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Neural Architecture Search based on Tabu Search and Evolutionary Algorithm 11

Neural Architecture Search Based on Tabu Search and Evolutio...

引用

11th ieee Annual international conference on CYBER technology in Automation, Control, and Intelligent systems, CYBER 2021

作者： Fan, Zhun Long, Zhoubin Li, Wenji Wang, Zhaojun Yang, Zhi Wang, Liu Guangdong Province Engineering College Shantou University Department of Key Lab of Digital Signal and Image Processing GuangdongShantou China

ISBN: (纸本)9781665425278

Most existing optimization methods for neural architecture search (NAS), including evolutionary algorithms, reinforcement learning and gradient-based approaches, have not employed memory strategies explicitly, which may lack of efficiency when searching neural architectures. To solve this issue, we propose a new NAS approach by using an evolutionary algorithm which employs a tabu mechanism to help to improve the search efficiency. To be more specific, the individuals of parent population are selected by tournament selection and tabu list. The tournament selection select parent population according to the accuracy of each individual. And the tabu mechanism builds a tabu list to record the chosen operations in the last previous search process, which employs a search memory mechanism to improve the efficiency explicitly. To confirm the superior performance of our approach, a well-designed surrogate model is used to accelerate the process of performance evaluation on CIFAR-10. The comprehensive experimental results show that the proposed method can reach to 2.48% error rate with about 2 GPU days, which demonstrates the superiority of the suggested method. © 2021 ieee.

关键词： Evolutionary algorithms

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A2-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV systems 42

A2-UAV: Application-Aware Content and Network Optimization o...

引用

42nd ieee international conference on Computer Communications, INFOCOM 2023

作者： Coletta, Andrea Giorgi, Flavio Maselli, Gaia Prata, Matteo Silvestri, Domenicomichele Ashdown, Jonathan Restuccia, Francesco J.P. Morgan Ai Research New York United States Sapienza University of Rome Department of Computer Science Italy Air Force Research Laboratory United States Northeastern University Institute for the Wireless Internet of Things United States

ISBN: (纸本)9798350334142

To perform advanced surveillance, Unmanned Aerial Vehicles (UAVs) require the execution of edge-assisted computer vision (CV) tasks. In multi-hop UAV networks, the successful transmission of these tasks to the edge is severely challenged due to severe bandwidth constraints. For this reason, we propose a novel A2-UAV framework to optimize the number of correctly executed tasks at the edge. In stark contrast with existing art, we take an application-aware approach and formulate a novel Application-Aware Task Planning Problem (A2-TPP) that takes into account (i) the relationship between deep neural network (DNN) accuracy and image compression for the classes of interest based on the available dataset, (ii) the target positions, (iii) the current energy/position of the UAVs to optimize routing, data pre-processing and target assignment for each UAV. We demonstrate A2-TPP is NP-Hard and propose a polynomial-time algorithm to solve it efficiently. We extensively evaluate A2-UAV through real-world experiments with a testbed composed by four DJI Mavic Air 2 UAVs. We consider state-of-the-art image classification tasks with four different DNN models (i.e., DenseNet, ResNet152, ResNet50 and MobileNet-V2) and object detection tasks using YoloV4 trained on the imageNet dataset. Results show that A2-UAV attains on average around 38% more accomplished tasks than the state of the art, with 400% more accomplished tasks when the number of targets increase significantly. To allow full reproducibility, we pledge to share datasets and code with the research community. © 2023 ieee.

关键词： Deep neural networks

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Energy-Efficient Approximate Floating-Point Multiplier based on Radix-8 Booth Encoding 14

Energy-Efficient Approximate Floating-Point Multiplier Based...

引用

14th ieee international conference on ASIC, ASICON 2021

作者： Ding, Rongyu Guo, Yi Sun, Heming Kimura, Shinji Waseda University Graduate School of Information Production and Systems Kitakyushu Japan Waseda Research Institute for Science and Engineering Tokyo Japan

ISBN: (纸本)9781665411493

In applications such as digital signal processing and machine learning, the accuracy of internal operations is not so strict due to the limitation of human perception. Approximate computing has been focused as an effective way to trade off energy against accuracy. In this paper, a new type of approximate floating-point (FP) multiplier is proposed by applying radix-8 Booth encoding to the mantissa part. We devise the addition of the triple of multiplicand in radix-8 Booth encoding. Experimental results show the proposed design can achieve significant reduction in area, delay and power up to 66.48%, 23.39% and 69.02% while losing only 0.18% accuracy when compared with the ieee-754 single precision FP multiplier. The proposed multipliers are applied to image smoothing and image compression and show negligible quality loss. © 2021 ieee.

