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Towards efficient implementation of MLP-ANN classifier on the FPGA-based embedded system

IFAC-PapersOnLine 2022年第4期55卷 207-212页

作者： Rijad Sarić Nejra Beganović Dejan Jokić Edhem Čustović School of Computing Engineering and Mathematical Sciences La Trobe University 3086 Victoria Melbourne Australia Chair of Dynamics and Control University of Duisburg-Essen 47057 Duisburg Germany Department of Electrical and Electronics Engineering International Burch University 71000 Sarajevo Bosnia and Herzegovina

Epilepsy is a widely known neurological disease, causing atypical brain activity such as seizures. Apparently, it is critical to analyse possible triggers as well as establish appropriate medical treatment based on the seizure type. In general, diagnosis of epilepsy is usually conducted by applying various state-of-the-art algorithms to extract useful information from electroencephalography (EEG) signals. For effective real-time epilepsy diagnosis, the biomedical device integrated with the best performing machine learning algorithm is needed. Multilayer Perceptron (feed-forward) Artificial Neural Network (MLP-ANN) has proven to be a much better choice over traditional supervised machine learning algorithms in achieving high accuracy diagnosis. Challenges exist in handling extremely long data processing time in the case of many features in EEG signals plus complex MLP-ANN structure and computations. This research proposes the 5-12-3 MLP-ANN configuration to classify different types of epileptic seizures using Field Programmable Gate Array (FPGA) as a real-time embedded system.

关键词： MLP-ANN FPGA Epileptic seizure Real-time diagnosis

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Lightweight Trust Model with Machine Learning scheme for secure privacy in VANET 18

Lightweight Trust Model with Machine Learning scheme for sec...

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18th International Learning and Technology Conference, L and T 2021

作者： Junejo, Muhammad Haleem Ab Rahman, Ab Al-Hadi Shaikh, Riaz Ahmed Yusof, Kamaludin Mohamad Kumar, Dileep Memon, Imran Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai81310 Malaysia Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Department of Computer Science Bahria University Karachi campus sindh Pakistan

A vehicular ad hoc network (VANETs) is transforming public transport into a safer wireless network, increasing its safety and efficiency. The VANET consists of several nodes which include RSU (Roadside Units), vehicles, traffic signals, and other wireless communication devices that are communicating sensitive information in a network. Nevertheless, security threats are increasing day by day because of dependency on network infrastructure, dynamic nature, and control technologies used in VANET. The security threats could be addressed widely by using machine learning and artificial intelligence on the road transport nodes. In this paper, a comparison of trust and cryptography was presented based on applications and security requirements of VANET. © 2021 The Authors. Published by Elsevier B.V.

关键词： Machine learning

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Attention-Driven Graph Convolutional Networks for Deadline-Constrained Virtual Machine Task Allocation in Edge computing

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IEEE Transactions on Consumer Electronics 2025年

作者： Ali, Ahmad Ullah, Inam Singh, Sushil Kumar Jiang, Weiwei Alturise, Fahad Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen 518060 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen 518060 China Beijing University of Posts and Telecommunications Beijing China Qassim University College of Computer Department of Cybersecurity Buraydah Saudi Arabia

Effective load balancing is critical in contemporary mobile computing environments to ensure optimal resource utilization and adherence to strict deadlines. This study presents a novel framework for deadline-constrained load balancing, termed DCLD-net, which leverages the integration of Graph Convolutional Networks (GCN) and an attention-based Bidirectional Gated Recurrent Unit (BiGRU). The proposed framework utilizes the spatial modeling capabilities of GCNs to capture intricate network topologies, while the temporal dynamics and attention mechanisms of BiGRU enable the identification of critical patterns in traffic flow data. By incorporating key factors such as sticky sessions, load distribution, content-based factors such as videos or file downloads, instance health checks, and other relevant parameters, the DCLD-net model achieves high accuracy in predicting task schedules. These schedules allocate tasks to virtual machines (VMs), enabling dynamic traffic routing through a load balancer that efficiently assigns tasks. Extensive experimental evaluations demonstrate that the proposed attention-based BiGRU-GCN model significantly outperforms traditional methods in both deadline compliance and load balancing efficiency, offering a robust solution to the challenges of modern cloud computing. Experimental results demonstrate that DCLD-net outperforms traditional methods by 27% in deadline compliance and 29.3% in load balancing efficiency. © 1975-2011 IEEE.

