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A data alternating extraction general structure and its algorithms for adaptive space-time wideband beamforming

DIGITAL SIGNAL PROCESSING 2022年第0期126卷 103478-103478页

作者： Zhang, Shurui Gu, Qiong Ma, Xiaofeng Sheng, Weixing Kirubarajan, Thia Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada

The adaptive space-time processing is a well-substantiated method to build a wideband beamformer. This paper proposes a low complexity general structure for the adaptive space-time wideband beamforming. The new general structure uses a data alternating extraction approach for adjacent arrays to reduce the scales of received data, resulting in obtaining the optimum adaptive weights with much lower computational complexity. Then, the adaptive wideband beamforming algorithms are proposed for the general structure. Among the novel algorithms, the singular value decomposition alternating extraction frequency constrained Frost beamformer (SVD-AEFCFB) and SVD-AEFC Laguerre beamformer (SVDAEFCLB) both have an undistorted response to the wideband signal of interest and satisfactory anti interference capability with the lowest computational complexity. Importantly, the proposed structure and algorithms have the same generalized and popularized characteristics for the other space-time methods. Simulation results highlight the correctness and effectiveness of the proposed methods. (C)& nbsp;2022 Elsevier Inc. All rights reserved.

关键词： Adaptive wideband beamformer Space-time processing Data alternating extraction general structure low computational complexity

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Inverse-free zeroing neural network for time-variant nonlinear optimization with manipulator applications

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NEURAL NETWORKS 2024年 178卷 106462页

作者： Chen, Jielong Pan, Yan Zhang, Yunong Li, Shuai Tan, Ning Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Univ Oulu Fac Informat Technol & Elect Engn Oulu 905706 Finland VTT Technol Res Ctr Finland Oulu 905706 Finland

In this paper, the problem of time -variant optimization subject to nonlinear equation constraint is studied. To solve the challenging problem, methods based on the neural networks, such as zeroing neural network and gradient neural network, are commonly adopted due to their performance on handling nonlinear problems. However, the traditional zeroing neural network algorithm requires computing the matrix inverse during the solving process, which is a complicated and time-consuming operation. Although the gradient neural network algorithm does not require computing the matrix inverse, its accuracy is not high enough. Therefore, a novel inverse -free zeroing neural network algorithm without matrix inverse is proposed in this paper. The proposed algorithm not only avoids the matrix inverse, but also avoids matrix multiplication, greatly reducing the computational complexity. In addition, detailed theoretical analyses of the convergence performance of the proposed algorithm is provided to guarantee its excellent capability in solving time -variant optimization problems. Numerical simulations and comparative experiments with traditional zeroing neural network and gradient neural network algorithms substantiate the accuracy and superiority of the novel inverse -free zeroing neural network algorithm. To further validate the performance of the novel inverse -free zeroing neural network algorithm in practical applications, path tracking tasks of three manipulators (i.e., Universal Robot 5, Franka Emika Panda, and Kinova JACO2 manipulators) are conducted, and the results verify the applicability of the proposed algorithm.

关键词： Time-variant nonlinear optimization Inverse-free algorithm Zeroing neural network Robot control low computational complexity

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An FPGA-Based Simple RGB-HSI Space Conversion Algorithm for Hardware Image Processing

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IEEE ACCESS 2020年 8卷 173838-173853页

作者： Zhi, Shuaiqing Cui, Yani Deng, Jiaxian Du, Wencai Hainan Univ Coll Informat & Commun Engn Haikou 570228 Hainan Peoples R China City Univ Macau Inst Data Sci Taipa 999078 Macao Peoples R China

In this paper, a new, low-complexity, easy-to-implement hardware method for color space conversion between the red-green-blue (RGB) and the hue-saturation-intensity (HSI) color spaces called the simple RGB-HSI space conversion (S-SC) algorithm is proposed, which aims to provide more rapid computing due to the need for fewer operations. In the S-SC algorithm, we reconstruct the model of space conversion between the RGB color space and the HSI color space (RGB-HSI) by inverting the conversion from the HSI color space to the RGB color space of the traditional geometric derivation algorithm. As a result, the nonlinear model-realized RGB-HSI color space conversion by the geometric derivation algorithm is transformed into a linear conversion model, which can avoid complicated calculations such as trigonometric and inverse trigonometric functions in the color space conversion process. The model can effectively reduce the computational complexity of the algorithm and facilitate hardware implementation at the same time. To evaluate the performance of the S-SC algorithm, we first compare the S-SC algorithm with the geometric derivation algorithm from the computational complexity perspective. On this basis, we compare the S-SC algorithm with five other RGB-HSI color space conversion algorithms from the perspectives of error and conversion effect. Finally, we use the field programmable gate array (FPGA) hardware platform to analyze and verify the timing sequence and logical resource consumption and verify the effectiveness of the proposed algorithm with experimental results. We show that the S-SC algorithm achieves good performance in terms of conversion accuracy, logical unit resource occupancy, and output timing.

