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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： P. Ebby Darney A. Vegi Fernando R. Santhana Krishnan J. Relin Francis Raj V. Lavanya V. Vinoth Kumar Department of Electrical and Electronics Engineering Rajarajeswari College of Engineering Bangalore India Department of AIML (CS Cluster) Dayananda Sagar University Bangalore India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The Javan rhinoceros (Rhinoceros sondaicus), one of the most critically endangered species, faces numerous threats such as poaching, habitat destruction, and limited monitoring capabilities. Traditional conservation methods are often ineffective due to the vast and difficult-to-access nature of rhino habitats. This paper proposes a Hybrid Deep Learning (HDL) framework, combining CNNs, RNNs, and GNNs, to address these challenges and enhance conservation efforts. The proposed system offers a comprehensive solution for real-time rhino monitoring, poacher detection, and habitat preservation. The framework integrates CNNs for image-based monitoring, enabling the detection of rhinos, poachers, and habitat changes using camera trap images, drone footage, and satellite images. RNNs, specifically LSTM networks, are employed to analyze bioacoustic recordings from forest microphones, capturing long-term dependencies in sound data to detect rhino vocalizations and poaching sounds like gunshots or chainsaws. In addition, GNNs are utilized to process GPS tracking data to model spatial-temporal relationships and predict rhino movement, optimizing anti-poaching patrol routes and identifying high-risk zones for poaching. The proposed framework has been assessed using various evaluation metrics such as mAP for object detection, F1-Score for audio classification, and RMSE for movement prediction. The system is designed to be deployed in the field on a cloud-based platform, enabling real-time monitoring and decision-making. Edge computing on drones and sensor devices ensures low-latency processing and immediate action in response to detected threats. This model is expected to significantly enhance Javan rhino conservation by providing accurate, real-time data and actionable insights.

关键词： Deep learning Tracking Habitats Real-time systems Data models Threat assessment Satellite images Recording Monitoring Drones

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Development of Neuron Processing for DCNN with Weight and Activation for IoT

Development of Neuron Processing for DCNN with Weight and Ac...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Gummagatta Vybhavi Yajaman Adiba Sultana C. Preethi Deva Vengal Haideer M. Alabdeli Sandeep C S Department of CSE Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India Department of CSE CMR College of Engineering & Technology Hyderabad Telangana Department of Computer Science Engineering Sethu Institute of Technology Pulloor Kariapatti Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Head Department of ECE Jawaharlal College of Engineering and Technology Palakkad Kerala

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Convolutional neural network (CNN) accelerators powered by FPGA have garnered a lot of interest lately. This is mainly because they provide a higher degree of energy efficiency when compared to Graphics processing units (G However, it can be difficult for FPGA-based solutions to outperform their Graphics Processing Unit (GPU) substitutes in terms of performance. In this research, we have demonstrated that for a neural network (CNN) taught with binarized values and the activation aspect, it can be more advantageous to use FPGA-based accelerating in regards to productivity and energy usage. An effective, fully mapped FPGA acceleration design featuring deep pipeline phases and layer normalization is demonstrated, designed to function with tiny batch sizes. Unlike the Graphics Processing Unit acceleration, the quantity of the information batch being handled does not significantly impact the processing speed of FPGA accelerators. On the other hand, the total amount of the information batch that is processed has a significant impact on graphic processing acceleration. Test findings show that compared to a Titan X Graphics Processing Unit the proposed BCNN design running on a Virtex-7 FPGA handles specific requests in small batches 8.3 instances quicker and seventy-five times more effectively.

关键词： Productivity Pipelines Graphics processing units Process control Parallel processing Throughput Energy efficiency Convolutional neural networks Field programmable gate arrays Biological neural networks

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Multi-Objective Mobile Damped Wave Algorithm (MOMDWA): A Novel Approach For Quantum System Control

arXiv

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arXiv 2025年

作者： Yu, Juntao Yu, Jiaquan Wei, Dedai Sha, Xinye Fu, Shengwei Qiu, Minyu Jin, Yurun OuYang, Kaichen Department of Mathematics University of Science and Technology of China Hefei230026 China College of Economics Shenyang University Shenyang110000 China Graduate School of Arts and Sciences Columbia University NY10027 United States Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang550025 China China Jiliang University Information Engineering College Zhejiang Hangzhou China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China