关键词： attention consumer electronics Edge computing load balancing machine learning resource utilization virtual machine

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Editorial:Special Topic on Federated Learning for IoT and Edge computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud computing: An Algorithm-Level Review

arXiv

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arXiv 2025年

作者： Gu, Yan Liu, Zhaoze Dai, Shuhong Liu, Cong Wang, Ying Wang, Shen Theodoropoulos, Georgios Cheng, Long The School of Control and Computer Engineering North China Electric Power University Beijing China The School of Computer Science and Technology Shandong University of Technology China The Institute of Computing Technology Chinese Academy of Sciences Beijing China The School of Computer Science University College Dublin Ireland Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Cloud computing has revolutionized the provisioning of computing resources, offering scalable, flexible, and on-demand services to meet the diverse requirements of modern applications. At the heart of efficient cloud operations are job scheduling and resource management, which are critical for optimizing system performance and ensuring timely and cost-effective service delivery. However, the dynamic and heterogeneous nature of cloud environments presents significant challenges for these tasks, as workloads and resource availability can fluctuate unpredictably. Traditional approaches, including heuristic and meta-heuristic algorithms, often struggle to adapt to these real-time changes due to their reliance on static models or predefined rules. Deep Reinforcement Learning (DRL) has emerged as a promising solution to these challenges by enabling systems to learn and adapt policies based on continuous observations of the environment, facilitating intelligent and responsive decision-making. This survey provides a comprehensive review of DRL-based algorithms for job scheduling and resource management in cloud computing, analyzing their methodologies, performance metrics, and practical applications. We also highlight emerging trends and future research directions, offering valuable insights into leveraging DRL to advance both job scheduling and resource management in cloud computing. © 2025, CC BY.

关键词： Job shop scheduling

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Ru Li Aimin Li Rong Fei Xie Guo Fang-Xiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions.

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Memory-Based Contrastive Learning with Optimized Sampling for Incremental Few-Shot Semantic Segmentation

Memory-Based Contrastive Learning with Optimized Sampling fo...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Yuxuan Zhang Miaojing Shi Taiyi Su Hanli Wang Department of Computer Science & Technology Tongji University Shanghai P. R. China Key Laboratory of Embedded System & Service Computing Ministry of Education Tongji University Shanghai P. R. China Department of Control Science & Engineering Tongji University Shanghai P. R. China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Incremental few-shot semantic segmentation (IFSS) aims to incrementally expand a semantic segmentation model’s ability to identify new classes based on few samples. However, it grapples with the dual challenges of catastrophic forgetting (due to feature drift in old classes) and overfitting (triggered by inadequate samples in new classes). To address these issues, a novel approach is proposed to integrate pixel-wise and region-wise contrastive learning, complemented by an optimized example and anchor sampling strategy. The proposed method incorporates a region memory and pixel memory designed to explore the high-dimensional embedding space more effectively. The memory, retaining the feature embeddings of known classes, facilitates the calibration and alignment of seen class features during the learning process of new classes. To further mitigate overfitting, the proposed approach implements an optimized example and anchor sampling strategy. Extensive experiments show the competitive performance of the proposed method. The source code of this work can be found in https://***.

关键词： Circuits and systems Semantic segmentation Source coding Semantics Calibration

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Bio-inspired Swarm Intelligence: a Flocking Project With Group Object Recognition

Bio-inspired Swarm Intelligence: a Flocking Project With Gro...

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Consumer Electronics and Computer engineering (ICCECE), 2021 IEEE International Conference on

作者： Song Tianbo Hu Weijun Cai Jiangfeng Liu Weijia Yuan Quan He Kun Ira A. Fulton Schools of Engineering Arizona State University Tempe USA Faculty of Applied Mathematics and Control Processes Saint Petersburg State University Saint Petersburg Russia Department of Chemistry Lomonosov Moscow State University Moscow Russia Allen School of Engineering & Computing Trine University Angola USA Faculty of Engineering The Open University of China Hangzhou China College of Computing Illinois Institute of Technology Chicago USA

With the help of the adaptive gain control algorithm and the flocking SWARM algorithm with adversarial agents, the project described in this article makes the entire system communication-free while maintaining the same functionality and performance. It outlines the entirety of this biologically inspired swarm intelligence's thought process, from inception to conclusion (Flocking algorithm). The entire project is dependable and practically impenetrable under a variety of conditions. The neighbor observation for group conjecture is then strengthened by the use of the adaptive gain control technique and a partial Kalman filter. After the system has been stabilized, use the built-in camera of the SWARM drone to introduce the Object Recognition method in computer vision. As a result, the system enters the real world from the simulation level.

