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IEEE International Conference on radar

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

ISBN: (纸本)9781665468893

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.

关键词： Protocols Scalability Streaming media radar imaging Data processing Software Hardware

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X-Band Polinsar Vegetation Canopy Height Inversion Strategy Based on Frequency Segmentation

X-Band Polinsar Vegetation Canopy Height Inversion Strategy ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Fanyi Tang Jinwei Xie Zhiyong Suo Han Li Zhenfang Li National Laboratory of Radar Signal Processing Xidian University Xi'an China Nanjing Research Institute of Electronic Technology China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Vegetation canopy height inversion is a significant research content in Polarimetric Interferometry Synthetic Aperture radar (PolInSAR) surface parameter inversion. One practical challenge is that the traditional three-stage inversion method will lead to the inaccurate inversion of vegetation canopy height in X-band because of the too concentrated distribution of polarization interference coherence coefficients (PolInCc). In this paper, an X-band PolInSAR vegetation canopy height inversion strategy is proposed based on Frequency Segmentation (FS). The FS divides the original PolInSAR images into multiple PolInSAR images called sub-images, and the sub-images are used to estimate the more near-linear PolInCc distribution of the sub-images by interferometry. Then the precise terrain phase and effective volume coherence coefficient can be obtained to invert the vegetation canopy height accurately. The validity of the proposed method is verified by N-SAR X-band real data.

关键词： Vegetation mapping Scattering Azimuth Coherence Fitting Interferometry Extinction coefficients

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One to multiple mapping dual learning: Learning multiple sources from one mixed signal

arXiv

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arXiv 2021年

作者： Liu, Ting Wang, Wenwu Zhang, Xiaofei Yan, Jingwen Guo, Yina The Department of Electronics and Communication Engineering Taiyuan University of Science and Technology Taiyuan030024 China The Centre for Vision Speech and Signal Processing University of Surrey Guildford SurreyGU2 7XH United Kingdom The Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Technology

Single channel blind source separation (SCBSS) refers to separate multiple sources from a mixed signal collected by a single sensor. The existing methods for SCBSS mainly focus on separating two sources and have weak generalization performance. To address these problems, an algorithm is proposed in this paper to separate multiple sources from a mixture by designing a parallel dual generative adversarial Network (PDualGAN) that can build the relationship between a mixture and the corresponding multiple sources to realize one-to-multiple cross-domain mapping. This algorithm can be applied to any mixed model such as linear instantaneous mixed model and convolutional mixed model. Besides, one-to-multiple datasets are created which including the mixtures and corresponding sources for this study. The experiment was carried out on four different datasets and tested with signals mixed in different proportions. Experimental results show that the proposed algorithm can achieve high performance in peak signal-to-noise ratio (PSNR) and correlation, which outperforms state-of-the-art algorithms. © 2021, CC BY.

关键词： Mapping

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Novel explanation of the Active Galactic Nuclei. The Power Source of Quasars as a result of vacuum polarization by the gravitational singularities on the distributional BHs horizon 9

Novel explanation of the Active Galactic Nuclei. The Power S...

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9th International Conference on Mathematical Modeling in Physical sciences, IC-MSQUARE 2020

作者： Foukzon, Jaykov Men'Kova, Elena R Potapov, Alexander A Department of Mathematics Israel Institute of Technology Haifa3200003 Israel All-Russian Research Institute for Optical and Physical Measurements Moscow119361 Russia Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences Moscow125009 Russia JNU-IREE Joint Laboratory of Fractal Method and Signal Processing Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou China

In this paper we argue that the current paradigm for AGN and quasars is essentially incomplete and a rivision is needed. Remind that the current paradigm for AGN and quasars is that their radio emission is explained by synchrotron radiation from relativistic electrons that are Doppler boosted through bulk motion. In this model, the intrinsic brightness temperatures cannot exceed 1011 to 1012 K. Typical Doppler boosting is expected to be able to raise this temperature by a factor of 10. The observed brightness temperature of the most compact structures in BL Lac, constrained by baselines longer than 5.3G, must indeed exceed 2 × 1013K and can reach as high as 3 × 1014K. This is difficult to reconcile with current incoherent synchrotron emission models from relativistic electrons, requiring alternative models such as emission from relativistic protons. However the proton, as we know, is 1836 times heavier than an electron and absolutely huge energy is required to accelerated it to sublight speed. These alternative models such as emission from relativistic protons can be suported by semiclassical gravity effect finds its roots in the singular behavior of quantum fields on curved distributional space- times presented by rotating gravitational singularities. © Published under licence by IOP Publishing Ltd.

