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SSRN 2022年

作者： Roger Magloire, Keumo Tsiaze Tsague Fotio, Carlos Fotue, Alain Jerve Lukong Fai, Cornelius Talla, Pierre Kisito El Moussaouy, Abdelaziz Mommadi, Omar Kenfack Sadem, Christain Laboratory of Condensed Matter-Electronics and Signal Processing Department of Physics Faculty of Science University of Dschang P.O. Box 479 Dschang Cameroon University of Abomey-Calavi 072 P O Box 50 Cotonou Benin Laboratory of Mechanics Materials and Structures Faculty of Science University of Yaoundé I P.O. Box 812 Yaoundé Cameroon OAPM Group Laboratory of Mechanics Waves Energy and Environment Department of Physics Faculty of Sciences University Mohamed I Oujda60000 Morocco Laboratory of Innovation in Science Technology and Education Regional Centre for the Professions of Education and Training Oujda60000 Morocco Department of Physics Faculty of Science University of Dschang P. O. Box 67 Dschang Cameroon

The letter presents a Ginzburg-Landau theory of fluctuations in superconductors in the zero-dimensional (0D) limit. The central descriptive tool is a complex order parameter interpreted as a self-consistent field describing the centre-of-mass motion of electrons pairs and highlighting a resistance between Cooper pairs. We describe the temperature and size dependences of superconductivity in single, isolated 0D superconductors taking into account an intrinsic energy parameter related to confined geometries. It is shown that all the superconducting effects arise only from the existence of fluctuation-induced superconductivity. The results obtained show that the electronic structure of 0D superconductors goes through several phases with qualitatively different properties. The dimensional dependence of thermodynamic quantities is particularly highlighted and thermodynamic properties of 0D fields are discussed and compared in relation to those of bulk fields. Individual grains of $^{127}\mathrm{Al}$ and $^{207}\mathrm{Pb}$ as well as granular superconductors are provided in support of the analysis © 2022, The Authors. All rights reserved.

关键词： Thermodynamic properties

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Distribution Preserving Deep Semi-Nonnegative Matrix Factorization

Distribution Preserving Deep Semi-Nonnegative Matrix Factori...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhuolin Tan Anyong Qin Yongqing Sun Yuan Yan Tang School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Signal and Information Processing Chongqing China NTT Media Intelligence Laboratories Yokosuka Japan Zhuhai UM Science & Technology Research Institute University of Macau Macau China

ISBN: (纸本)9781665442084

Deep semi-nonnegative matrix factorization can obtain the hidden hierarchical representations according to the unknown attributes of the given data. On the other hand, the inherent structure of the each data cluster can be described by the distribution of the intra-class data. Then one hopes to learn a new low dimensional representation which can preserve the intrinsic structure embedded in the original high dimensional data space perfectly. Here we propose a novel distribution preserving deep semi-nonnegative matrix factorization method (DPNMF) to achieve this goal. As a result, the manifold structures in the raw data are well preserved in the feature space being from the top layer. The experimental results on the real-world datasets show that the proposed algorithm has good performance in terms of cluster accuracy and normalized mutual information (NMI).

关键词： Manifolds Conferences Clustering algorithms Approximation algorithms Mutual information Cybernetics

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Enabling User Relaying In MCM-NOMA Under Doubly Selective Channels Using Iterative Interference Cancellation Schemes For Wireless IoT Networks

TechRxiv

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TechRxiv 2022年

作者： Hamza, Anis Amazigh Dayoub, Iyad Alouani, Ihsen Amrouche, Abderrahmane Laboratory of Speech Communication and Signal Processing Faculty of Electronics and Computer Science University of Science and Technology Houari Boumediene UPHF Bab Ezzouar Algiers16111 Algeria Polytechnic University Hauts-de-France CNRS Univ. Lille ISEN Centrale Lille UMR8520- IEMN Institut D’Electronique de Microlectronique Et de Nanotechnologie - DOAE Dpartement Opto-Acousto-Electronique Valenciennes59313 France INSA Hauts-de-France Valenciennes59313 France

Cell-edge users of the future cellular internet of things (IoT) with massive IoT sensors can suffer from extremely severe channel conditions, especially under very high-speed scenarios. In this paper, we present a performance improvement method for cell-edge users of multi-carrier modulation (MCM)-based non-orthogonal multiple access (NOMA) downlink systems. To this end, we consider the implementation of cooperative user relaying NOMA (CUR-NOMA) and derive its lower bound E2E-BER under doubly selective channels. In addition, the imperfect successive interference cancellation (SIC) process is analyzed, wherein two interference cancellation schemes are combined to remove the NOMA induced inter-user interference (IUI) and the doubly selective channel induced inter-carrier interference (ICI). © 2022, CC BY.

关键词： Internet of things

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An Equivalent Range Model Based on Time Resampling for High-Speed Maneuvering Platform SAR

An Equivalent Range Model Based on Time Resampling for High-...

