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tensor-Based Sparse Recovery Space-Time Adaptive processing for Large Size Data Clutter Suppression in Airborne Radar

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第2期59卷 907-922页

作者： Cui, Ning Xing, Kun Yu, Zhongjun Duan, Keqing Chinese Acad Sci Aerosp Informat Res Inst Beijing 100864 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100094 Peoples R China Sun Yat Sen Univ SYSU Sch Elect & Commun Engn Guangzhou 510275 Peoples R China

Sparse recovery space-time adaptive processing (SR-STAP) can achieve an ideal clutter suppression with very few training samples, however, its application faces two challenges: 1) severe gird mismatch effect and 2) large time-resources requirement. In practice, a coarse space-time grids will bring a serious mismatch between the true clutter points and the divided grids, which leads to a significant performance degradation of clutter suppression. Although the high-resolution mesh can effectively reduce the grid mismatch effect, its cost is huge computational load. Thus, it is meaningful to reduce the large-scale dictionary operation complexity while maintaining suboptimal clutter suppression performance for SR-STAP when applying in real airborne radar system. This article proposed a tensor-based SR-STAP scheme aims at large-scale dictionary application. In the proposed framework, traditional vector-based operations are replaced by their corresponding low-complexity tensor representation. As a result, a large-scale matrix operation can be degraded into multiple small-scale matrix calculation, thus the huge computational loading can be saved in recovery. A comparison of tensor-based SR-STAP and traditional vector-based SR-STAP in large-scale dictionary application is also exhaustive discussed here. Based on this framework, a tensor-based sparse Bayesian learning and its fast matrix-realization form are developed. A series of carefully designed numerical simulation and measurement experiments indicate that the significant advantages of the tensor-based SR-STAP whether in performance or computation loading.

关键词： Clutter Dictionaries Airborne radar tensors Estimation Bayes methods Training clutter suppression sparse recovery space-time adaptive processing (SR-STAP) tensor processing

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Vtensor:Using Virtual tensors to Build a Layout-Oblivious AI Programming Framework

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Journal of Computer Science & Technology 2023年第5期38卷 1074-1097页

作者：俞峰赵家程崔慧敏冯晓兵薛京灵 Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100080China School of Computer Science and Engineering University of New South WalesSydney 1466Australia

tensors are a popular programming interface for developing artificial intelligence(AI)*** refers to the order of placing tensor data in the memory and will affect performance by affecting data locality;therefore the deep neural network library has a convention on the *** AI applications can use arbitrary layouts,and existing AI systems do not provide programming abstractions to shield the layout conventions of libraries,operator developers need to write a lot of layout-related code,which reduces the efficiency of integrating new libraries or developing new ***,the developer assigns the layout conversion operation to the internal operator to deal with the uncertainty of the input layout,thus losing the opportunity for layout *** on the idea of polymorphism,we propose a layout-agnostic virtual tensor programming interface,namely the Vtensor framework,which enables developers to write new operators without caring about the underlying physical layout of *** addition,the Vtensor framework performs global layout inference at runtime to transparently resolve the required layout of virtual tensors,and runtime layout-oriented optimizations to globally minimize the number of layout transformation *** results demonstrate that with Vtensor,developers can avoid writing layout-dependent *** with tensorFlow,for the 16 operations used in 12 popular networks,Vtensor can reduce the lines of code(LOC)of writing a new operation by 47.82%on average,and improve the overall performance by 18.65%on average.

关键词： artificial intelligence(AI)programming layout-oblivious tensor processing

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A novel dimensionality reduction algorithm for Cholangiocarcinoma hyperspectral images

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OPTICS AND LASER TECHNOLOGY 2023年第1期167卷

作者： Li, Chenming Wang, Meiling Sun, Xinyu Zhu, Min Gao, Hongmin Cao, Xueying Ullah, Inam Liu, Qin Xu, Peipei Hohai Univ Coll Comp & Informat Nanjing 211100 Peoples R China Nanjing Med Univ Nanjing Drum Tower Hosp Dept Hematol Clin Coll Nanjing Peoples R China Nanjing Univ Nanjing Drum Tower Hosp Affiliated Hosp Dept HematolMed Sch Nanjing Peoples R China Nanjing Univ Nanjing Drum Tower Hosp Affiliated Hosp Dept OncolMed Sch Nanjing Peoples R China Gachon Univ Dept Comp Engn Seongnam 13120 South Korea

Medical hyperspectral imagery (HSI) has become a promising auxiliary diagnostic tool in the field of medical diagnosis and being offered noninvasive disease diagnosis in many cases. However, the huge number of spectral bands may lead to the curse of dimensionality and increase computational complexity. Thus, dimensionality reduction (DR) is an essential step for hyperspectral preprocessing. To maintain the cubic nature of HSI and extract more discriminative and representative information from original data, we proposed a novel DR method termed tensor-based weight-modified multi-manifold discriminant analysis (TWMDA). In this paper, two weight modified intra-class and inter-class adjacency affinity matrices are constructed to make full use of the class in-formation and strengthen the power of capturing discriminant information from high-dimensional data. After that, balancing the effect of within-class adjacency compactness and between-class adjacency separation to obtain better classification performance in tensor space instead of vector space. Experimental results on Chol-angiocarcinoma (CCA) microscope hyperspectral data sets prove the efficiency and superiority of the proposed method. Hence, this novel DR approach would be more beneficial for cancer diagnosis based on HSI.