关键词： Synchrotron radiation

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Acoustic echo suppression using speech uncertainty in modulation domain

TechRxiv

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TechRxiv 2021年

作者： Shifas, Muhammed P.V. Jayakumar, E.P. Sathidevi, P.S. The Speech Signal Processing Laboratory Department of Computer Science University of Crete Greece Department of Electronics and Communication Engineering National Institute of Technology Calicut Kerala Kozhikode India

Quality degradation of near-end speech in mobile communication or hands free devices is mainly due to acoustic echoes and background noises. The received far-end speech gets reflected from the obstacles present in the surroundings creating acoustic echo. All other disturbances from the near-end environment are considered as background noises. A novel acoustic echo suppression scheme using speech uncertainty in modulation domain (MD) is proposed in this paper. State of the art acoustic echo suppression systems are based on either time domain or frequency domain analysis. In recent times, the modulation domain analysis is popularly used in speech processing, as it captures the human perceptual properties. Modulation domain provides the temporal variation of the acoustic magnitude spectra which acts as an information bearing signal. In this paper, a new method is developed and implemented to model the echo path and estimate the echo in modulation domain. Echo cancellation is done effectively by manipulating the modulation spectrum and employing speech uncertainty. In this method, the microphone input is modelled as a binary hypothesis process and the gain function is modified accordingly. The proposed method shows better performance as compared to other competitive methods for acoustic echo suppression with no audible degradation in the near-end speech. © 2021, CC BY.

关键词： Speech enhancement

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Challenge-Aware RGBT Tracking 1

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16th European Conference on Computer Vision, ECCV 2020

作者： Li, Chenglong Liu, Lei Lu, Andong Ji, Qing Tang, Jin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (数字)9783030585426

ISBN: (纸本)9783030585419

RGB and thermal source data suffer from both shared and specific challenges, and how to explore and exploit them plays a critical role to represent the target appearance in RGBT tracking. In this paper, we propose a novel challenge-aware neural network to handle the modality-shared challenges (e.g., fast motion, scale variation and occlusion) and the modality-specific ones (e.g., illumination variation and thermal crossover) for RGBT tracking. In particular, we design several parameter-shared branches in each layer to model the target appearance under the modality-shared challenges, and several parameter-independent branches under the modality-specific ones. Based on the observation that the modality-specific cues of different modalities usually contains the complementary advantages, we propose a guidance module to transfer discriminative features from one modality to another one, which could enhance the discriminative ability of some weak modality. Moreover, all branches are aggregated together in an adaptive manner and parallel embedded in the backbone network to efficiently form more discriminative target representations. These challenge-aware branches are able to model the target appearance under certain challenges so that the target representations can be learnt by a few parameters even in the situation of insufficient training data. From the experimental results we will show that our method operates at a real-time speed while performing well against the state-of-the-art methods on three benchmark datasets. © 2020, Springer Nature Switzerland AG.

关键词： Computers

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Novel Personalized Multimedia Recommendation Systems Using Tensor Singular-Value-Decomposition

Novel Personalized Multimedia Recommendation Systems Using T...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Shih Yu Chang Hsiao-Chun Wu Kun Yan Xinjia Chen Yiyan Wu Department of Applied Data Science San Jose State University San Jose CA USA School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge LA USA Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin China Department of Information and Telecommunication Guilin University of Electronic Technology Guilin China Department of Engineering Technology Northwestern State University of Louisiana Natchitoches LA USA Communications Research Centre Canada Ottawa ON Canada

Nowadays, multimedia data are often produced by various sources, e.g., Internet of Things (IoT), social media, customer databases, etc. As tremendous multimedia data are produced every day, users or E-commerce customers cannot infer information from such data by themselves and therefore recommender systems have been developed to help users to select the products or services which better fit users’ preferences or requirements. Nonetheless, there exists few works on the incorporation of side information or multiple attributes about items into the design of a more robust recommender system. In this work, we propose a novel approach based on the third-order tensor singular-value-decomposition (T3-SVD) to design new personalized multimedia recommender systems (PMRSs) for internet users. A PMRS can dynamically adjust its recommendation strategy subject to a particular user’s on-line transaction behavior. To evaluate the effectiveness of our proposed PMRS based on T3-SVD, we compare the performances of our proposed new PMRS and two other existing tensor-based recommendation systems over realworld data in terms of normalized root-mean-square error (NRMSE). As a result, our proposed new PMRS greatly outperforms the other two existing systems.