In this paper, we introduce a novel multi-objective optimization algorithm, the Multi-Objective Mobile Damped Wave Algorithm (MOMDWA), specifically designed to address complex quantum control problems. Our approach extends the capabilities of the original Mobile Damped Wave Algorithm (MDWA) by incorporating multiple objectives, enabling a more comprehensive optimization process. We applied MOMDWA to three quantum control scenarios, focusing on optimizing the balance between control fidelity, energy consumption, and control smoothness. The results demonstrate that MOMDWA significantly enhances quantum control efficiency and robustness, achieving high fidelity while minimizing energy use and ensuring smooth control pulses. This advancement offers a valuable tool for quantum computing and other domains requiring precise, multi-objective control. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum computers

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Inference-Aware State Reconstruction for Industrial Metaverse under Synchronous/Asynchronous Short-Packet Transmission

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IEEE Transactions on Communications 2025年

作者： Xiong, Qinqin Cao, Jie Zhu, Xu Jiang, Yufei Pappas, Nikolaos Harbin Institute of Technology School of Electronic and Information Engineering Shenzhen518055 China The Shenzhen Key Laboratory of Wireless AloT Communications Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen Guangdong China Linkoping University Department of Computer and Information Science Linköping58183 Sweden

We consider a real-time state reconstruction system for industrial metaverse. The time-varying physical process states in real space are captured by multiple sensors via wireless links, and then reconstructed in virtual space. In this paper, we use the spatial-temporal correlation of the sensor data of interest to infer the real-time data of the target sensor to reduce the mean squared error (MSE) of reconstruction for industrial metaverse under short-packet transmission (SPT). Both synchronous and asynchronous transmission modes for multiple sensors are considered. It is proved that the average reconstruction MSE and average block error probability (BLEP) have a positive correlation under inference with synchronous transmission scheme, whereas they have a negative correlation under inference with asynchronous transmission scheme in certain conditions. Additionally, the average reconstruction MSE decreases monotonically with the mean squared spatial correlation (MSSC), which characterizes the global spatial correlation level. With a high BLEP or long transmission period, even under weak MSSC, the inference scheme still significantly reduces the average reconstruction MSE compared to the no inference case. Moreover, closed-form MSSC thresholds are derived for the superiority regions of the inference with synchronous transmission and inference with asynchronous transmission schemes, respectively. Adaptations of blocklength and time shift of asynchronous transmission are conducted to minimize the average reconstruction MSE. Simulation results show that the two inference schemes outperform the no inference case, with an average MSE reduction of more than 50%. © 1972-2012 IEEE.

关键词： Packet networks

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How to Securely Outsource the Multiple Kernel Fuzzy Clustering Task in Edge Computing

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IEEE Internet of Things Journal 2025年

作者： Sun, Xinrong Tao, Yunting Kong, Fanyu Yu, Qian Ge, Chunpeng Xu, Qiuliang Zhang, Hanlin Binzhou Polytechnic College of Information Engineering Shandong Binzhou256603 China Shandong University School of Software Jinan250101 China Shandong Sansec Information Technology Co. Ltd Jinan250101 China Singapore Singapore Qingdao University College of Computer Science and Technology Qingdao266071 China

For the huge amount of data from the Internet of Things (IoT) devices, multiple kernel learning is a widely concerned issue in data analyzing, among which the multiple kernel fuzzy clustering (MKFC) algorithm is an effective approach for extracting linear features in high-dimensional space. For a time-consuming multiple kernel clustering task, it's meaningful to find a secure and efficient outsourcing scheme in the edge-end collaborative architecture, which utilizes edge computing resources while resisting untrusted edge servers. However, existing secure outsourcing schemes cannot align well with the distributed and real-Time characteristics of edge computing due to their complex encryption processes. In this paper, we propose a secure multiple kernel fuzzy clustering outsourcing scheme based on a novel matrix blinding method. The proposed novel matrix blinding method conducts two related encryption operations with disturbance terms, which avoids specific disturbance elimination computations, to reduce the computational burdens in the decryption phase. Additionally, we introduce a sampling verification method to detect the server's deceptive behaviors. The theoretical analysis demonstrates that our scheme guarantees data privacy and has the capability to verify incorrect results. The experimental results indicate that our scheme is 6.73% superior to other schemes on average when conducting matrix outsourcing computation and enhances the efficiency of conducting the MKFC algorithm by 10.44% to 55.70% on different datasets. © 2014 IEEE.