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作者： Anyi Wang Xiaoyang Jiao Ping Guo Chunhui Lin College of Communication and Information Engineering Xi'an University of Science and Technology National Laboratory of Radar Signal Processing Xidian University

For the high-speed maneuvering platform SAR,the high-speed state will lead to stop-go assumption invalid and the forward acceleration would severely reduce the depth of focusing field in azimuth,thus the traditional range model with stop-go assumption will be *** this paper,the accurate range history is derived in a geometric configuration for high-speed maneuvering platform SAR and a new range equation is derived by transformation of bistatic to monostatic *** on the new range equation,the azimuth time resampling is proposed by which one can obtain a simplified range equation with a form similar to that of the conventional SAR *** the typical imaging methods can be applied directly without any *** results verify the effectiveness of the proposed method.

关键词： high-speed maneuvering platform azimuth time resampling range equation synthetic aperture radar(SAR)

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Community-Grouping Based Particle Swarm optimisation Algorithm for Feature Selection

Community-Grouping Based Particle Swarm optimisation Algorit...

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Congress on Evolutionary Computation

作者： Jianfeng Qiu Jiangchuan Wan Lei Zhang Fan Cheng Yongkang Luo Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University China School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

As a frequently-used dimensionality reduction technique in machine learning, feature selection has attracted interests in the last decade. Since feature selection is essentially a combinatorial optimization problem, how to search the valuable feature subset is a challenging optimization task. Particle swarm optimization (PSO) algorithm and its variations have shown their competitiveness in solving feature selection problem. However, they have been proven to be easily trapped into the local optimal in high-dimensional space due to their intrinsic characteristic of quick convergence. To this end, an effective binary particle swarm optimization algorithm, named CBPSOFS, is proposed for feature selection, where a community-grouping based adaptive updating strategy is designed to avoid trapping into the local optimum and enhance the performance of PSO algorithm in feature selection. To be specific, the correlationship among features is used to construct the feature network, where multiple feature groups are obtained by dividing the achieved feature network. Considering that a community usually contains multiple similar features, the proposed adaptive updating strategy utilizes these feature groups to make the similar features not be included in the same particle so as to maintain the diversity of the population in the evolution. In addition, an information gain based initialization strategy and a history information based resetting strategy are also developed to improve the quality of obtained feature subset. Experimental results on several real world datasets have demonstrated the effectiveness of CBPSOFS in feature selection when compared with the several state-of-the-art baselines.

关键词： Feature extraction Sociology Statistics Particle swarm optimization Optimization History signal processing algorithms

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Enhanced Beamforming via Bayesian Postprocessing

Enhanced Beamforming via Bayesian Postprocessing

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OCEANS

作者： Chennan Shi Jun Tao Key Laboratory of Underwater Acoustic Signal Processing of the Ministry of Education School of Information Science and Engineering Southeast University Nanjing China Acoustic Science and Technology Laboratory Harbin Engineering University Harbin China

ISBN: (数字)9781728154466

ISBN: (纸本)9781728184098

A Bayesian postprocessing (BP) scheme for improving beamforming performance is proposed. It is implemented as a signal beam power estimator at a beamformer output based on the Bayesian minimum mean square error (BMMSE) criterion, boosting signal components by suppressing noise and interferences. The BMMSE estimation of the signal beam power boils down to the track of the noise-plus-interference power and the signal-to-interference-plus-noise ratio (SINR), which are achieved with the minimum-statistics (MS) method and the decision-directed approach (DDA), respectively. The Bayesian postprocessing scheme is applied to the conventional beamforming (CBF), leading to the BP-CBF. Simulation results showed the BP-CBF significantly outperforms the CBF without sacrificing its robustness to signal mismatch.

关键词： Array signal processing Simulation Estimation Mean square error methods Robustness Bayes methods signal to noise ratio

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Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer

arXiv

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arXiv 2023年

作者： Pati, Pushpak Jaume, Guillaume Ayadi, Zeineb Thandiackal, Kevin Bozorgtabar, Behzad Gabrani, Maria Goksel, Orcun IBM Research Europe Switzerland Department of Pathology Brigham and Women’s Hospital Harvard Medical School United States Department of Pathology Massachusetts General Hospital Harvard Medical School United States Cancer Program Broad Institute of Harvard MIT United States Data Science Program Dana-Farber Harvard Cancer Center United States Computer-Assisted Applications in Medicine ETH Zurich Switzerland Signal Processing Laboratory 5 EPFL Switzerland EPFL Switzerland Department of Information Technology Uppsala University Sweden

The segmentation and automatic identification of histological regions of diagnostic interest offer a valuable aid to pathologists. However, segmentation methods are hampered by the difficulty of obtaining pixel-level annotations, which are tedious and expensive to obtain for Whole-Slide images (WSI). To remedy this, weakly supervised methods have been developed to exploit the annotations directly available at the image level. However, to our knowledge, none of these techniques is adapted to deal with WSIs. In this paper, we propose WHOLESIGHT, a weakly-supervised method, to simultaneously segment and classify WSIs of arbitrary shapes and sizes. Formally, WHOLESIGHT first constructs a tissue-graph representation of the WSI, where the nodes and edges depict tissue regions and their interactions, respectively. During training, a graph classification head classifies the WSI and produces node-level pseudo labels via post-hoc feature attribution. These pseudo labels are then used to train a node classification head for WSI segmentation. During testing, both heads simultaneously render class prediction and segmentation for an input WSI. We evaluated WHOLESIGHT on three public prostate cancer WSI datasets. Our method achieved state-of-the-art weakly-supervised segmentation performance on all datasets while resulting in better or comparable classification with respect to state-of-the-art weakly-supervised WSI classification methods. Additionally, we quantify the generalization capability of our method in terms of segmentation and classification performance, uncertainty estimation, and model calibration. © 2023, CC BY-NC-ND.

