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International Joint Conference on Neural Networks (IJCNN)

作者： Songlin Li Zhe Li Boyuan Li Xiuhong Li Jiabao Sheng School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China Department of Electrical and Electronic Engineering Hong Kong Polytechnic University HongKong China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Camouflaged objects, exhibiting high similarity with their surroundings, pose a substantial challenge for both humans and machines to detect when concealed within the environment. Existing methods for camouflage object detection (COD) struggle in accurately segmenting the overall structure of camouflaged objects. To address this issue, we propose a novel boundary-guided fusion of multi-level features network (BGFM-Net) for COD. In contrast to existing boundary-guided methods, we pay more attention to addressing the significant imbalance in the pixel quantities between boundary and background features, allowing for a more comprehensive representation of boundary features. BGFM-Net primarily consists of a multi-scale aggregation module (MSAM), a boundary-guided feature module (BFM), and a cross-Level fusion module (CLFM). MSAM effectively integrates contextual semantics at different scales, achieving a powerful and efficient feature representation. BFM adeptly combines edge features while constraining interference from background features, guiding the learning of camouflaged object boundary representation. CLFM integrates multi-level features for predicting camouflaged objects while adaptively adjusting channel weights to emphasize important channels and diminish the impact of less relevant channels for the task. Extensive experiments on three benchmark camouflage datasets demonstrate that our BGFM-Net outperforms other state-of-the-art COD models.

关键词： Adaptation models Semantics Neural networks Object detection Interference Benchmark testing Feature extraction

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multi-Task Sparse signal Recovery with Dirichlet Process Priors Based on Expectation Propagation Technique 7

Multi-Task Sparse Signal Recovery with Dirichlet Process Pri...

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7th International Conference on signal and Image processing, ICSIP 2022

作者： Fu, Yin Wu, Qisong Zhang, Yimin D. Amin, Moeness G. Southeast University Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Nanjing210096 China Temple University Department of Electrical and Computer Engineering PhiladelphiaPA19122 United States Villanova University Center for Advanced Communications VillanovaPA19085 United States

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Exploiting the shared information among tasks to significantly improve the sparse reconstruction performance lays the essence of multi-task compressive sensing. In this paper, a novel generative model of multi-task compressive sensing with Dirichlet process (DP) priors is proposed and the sharing mechanisms among tasks are revealed, yielding a principled means of inferring the clusters as well as performing compressive sensing inversion simultaneously. The spike-and-slab priors are first used to model the group sparsity among tasks within the identical cluster, and the DP priors are then introduced to automatically perform clustering for the tasks. The expectation propagation method is finally carried out to take the inference for posterior distribution approximation. The superiority of the proposed method over state-of-the-art algorithms is demonstrated by using experimental results on both numerical data and real data sets. © 2022 IEEE.

关键词： Compressed sensing

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Direct multipath-Based SLAM

arXiv

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arXiv 2024年

作者： Liang, Mingchao Leitinger, Erik Meyer, Florian The Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA92093 United States The Signal Processing and Speech Communication Laboratory Graz University of Technology Graz8010 Austria The Scripps Institution of Oceanography The Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA92093 United States

In future wireless networks, the availability of information on the position of mobile agents and the propagation environment can enable new services and increase the throughput and robustness of communications. multipath-based simultaneous localization and mapping (SLAM) aims at estimating the position of agents and reflecting features in the environment by exploiting the relationship between the local geometry and multipath components (MPCs) in received radio signals. Existing multipath-based SLAM methods preprocess received radio signals using a channel estimator. The channel estimator lowers the data rate by extracting a set of dispersion parameters for each MPC. These parameters are then used as measurements for SLAM. Bayesian estimation for multipath-based SLAM is facilitated by the lower data rate. However, due to finite resolution capabilities limited by signal bandwidth, channel estimation is prone to errors and MPC parameters may be extracted incorrectly and lead to a reduced SLAM performance. We propose a multipath-based SLAM approach that directly uses received radio signals as inputs. A new statistical model that can effectively be represented by a factor graph is introduced. The factor graph is the starting point for the development of an efficient belief propagation (BP) method for multipath-based SLAM that avoids data preprocessing by a channel estimator. Numerical results based on synthetic and real data in challenging single-input, single-output (SISO) scenarios demonstrate that the proposed method outperforms conventional methods in terms of localization and mapping accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel estimation

