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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2024年 10卷 487-499页

作者： Oren-Loberman, Mor Azar, Vered Huleihel, Wasim Tel Aviv Univ Dept Elect Engn Syst IL-6997801 Tel Aviv Israel

Modern social media platforms play an important role in facilitating rapid dissemination of information through their massive user networks. Fake news, misinformation, and unverifiable facts on social media platforms propagate disharmony and affect society. In this paper, we consider the problem of online auditing of information flow/propagation with the goal of classifying news items as fake or genuine. Specifically, driven by experiential studies on real-world social media platforms, we propose a probabilistic Markovian information spread model over networks modeled by graphs. We then formulate our inference task as a certain sequential detection problem with the goal of minimizing the combination of the error probability and the time it takes to achieve the correct decision. For this model, we find the optimal detection algorithm minimizing the aforementioned risk and prove several statistical guarantees. We then test our algorithm over real-world datasets. To that end, we first construct an offline algorithm for learning the probabilistic information spreading model, and then apply our optimal detection algorithm. Experimental study show that our algorithm outperforms state-of-the-art misinformation detection algorithms in terms of accuracy and detection time.

关键词： Fake news Social networking (online) Probabilistic logic Feature extraction Information processing Hidden Markov models detection algorithms Misinformation flow online auditing sequential detection bounds algorithms processing over graphs

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Learnable Matched Filter for Methane Plume Segmentation in Hyperspectral Imagery

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： de Araujo, Ronald Albert Zortea, Maciel IBM Res Brazil Lab BR-20031170 Rio De Janeiro Brazil

Methane emissions are a major contributor to global warming, making the detection and monitoring of anthropogenic methane sources, such as oil and gas operations and landfills, a critical task. Remote sensing using hyperspectral sensors has proven to be a valuable tool for this purpose. However, traditional methane detection algorithms based on matched filters often produce spurious results and require laborious postprocessing to accurately identify actual plumes. In this letter, we propose a novel approach for estimating the target signature of a matched filter as a learnable layer integrated into a user-selected segmentation model, trainable in an end-to-end manner. By considering the signature as a parameter of the segmentation model, our methodology enables the signature to adapt both to the task and the user-selected segmentation approach. We use hyperspectral images from the EMIT instrument and their associated publicly available methane plumes to train deep learning segmentation models. Our results show consistent improvements in both convolutional neural networks (CNNs) and transformer architectures, as demonstrated by the $F1$ segmentation score increasing by approximately 18% compared to the baseline values. This allows for more accurate and automated plume segmentation, ultimately aiding in the identification of methane leaks from point source emitters.

关键词： Climate change Methane detection algorithms Deep learning Remote sensing Greenhouse gases Global warming Hyperspectral imaging greenhouse gas methane detection remote sensing

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Low-Complexity Polar-Coded Iterative OTFS Receiver

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IEEE COMMUNICATIONS LETTERS 2025年第1期29卷 60-64页

作者： Zhang, Huihou Jing, Lianyou Zhao, Nan He, Chengbing Li, Yonghui Dalian Univ Technol Sch Informat & Commun Engn Dalian 116024 Liaoning Peoples R China Northwestern Polytech Univ Ocean Inst Taicang 215400 Jiangsu Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Shaanxi Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

The orthogonal time-frequency space (OTFS) modulation has gained significant attention, due to its robust performance in high-speed mobile environment. In this correspondence, we develop a novel approach wherein delay-Doppler grid symbols into vectors are organized according to their Doppler index for OTFS. A novel detection algorithm is proposed in the Doppler transform domain, which offers lower complexity and better bit error rate (BER) performance compared to existing OTFS detection algorithms. In addition, considering the superior performance of short-block polar codes, we integrate them to further enhance the performance. Simulation results validate the performance advantages of the proposed algorithm over existing benchmarks in terms of BER for OTFS. Specifically, BER performance can be improved by at least 1 dB.