关键词： Digital signal processing

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Data Classification Model of Visual Art Works based on Sparse Coding 5

Data Classification Model of Visual Art Works Based on Spars...

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5th international conference on Electronics, Communication and Aerospace technology, ICECA 2021

作者： Zhang, Wenyi Wuhan University of Technology Hubei Wuhan430070 China

ISBN: (纸本)9781665435246

The vision system is more perfect than any artificial digital image signal processing system. Bionics is a good way to improve the technology of digital image processing. Sparse coding is an artificial neural network method that simulates simple cell receptive fields in the main visual cortex of the mammalian primary visual system. It can effectively extract the internal features of the image and also obtain the maximum amount of information in the image. based on the sparse coding algorithm, this paper designs a style understanding and style classification model for dead artworks. © 2021 ieee.

关键词： image enhancement

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Matrix Recovery using Deep Generative Priors with Low-Rank Deviations

Matrix Recovery using Deep Generative Priors with Low-Rank D...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Pengbin Yu Jianjun Wang Chen Xu School of Mathematics and Statistics Southwest University Chongqing China Department of Mathematics and Statistics University of Ottawa Ottawa ON Canada

In matrix recovery, an unknown matrix can be reconstructed by a small number of limited and noisy measurements. Deep learning-based methods, such as deep generative models, pro-vide stronger priors that can serve to mitigate the pressure of sampling during image recovery. But such methods require that the recovered data be limited to the scope of the generator, otherwise it will lead to large recovery error. To circumvent this problem, in this paper, a framework for matrix recovery from limited measurements is proposed, which employs low rank approximation to characterize the deviation of generator, referred to as Low-Rank-Gen. Theoretically, we propose Matrix Set-Restricted Eigenvalue Condition (M-S-REC), and further prove the existence of decoders and upper bound of reconstruction error using certain number of measurements corresponding to such decoder. Empirically, we observe consistent improvements in reconstruction accuracy, PSNR index over competing approaches.

关键词： Learning systems Upper bound Measurement uncertainty signal processing Generators Eigenvalues and eigenfunctions Decoding

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Basketball Video Analysis for Automated Game Data Acquisition Deep Learning

Basketball Video Analysis for Automated Game Data Acquisitio...

引用

ieee international conference on Electro-Information technology

作者： Diego Rodriguez Garcia Xinrui Yu Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

The utilization of data analytics to gain insights into the game of basketball has seen a remarkable surge in the past decade. Leagues such as the National Basketball Association are continuously exploring innovative methods to analyze game data, an approach that has significantly influenced the dynamics of the game. But to perform these analyses, a growing amount of data is needed, which is traditionally annotated by humans. This work proposes a 3-stage system able to automatically acquire relevant basketball game data from a broadcast video. The first stage is an object detector combined with a tracking algorithm to extract the main elements present in a basketball game video. Then, the players' visual information is analyzed to identify the players based on pixel color analysis and number recognition. Finally, a statistics generation algorithm assigns the game events to the corresponding player and team, so that the system can be used as an aid for box score annotation in major leagues, low-cost annotation in amateur games, or in-depth game video analysis.

关键词： Visualization Annotations image color analysis Data acquisition Optical character recognition Games Detectors

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ForensiCam-215K: A Large Scale image and Video Dataset for Forensic Analysis

ForensiCam-215K: A Large Scale Image and Video Dataset for F...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Suwen Du Pengpeng Yang Daniele Baracchi Jinglian Jin Dasara Shullani Alessandro Piva Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang China Department of Information Engineering University of Florence Florence Italy

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Determining the origin of a digital image or video, namely device source identification, is widely used in courtroom evidence and copyright protection. Currently, device source identification primarily focuses on images captured using single camera with default settings. However, with the advancement of imaging technology, there is a large number of smartphones equipped with multiple cameras and various shooting modes for acquiring images, which may pose a significant challenge to device source identification. Therefore, to assess the performance of image source identification algorithm for modern smartphones and promote further research, it is crucial to build a dataset of image and video captured by modern smartphones. In this paper, we present a large-scale image and video dataset for forensic analysis, ForensiCam-215K. The dataset includes over 215K media contents captured by 130 modern smartphones of 10 major brands. We used the latest equipment to capture images from the main, wide-angle, and telephoto cameras in six different shooting modes, and the media were collected under a strictly controlled procedure to reduce the bias caused by differences in the acquisition process between different devices. Additionally, we used the Photo Response Non-Uniformity (PRNU) method to perform device source identification tests on the dataset. The results indicate that device source identification is a challenging task especially for images and videos captured by smartphones with multiple cameras and various shooting modes. The dataset will be released as open-source and freely available for use by the multimedia forensics research community at https://***/dswdsw21072/ForensiCam-215K.