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Variability in Spread Spectrum Time Domain Reflection and Transmission Measurements

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Makin, Samuel Addad, Mouad Redhead, Brent Furse, Cynthia M. University of Utah Electrical and Computer Engineering Department Salt Lake City 84112 UT United States Djillali Liabes University of Sidi Bel Abbes Telecommunications and Digital Signal Processing Laboratory SBA Algeria

Spread spectrum time-domain reflectometry (SSTDR) offers a new nonintrusive measurement technique for microwave imaging. While vector network analyzers (VNAs) are conventional tools in this domain, they present significant drawbacks such as high cost and lengthy scan times. This article explores the viability of substituting SSTDR devices for the VNA by validating their accuracy in comparison to traditional VNAs in the 0.5–4-GHz band. We conducted several tests of reflection and transmission measurements of resistor–capacitor (RC) loads, analysis of variations due to cable movement, calibration consistency, and temperature-induced variations. We also measured antenna S11 and S12 in two oil-based phantoms to simulate human breast tissue, comparing changes with the insertion of a metal tumor substitute. We found that the mean confidence intervals (CIs) of the VNA and SSTDR are better (less) than 0.03 for |S11| and 0.02 for |S21| from 0.5 to 4 GHz and that metal “tumors” in the oil are detectable. These results suggest that SSTDR may be a viable alternative to VNAs for applications such as microwave breast imaging, warranting further investigation in more robust setups.

关键词： Antenna measurements reflectometry spread spectrum time-domain reflectometry (SSTDR) variability

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MN-Net: Speech Enhancement Network via Modeling the Noise

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 1208-1219页

作者： Ying Hu Qin Yang Wenbing Wei Li Lin Liang He Zhijian Ou Wenzhong Yang School of Computer Science and Technology Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China Xinjiang Institute of Electronics Research Shares Company Ltd. Urumqi China Department of Electrical Engineering Tsinghua University Beijing China Beijing National Research Center for information Science and Technology Department of Electronics Tsinghua University Beijing China

Currently, deep learning-based speech enhancement methods generally focus on target speech extraction while neglecting modeling the other sound sources in the mixture. These methods still can't distinguish the target speech from the interference well. In this paper, we present a monaural speech enhancement network via Modeling the Noise (MN-Net), which includes a shared Encoder and three separate Decoders for parallel modeling the magnitude and phase spectrogram of target speech, and the complex spectrogram of noise. Specifically, we propose a multi-Branch Feature Extractor (MBFE) module to capture the richer contextual information in mixture, and a Spatial Reconstruction Unit (SRU) to remove the redundancy from extracted features. We compared our proposed MN-Net with 18 classical speech enhancement methods on the VoiceBank+DEMAND dataset, and with 9 ones on DNS-Challenge dataset for denoising task, and with 7 ones on the WHAMR! dataset for simultaneous denoising & de-reverberation task. Our proposed MBFE module was applied to two classical speech enhancement methods, DB-AIAT and CMGAN, replacing their DenseBlocks module. The results demonstrate that applying the MBFE module can boost their performances while keeping smaller model size. A series of visualization analysis intuitively verify that modeling the noise can enable the network to distinguish the target speech from noise and other interference more accurately.