关键词： Vectors Complexity theory Channel estimation Bit error rate Time-frequency analysis Detectors detection algorithms Reliability Indexes OTFS detection low complexity polar codes polar codes

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Quantization for Communication-Efficient Change-Point detection Over Networks

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 142-157页

作者： Jiao, Yuchen Xu, Xingyu Gu, Yuantao Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Change-Point detection (CPD) has been researched for a long time in statistical signal processing, and has been widely used in many applications such as monitoring and anomaly detection. When detecting over networks, existing algorithms require sensors to exchange information via transmitting full-precision variables, which requires large bandwidth and consumes a large amount of energy. To address these problems, we explore the idea of quantizing data with low precision without deteriorating the detection performance. Specifically, considering the strong autocorrelation of the transmitted sequence in time domain and the accumulation of quantization error due to the autocorrelation, we design a scheme that combines the differential quantization technique and the error feedback technique. Based on this scheme, we propose a communication-efficient decentralized CPD algorithm named Quantized CUSUM (Q-CUSUM), which only requires small bandwidth and has low energy consumption. We theoretically analyze two commonly-used detection criteria, namely Average Run Length (ARL) and Expected detection Delay (EDD), and compute their theoretical bounds. It is proved that the detection performance is robust to the quantization error. The proposed quantization scheme can be easily extended to the partial-communication case, where at each time only a subset of sensor pairs are allowed to exchange information. Experiments are implemented on both synthetic and real-world datasets. The results indicate that the proposed quantization scheme can effectively reduce the communication cost without deteriorating the detection performance.

关键词： Quantization (signal) Sensors Signal processing algorithms Bandwidth detection algorithms Delays Protocols Change-point detection decentralized algorithms CUSUM differential quantization error feedback

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Automated Identification of Malaria-Infected Cells and Classification of Human Malaria Parasites Using a Two-Stage Deep Learning Technique

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IEEE ACCESS 2024年 12卷 135746-135763页

作者： Sukumarran, Dhevisha Loh, Ee Sam Khairuddin, Anis Salwa Mohd Ngui, Romano Sulaiman, Wan Yusoff Wan Vythilingam, Indra Divis, Paul Cliff Simon Hasikin, Khairunnisa Univ Malaya Fac Engn Dept Biomed Engn Kuala Lumpur 50603 Malaysia Univ Malaya Fac Engn Ctr Intelligent Syst Emerging Technol CISET Kuala Lumpur 50603 Malaysia Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia Univ Malaysia Sarawak Fac Med & Hlth Sci Malaria Res Ctr Kota Samarahan 94300 Sarawak Malaysia Univ Malaya Fac Med Dept Parasitol Kuala Lumpur 50603 Malaysia

The gold standard for diagnosing malaria remains microscopic examination;however, its application is frequently impeded by the lack of a standardized framework that guarantees uniformity and quality, particularly in scenarios with limited resources and high volume. This study suggests a novel and highly effective automated diagnostic approach that employs deep-learning object detectors to improve the accuracy and efficiency of malaria-infected cell detection and Plasmodium species classification to overcome these challenges. Plasmodium parasites were detected within thin blood stain images using the YOLOv4 and YOLOv5 models, which were optimized for this purpose. YOLOv5 obtains a slightly higher accuracy on the source dataset (mAP@ 0.5=96 %) than YOLOv4 (mAP@ 0.5=89 %), but YOLOv4 exhibits superior robustness and generalization across diverse datasets, as demonstrated by its performance on an independent validation set (mAP@ 0.5=90 %). This robustness emphasizes the dependability of YOLOv4 for deployment in a variety of clinical settings. Furthermore, an automated process was implemented to produce bound single-cell images from YOLOv4's localization outputs, thereby eradicating the necessity for conventional and time-consuming segmentation methods. The DenseNet-121 model, which was optimized for species identification, obtained an impressive overall accuracy of 95.5% in the subsequent classification stage, indicating excellent generalization across all malaria species. Accurate classification of Plasmodium species on microscopically thin blood films is essential for guiding appropriate therapy and preventing unnecessary anti-malarial treatments, which can lead to adverse effects and contribute to drug resistance. This research contributes to the field of automated malaria diagnosis by offering a comprehensive framework that substantially improves clinical decision-making, particularly in resource-limited environments.

