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DPT‐tracker:Dual pooling transformer for efficient visual tracking

CAAI Transactions on Intelligence Technology 2024年第4期9卷 948-959页

作者： Yang Fang Bailian Xie Uswah Khairuddin Zijian Min Bingbing Jiang Weisheng Li Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Mechanical Precision Engineering Malaysia‐Japan International Institute of TechnologyUniversity of Technology MalaysiaKuala LumpurMalaysia Department of Electrical and Computer Engineering Inha UniversityIncheonRepublic of Korea School of Information Science and Technology Hangzhou Normal UniversityHangzhouChina

Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model complexity will grow quadratically with the number of input *** alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers,we propose a dual pooling transformer tracking framework,dubbed as DPT,which consists of three components:a simple yet efficient spatiotemporal attention model(SAM),a mutual correlation pooling Trans-former(MCPT)and a multiscale aggregation pooling Transformer(MAPT).SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time *** aims to capture multi‐scale pooled and correlated contextual features,which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking *** tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on Track-ingNet while maintaining a shorter sequence length of attention tokens,fewer parameters and FLOPs compared to existing state‐of‐the‐art(SOTA)Transformer tracking *** experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency.

关键词： human‐computer interfacing image motion analysis pattern recognition signal processing tracking

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Accelerate Processing of Image with the Keccak-512 Algorithm on Cryptoprocessor 8

Accelerate Processing of Image with the Keccak-512 Algorithm...

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8th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2023

作者： Sideris, Argyrios Sanida, Theodora Sanida, Maria Vasiliki Dossis, Michael Dasygenis, Minas University of Western Macedonia Department of Electrical & Computer Engineering Kozani50131 Greece University of Piraeus Department of Digital Systems Piraeus18534 Greece

ISBN: (纸本)9798350383676

Image hashing is a crucial technique for content authentication and image retrieval in various applications, including multimedia databases and copyright protection. This study introduces a novel approach to image hashing that leverages the capabilities of FPGAs and harnesses the cryptographic strength and computational efficiency of the Keccak-512 algorithm. Our experimentations were conducted on the Intel Arria 10 GX FPGA board, and the versatile Nios II enhanced micropro-cessor. The results gleaned from these experiments underscore the tremendous potential of our FPGA-based image-hashing approach. It presents an alluring solution, merging efficiency with security, effectively addressing the burgeoning demands for content authentication and image integrity verification across diverse image-centric applications. This approach provides a powerful tool for addressing the increasing demand for image authentication and integrity verification in the digital age. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Decisioning-Based Approach for Optimising Control engineering Tools Using Digital Twin Capabilities and Other Cyber-Physical Metaverse Manufacturing System Components

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3212-3221页

作者： Mourad, Nahia Alsattar, Hassan A. Qahtan, Sarah Zaidan, Aws Alaa Deveci, Muhammet Sangaiah, Arun Kumar Pedrycz, Witold British University in Dubai Faculty of Engineering and IT Dubai United Arab Emirates University of Mashreq College of Administrative Science Department of Business Administration Baghdad10067 Iraq Middle Technical University College of Health and Medical Technology-Baghdad Department of Computer Center Baghdad10047 Iraq SP Jain School of Global Management SydneyNSW2141 Australia National Defence University Turkish Naval Academy Department of Industrial Engineering Istanbul34040 Turkey Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon International Graduate Institute of AI National Yunlin University of Science and Technology Yunlin64002 Taiwan University of Alberta Faculty of Engineering Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul34010 Turkey

The optimisation of control engineering tools based on digital twin capabilities and other cyber-physical metaverse manufacturing system (CPMMS) components are crucial for the successful performance. This study proposes a model for optimising control engineering tools using digital twin capabilities and other CPMMS components to solve the open issues. The main contributions and novelty aspects of the methodological process are outlined as follows: Formulated and developed is a decision matrix based on a utility procedure for 10 control engineering tools with digital twin capabilities and other three CPMMS components (Programmable-Logic-Controller and Human-Machine-Interface, Internet of Things connectivity and cybersecurity features). This matrix accounts for the uncertainty associated with tool assessment and transformation evaluation issue;formulated and develop an integrating fuzzy weighted with zero-inconsistency-interval-valued spherical fuzzy rough sets (IvSFRS-FWZIC) and combined compromise solution (CoCoSo) methods. The IvSFRS-FWZIC method is utilised to assign importance degrees to the digital twin capabilities and other CPMMS components. The applicability and robustness of the proposed approach are validated and evaluated through conducting sensitivity, correlation, and comparative analyses. The proposed approach can assist managers in analysing and selecting the most suitable tool for developing CPMMS. © 1975-2011 IEEE.