关键词： Noise Feature extraction Spectrogram Speech enhancement Decoding Kernel Transformers Noise reduction Data mining Training

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Direct multipath-Based SLAM

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IEEE Transactions on signal processing 2025年 73卷 2336-2352页

作者： Liang, Mingchao Leitinger, Erik Meyer, Florian University of California San Diego Department of Electrical and Computer Engineering La Jolla CA92093 United States Graz University of Technology Signal Processing and Speech Communication Laboratory Graz8010 Austria University of California San Diego Scripps Institution of Oceanography La Jolla CA92093 United States

Radio-based localization approaches that make use of reflections in the propagation environment to improve the accuracy and robustness of location estimates have a variety of potential applications in future wireless communication networks. multipath-based simultaneous localization and mapping (SLAM) aims at estimating the position of agents and reflecting features in the environment by exploiting the relationship between the local geometry and multipath components (MPCs) in received radio signals. Existing multipath-based SLAM methods preprocess received radio signals using a channel estimator. The channel estimator lowers the data rate by extracting a set of dispersion parameters for each MPC. These parameters are then used as measurements for SLAM. Bayesian estimation for multipath-based SLAM is facilitated by the lower data rate. However, due to finite resolution capabilities limited by signal bandwidth, channel estimation is prone to errors and MPC parameters may be extracted incorrectly and lead to a reduced SLAM performance. We propose a multipath-based SLAM approach that directly uses received radio signals as inputs. A new statistical model that can effectively be represented by a factor graph is introduced. The factor graph is the starting point for the development of an efficient belief propagation (BP) method for multipath-based SLAM that avoids data preprocessing by a channel estimator. Numerical results based on synthetic and real data in challenging single-input, single-output (SISO) scenarios demonstrate that the proposed method outperforms conventional methods in terms of localization and mapping accuracy. © 1991-2012 IEEE.

关键词： Mapping

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Exploration of Data Scene Characterization and 3D ROC Evaluation for Hyperspectral Anomaly Detection

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Remote Sensing 2024年第1期16卷 135-135页

作者： Chang, Chein-I Chen, Shuhan Zhong, Shengwei Shi, Yidan Information and Technology College Dalian Maritime University Dalian116026 China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland BaltimoreMD21250 United States Department of Electrical Engineering National Cheng Kung University Tainan70101 Taiwan Department of Electrical Engineering Zhejiang University Hangzhou310027 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Whether or not a hyperspectral anomaly detector is effective is determined by two crucial issues, anomaly detectability and background suppressibility (BS), both of which are very closely related to two factors, the datasets used for a selected hyperspectral anomaly detector and detection measures used for its performance evaluation. This paper explores how anomaly detectability and BS play key roles in hyperspectral anomaly detection (HAD). To address these two issues, we investigate three key elements attributed to HAD. One is a selected hyperspectral anomaly detector, and another is the datasets used for experiments. The third one is the detection measures used to evaluate the effectiveness of a hyperspectral anomaly detector. As for hyperspectral anomaly detectors, twelve commonly used anomaly detectors were evaluated and compared. To address the appropriate use of datasets for HAD, seven popular and widely used datasets were studied for HAD. As for the third issue, the traditional area under a receiver operating characteristic (ROC) curve of detection probability—PD versus false alarm probability, PF, (AUC(D,F))—was extended to 3D ROC analysis where a 3D ROC curve was developed to generate three 2D ROC curves from which eight detection measures could be derived to evaluate HAD in all round aspects, including anomaly detectability, BS and joint anomaly detectability and BS. Qualitative analysis showed that many works reported in the literature which claimed that their developed hyperspectral anomaly detectors performed better than other anomaly detectors are actually not true because they overlooked these two issues. Specifically, a comprehensive study via extensive experiments demonstrated that these 3D ROC curve-derived detection measures can be further used to address the various characterizations of different data scenes and also to provide explanations as to why certain data scenes are not suitable for HAD. © 2023 by the authors.