关键词： Malaria Microscopy Deep learning Cells (biology) Accuracy Support vector machines Films Machine learning Surveillance Artificial intelligence detection algorithms deep learning biosurveillance AI-monitoring detection

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Performance Analysis and Low-Complexity Design for XL-MIMO With Near-Field Spatial Non-Stationarities

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2024年第6期42卷 1656-1672页

作者： Zhi, Kangda Pan, Cunhua Ren, Hong Chai, Kok Keong Wang, Cheng-Xiang Schober, Robert You, Xiaohu Tech Univ Berlin Sch Elect Engn & Comp Sci D-10623 Berlin Germany Southeast Univ Natl Mobile Commun Res Lab Nanjing 211189 Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Friedrich Alexander Univ Erlangen Nurnberg FAU Inst Digital Commun D-91054 Erlangen Germany

Extremely large-scale multiple-input multiple-output (XL-MIMO) is capable of supporting extremely high system capacities with large numbers of users. In this work, we build a framework for the analysis and low-complexity design of XL-MIMO in the near field with spatial non-stationarities. Specifically, we first analyze the theoretical performance of discrete-aperture XL-MIMO using an electromagnetic (EM) channel model based on the near-field spherical wavefront. We analytically reveal the impact of the discrete aperture and polarization mismatch on the received power. We also complement the classical Fraunhofer distance based on the considered EM channel model. Our analytical results indicate that a limited part of the XL-array receives the majority of the signal power in the near field, which leads to a notion of visibility region (VR) of a user. Thus, we propose a VR detection algorithm and leverage the acquired VR information to devise a low-complexity symbol detection scheme. Furthermore, we propose a graph theory-based user partition algorithm, relying on the VR overlap ratio between different users. Partial zero-forcing (PZF) is utilized to eliminate only the interference from users allocated to the same group, which further reduces computational complexity in matrix inversion. Numerical results confirm the correctness of the analytical results and the effectiveness of the proposed algorithms. It reveals that our algorithms approach the performance of conventional whole array (WA)-based designs but with much lower complexity.

关键词： Channel models Antenna arrays detection algorithms Apertures Transmission line matrix methods MIMO communication Vectors Extremely-large-scale MIMO electromagnetic channel model visibility region spatial non-stationarities near-field communications

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Automatic detection and Tracking of Random Frequency Signals Using Magnitude and Phase Information

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IEEE ACCESS 2022年 10卷 67373-67382页

作者： Carevic, Dragana Def Sci & Technol Grp Maritime Div HMAS Stirling Rockingham WA 6168 Australia

This paper presents an approach to the problems of detection and frequency estimation of a frequency modulated narrow-band signal in additive complex Gaussian noise. The signal is assumed to have an unknown amplitude, initial phase and frequency trajectory over time, while a priori information regarding random frequency variability is taken to be available. The proposed approach operates in the frequency domain and uses the magnitude and phase of the discrete-time Fourier transforms computed over nonoverlapping signal segments. Robustness at low signal-to-noise ratios is achieved by suppressing the segment-related likelihoods for which the phase estimation error is large. The approach utilises unthresholded transform data and thus works in a track-before-detect manner, and the frequency trajectory is estimated by applying a search over the frequency-time bins. The results of a simulation study involving two signal types with different frequency variability are described.

关键词： Time-frequency analysis Frequency estimation Hidden Markov models Radar tracking Phase frequency detectors Estimation Signal to noise ratio detection algorithms frequency estimation Markov processes maximum a posteriori estimation phase frequency detectors phase estimation signal detection signal processing algorithms state estimation Viterbi algorithm

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EMD-based noise reduction study of steel cored conveyor belt containing slag signal

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ALEXANDRIA ENGINEERING JOURNAL 2024年 98卷 56-67页