关键词： Dimensionality reduction Graph embedding tensor processing Cholangiocarcinoma Hyperspectral image

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In Search of the Performance- and Energy-Efficient CNN Accelerators

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IEICE TRANSACTIONS ON ELECTRONICS 2022年第6期E105C卷 209-221页

作者： Sedukhin, Stanislav Tomioka, Yoichi Yamamoto, Kohei Univ Aizu Aizu Wakamatsu Fukushima 9658580 Japan Oki Elect Ind Co Ltd Osaka 5410051 Japan

In this paper, starting from the algorithm, a performance- and energy-efficient 3D structure or shape of the tensor processing Engine (TPE) for CNN acceleration is systematically searched and evaluated. An optimal accelerator's shape maximizes the number of concurrent MAC operations per clock cycle while minimizes the number of redundant operations. The proposed 3D vector-parallel TPE architecture with an optimal shape can be very efficiently used for considerable CNN acceleration. Due to implemented support of inter-block image data independency, it is possible to use multiple of such TPEs for the additional CNN acceleration. Moreover, it is shown that the proposed TPE can also be uniformly used for acceleration of the different CNN models such as VGG, ResNet, YOLO, and SSD. We also demonstrate that our theoretical efficiency analysis is matched with the result of a real implementation for an SSD model to which a state-of-the-art channel pruning technique is applied.

关键词： multi-channel convolution tensor processing vector-parallel computing data reusing computing efficiency

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Structured tensor CP Decomposition-Aided Pilot Decontamination for UAV Communication in Cell-Free Massive MIMO Systems

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IEEE COMMUNICATIONS LETTERS 2022年第9期26卷 2156-2160页

作者： Li, Jiamin Wu, Zhenggang Wan, Ziqian Zhu, Pengcheng Wang, Dongming You, Xiaohu Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

This letter proposes a structured CANDECOMP/PARAFAC (CP) decomposition-aided (SCPD) pilot decontamination (PDC) scheme for UAV communication in cell-free massive MIMO systems. We design a two-stage pilot transmission scheme and intentionally generate pilot contamination for pilot decontamination. At first, we formulate the received signals as a second-order tensor. Then we turn the PDC problem into a joint channel parameter estimation problem. By exploiting the Vandermonde structure of the factor matrix, we propose a SCPD-aided scheme for channel estimation, which only leverages standard linear algebra and avoids numerous iterations. The simulation results demonstrate the effectiveness of the proposed SCPD scheme in terms of accuracy and complexity.

关键词： tensors Channel estimation Autonomous aerial vehicles Matrix decomposition Estimation Decontamination Delay effects Pilot decontamination cellular-connected UAV communication structured CP decomposition (SCPD) tensor processing

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Low-Rank tensor Models for Improved Multidimensional MRI: Application to Dynamic Cardiac T1 Mapping

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2020年 6卷 194-207页

作者： Yaman, Burhaneddin Weingartner, Sebastian Kargas, Nikolaos Sidiropoulos, Nicholas D. Akcakaya, Mehmet Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Univ Minnesota Ctr Magnet Resonance Res Minneapolis MN 55455 USA Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22904 USA

Multidimensional, multicontrast magnetic resonance imaging (MRI) has become increasingly available for comprehensive and time-efficient evaluation of various pathologies, providing large amounts of data and offering new opportunities for improved image reconstructions. Recently, a cardiac phase-resolved myocardial T-1 mapping method has been introduced to provide dynamic information on tissue viability. Improved spatio-temporal resolution in clinically acceptable scan times is highly desirable but requires high acceleration factors. tensors are well-suited to describe interdimensional hidden structures in such multi-dimensional datasets. In this study, we sought to utilize and compare different tensor decomposition methods, without the use of auxiliary navigator data. We explored multiple processing approaches in order to enable high-resolution cardiac phase-resolved myocardial T-1 mapping. Eight different low-rank tensor approximation and processing approaches were evaluated using quantitative analysis of accuracy and precision in T-1 maps acquired in six healthy volunteers. All methods provided comparable T-1 values. However, the precision was significantly improved using local processing, as well as a direct tensor rank approximation. Low-rank tensor approximation approaches are well-suited to enable dynamic T-1 mapping at high spatio-temporal resolutions.