关键词： Curve fitting

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AI-Based Eye Tracking for Human-computer Interaction

AI-Based Eye Tracking for Human-Computer Interaction

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IEEE International Conference on Electro-Information Technology

作者： David Sancho Crespo Xinrui Yu Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A E.T.S.I Industriales Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

Human-computer interaction (HCI) is a field that has grown enormously in importance in recent times, with the spread of the personal computer. However, HCI devices have had little evolution since their first designs. Prolonged use of the mouse can cause injury or permanent damage to the hands and wrists. So, this paper proposes an alternative to the conventional mouse that does not require the use of hands and works only with a webcam. A program has been developed that performs all the functions of the mouse based on the image of the user's face captured by the camera. The cursor moves to the position of the screen where the user is looking at in real time, while the rest of the functionalities such as clicks and scrolling are controlled with facial commands, such as winking or tilting the head. To implement these functions, several tools based on artificial intelligence have been used, such as a facial landmark detector, a gaze vector estimator, and a fully connected multilayer neural network.

关键词： Wrist Webcams Tracking Neural networks Nose Nonhomogeneous media Vectors

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Reduced KPCA based Ensemble Learning Approach for Fault Diagnosis of Grid-Connected PV Systems 19

Reduced KPCA based Ensemble Learning Approach for Fault Diag...

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19th IEEE International multi-Conference on Systems, signals and Devices, SSD 2022

作者： Dhibi, Khaled Mansouri, Majdi Bouzrara, Kais Nounou, Hazem Nounou, Mohamed Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha23874 Qatar Department of Mathematics and Sciences Prince Sultan University Riyadh11586 Saudi Arabia Chemical Engineering Program Texas A&M University at Qatar Doha23874 Qatar

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

The main objective of this work is to develop novel fault diagnosis techniques using ensemble learning and multivariate statistical techniques. The proposed methods are capable of identifying and classifying PV faults. First, an ensemble learning (EL) method that combines several base models is proposed. Next, kernel principal components analysis (KPCA) and reduced KPCA are proposed to extract and select the pertinent characteristics from raw data. Then, the extracted significant characteristics are transmitted to the EL model for classification purposes. The main idea behind these proposals is to provide the best accuracy and also improve the results in terms of computation time. The diagno-sis results demonstrated the efficiency of the proposed frameworks. © 2022 IEEE.

关键词： multivariant analysis

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Two-in-One Solution: Simultaneously Enhancing Security and Privacy for Data-Driven Models in Mobile Edge Computing

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IEEE Transactions on Consumer Electronics 2024年

作者： Liu, Pengrui Yuan, Xiaohan Wang, Wei Xu, Xiangrui Li, Tao Wang, Junyong Wang, Bin Pedrycz, Witold Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation 3 Shangyuancun Beijing100044 China Haihe Lab of ITAI Tianjin300450 China Zhejiang Key Laboratory of Multi-dimensional Perception Technology Application and Cybersecurity Hangzhou310053 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Data-driven models are widely employed in Mobile Edge Computing to satisfy the demands of Emerging Consumer Applications. However, previous work demonstrates that data-driven models are susceptible to security threats like Backdoor and Evasion Attacks or privacy threats like Membership Inference Attacks. Numerous existing methods for mitigating these threats have been proposed. However, these methods focus solely on enhancing model security or only on improving model privacy. Ideally, data-driven models should enhance model security and privacy simultaneously. In this paper, we propose methods that combine individual security-enhancing and privacy-enhancing methods to mitigate the security and privacy threats of data-driven models simultaneously. We evaluate the effectiveness of individual security-enhancing methods, individual privacy-enhancing methods, and our methods in simultaneously enhancing model security and privacy. Our comprehensive experimental analysis reveals two-fold insights. First, individual security-enhancing methods can either enhance or diminish model privacy, while individual privacy-enhancing methods face challenges in enhancing model security. Second, our methods improve the effectiveness of simultaneously enhancing model security and privacy compared to the individual methods. © 1975-2011 IEEE.

关键词： Differential privacy

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