关键词： Internet of things

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DASA: Delay-Adaptive Multi-Agent Stochastic Approximation

DASA: Delay-Adaptive Multi-Agent Stochastic Approximation

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IEEE Conference on Decision and Control

作者： Nicolò Dal Fabbro Arman Adibi H. Vincent Poor Sanjeev R. Kulkarni Aritra Mitra George J. Pappas Electrical and Systems Engineering Department University of Pennsylvania Department of Electrical and Computer Engineering Princeton University Electrical and Computer Engineering Department North Carolina State University

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We consider a setting in which N agents aim to speedup a common Stochastic Approximation (SA) problem by acting in parallel and communicating with a central server. We assume that the up-link transmissions to the server are subject to asynchronous and potentially unbounded time-varying delays. To mitigate the effect of delays and stragglers while reaping the benefits of distributed computation, we propose DASA, a Delay-Adaptive algorithm for multi-agent Stochastic Approximation. We provide a finite-time analysis of DASA assuming that the agents’ stochastic observation processes are independent Markov chains. Significantly advancing existing results, DASA is the first algorithm whose convergence rate depends only on the mixing time $\tau_{m i x}$ and on the average delay $\tau_{a v g}$ while jointly achieving an N-fold convergence speedup under Markovian sampling. Our work is relevant for various SA applications, including multi-agent and distributed temporal difference (TD) learning, Q-learning and stochastic optimization with correlated data.

关键词： Q-learning Temporal difference learning Distributed databases Approximation algorithms Delays Servers Optimization Convergence

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Invited Paper: Learned In-Sensor Visual Computing: From Compression to Eventification 42

Invited Paper: Learned In-Sensor Visual Computing: From Comp...

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42nd IEEE/ACM International Conference on computer-Aided Design, ICCAD 2023

作者： Feng, Yu Ma, Tianrui Boloor, Adith Zhu, Yuhao Zhang, Xuan University of Rochester Department of Computer Science United States Washington University Department of Electrical and Systems Engineering St. Louis United States

ISBN: (纸本)9798350315592

Visual computing is vital for numerous applications. In conventional visual computing systems, CMOS image sensors (CIS) act as pure imaging devices for capturing images, however, recent CIS designs increasingly integrate processing capabilities such as Deep Neural Networks (DNN), which give rise to a notion of in-sensor computing. In this paper, we propose a new concept, learned in-sensor visual computing, which exploits end-to-end optimization of in-sensor processing and downstream vision tasks to achieve better overall algorithm accuracy and adopts hardware/algorithm co-design to achieve ultra-low sensor energy consumption. Two examples of the learned in-sensor visual computing, Leca and EDGAzE, are demonstrated. © 2023 IEEE.

关键词： Deep neural networks

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Fundamental Limits of Distributed Covariance Matrix Estimation Under Communication Constraints 41

Fundamental Limits of Distributed Covariance Matrix Estimati...

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41st International Conference on Machine Learning, ICML 2024

作者： Rahmani, Mohammad Reza Yassaee, Mohammad Hossein Maddah-Ali, Mohammad Ali Aref, Mohammad Reza Department of Electrical Engineering Sharif University of Technology Tehran Iran Sharif Center for Information Systems and Data Science Sharif Institute for Convergence Science & Technology Tehran Iran Department of Electrical and Computer Engineering University of Minnesota Minneapolis United States

Estimating high-dimensional covariance matrices is crucial in various domains. This work considers a scenario where two collaborating agents access disjoint dimensions of m samples from a high-dimensional random vector, and they can only communicate a limited number of bits to a central server, which wants to accurately approximate the covariance matrix. We analyze the fundamental trade-off between communication cost, number of samples, and estimation accuracy. We prove a lower bound on the error achievable by any estimator, highlighting the impact of dimensions, number of samples, and communication budget. Furthermore, we present an algorithm that achieves this lower bound up to a logarithmic factor, demonstrating its near-optimality in practical settings. Copyright 2024 by the author(s)

关键词： Budget control

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Gen Z Oriented engineering Education in the 'Industry 4.0' Age 64

Gen Z Oriented Engineering Education in the 'Industry 4.0' A...

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64th IEEE Annual International Scientific Conference on Power and electrical engineering of Riga Technical University, RTUCON 2023

作者： Raud, Zoja Vodovozov, Valery Petlenkov, Eduard Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia Tallinn University of Technology Department of Computer Systems Tallinn Estonia

ISBN: (纸本)9798350317732

The study analyses and offers the active blended learning methodology oriented on the students belonging to the so-called Generation Z (Gen Z). The goal is to support achieving the creativity, innovation, and other targets stated by the 'Industry 4.0' age. Two disciplines are examined, namely Robotics at the bachelor's level and Advanced Robotics at the master's level for the local, Erasmus+, EuroTeQ, Asian, and African students. The results represent the specificity of active blended learning in view of the formative assessment, poll-supported lectures, and online quizzing. The main strengths of the Gen Z representatives are most strongly manifested in computer forms of training, especially in exercises and labs, where practical skills are acquired. © 2023 IEEE.