关键词： Image color analysis Hardware Signal processing algorithms Field programmable gate arrays Approximation algorithms computational modeling RGB-HSI space conversion FPGA implementation low computational complexity low logic resource consumption

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Design of airborne target tracking accelerator based on KCF

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JOURNAL OF ENGINEERING-JOE 2019年第23期2019卷 8966-8971页

作者： Yang, Haoran Yu, Jinxiang Wang, Shaojun Peng, Xiyuan Harbin Inst Technol Sch Elect & Informat Engn Harbin 150080 Peoples R China

The on-board target tracking of unmanned aerial vehicle (UAV) relies on on-board computer to process the collected ground image information, and then use the target tracking algorithm to achieve the target tracking function, which provides the user with target position, trajectory and other information. UAVs are limited by load and power consumption. Due to the high resolution and frame rate of UAV's image collection system, traditional target tracking platforms cannot meet the requirements. As a result, the real-time nature of the tracking platform urgently needs to be resolved. This study designs and implements the airborne ground target tracking accelerator for UAV. The kernelised correlation filters (KCFs) algorithm with low computational complexity and precision is selected to achieve the airborne ground target tracking function and its performance bottleneck is analysed. In order to improve the real-time performance of the tracking task processing, the ZYNQ platform based on ARM and field programmable gate arrays is chosen to achieve the hardware acceleration of the KCF algorithm. The KCF algorithm is implemented in hardware using the high-level synthesis technology, and a high-speed data transmission link is established. Experiments show that the design can achieve a tracking rate of more than 30 FPS under 960x540 resolution, which meets the real-time requirements of UAV target tracking task.

关键词： autonomous aerial vehicles target tracking remotely operated vehicles field programmable gate arrays filtering theory tracking task processing ZYNQ platform hardware acceleration KCF algorithm tracking rate UAV target tracking task airborne target tracking accelerator on-board target tracking unmanned aerial vehicle on-board computer collected ground image information target tracking algorithm target tracking function target position power consumption frame rate UAV's image collection system traditional target tracking platforms tracking platform study designs airborne ground target tracking accelerator correlation filters algorithm low computational complexity

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BSN-ESC: A Big-Small Network-Based Environmental Sound Classification Method for AIoT Applications

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SENSORS 2023年第15期23卷 6767-6767页

作者： Peng, Lujie Yang, Junyu Yan, Longke Chen, Zhiyi Xiao, Jianbiao Zhou, Liang Zhou, Jun Univ Elect Sci & Technol China Sch Informat & Commun Engn Dept Internet Things Engn Chengdu 611731 Peoples R China

In recent years, environmental sound classification (ESC) has prevailed in many artificial intelligence Internet of Things (AIoT) applications, as environmental sound contains a wealth of information that can be used to detect particular events. However, existing ESC methods have high computational complexity and are not suitable for deployment on AIoT devices with constrained computing resources. Therefore, it is of great importance to propose a model with both high classification accuracy and low computational complexity. In this work, a new ESC method named BSN-ESC is proposed, including a big-small network-based ESC model that can assess the classification difficulty level and adaptively activate a big or small network for classification as well as a pre-classification processing technique with logmel spectrogram refining, which prevents distortion in the frequency-domain characteristics of the sound clip at the joint part of two adjacent sound clips. With the proposed methods, the computational complexity is significantly reduced, while the classification accuracy is still high. The proposed BSN-ESC model is implemented on both CPU and FPGA to evaluate its performance on both PC and embedded systems with the dataset ESC-50, which is the most commonly used dataset. The proposed BSN-ESC model achieves the lowest computational complexity with the number of floating-point operations (FLOPs) of only 0.123G, which represents a reduction of up to 2309 times in computational complexity compared with state-of-the-art methods while delivering a high classification accuracy of 89.25%. This work can achieve the realization of ESC being applied to AIoT devices with constrained computational resources.

关键词： environmental sound classification low computational complexity neural network AIoT

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DOA Estimation Based on Reduced-rank Multistage Wiener Filter

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Procedia Computer Science 2017年 107卷 652-656页

作者： Ming Zhang Rui Lu Shitao Zhu Anxue Zhang Xi’an Jiaotong University Xi’an 710049 China

A new DOA estimator based on the reduced-rank multistage Wiener filter (MWF) is proposed this paper. This method estimates the DOAs of the impinging signals in the framework of beamforming and the number of sources (NOS) is hence not required. Besides improving performance over the classical subspace-based methods under small sample size condition, the new algorithm offers a significant reduction in computational complexity. In addition, a computation-free rank selection criterion is developed for the reduced-rank MWF, which plays the role of determining the NOS in the subspace-based methods. This criterion has better performance than the conventional information theoretic criteria when the sample size is small.