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A domain decomposition strategy for natural imposition of mixed boundary conditions in port-Hamiltonian systems

arXiv

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arXiv 2025年

作者： de Jong, Sjoerd D.M. Brugnoli, Andrea Rashad, Ramy Zhang, Yi Stramigioli, Stefano Department of Microelectronics Delft University of Technology Delft Netherlands ICA Université de Toulouse ISAE–SUPAERO INSA CNRS MINES ALBI UPS Toulouse France Control and Instrumentation Engineering Department King Fahd University of Petroleum and Minerals Saudi Arabia School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China Robotics and Mechatronics Department University of Twente Netherlands

[Abstract] In this contribution, a finite element scheme to impose mixed boundary conditions without introducing Lagrange multipliers is presented for wave propagation phenomena described as port-Hamiltonian systems. The strategy relies on finite element exterior calculus and a domain decomposition to interconnect two systems with different causalities. The spatial domain is split into two parts by introducing an arbitrary interface. Each subdomain is discretized with a mixed finite element formulation that introduces a uniform boundary condition in a natural way as the input. In each subdomain the spaces are selected from a finite element subcomplex to obtain a stable discretization. The two systems are then interconnected together by making use of a feedback interconnection. This is achieved by discretizing the boundary inputs using appropriate spaces that couple the two formulations. The final systems includes all boundary conditions explicitly and does not contain any Lagrange multiplier. Each subdomain is integrated using an implicit midpoint scheme in an uncoupled way from the other by means of a leapfrog scheme. The proposed strategy is tested on three different examples: the Euler-Bernoulli beam, the wave equation and the Maxwell equations. Numerical tests assess the conservation properties of the scheme and the effectiveness of the methodology. © 2025, CC BY.

关键词： Lagrange multipliers

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Rational Modulation of Redox Equilibrium Over Mn Modified Ce-Sn Catalyst for Low-Temperature Elimination of No and Hg0

SSRN

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SSRN 2024年

作者： Shi, Qiqi Lu, Litong Kang, Dongrui Shen, Boxiong Bian, Yao Zhang, Xiao Lyu, Honghong Li, Shuhao Tianjin Key Laboratory of Clean Energy and Pollution Control School of Energy and Environmental Engineering Hebei University of Technology Tianjin300401 China Department of Chemistry and Biotechnology School of Science Computing and Engineering Technologies Swinburne University of Technology MelbourneVIC3122 Australia CSIRO Manufacturing Private Bag 10 Clayton SouthVIC3169 Australia

The synergistic catalytic removal of NO and Hg0 under low temperatures is still a great challenge. Here, we report a Mn-modified Ce-Sn catalyst for simultaneous removal of NO and Hg0 at low temperature. The optimum Mn0.2/Ce0.33Sn0.67 exhibited more than 95% NO conversion and N2 selectivity above 86% at 150-250 °C as well as Hg0 removal of nearly 100% within 150-300 °C. SnO2-promoted Ce-Sn solid solution enhanced the surface proportion of Ce3+, the number of surface chemisorbed oxygen and the electronic interaction within Ce-Sn linkage. Mn modification further reasonably promoted redox cycling at low temperatures and improved acidic distribution of catalyst. Under the cooperation of Ce-Sn and Mn sites, the presence of Hg0 could substantially facilitate the reduction of NO at 250-300 °C and the SCR atmosphere had a weak inhibitory effect on Hg0 removal. During the SCR process, the NH4+ and NH2 species that directly reacted with gaseous NO following the Eley−Rideal mechanism and the improved reactivity of NH2 with nitrates following the Langmuir−Hinshelwood mechanism enhanced the NO conversion efficiency at low temperature. Meanwhile, Mn modification could alleviate competitive adsorption of NH3 on Hg0 and promote the formation of more chemically adsorbed mercury species. This work will provide guidance for the design of low-temperature catalysts for the simultaneous removal of NO and Hg0 in flue gas. © 2024, The Authors. All rights reserved.

关键词： Temperature

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