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Receiver distortion nornalization method for specific emitter identification 9

Receiver distortion nornalization method for specific emitte...

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9th IEEE Joint International Information technology and Artificial Intelligence Conference, ITAIC 2020

作者： Shi, Mentkai Huang, Yuanling Wang, Guiliang University School of Information System Engineering PLA Strategic Support Force Information Engineering University Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

Specific emitter identification (SEI) is an identification technique that identify the transmitting device with the hardware imperfection. However, the identification features are influenced by the receiver distortions and can be different when the receivers are changed. Previous SEI studies rarely concern the method to normalize the distortions among different receivers. To solve this problem, a SEI receiver normalization method is proposed. In the proposed method, a frequency-domain adaptive equalization system is contained to normalize the distortions in receivers. According to the experiment, this method can effectively eliminate the SEI feature difference caused by receivers. Then the robust of the proposed method is discussed. © 2020 IEEE.

关键词： Equalizers

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Weakly-Supervised Depression Detection in Speech Through Self-Learning Based Label Correction

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 748-758页

作者： Yanfei Sun Yuanyuan Zhou Xinzhou Xu Jin Qi Feiyi Xu Zhao Ren Björn W. Schuller School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China Wuxi University Wuxi China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria Key Laboratory of Modern Acoustics MOE Nanjing University Nanjing China Cognitive Systems Lab University of Bremen Bremen Germany Chair of Health Informatics Technische Universität München (TUM) München Germany Munich Data Science Institute Munich Germany Munich Center for Machine Learning Munich Germany GLAM – the Group on Language Audio & Music Imperial College London London U.K.

Automated Depression Detection (ADD) in speech aims to automatically estimate one's depressive attributes through artificial intelligence tools towards spoken signals. Nevertheless, existing speech-based ADD works fail to sufficiently consider weakly-supervised cases with inaccurate labels, which may typically appear in intelligent mental health. In this regard, we propose the Self-Learning-based Label Correction (SLLC) approach for weakly-supervised depression detection in speech. The proposed approach employs a self-learning manner connecting a label correction module and a depression detection module. Within the approach, the label correction module fuses likelihood-ratio-based and prototype-based label correction strategies in order to effectively correct the inaccurate labels, while the depression detection module aims at detecting depressed samples through a 1D convolutional recurrent neural network with multiple types of losses. The experimental results on two depression detection corpora show that our proposed SLLC approach performs better compared with existing state-of-the-art speech-based depression detection approaches, in the case of weak supervision with inaccurate labels for depression detection in speech.

关键词： Depression Feature extraction Training Speech processing Prototypes Noise measurement Telecommunications Sun Internet of Things Weak supervision

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Atmospheric Effects on Ultra-High-Resolution Millimeter-Wave ISAR Imagery for Space Targets

Atmospheric Effects on Ultra-High-Resolution Millimeter-Wave...

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IEEE International Conference on radar

作者： Minghui Wan Jialian Sheng Xia Ding Mengdao Xing Shanghai Radio Equipment Research Institute Shanghai China Shanghai Engineering Research Center of Target Identification and Environment Perception Shanghai China Traffic Perception Radar Technology Research&Development Center of CASC Shanghai China National Laboratory of Radar Signal Processing Xidian University Xi'an China

ISBN: (纸本)9781665468893

In order to get more detail information about targets, ultra-high-resolution (UHR) millimeter-wave radar (MMW) is widely used. UHR MMW radar is sensitive to errors, and the atmosphere effect is one of the major factors affecting the final imaging quality during the exploring of space targets. In this paper, high-precision radar signal propagation models of the background troposphere and ionosphere are established. Based on the models, the influence mechanism of the atmospheric effect on radar signal echo is investigated. The simulation results show that the tropospheric effect lead to azimuth defocusing and a large shift in the range direction on the image. The ionospheric effect produces the intra-pulse phase error which is inversely proportional to frequency.

关键词： Atmospheric modeling Simulation Spaceborne radar Imaging Interference radar imaging Millimeter wave radar

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