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2021 CIE International Conference on radar, radar 2021

作者： Lyu, Xiaoyong Xu, Jin Wang, Jun School of Information Engineering Zhengzhou University Zhengzhou China The 38th Research Institute of China Electronics Technology Group Corporation Hefei China Xidian University National Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9781665498142

Clutter cancellation in passive radar is gaining increasing interest. The extensive cancellation algorithm (ECA) has received a range of applications in passive radar clutter cancellation. However, the ECA suffers from great computational complexity. Several variants of ECA have been proposed to increase the computational efficiency. Among those methods, a parallel implementation algorithm obtains an interesting conclusion that, the cancellation weight vector of ECA can be calculated as the weighted mean of several weight vectors, each of which is computed using only a small segment of the reference and surveillance signals. The algorithm can get very similar cancellation weight vector compared with the ECA, but increases the computational efficiency. However, the derivation of this algorithm in the original paper is very complicated and not straightforward to be understood and the obtaining of the final conclusion is not explicit. In this paper, we rederive this algorithm from the maximum likelihood (ML) perspective. We can get the same algorithm, but use a very simple and straightforward derivation. Furthermore, we show explicitly how the finial conclusion is derived, by making some assumptions on the statistical characteristics of the illuminating signal, which is not discussed in the original paper. Finally, we analyze through simulations how the assumptions made on the illuminating signal affect the estimation of the cancellation weight vector. © 2021 IEEE.

关键词： clutter cancellation maximum likelihood parallel algorithm passive radar

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An Improved Optimal Transport Kernel Embedding Method with Gating Mechanism for Singing Voice Separation and Speaker Identification

An Improved Optimal Transport Kernel Embedding Method with G...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Weitao Yuan Yuren Bian Shengbei Wang Masashi Unoki Wenwu Wang Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tiangong University China Japan Advanced Institute of Science and Technology Japan Centre for Vision Speech and Signal Processing University of Surrey Guildford UK

Singing voice separation (SVS) and speaker identification (SI) are two classic problems in speech signal processing. Deep neural networks (DNNs) solve these two problems by extracting effective representations of the target signal from the input mixture. Since essential features of a signal can be well reflected on its latent geometric structure of the feature distribution, a natural way to address SVS/SI is to extract the geometry-aware and distribution-related features of the target signal. To do this, this work introduces the concept of optimal transport (OT) to SVS/SI and proposes an improved optimal transport kernel embedding (iOTKE) to extract the target-distribution-related features. The iOTKE learns an OT from the input signal to the target signal on the basis of a reference set learned from all training data. Thus it can maintain the feature diversity and preserve the latent geometric structure of the distribution for the target signal. To further improve the feature selection ability, we extend the proposed iOTKE to a gated version, i.e., gated iOTKE (G-iOTKE), by incorporating a lightweight gating mechanism. The gating mechanism controls effective information flow and enables the proposed method to select important features for a specific input signal. We evaluated the proposed G-iOTKE on SVS/SI. Experimental results showed that the proposed method provided better results than other models.

关键词： Deep learning Neural networks Training data Logic gates signal processing Feature extraction Data mining

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Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning

arXiv

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arXiv 2024年

作者： Tan, Chuangchuang Zhao, Yao Wei, Shikui Gu, Guanghua Liu, Ping Wei, Yunchao Institute of Information Science Beijing Jiaotong University China Beijing Key Laboratory of Advanced Information Science and Network Technology China School of Information Science and Engineering Yanshan University China Hebei Key Laboratory of Information Transmission and Signal Processing China Center for Frontier AI Research IHPC A*STAR Singapore

This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images despite limited training data. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. However, the rapid advancements in synthesis technology have led to specific artifacts for each generation model. Consequently, these detectors have exhibited a lack of proficiency in learning the frequency domain and tend to overfit to the artifacts present in the training data, leading to suboptimal performance on unseen sources. To address this issue, we introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors. Our method forces the detector to continuously focus on high-frequency information, exploiting high-frequency representation of features across spatial and channel dimensions. Additionally, we incorporate a straightforward frequency domain learning module to learn source-agnostic features. It involves convolutional layers applied to both the phase spectrum and amplitude spectrum between the Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (iFFT). Extensive experimentation involving 17 GANs demonstrates the effectiveness of our proposed method, showcasing state-of-the-art performance (+9.8%) while requiring fewer parameters. The code is available at https://***/chuangchuangtan/FreqNetDeepfakeDetection. Copyright © 2024, The Authors. All rights reserved.

