High-precision fault diagnosis is crucial to ensuring the normal operation of the system. In recent years, fault diagnosis methods based on traditional deep learning models have matured, but these models have problems capturing long distance dependencies in sequences and are limited to modeling in the temporal dimension. To address these challenges, this article proposes a novel fault diagnosis method for space power systems, namely dual-aspect time series transformer (DTST). DTST first adopts a token sequence generation method to decompose the data into two sets of sequence tokens in the temporal and spatial dimensions. Then, by introducing the Transformer, it obtains class tokens for these two sets of sequence tokens and merges them into a global class token for performing fault diagnosis tasks. To validate the rationality of the DTST structural design, this article conducts comprehensive experiments on the space power system dataset and real telemetry dataset. The experimental results show that, compared to single-structure models, DTST with a dual-structure design performs superiorly in diagnostic performance. Meanwhile, the fusion of dual-structure design has also been adequately demonstrated. Compared to traditional deep learning models and Transformer variant models, DTST demonstrates superior performance and robustness. |
| Autors: Zhiqiang Xu;Mingyang Du;Yujie Zhang;Qiang Miao; |
| Appeared in: IEEE Transactions on Instrumentation and Measurement |
| Publication date: May 2024, volume: 73, issue:null, pages: 1 - 10 |
| Publisher: IEEE |
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|
| » Dual Structural Consistent Partial Domain Adaptation Network for Intelligent Machinery Fault Diagnosis |
Abstract:
In industrial scenarios, the source-domain (SD) data typically encompasses condition monitoring (CM) data from all machines within a workshop or factory setting, while the target-domain (TD) data may only include CM data from one or a small number of machines. The intelligent diagnostic method based on partial domain adaptation (PDA) represents a powerful tool for aligning features between SD and TD data within partial categories. However, existing PDA techniques can only align either the marginal or conditional distributions (CDs) between SD and TD data within the shared label space, but not both simultaneously. To overcome this limitation, our study introduces a dual structural consistent PDA network. This network leverages the vision transformer (ViT) as its foundation, ensuring effective extraction of distinguishable features from both SD and TD data. A weight balance mechanism is integrated into the partial adversarial training (PAT) process, facilitating marginal distribution alignment (MDA) between SD and TD data within the shared label space. Additionally, a knowledge distillation (KD)-based approach is employed for CD alignment (CDA) across the two structural consistent networks (SCNs), ensuring consistency in predictions for TD data. The effectiveness of our proposed method is demonstrated through its application on two sets of experimental faulty data, confirming its ability to provide a feature distribution that is not affected by domain changes but is discriminative for different classes when dealing with PDA tasks. |
| Autors: Kun Yu;Xuesong Wang;Yuhu Cheng;Ke Feng;Yongchao Zhang;Bin Xing; |
| Appeared in: IEEE Transactions on Instrumentation and Measurement |
| Publication date: May 2024, volume: 73, issue:null, pages: 1 - 13 |
| Publisher: IEEE |
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|
| » Dynamics of Ideological Biases of Social Media Users |
Abstract:
Humanity for centuries has perfected skills of interpersonal interactions and evolved patterns that enable people to detect lies and deceiving behavior of others in face-to-face settings. Unprecedented growth of people's access to mobile phones and social media raises an important question: How does this new technology influence people's interactions and support the use of traditional patterns? In this article, we answer this question for homophily-driven patterns in social media. In our previous studies, we found that, on a university campus, changes in student opinions were driven by the desire to hold popular opinions. Here, we demonstrate that the evolution of online platform-wide opinion groups is driven by the same desire. We focus on two social media: Twitter and Parler, on which we tracked the political biases of their users. On Parler, an initially stable group of Right-biased users evolved into a permanent Right-leaning echo chamber dominating weaker, transient groups of members with opposing political biases. In contrast, on Twitter, the initial presence of two large opposing bias groups led to the evolution of a bimodal bias distribution, with a high degree of polarization. We capture the movement of users from the initial to final bias groups during the tracking period. We also show that user choices are influenced by side-effects of homophily. Users entering the platform attempt to find a sufficiently large group whose members hold political biases within the range sufficiently close to their own. If successful, they stabilize their biases and become permanent members of the group. Otherwise, they leave the platform. We believe that the dynamics of users' behavior uncovered in this article create a foundation for technical solutions supporting social groups on social media and socially aware networks. |