关键词： Accelerated imaging multi-dimensional MRI myocardial T-1 mapping tensor processing low-rank tensors PARAFAC Tucker

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Adaptive 3D Interpolation of Images 55

Adaptive 3D Interpolation of Images

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55th International Scientific Conference on Information, Communication and Energy Systems and Technologies (IEEE ICEST)

作者： Mironov, Rumen P. Tech Univ Sofia Fac Telecommun Boulv Kl Ohridsky 8 Sofia 1000 Bulgaria

ISBN: (纸本)9781728171432

In this paper a scheme of adaptive 3D interpolator, suitable with the structure of neighborhood picture elements in local area 2x2x2 is developed. The type of the interpolation changes adaptively depending on the presence or absence of contours in the 3D area of the studied fragment, which improves the visual quality. On this base an algorithm for adaptive 3D interpolation of halftone images, worked in Matlab environment for operating system Windows 7 is developed. The given experimental results from working of developed interpolator in different directions, based on a set of real test video sequences are obtained.

关键词： Digital image processing 3D adaptive image sequences interpolation tensor processing

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Vtensor: Using Virtual tensors to Build a Layout-Oblivious AI Programming Framework 20

VTensor: Using Virtual Tensors to Build a Layout-Oblivious A...

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ACM International Conference on Parallel Architectures and Compilation Techniques (PACT)

作者： Yu, Feng Zhao, Jiacheng Cui, Huimin Feng, Xiaobing Xue, Jingling Chinese Acad Sci ICT SKLCA Beijing Peoples R China UCAS Sch Comp Sci & Technol Beijing Peoples R China UNSW Sydney Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (纸本)9781450380751

tensors are a popular programming interface for developing AI algorithms. Representative AI programming frameworks require developers to be always aware of tensor layouts, thereby reducing their productivity in integrating an existing operation with a new library and/or writing a new operation. We propose Vtensor, a layout-oblivious virtual tensor programming interface, together with a global layout inference mechanism to resolve the layout required by virtual tensors. Furthermore, Vtensor leverages a layoutoriented optimization to globally minimize the number of layout conversion operations, together with a straggler-ware scheduling algorithm and a pool-based memory allocation scheme to globally allocate resources. Vtensor yields significant speedup and LOC (Lines of Codes) reduction compared to tensorFlow.

关键词： deep neural networks layout-oblivious ai programming tensor processing

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Visual front-end for underwater scene change detection and environment monitoring by the autonomous drone

Visual front-end for underwater scene change detection and e...

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Conference on Real-Time Image processing and Deep Learning

作者： Cyganek, Boguslaw Smolka, Bogdan AGH Univ Sci & Technol Al Mickiewicza 30 PL-30059 Krakow Poland Silesian Tech Univ St Akad 16 PL-44101 Gliwice Poland

ISBN: (纸本)9781510626584

Autonomous underwater drone operation requires on-line analysis of signals coming from various sensors. In this paper we focus on design of the visual front-end of an underwater drone which is optimized for abrupt signal change detection for help in maneuvering and underwater object search operations. The proposed method relies on tensor space comparison with the chordal kernel function. This kernel measures a distance expressed as principal angles on Grassman manifolds of unfolded tensors. Although tested on color videos, the method can be scaled to accept more signal types in the input tensors. Experiments show promising results.

关键词： Underwater object search underwater signals processing tensor processing chordal kernel data stream analysis

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Structured tensor Decomposition-Based Channel Estimation for Wideband Millimeter Wave MIMO 53

Structured Tensor Decomposition-Based Channel Estimation for...

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53rd Asilomar Conference on Signals, Systems, and Computers (ACSSC)

作者： Lin, Yuxing Jin, Shi Matthaiou, Michail You, Xiaohu Southeast Univ Natl Mobile Commun Res Lab Nanjing Peoples R China Queens Univ Belfast Inst Elect Commun & Informat Technol ECIT Belfast Antrim North Ireland

ISBN: (数字)9781728143002

ISBN: (纸本)9781728143002

In this paper, we focus on the channel estimation of millimeter wave (mmWave) multiple-input multiple-output orthogonal frequency division multiplexing systems with hybrid analog-digital structures. By exploiting the sparse characteristics of mmWave channels, we transform the channel estimation issue to the recovery problem of multi-path parameters, e.g., angle of arrival/departure, time delay and path gain. We formulate the downlink training signals as a third-order low-rank tensor, which fits a canonical polyadic (CP) model with three factor matrices containing the channel parameters. We further exploit the Vandermonde structure of the factor matrix and develop a structured CP decomposition-aided channel estimation algorithm, which leverages standard linear algebra and avoids random initialization and iterations. The simulation results show that the proposed scheme outperforms the traditional strategies in terms of accuracy, complexity and robustness.

关键词： Channel estimation hybrid beamforming structured CP decomposition tensor processing wideband mmWave systems

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