关键词： Image segmentation

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DDMA-MIMO radar Maximum Unambiguous Velocity Extension Based on Global Optimization Phase Modulation

DDMA-MIMO Radar Maximum Unambiguous Velocity Extension Based...

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IEEE International Conference on radar

作者： Hongfei Lian Fugang Lu Qiao Chen Jiamin Long Xueyao Hu Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China No. 203 Research Institute of China Ordnance Industries Xi'an China Beijing Science and Technology Ruixing Electronic Technology Co. Ltd Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (纸本)9781665468893

Doppler division multiple access (DDMA) waveform shows great orthogonality for multiple-input multiple-output (MIMO) radar. However, it leads to a serious velocity ambiguous problem and hinders its application in automotive radar. In this work, a maximum unambiguous velocity extension approach is developed for DDMA MIMO radar by using a global ergodic optimization phase modulation during transmission. To be specific, we take the cumulative sum of the peak amplitude difference of global as the optimization cost function to obtain modulation codes. Furthermore, a matched filter phase demodulation carried out at the receiver to determine the results of angle measurement and velocity expansion. Finally, the simulation analysis and real-data experiments demonstrate our approach effectively improves velocity ambiguity resolving capabilities of DDMA waveform and suitable for massive MIMO array in nowadays automotive radar.

关键词： Matched filters Codes Phase modulation MIMO radar radar detection radar Doppler radar

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Dynamic Compressive Sensing Based Adaptive Equalization for Underwater Acoustic Communications

Dynamic Compressive Sensing Based Adaptive Equalization for ...

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OCEANS

作者： Zhen Qin Jun Tao Xiao Han Key Laboratory of Underwater Acoustic Signal Processing of the Ministry of Education School of Information Science and Engineering Southeast University Nanjing China Acoustic Science and Technology Laboratory Harbin Engineering University Harbin China

ISBN: (数字)9781728154466

ISBN: (纸本)9781728184098

The wide-sense sparsity of a direct adaptive equalizer (DAE) for single-carrier underwater acoustic (UWA) communications has been recognized and utilized to reduce the equalization complexity via the idea of partial tap updating (PTU). Existing PTU schemes run in a batch mode so positions of the partial taps to be updated, once chosen, remain unchanged over a block. Under harsh channel conditions, however, the sparse structure of a DAE may change even within one block and existing PTU schemes will fail. This fact motivates us to seek a way to track also the dynamics of the DAE structure, in addition to its coefficients. In this paper, we propose to adopt the dynamic compressed sensing (DCS) technique to achieve the aforementioned goal. Specifically, the sparse adaptive orthogonal matching pursuit (SpAdOMP) as a typical greedy-type DCS algorithm, is employed. The taps of the resulting SpAdOMP DAE are updated with the affine projection algorithm (APA), leading to the SpAdOMP-APA-DAE. The SpAdOMP-APA-DAE was tested by experimental data collected in an at-sea UWA communication trial. Experimental results showed it significantly outperformed existing DAEs attributed to tracking only significant DAE taps while dropping negligible ones.

关键词： Heuristic algorithms Matching pursuit algorithms Adaptive equalizers Complexity theory Underwater acoustics Projection algorithms Compressed sensing

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A review on InSAR phase denoising

arXiv

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arXiv 2020年

作者： Xu, Gang Gao, Yandong Li, Jinwei Xing, Mengdao State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China School of Environment Science and Spatial Informatics China University of Mining and Technology Xuzhou China Xi'an Institute of Space Radio Technology Xi'an710071 China National Laboratory of Radar Signal Processing Xidian University Xi'an710071 China

Nowadays, interferometric synthetic aperture radar (InSAR) has been a powerful tool in remote sensing by enhancing the information acquisition. During the InSAR processing, phase denoising of interferogram is a mandatory step for topography mapping and deformation monitoring. Over the last three decades, a large number of effective algorithms have been developed to do efforts on this topic. In this paper, we give a comprehensive overview of InSAR phase denoising methods, classifying the established and emerging algorithms into four main categories. The first two parts refer to the categories of traditional local filters and transformed-domain filters, respectively. The third part focuses on the category of nonlocal (NL) filters, considering their outstanding performances. Latter, some advanced methods based on new concept of signal processing are also introduced to show their potentials in this field. Moreover, several popular phase denoising methods are illustrated and compared by performing the numerical experiments using both simulated and measured data. The purpose of this paper is intended to provide necessary guideline and inspiration to related researchers by promoting the architecture development of InSAR signal processing. Copyright © 2020, The Authors. All rights reserved.

关键词： Synthetic aperture radar

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