关键词： DOA estimation low computational complexity multistage Wiener filter reduced-rank processing

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A Secure Visual Secret Sharing Scheme with Authentication Based on QR Code

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Journal on Big Data 2021年第2期3卷 85-95页

作者： Xinwei Zhong Lizhi Xiong Zhihua Xia Nanjing University of Information Science&Technology Nanjing210044China

With the rise of the Internet of Things(IoT),various devices in life and industry are closely *** of its high payload,stable error correction capability,and convenience in reading and writing,Quick Response(QR)code has been widely researched in ***,the security of privacy data in IoT is also a very important *** the same time,because IoT is developing towards low-power devices in order to be applied to more fields,the technology protecting the security of private needs to have the characteristics of low computational *** Secret Sharing(VSS),with its features of safety and low computational cost,can fully meet the requirements of communication security in ***,a VSS scheme with QR code(VSS-QR)was proposed and has been applied to some *** VSS-QR,the secret is shared into a series of *** shares are usually common QR codes,which cannot cause the attention of the ***,if there is dishonesty among participants,the secret cannot be recovered,which will lead to VSS-QR cannot be widely used due to its inadequate *** this paper,we propose a visual secret sharing scheme with authentication based on QR code(VSSA-QR).Both the reconstructed secret QR code and shares can be verified whether they are forged by *** above-mentioned operations conveniently are performed on low-power QR scanning *** only does the proposed scheme prevent some dishonest participants or attackers from cheating,but also prevent all participants from *** addition,the payload is the QR code itself,which is higher than other *** analysis and experiments prove that the proposed scheme is effective.

关键词： QR code visual secret sharing low computational complexity authentication

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Fast algorithm for moving target localisation using FDA-MIMO radar

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JOURNAL OF ENGINEERING-JOE 2019年第19期2019卷 5749-5752页

作者： Xu, Jian Wang, Wen-Qin Cui, Can Huang, Bang Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Sichuan Peoples R China Nanjing Univ Sci & Technol Sch Elect Engn & Optoelect Technol Nanjing Jiangsu Peoples R China

Target localisation using frequency diverse array (FDA) has drawn much attention owing to the additional degree of freedom in the range dimension. However, the Doppler effect was often ignored in previous FDA works because of the stationary target assumption, and in contrast, even stationary target localisation could be of quite a high computational complexity. In this study, a method is proposed that can jointly estimate range, angle, and Doppler for FDA multiple-input-multiple-output radar with low computational complexity. First, Doppler is estimated independently by utilising unstructured maximum likelihood method. Next, traditional two-dimensional (2D) MUSIC is divided into multiple one-dimensional (1D) MUSIC to estimate the angle. Then substituting the estimated angle into the former 2D MUSIC results in a 1D searching over range dimension to get the range estimation. The advantage of the proposed method was verified by simulation results.

关键词： computational complexity direction-of-arrival estimation MIMO radar Doppler effect radar signal processing MIMO communication maximum likelihood estimation fast algorithm FDA-MIMO radar frequency diverse array range dimension Doppler effect previous FDA works stationary target assumption stationary target localisation high computational complexity FDA multiple-input-multiple-output radar low computational complexity maximum likelihood method two-dimensional MUSIC one-dimensional MUSIC estimated angle 2D MUSIC results range estimation

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Searching Optimal Symbol Mapping for Multi-D BICM-ID Systems Using SA Algorithm

Searching Optimal Symbol Mapping for Multi-D BICM-ID Systems...

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第十七届计算机工程与工艺年会暨第三届微处理器技术论坛

作者： Liang Zhengfa Liu Hengzhu School of Computer National University of Defense Technology

Symbol mapping is the crucial parameter to achieve high coding gain over the iterations in BICM-ID *** problem of finding the optimal symbol mapping can be formulated as an instance of the combinatorial optimization problem known as the quadratic assignment problem(QAP),which is of Non-Deterministic Polynomial(NP) *** multi-dimension(multi-D) modulation is used,the computational complexity rapidly *** this paper,we investigate Simulated Annealing(SA) algorithm to solve the problem,which can efficiently find the optimal symbol mapping with low computational *** addition,the SA algorithm can be used for any modulation schemes,and this is another remarkable advantage of SA algorithm.

关键词： symbol mapping Multi-D BICM-ID systems QAP SA low computational complexity

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A Recursive Partitioning Algorithm for Space Information Flow

A Recursive Partitioning Algorithm for Space Information Flo...

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IEEE Global Communications Conference

作者： Jiaqing Huang Zongpeng Li Department of Electronics and Information Engineering Huazhong University of Science and Technology Department of Computer Science University of Calgary

ISBN: (纸本)9781479935130

Space Information Flow (SIF) is a new research paradigm that studies network coding in a geometric space, which is different with Network Information Flow (NIF) that studies network coding in a graph. One of the key open problems at the core of SIF is to design an algorithm that computes optimal SIF solutions. A new heuristic SIF algorithm based on non-uniform recursive space partitioning is proposed in this work, for computing SIF for any density distribution of given terminal nodes in 2-D Euclidean space. Simulation results show that the new algorithm has low computational complexity and converges to optimal solutions promptly.

关键词： computational complexity graph theory network coding 2D Euclidean space NIF geometric space heuristic SIF algorithm low computational complexity network information flow nonuniform recursive space partitioning algorithm recursive partitioning algorithm space information flow terminal node density distribution Clustering algorithms Equations Mathematical model Network coding Partitioning algorithms Relays Routing

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