关键词： Differential privacy

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Enhancing Microgrid Energy Security Through Optimal Power Sharing in Multi-Modal Energy Storage Systems

Enhancing Microgrid Energy Security Through Optimal Power Sh...

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International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Shaikh Tawhidul Islam Anik Md Salman Khan Mithil Shameem Ahmad Md. Rifat Hazari Mahmoodul Islam Tofael Ahmed Md. Shahariar Parvez Mahamudul Hassan Department of Electrical and Electronic Engineering Faculty of Engineering American International University-Bangladesh Dhaka Bangladesh Department of Electrical Engineering and Computer Science Electrical Engineering (MS) South Dakota School of Mines & Technology Rapid City SD United States Department of Electrical and Electronic Engineering Chittagong University of Engineering & Technology Chattogram Bangladesh Department of Industrial and Production Engineering Faculty of Engineering American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

Microgrids (MGs), which combine distributed energy resources (DERs) with energy storage systems (ESS), offer improved energy security, reliability and sustainability by utilizing renewable energy sources for subsequent use. However, single ESS options often face challenges such as short life cycles, low power density etc. More than one ESS can resolve the mentioned issues though it increases complexity and maintaining stability becomes an issue. This paper proposes an effective energy management strategy between two ESS namely battery and supercapacitor to increase energy security in MG. The proposed strategy is designed in such a way that one of the two ESS stays connected all the time to the DC bus to ensure uninterruptable power supply to loads. Due to the use of DC bus, the system complexity decreases as there is no need to control frequency. Two cases were considered to verify the performance of the proposed energy management strategy in Matlab/Simulink. In the first case, during the unavailability of supercapacitor, the battery supplies 50 W to the load in addition to PV whereas supercapacitor supplies 136 W in the absence of battery along with PV. Furthermore, it was shown that the absence of PV has no negative effect in terms of power flow by ESS to meet load demand.

关键词： Power supplies Heuristic algorithms Signal processing algorithms Microgrids Supercapacitors Batteries Complexity theory Security Energy management Sustainable development

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SMTFL: Secure Model Training to Untrusted Participants in Federated Learning

arXiv

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arXiv 2025年

作者： Zhao, Zhihui Dong, Xiaorong Ren, Yimo Wang, Jianhua Yu, Dan Zhu, Hongsong Chen, Yongle College of Computer Science and Technology Taiyuan University of Technology Taiyuan030024 China College of Artificial Intelligence Taiyuan University of Technology Taiyuan030024 China Institute of Information Engineering Chinese Academy of Sciences The School of Cyberspace Security University of Chinese Academy of Sciences Beijing100085 China

Federated learning is an essential distributed model training technique. However, threats such as gradient inversion attacks and poisoning attacks pose significant risks to the privacy of training data and the model correctness. We propose a novel approach called SMTFL to achieve secure model training in federated learning without relying on trusted participants. To safeguard gradients privacy against gradient inversion attacks, clients are dynamically grouped, allowing one client’s gradient to be divided to obfuscate the gradients of other clients within the group. This method incorporates checks and balances to reduce the collusion for inferring specific client data. To detect poisoning attacks from malicious clients, we assess the impact of aggregated gradients on the global model’s performance, enabling effective identification and exclusion of malicious clients. Each client’s gradients are encrypted and stored, with decryption collectively managed by all clients. The detected poisoning gradients are invalidated from the global model through a unlearning method. To our best knowledge, we present the first practical secure aggregation scheme, which does not require trusted participants, avoids the performance degradation associated with traditional noise-injection, and aviods complex cryptographic operations during gradient aggregation. Evaluation results are encouraging based on four datasets and two models: SMTFL is effective against poisoning attacks and gradient inversion attacks, achieving an accuracy rate of over 95% in locating malicious clients, while keeping the false positive rate for honest clients within 5%. The model accuracy is also nearly restored to its pre-attack state when SMTFL is deployed. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Towards Reliable and Energy-Efficient Communication Protocols for Wireless Body Area Networks in Medical Applications