| Autors: Mohammed Shahid Modi;James Flamino;Boleslaw K. Szymanski; |
| Appeared in: IEEE Communications Magazine |
| Publication date: May 2024, volume: 62, issue:5, pages: 36 - 42 |
| Publisher: IEEE |
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|
| » Editorial Publishing in a Practice-Oriented Journal: Why and How You Should Do It |
Abstract:
Academics publish in academic outlets. So far, this is nothing new. However, publishing in such journals leaves a big gap. Traditional academic peer-reviewed journals have a limited readership. First, academic articles are usually written in a technical language, using specialized jargon that makes it difficult for a nonexpert to follow. Second, such journal articles are usually very narrow on a specific topic. So, if you want to get an understanding of a bigger topic, you must collect different articles from various journals. Third, papers focus on describing how the research contributes to theoretical advancement, not on how the research can be used by managers in companies or policymakers. Even though there is usually such a section, this is a rather minor focus. Fourth, peer-review processes can take a very long time, sometimes even years. So, whatever you read, especially in high-ranked outlets, is research that was mostly completed several years ago. Last, more easily accessible formats, such as books or printed magazines, are less and less available, in general. Earlier, it was a common practice to publish books that managers and policymakers purchased regularly. The same is true for openly accessible (conference) proceedings, with some notably exemptions, such as information systems or computer science. Reaching the nonacademic audience has become more and more detached from regular academic work. |
| Autors: Alexander Brem; |
| Appeared in: IEEE Engineering Management Review |
| Publication date: May 2024, volume: 52, issue:2, pages: 6 - 8 |
| Publisher: IEEE |
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|
| » Editorial: Special Issue for the 75th Anniversary of the IEEE Circuits and Systems Society [Editorial] |
Abstract:
IEEE Circuits and Systems (CAS) Society (CASS) celebrates the 75th Anniversary of the Society in 2024. This is a major celebration for CASS after the Golden Jubilee celebration in 1999. This special Issue of the IEEE Circuits and Systems Magazine is one of many celebrations and events planned this year by and for CASS members. The Celebrations not only reflect upon the history of our Society from multiple angles but also look forward to the future. The planning for this Special Issue started in July 2023 when Gabriele Manganaro, VP-Publications of IEEE CASS, tasked the incoming Editor-in-Chief (KP) to plan for the Special Issue. A Call for White Papers was issued to the CASS membership on 20 July 2023. The authors were asked to provide historical progress over the last 25 years and point out future directions for the next 25 years. We received numerous White Papers in September 2023, and were able to invite only few authors to submit Full Papers. Authors of Invited White Papers submitted their Full papers in second half of December 2023. All reviews were completed by March 2024. Before providing an overview of the papers in this Special Issue, we begin by thanking all the authors who took the time to submit the White Papers (whether Invited or not) and the Full papers. We are very grateful to all the reviewers who provided reviews in short notice due to the time constraints to publish the Special Issue. |
| Autors: Keshab K. Parhi;Hai Li;Krishnan Kailas;Harish Krishnaswamy;Massimo Alioto;Maciej Ogorzalek; |
| Appeared in: IEEE Circuits and Systems Magazine |
| Publication date: May 2024, volume: 24, issue:2, pages: 3 - 3 |
| Publisher: IEEE |
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|
| » Educating Venture Scientists: Inspiring Students and Young Professionals To Commercialize Their Deep Tech Research Supported by Their Institutions |
Abstract:
Students and young professionals are truly energized by pursuing deep tech research that can underpin a successful tech startup. This global innovation strategy is inspired by role model students at premier U.S. universities. |
| Autors: Philip Treleaven; |
| Appeared in: Computer |
| Publication date: May 2024, volume: 57, issue:5, pages: 20 - 28 |
| Publisher: IEEE |
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|