关键词： Fast Fourier transforms

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Linear-Geometry Distortion Correction for Bistatic Inverse Synthetic Aperture radar Imaging Based on Deep Learning Model

Linear-Geometry Distortion Correction for Bistatic Inverse S...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jiannan Wang Jingtao Ma Penghui Huang Shengqi Zhu Haojuan Yuan Yanyang Liu Anjie Cao Xiangcheng Wan Muyang Zhan School of Electronic Information and Engineering Shanghai Jiao Tong University Shanghai China School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China National Laboratory of Radar Signal Processing Xidian University Xi’an China Shanghai Aerospace Electronics Technology Institute Shanghai China Shanghai Institute of Satellite Engineering Shanghai China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Compared with a monostatic inverse synthetic aperture radar (ISAR) imaging system, a bistatic ISAR (Bi-ISAR) system offers more comprehensive target information, along with higher system security and resistance to interference. However, the inherent geometric configuration of Bi-ISAR will introduce the linear-geometry distortion (LGD), causing the target to appear sheared in shape and impacting subsequent target recognition. To deal with this issue, in this paper, a novel deep learning-based algorithm is proposed to realize the LGD correction. In the proposed algorithm, a neural network called DoubleUNet is employed for the accurate semantic segmentation of target's ISAR image. Then the initial target ISAR image is transformed into two-dimensional point clouds based on the estimated radar parameters. Finally, the linear coupling relationship between azimuth and range dimensions is removed, beneficial to the target ISAR shape restoration and subsequent recognition. Simulation experiments validate the proposed algorithm.

关键词： Deep learning Target recognition Shape Simulation Semantic segmentation Imaging radar imaging

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Continuous Distributed processing of Software Defined radar

Continuous Distributed Processing of Software Defined Radar

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2021 CIE International Conference on radar, radar 2021

作者： Li, Bing Qiu, Qiang Gong, Shiqi Liu, Yongjun Lei, Yu Institute of Computing Technology CAS Key Lab of Network Data Science and Technology Chinese Academy of Sciences Beijing China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Xidian University National Laboratory of Radar Signal Processing Xi'an China Golaxy Data Technology Co. Ltd. Beijing China

ISBN: (纸本)9781665498142

Software-defined radar has been an active research field for more than ten years. However, the low performance and low scalability of the traditional processing techniques of SDR make it hard to deal with complex radar tasks. The stream processing engine is a software that supports low latency and high scalability distributed data processing. It is widely used in log, image, and video processing scenarios but rarely used in radar applications. This paper reviews processing software-defined radar data by using the stream processing engine, and we illustrate the differences between processing log data and radar data. Furthermore, we discuss some optimizations for stream processing engines to get better performance in the radar data processing. © 2021 IEEE.

关键词： distributed processing Software-defined radar stream processing engine

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Comparative Study on Outage Probability of mmWave Vehicle-to-Vehicle Communications

Comparative Study on Outage Probability of mmWave Vehicle-to...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yanjie Pu Fuxi Wen Yong Zeng Shenghua Zhou Hangzhou Institute of Technology Xidian University China School of Vehicle and Mobility Tsinghua University China National Mobile Communications Research Laboratory Southeast University China Purple Mountain Laboratories Southeast University China National Laboratory of Radar Signal Processing Xidian University China

Integrated sensing and communication is a promising technology for enabling high-speed and low-latency commu-nication between connected vehicles. However, due to the unique characteristics of millimeter wave (mmWave) communication, such as severe path loss, susceptibility to blockage and beam alignment mismatch caused by positioning or sensing errors, outage probability analysis is critical to evaluate the performance of such systems. In this paper, we investigate the outage probability of mm Wave V2V communication systems in highway and urban scenarios with varying propagation environments and traffic densities. Furthermore, the optimal 3dB beamwidth is also derived for a given transmitting power and communication distance.

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Application of multi-classification method of skin cancer based on dermoscopic image 21

Application of multi-classification method of skin cancer ba...

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2nd International Symposium on Artificial Intelligence for Medicine sciences, ISAIMS 2021

作者： Chen, Qian Li, Min Chen, Cheng Chen, Chen Lv, Xiaoyi College of Software Key Laboratory of Software Engineering Technology Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China College of Information Science and Engineering Xinjiang University Urumqi China

ISBN: (纸本)9781450395588

As a kind of cancer with high incidence, skin cancer seriously threatens people's life and health. Early detection, early diagnosis, and early treatment are one of the effective ways to increase the survival rate of patients with skin diseases. Therefore, this paper used the ResNet50 model as a feature extractor based on deep learning, and combined machine learning algorithms to explore the heterogeneity among multi-type of skin cancer, and tried to construct the best performance computer-aided diagnosis model. The research first used preprocessing techniques such as hair noise removal and data enhancement, and then achieved a classification accuracy of 90.497% on the public data set of ISIC2019. The proposed model effectively relieves the huge work pressure of dermatologists, and provides a reference for the better use of dermoscopic images for intelligent diagnosis and classification of skin cancer. © 2021 ACM.