Towards Reliable and Energy-Efficient Communication Protocol...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Jayaprakash, B. Premkumar, J. Das, Neeraj Punia, Ankit Nayak, Subrat Kumar Ojha, Varun Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Biomedical Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India

ISBN: (纸本)9798331542375

Wireless body area networks are an attractive technology for measuring health data in medical applications. Several challenges exist in designing and implementing such reliable, energy-efficient communication protocols for these networks. This new strategy solution strategy is based on a hybrid communication protocol that simultaneously obtains the positive aspects of centralized and distributed structures. By centralizing network maintenance and control functions such as health monitoring of nodes and transmission control, the proposed protocol supports reliable data transmission with minimal energy consumption in WBANs. This reduces the overhead and collisions, giving the network better reliability. Unlike other protocols mentioned where the data flow from the sensor node to the central node is at least distributed, in our case, a few nodes send their data at a time to the Central Node. It enables low-power communication since nodes only need to wake up during their allotted transmission time slot. Through simulations, we demonstrate that the new protocol surpasses existing protocols in reliability and energy consumption performance. It can also help to minimize the energy consumption of WBAN nodes, which makes it an appropriate candidate for long-time medical monitoring applications. All in all, this work helps to pave the way for the efficient use of WBANs in a wide range of medical applications that require robustness and low-energy communications. © 2025 IEEE.

关键词： Signaling

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The Role of Blockchain technology in Enhancing Network Security

The Role of Blockchain Technology in Enhancing Network Secur...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Dhingra, Lovish Meetei, Loitongbam Sanayai Ojha, Varun Lodi, M.K. Sheela, A.C. Santha Agarwal, Ankita Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India School of Mangement-PG Management Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331542375

Blockchain technology has received much attention for its digital storage and security applications. Blockchain technology has many benefits regarding network security, including decentralization, immutability and transparency, which can significantly increase the level of protection for networks. Decentralization is one of the key advantages that blockchain can bring to network security. Conventional network security systems have control of data, which is all centralized in front of a single entity, making them easily attacked. On the other hand, blockchain technology decentralizes data control and therefore, the distribution of potential hacking in nodes makes it almost impossible to hack or breach the entire network. Specifically, the blockchain is immutable;its construction protects against any data modification occurring on the network when no vote by a majority of nodes permits a change. It renders cybercriminals nearly incapable of modifying the network data, which adds an extra layer of protection. Transparency is another key feature of blockchain technology since all participants in the network will have access to view any transaction conducted on the network. This functionality helps monitor any unauthorized or suspicious activity on the network, providing a chance to be vigilant and address the issue. © 2025 IEEE.

关键词： Authentication

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Energy Harvesting Techniques for Self-Powered Body-Worn Sensors in Wireless Body Area Networks

Energy Harvesting Techniques for Self-Powered Body-Worn Sens...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Gupta, Bhupendra Ku. Ghosal, Shreya Ganesan, Subbulakshmi Sudhakar, T. Mishra, Awakash Pandey, Neelesh Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Biomedical Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India

ISBN: (纸本)9798331542375

Self-powered body-worn sensors are considered one of the potential candidates for health and fitness monitoring in wireless body area networks. Sensors extract energy from the human body or its environment to power themselves, making them self-contained with no external power supply, thus improving portability and wear ability. According to this, energy harvesting methods are essential for developing selfpowered body-worn sensors in WBANs. This technical paper reviews different methods of energy harvesting used for bodyworn sensors, such as Body heat, motion, ambient light harvesting, etc. It also discusses some drawbacks and limitations, such as low energy production and inconsistent energy sources. It also features some of the latest blasting techniques and technologies concerning energy recovery. Using self-powered body-worn sensors can lead to various applications and advantages in WBANs. Such uses included continuous & non-invasive health monitoring, data collection for medical research and sports performance tracking. Self-powered sensors reduce the frequency of maintenance and minimize environmental pollution caused by battery disposal. © 2025 IEEE.

关键词： Body sensor networks

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