| » Effectiveness of Intelligent Control Strategies in Robot-Assisted Rehabilitation—A Systematic Review |
Abstract:
This review aims to provide a systematic analysis of the literature focused on the use of intelligent control systems in robotics for physical rehabilitation, identifying trends in recent research and comparing the effectiveness of intelligence used in control, with the aim of determining important factors in robot-assisted rehabilitation and how intelligent controller design can improve them. Seven electronic research databases were searched for articles published in the years 2015 – 2022 with articles selected based on relevance to the subject area of intelligent control systems in rehabilitation robotics. It was found that the most common use of intelligent algorithms for control is improving traditional control strategies with optimization and learning techniques. Intelligent algorithms are also commonly used in sensor output mapping, model construction, and for various data learning purposes. Experimental results show that intelligent controllers consistently outperform non-intelligent controllers in terms of transparency, tracking accuracy, and adaptability. Active participation of the patients and lowered interaction forces are consistently mentioned as important factors in improving the rehabilitation outcome as well as the patient experience. However, there are limited examples of studies presenting experimental results with impaired participants suffering limited range of motion, so the effectiveness of therapy provided by these systems is often difficult to quantify. A lack of universal evaluation criteria also makes it difficult to compare control systems outside of articles which use their own comparison criteria. |
| Autors: Dexter Felix Brown;Sheng Quan Xie; |
| Appeared in: IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Publication date: May 2024, volume: 32, issue:null, pages: 1828 - 1840 |
| Publisher: IEEE |
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|
| » Effects of Probabilistic Priority on Delay at Permissive Left-Turn Signalized Intersections |
Abstract:
Probabilistic yielding is a commonly observed behavior at permissive left-turn signalized intersections in some developing countries, which has been studied in the past to reveal its effect on traffic capacity. However, the effect on left-turn traffic delay is still unrevealed and requires further investigation. Different from the method established for investigating traffic capacity, the most challenging issue in studying traffic delay is the recognition of queued and non-queued left-turn vehicles. Based on queuing theory, an analytical delay estimation model was developed in this paper, wherein the probabilistic yielding behavior at a permissive left-turn intersection was put into consideration. To estimate left-turn traffic delay by the M/G2/1 queuing model, the service time distributions for both queued and non-queued left-turn vehicles were derived. Then, stochastic simulations were performed to validate the established analytical model in case of various combinations of left-turn yielding rates, volume-to-capacity ratios (v/c ratios) of left-turn traffic, and through traffic flow rates. Considering that the traffic scenario is consistent with the traffic situation of a permissive left-turn signalized intersection described in HCM when the yielding rate equals 1, the proposed estimation model and simulation results were also validated by the HCM method. It was found that the proposed left-turn traffic delay estimation model is a cost-effective alternative to the microscopic simulation and HCM method. Through sensitivity tests, this research found that left-turn traffic delay increased rapidly with the left-turn yielding rate, the through traffic flow rate, and the left-turn v/c ratio, among which the through traffic flow rate is the most critical factor. Furthermore, it was found that the effect of the left-turn yielding rate on left-turn traffic delay appeared to be more prominent when the left-turn yielding rate was between 0 and 0.2. The results of this study can contribute to the guidance of traffic signal warrants for permissive left-turn phases. |
| Autors: Daobin Wang;Guangchuan Yang;Zong Tian;Dali Wei;Xuesong Mao; |
| Appeared in: IEEE Transactions on Intelligent Transportation Systems |
| Publication date: May 2024, volume: 25, issue:5, pages: 3402 - 3419 |
| Publisher: IEEE |
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|
| » Efficient Cloud Removal Network for Satellite Images Using SAR-Optical Image Fusion |
Abstract:
Clouds in remote sensing optical images often obscure essential information. They may lead to occlusion or distortion of ground features, thereby affecting the subsequent analysis and extraction of target information. Therefore, the removal of clouds in optical images is a critical task in various applications. Synthetic aperture radar (SAR)-optical image fusion has achieved encouraging performance in the reconstruction of cloud-covered information. Such methods, however, are extremely time-consuming and computationally intensive, making them difficult to apply in practice. This letter proposes a novel feature pyramid network (FPNet) that effectively reconstructs the missing optical information. FPNet enables the extraction and fusion of multiscale features from the SAR image and the cloudy optical image, as the FPNet leverages the power of convolutional neural networks by merging the feature maps from different scales. It can learn useful features efficiently because it downsamples the input images while preserving important information, thus reducing the computational workload. Experiments are conducted on a benchmark global SEN12MS-CR dataset and a regional South Sudan dataset. Results are compared with those of state-of-the-art methods such as DSen2-CR and GLF-CR. The experimental results demonstrate that FPNet accomplishes superior performance in terms of accuracy and visual effects. Both the inference and training speeds of FPNet are fast. Specifically, it runs at 96 FPS and requires less than 4 h to train a single epoch using SEN12MS-CR on two 2080ti GPUs. Therefore, it is suitable for applying to various study areas. |
| Autors: Chenxi Duan;Mariana Belgiu;Alfred Stein; |
| Appeared in: IEEE Geoscience and Remote Sensing Letters |
| Publication date: May 2024, volume: 21, issue:null, pages: 1 - 5 |
| Publisher: IEEE |
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|
| » Efficient Driving with Automated and Connected Vehicles Algorithms, Microsimulations, and Cyber-Physical Experiments |
Abstract:
The shift that we are witnessing toward automated, connected, electric, and shared vehicular transportation is likely to be the most disruptive since the early days of automobiles. Improved safety, increased comfort, and time saving potential are among the most anticipated positive impacts of automated and connected vehicles. With much easier access to information, increased processing power, and precision control, automated and connected vehicles also offer unprecedented opportunities for energy efficient movement of people and goods and enable more efficient road use. This talk takes a closer look at the energy saving and traffic efficiency potentials of automated and connected vehicles based on first principles of motion, optimal control theory, and practical examples from our past and ongoing research. For instance, the talk shows through experiments on streets of San Francisco, that considerable energy can be saved by coordinated movement at traffic lights. In highway driving, our optimal motion planning algorithms that adjust the speed and select the best lane via mixed integer optimization, save energy by anticipating the motion of human drivers or by wirelessly receiving the intentions of neighboring automated vehicles. Energy efficient motion of these automated vehicles has a harmonizing effect on mixed traffic, leading to additional benefits for upstream human-driven vehicles. Opportunities for cooperative driving further increases efficiency of a group of vehicles by allowing them to move in a coordinated manner. In this talk, these benefits are shown in mixed traffic microsimulations, as well as shown in a novel cyber-physical experiment with virtual traffic surrounding real automated vehicles on a test track. Throughout the talk the gaps and future research directions are also highlighted. |
| Autors: Ardalan Vahidi; |
| Appeared in: IEEE Transactions on Intelligent Transportation Systems |
| Publication date: May 2024, volume: 25, issue:4, pages: 214 - 256 |
| Publisher: IEEE |
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|
| » Electronically Assisted Astronomy > Take Detailed Images of the Heavens on the Cheap |
Abstract:
I hate the eye strain that often comes with peering through a telescope at the night sky—I'd rather let a camera capture the scene. But I'm too frugal to sink thousands of dollars into high-quality astrophotography gear. The Goldilocks solution for me is something that goes by the name of electronically assisted astronomy, or EAA. |
| Autors: David Schneider; |
| Appeared in: IEEE Spectrum |
| Publication date: May 2024, volume: 61, issue:5, pages: 16 - 18 |
| Publisher: IEEE |
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|
| » Enabling Analysis, Modeling, and Simulation for Cooperative Automated Vehicle Applications in Transportation Systems: The Pillar Diagram |
Abstract:
The objective of this research was to develop information on how analysis, modeling, and simulation (AMS) tools can be enabled to evaluate cooperative automated vehicle (CAV) applications, allowing transportation professionals using AMS to assess the safety, mobility, environmental, and energy benefits of CAVs. This effort aims to assist transportation agencies in developing information on the safe deployment and operation of these vehicles and to make informed decisions for infrastructure investments. The talk provides recommendations on how existing AMS tools can be complemented to better conduct CAV evaluation in a generalized modeling framework. We provide recommendations on the use of the proposed framework, including the pillar diagram, to facilitate the brainstorming process involved in using and complementing existing AMS tools to better conduct CAV evaluation. Using this framework, users can evaluate which AMS tools to use and how to augment them with programming and scripting when needed. Finally, the talk provides an example of a signalized arterial implementation to demonstrate how to bridge the gap between the current capabilities of AMS tools and desired capabilities for proper CAV evaluation from the perspective of transportation professionals and analysts. |
| Autors: Monty Abbas; |
| Appeared in: IEEE Transactions on Intelligent Transportation Systems |
| Publication date: May 2024, volume: 25, issue:4, pages: 2 - 28 |
| Publisher: IEEE |
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|
| » Enhanced Acetic Anhydride Detection Based on ZnO/La₂O₃ Nanoparticles With High Selectivity and Sensitivity |
Abstract:
Acetic anhydride is a crucial chemical composition for heroin synthesis and is also a hazardous volatile organic compound (VOC). Hence, the development of a reliable and sensitive chemical sensor for its detection is essential. In this study, ZnO/La2O3 nanoparticles were successfully synthesized via a simple one-step hydrothermal process to detect acetic anhydride. The surface morphology, composition, and microstructure of particles were thoroughly analyzed. The 0.2 at% La-ZnO-based gas sensor exhibits a maximum of 422 toward 100-ppm acetic anhydride at the optimal operating temperature of 300 °C. The synergistic catalysis between ZnO and La2O3 heterojunction solves the problem of low sensitivity of pure ZnO to acetic anhydride. The unique selectivity and the high sensitivity of sensors to acetic anhydride are associated with the Lewis acid–base interaction because of the enhanced Lewis base sites on the surface of sensing materials. Significantly, the gas-sensitive mechanism is elaborated, including the effect of heterojunction and oxygen vacancies, and the catalysis of La2O3. The most important thing is that the rapid detection of acetic anhydride has been realized, which is significant in curbing the spread of drugs. |
| Autors: Zhan Cheng;Hongmin Zhu;Hanyang Ji;Lu Kong;Zhenyu Yuan;Fanli Meng; |
| Appeared in: IEEE Transactions on Instrumentation and Measurement |
| Publication date: May 2024, volume: 73, issue:null, pages: 1 - 8 |
| Publisher: IEEE |
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|
| » Enhanced Few-Shot Malware Traffic Classification via Integrating Knowledge Transfer With Neural Architecture Search |
Abstract:
Malware traffic classification (MTC) is one of the important research topics in the field of cyber security. Existing MTC methods based on deep learning have been developed based on the assumption of enough high-quality samples and powerful computing resources. However, both are hard to obtain in real applications especially in availability of IoT. In this paper, we propose a few-shot MTC (FS-MTC) method combining knowledge transfer and neural architecture search (i.e. NAS-based FS-MTC) with limited training samples as well as acceptable computational resources, in order to mitigate the identified challenges. Specifically, our proposed method first converts the raw network traffic into traffic images through data pre-processing to serve as input data for the neural network. Second, we use neural architecture search to adaptively search for the effective feature extraction model on the source domain (including Edge-IIoTset, Bot-IoT, and benign USTC-TFC2016). Third, the searched model is pre-trained on source task to achieve the generic feature representation of malware traffic. Finally, we only use few-shot malware traffic samples to fine-tune the pre-trained model to quickly adapt to new types of MTC tasks in realistic network environments. The experimental results show that the proposed NAS-based FS-MTC method has great scalability and classification performance in different FS-MTC tasks, including 5-way K-shot USTC-TFC2016 dataset and 10-way K-shot CIC-IoT dataset. Compared with state-of-the-art methods in the field of malware classification, the proposed NAS-based FS-MTC has higher classification accuracy. Especially in the 1-shot case of the USTC-TFC2016 dataset, its average accuracy is as high as 86.91%. |
| Autors: Xixi Zhang;Qin Wang;Maoyang Qin;Yu Wang;Tomoaki Ohtsuki;Bamidele Adebisi;Hikmet Sari;Guan Gui; |
| Appeared in: IEEE Transactions on Information Forensics and Security |
| Publication date: May 2024, volume: 19, issue:null, pages: 5245 - 5256 |
| Publisher: IEEE |
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