关键词： Computer aided diagnosis

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Superhydrophobic Pet-Cu-Ni@Mwcnt with Electropositive Layer to Enhance the Electrical Properties of Teng for Human Wearable Sensors

SSRN

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SSRN 2024年

作者： Yu, Shuguo Li, Peng Ding, Hanqin Shi, Fei Li, Xingang Xinjiang Key Laboratory of Solid State Physics and Devices Xinjiang University Urumqi830046 China School of Physical Science and Technology Xinjiang University Urumqi830046 China School of Computer Science and Technology Xinjiang University Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi830046 China

Flexible triboelectric nanogenerator (TENG) are ideal for sustainable and wearable electronics applications due to their excellent performance and non-toxicity. In this study, multiwalled carbon nanotubes (MWCNT) loaded plain anti-radiation shielding cloth (PET-Cu-Ni) electropositive layer (PM) is synthesized, and then assembled with PDMS to make a superhydrophobicity and flexible PM-TENG. The alternating fabric-like PET in PET-Cu-Ni increases the contact area at the tribo-electrode interface, and the bimetallic atomic Cu-Ni layer increases the electrical conductivity and electron transfer capability of triboelectric material. MWCNT surface itself has a positive charge, which promotes the generation of triboelectric charges, and the deposition of the MWCNT layer also increases the contact area of interface. MWCNT with p-type conductive forms a metal-semiconductor junction with the Cu-Ni layer, and MWCNT layer acts as a hole-transporting layer that affects triboelectric charge separation to reduces triboelectric charge loss, which greatly improves the output current of TENG. The superhydrophobic PM prevents the loss of electrostatic charge on TENG surface to the extent that it promotes stable performance output. At 5 Hz operating frequency, the short-circuit current of PM-TENG is boosted to 13.7 μA, and the open-circuit voltage and power reach 231 V and 2.8 mW, respectively. Integration of PM-TENG as a power source with the SnS2 humidity sensor constitutes TENG-driven sensor, which realizes relative humidity detection with high sensitivity and fast response recovery. In addition, a remote human-machine interface (HMI) system is constructed by integrating PM-TENG, circuit module, and APP, which realizes real-time monitoring of finger curvature and promotes the development of biomedicine. © 2024, The Authors. All rights reserved.

关键词： Multiwalled carbon nanotubes (MWCN)

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Evaluation of LoRaWAN for Remote Health Monitoring: A Preliminary Study

Evaluation of LoRaWAN for Remote Health Monitoring: A Prelim...

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IEEE Conference on Standards for Communications and Networking (CSCN)

作者： Konstantinos Katzis Nina Evetovic Brian Cloteaux Kamran Sayrafian Dragana Bajić Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus Department of Communications & Signal Processing University of Novi Sad Novi Sad Serbia Currently with the Institute of Measurement science Slovak Academy of Sciences Information Technology Laboratory National Institute of Standards & Technology Maryland USA

ISBN: (数字)9798331507428

ISBN: (纸本)9798331507435

Lack of sufficient patient monitoring data patterns and scenarios are among the challenges for accurate traffic modeling and network performance optimization when considering IoT medical devices. This paper examines how health data-traffic models can be used to investigate the capability and proper configuration of Long-Range Wide Area Networks (LoRaWAN) for remotely monitoring patients in a hospital environment or at home. For the hospital environment, it is assumed that there are two types of patients at any given time i.e., patients in the intensive care unit (ICU) or surgical wards and those located in the general medical wards. The transmission times of the health data from patients in the ICU/Surgical wards and those in general wards are randomly distributed within one and four-hour periods, respectively. For personal health monitoring devices that people use at their homes, a two-state Markov chain is used to model the generated traffic pattern. Preliminary results indicate that LoRaWAN may be used to support a significant number of nodes with sufficient reliability in both scenarios.

关键词： Wide area networks Patient monitoring Medical devices Hospitals LoRaWAN Surgery Traffic control Reliability Standards Optimization

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Personalized Multimedia Recommendation Systems Using Higher-Order Tensor Singular-Value-Decomposition

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IEEE Transactions on Broadcasting 2024年第1期70卷 148-160页

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Huang, Scott Chih-Hao Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Department of Information and Telecommunication Guilin541004 China Northwestern State University Department of Engineering Technology NatchitochesLA71497 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Communications Research Centre Canada OttawaK2H 8S2 Canada

Because of rapid growth of multimedia data over the Internet, the infobesity has been emerging in recent years. Many recommender systems (RSs) have been proposed using a variety of techniques, including artificial intelligence, machine learning, statistical analysis, and data mining. Nowadays, E-commerce companies such as Amazon, eBay, Netflix, etc. have applied RSs conveniently to provide better customer services than before by enabling customized multimedia-content recommendations. It is well known that classical RSs are almost all based on the user-item attribute matrices. However, this two-fold user-item relationship should be extended to the emerging many-fold relationship among an arbitrary number of attribute groups so that the corresponding new RSs may lead to better performances. Therefore, in this paper, we introduce a novel personalized multimedia recommender system (PMRS) according to our proposed new parallel higher-order tensor singular-value-decomposition (PHOTSVD) algorithm. This PHOTSVD algorithm is capable of accommodating an arbitrary number of entities such as social-media channels, users, queries, webpages, etc. in a robust RS. Furthermore, we also investigate the cold start and data bias problems critical to RSs. Finally, we compare the performances of our proposed new PMRS and three other existing tensor-based RSs over realworld data with respect to the well-adopted metric, namely normalized root-mean-square error (NRMSE). Our proposed new PMRS greatly outperforms the other three existing tensor-based RSs in terms of NRMSE. © 1963-12012 IEEE.

关键词： Singular value decomposition

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