关键词： Performance evaluation Forensics signal processing algorithms Media Streaming media signal processing Cameras Object recognition Speech processing Smart phones

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Classification of image Classes based on the PCA Algorithm Optimized by the KNN Algorithm Improved by Genetic Algorithms

Classification of Image Classes Based on the PCA Algorithm O...

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ieee international conference on Information Communication and signal Processing (ICICSP)

作者： Constantin Doru Bălcău Costel Department of Mathematics and Computer Science The National University of Science and Technology POLITEHNICA Bucharest Piteşti Univ. Centre România

In this paper we propose an optimized version of the PCA algorithm by using genetic algorithms and the KNN technique with applications in the classification of image classes. The algorithm are developed by optimizing the images projection stage based on the eigenvectors that ensure the extraction of essential features from the data. In that stage, the optimal value regarding the size of the projection space of the initial images is determined by using a genetic algorithm and and the improved KNN algorithm regarding the key parameter represented by the number of nearest neighbors used in the testing stage. For the validation and testing of the developed algorithms, the classification of face image classes corresponding to several people is considered. The proposed algorithm includes several versions, analyzed in the experimental study, depending on the similarity measure used (the Euclidian distance, Manhattan distance, Mahalanobis distance, Minkowski distance and cosine) to determine the nearest neighbors to the KNN algorithm, as well as based on the crossover recombination method chosen at the level of binary genes with a crossover point or more many crossing points. The experimental study on the methods established in the work attests good performance through the percentages of correctness, resulting in applications for recognizing people through facial images.

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A Lightweight Deep Neural Network-based Optical image Detection Method for Underwater Submerged Rocket Wrecks

A Lightweight Deep Neural Network-Based Optical Image Detect...

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ieee international conference on signal and image Processing (ICSIP)

作者： Gangqi Chen Dongdong Hou Jinwei Xiao Shiqi Wei Yuxuan Wu Wenjun Ding Junge Shen School of Marine Science and Technology Northwestern Polytechnical University Xi’an China 713th ResearchInstitute China State Ship building Co. Ltd Zheng Zhou China Henan Key Laboratory of Underwater Intelligent Equipment Zheng Zhou China School of Astronautics Northwestern Polytechnical University Xi’an China School of Electronic Information Northwestern Polytechnical University Xi’an China Unmanned System Research Institute Northwestern Polytechnical University Xi’an China

Maritime recycling plays a significant role in reusing components from spacecraft. However, the current methods for detecting and recovering sunken spacecraft wrecks suffer from inaccurate detection and low efficiency. To improve the detection effectiveness of rocket wrecks, this paper proposes a method based on underwater optical images which leverage the high-resolution features of images. To address the issue of low accuracy and efficiency in detecting rocket wrecks, a lightweight deep neural network detection model is introduced to enhance accuracy while reducing the computational load and increasing efficiency. Finally, through quantitative and qualitative analyses, our proposed method achieves a detection accuracy of 96.0% with low computational power, which lays a solid foundation for spacecraft recycling.

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A VLSI Architecture for MQ Arithmetic Encoder Supporting Three-Context Parallel Processing

A VLSI Architecture for MQ Arithmetic Encoder Supporting Thr...

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international conference on Vision, image and signal Processing (ICVISP)

作者： Peijun Ma Cong Yao Jiangyi Shi Gongzhi Zhao Xidian University Xi'an China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

JPEG2000 is a popular still image compression standard, which has excellent compression performance in the field of still image compression. At present, it is widely believed that MQ arithmetic encoder has become the most critical speed bottleneck of JPEG2000 algorithm due to the restriction of the serial execution mode of the algorithm itself. The MQ encoding is serial in nature, which is due to the high dependence between adjacent Cx-D pairs which severely affects its processing speed. In order to solve this problem, this paper proposes a multi-context MQ coding hardware architecture based on a partially parallel coding strategy. This architecture can process up to three Cx-D pairs in one clock cycle, which greatly improves the processing speed of the MQ arithmetic encoder under the existing architecture. In terms of overall performance, the encoder designed in this paper can process 2.64 Cx-D pairs per cycle on average, and 743.16 Cx-D pairs per second under TSMC90nm technology. The architecture is only 208399.66 units in area while achieving high throughput. The proposed MQ encoder provides a useful reference for hardware implementation of high performance algorithmic encoders in JPEG2000 and other image compression standards. At the same time, we comprehensively analyze the possible output scenarios of multi-bytecode streams caused by multi-context processing, and propose a processing structure based on additional pipeline, which greatly reduces the number of module fan-out and FIFOs.

关键词： image coding Transform coding Voltage Very large scale integration Throughput Libraries Hardware Environmental factors Standards Arithmetic

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