关键词： Students

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Alternating Constrained Minimization Based Approximate Message Passing 48

Alternating Constrained Minimization Based Approximate Messa...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Thomas, Christo Kurisummoottil Slock, Dirk Virginia Tech Wireless@VT Bradley Department of Electrical and Computer Engineering ArlingtonVA United States Eurecom Communication Systems Department France

ISBN: (纸本)9781728163277

Generalized Approximate Message Passing (GAMP) allows for Bayesian inference in linear models with non-identically independently distributed (n.i.i.d.) priors and n.i.i.d. measurements of the linear mixture outputs. It represents an efficient technique for approximate inference, which becomes accurate when both rows and columns of the measurement matrix can be treated as sets of independent vectors and both dimensions become large. It has been shown that the fixed points of GAMP correspond to the extrema of a large system limit of the Bethe Free Energy (LSL-BFE), which represents a meaningful approximation optimization criterion regardless of whether the measurement matrix exhibits the independence properties. However, the convergence of (G)AMP can be notoriously problematic for certain measurement matrices, and the only sure fixes so far are damping (by a difficult-to-determine amount) or perform a double ADMM. In this paper, we revisit the GAMP algorithm (as e.g. for sparse Bayesian learning (SBL)) by more rigorously applying an alternating constrained minimization strategy to an appropriately reparameterized LSL BFE. This guarantees convergence, at least to a local optimum. We furthermore introduce a natural extension of the BFE to integrate the estimation of (the SBL) hyperparameters via Variational Bayes, leading to Variational AMBGAMP or VAM-BGAMP. © 2023 IEEE.

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A Method of Predicting Worker's Skill Level in SMEs for Estimating Changeover Time 13

A Method of Predicting Worker's Skill Level in SMEs for Esti...

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13th International Conference on the Internet of Things, IoT 2023

作者： Shibuya, Yuto Yamauchi, Taiga Ogihara, Shogo Kawasaki, Takafumi Okura, Yuki Iwamoto, Takeshi Electrical and Computer Engineering Graduate School of Engineering Toyama Prefectural University Toyama Japan Department of Information Systems Engineering Toyama Prefectural University Toyama Japan

ISBN: (纸本)9798400708541

IoT has been introduced to improve production efficiency in small and medium-sized manufacturing companies, and it is mainly aimed at measuring machinery statuses. The improvement of changeover time is significant for production efficiency and the changeover time is depends on the skill level of each worker. Thus, measuring the statuses of human-mediated processes is needed. Moreover, if the skill levels of each worker were quantified, it would be possible to predict setup changeover time based on the quantitative evaluation. Therefore, we define the components of worker proficiency in small and medium-sized manufacturing companies, measure and quantify them, and estimate the proficiency level of each worker. This study enables accurate prediction of setup time by realizing the above. It was 60% to predict the setup changeover time more accurately than the scheduled time, and some components showed a proportional relationship with skill level. © 2023 Owner/Author.

关键词： Production efficiency

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A Lightweight CNN Model for Tomato Crop Diseases on Heterogeneous Embedded System 12

A Lightweight CNN Model for Tomato Crop Diseases on Heteroge...

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12th International Conference on Modern Circuits and systems Technologies, MOCAST 2023

作者： Sanida, Theodora Sanida, Maria Vasiliki Sideris, Argyrios Dasygenis, Minas University of Western Macedonia Department of Electrical & Computer Engineering Kozani50100 Greece University of Piraeus Department of Digital Systems Piraeus18534 Greece

ISBN: (纸本)9798350321074

Tomato plants are vulnerable to several diseases, each of which may cause severe harm to the plant. These adverse conditions can significantly reduce the amount and quality of agricultural yields. In crop disease diagnostics, convolutional neural network (CNN) algorithms have recently materialised as the most critical area of research. Therefore, this work aims to develop a robust and lightweight CNN-based diagnostic tool for tomato leaf tissue diseases in a low-power heterogeneous embedded device. The research data consists of one category of healthy tomatoes obtained from PlantVillage and nine diseases that may affect them. We evaluated the effectiveness of the lightweight CNN diagnostic tool using conventional identification criteria, including accuracy, precision, area under the curve (AUC), f1-score, and recall, on 3,635 previously unseen test images. The outcomes are more than satisfactory, with 99.04% accuracy, 99.56% AUC, 99.00% f1-score, 98.78% precision, and 99.23% recall, respectively. The developed CNN-based diagnostic tool represents a significant advance in tomato leaf disease diagnosis, paving the way for future research to develop lightweight networks in low-power embedded devices for rapid and accurate diagnosis of plant diseases, benefiting farmers. © 2023 IEEE.

关键词： Convolutional neural networks

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