作者： Sun, Guoxin Zhang, Runze Liu, Zejiang Wu, Linfeng Yu, Qihui Tan, Xin Inner Mongolia Univ Sci & Technol Sch Mech Engn Baotou 014010 Peoples R China Key Lab Intelligent Diag & Control Electromech Sys Baotou 014010 Inner Mongolia Peoples R China China ShenhuaCoal Liquid & Chem Ordos Co Liquefact Ordos 017000 Peoples R China Peking Univ Ordos Res Inst Energy Ordos 017000 Peoples R China

This paper proposes a noise reduction technique that combines empirical mode decomposition (EMD) and wavelet thresholding to eliminate the noise interference caused by metal slag in the steel cord conveyor belt damage signal. Initially, the original signal undergoes decomposition by EMD into high- and low-frequency intrinsic mode function (IMF) components based on metal slag signal feature analysis. The wavelet threshold de-noising method is then applied to filter out the high-frequency noise while preserving the low-frequency IMF components. The IMF components containing slag noise are discarded, and the filtered signal and the lowfrequency IMF components are used for signal reconstruction to obtain an optimal noise reduction effect. The study demonstrates that this method is more effective in suppressing metal slag noise, environmental noise, and other noise interferences than traditional EMD decomposition noise reduction and wavelet threshold noise reduction methods. This research provides a valuable reference for noise reduction in steel cord conveyor belt detection data.

关键词： Metal slag Belt Empirical modal decomposition Wavelet thresholding detection algorithms

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Improved Differential Based Protection Scheme for Renewable Energy Based Microgrids With Low Communication Burden

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IEEE ACCESS 2024年 12卷 49007-49016页

作者： Alizadeh, Behzad Asle Mohammad Arani, Mohammadreza Fakhari Moghaddam Toronto Metropolitan Univ Dept Elect & Biomed Engn Toronto ON M5B 2K3 Canada

Due to prevalence of distributed energy resources, especially inverter interfaced ones, conventional protection systems meet substantial challenges for fault detection. Differential protection is the most reliable protection method for current networks;however, the conventional method leads to high communication burden. We propose a new differential protection based on disturbance detection and positive phase angle differences. The disturbance detection method is proposed based on Mathematical Morphology which has high speed and accuracy. Instead of transmitting the instantaneous values or the phasor of the currents which impose high communication burden on the network, the phase angle of the current is the only data which is transmitted when a sudden change is detected. The proposed method reduces the communication burden significantly and increases the resiliency of the protection scheme. The effectiveness of the proposed method is verified in modified IEEE 9 bus 3 machine network with three distributed energy resources. PSCAD is used to simulate the microgrid and the proposed method is implemented in MATLAB. It is proved that this method works properly in both islanded and grid connected modes and despite significant changes in the grid. The robustness of method in presence of noise and its high reliability and dependability is verified through various simulation case studies.

关键词： Microgrids Circuit faults Impedance Relays Current measurement Morphology Harmonic analysis detection algorithms Mathematical models Distributed power generation Renewable energy sources Differential protection microgrid disturbance detection mathematical morphology

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Persistent Wide-Area Maritime Surveillance Using Smallsats With MIMO Radar Beamforming

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IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE 2024年第3期39卷 16-27页

作者： Weissman, Isaac Independent Radar Consultant Washington DC USA

This article has presented an application, utilizing existing technologies, that would achieve uninterrupted radar surveillance of maritime traffic over wide ocean areas. The application entails the deployment in GEO of a large number of small satellites interconnected by MIMO processing in order to form large virtual arrays with acceptable angular resolution. The potential benefits of such an application are substantial. However, although there appear to be no fundamental obstacles to such an implementation, there are significant design challenges, including the minimizations of the aggregate launch mass and the data processing burden. A risk reduction program is suggested to address these challenges by implementing the application in distinct stages. For persistent maritime surveillance, there seems to be no better practical alternative to geostationary radar deployments.

关键词： Small satellites Surveillance Radar Marine vehicles Radar cross-sections Sea surface Radar antennas Maritime communications Array signal processing Radar applications Payloads MIMO radar detection algorithms GEO maritime surveillance MIMO persistence radar smallsats

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