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IoT-embedded Smart Clothing with CNN for Improved Spatial Awareness in the Visually Impaired 2

IoT-Embedded Smart Clothing with CNN for Improved Spatial Aw...

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2nd international conference on Intelligent Cyber Physical systems and Internet of Things (ICoICI)

作者： Aarthy, S. T. Amarnath, Raveendra N. Gopi, B. Selvakumar, R. Ganesh, E. N. Sujatha, S. SRM Inst Sci & Technol Dept Elect & Commun Engn Chennai Tamil Nadu India Mphasis Campbell Dr Melissa TX USA Sona Coll Technol Dept Elect & Commun Engn Salem Tamil Nadu India Saveetha Engn Coll Dept Artificial Intelligence & Machine Learning Chennai Tamil Nadu India St Peters Inst Higher Educ & Res SPIHER Dept Elect & Commun Engn R&D Chennai Tamil Nadu India Saveetha Univ Saveetha Sch Engn Saveetha Inst Med & Tech Sci Dept Biomed Engn Chennai Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

Smart clothes that include the Internet of Things (IoT) and convolutional neural network (CNN) technologies provide a fresh approach to helping the vision impaired with spatial awareness. This work presents a system that uses IoT sensors built into the fabric to get real-time location, orientation, and other environmental data. Using a CNN architecture, this data is processed to provide user feedback through tactile or auditory signals, allowing visually impaired individuals to navigate their environments more effectively. The efficacy of this technology was assessed by comprehensive user trials, which revealed significant improvements in movement and spatial awareness. IoT smart clothes with CNN help visually impaired people feel more confident and independent by giving them real-time input about their environment. A new possibility for assistive technology is demonstrated by how wearable technology may help the visually impaired. This technique improves spatial awareness and encourages accessibility in the design of assistive devices by seamlessly integrating IoT and CNN technologies into ordinary clothing.

关键词： Visually Impaired Assistive Technology Wearable Technology Navigation real-time Data Sensor Fusion Spatial Awareness

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FPGA Implementation of Systolic Array for 2D Convolution Using Residue Number System

FPGA Implementation of Systolic Array for 2D Convolution Usi...

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2023 ieee international conference on Electrical, Electronics, Communication and Computers, ELEXCOM 2023

作者： Bera, Tulip Dey, Shuvabrata Maulana Abul Kalam Azad University of Technology Microelectronics and Vlsi Technology West Bengal Haringhata Nadia India Maulana Abul Kalam Azad University of Technology Vlsi Design and Embedded Systems West Bengal Haringhata Nadia India

ISBN: (纸本)9798350305111

This paper presents an FPGA implementation of a systolic architecture for 2D convolution using the residue number system (RNS) with a designed new residue adder and residue multiplier. The proposed architecture offers improved processing speed by leveraging the benefits of RNS. Additionally, the use of a systolic array enhances parallelism, further boosting the overall performance of the convolution operation. Experimental results demonstrate the effectiveness of the approach, highlighting its potential for accelerated convolutional neural networks and real-time signal processing applications. © 2023 ieee.

关键词： Image processing

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Improving Distance Estimation in Sound Event Localization and Detection Using ResNet50 and Multi-ACCDDOA 10

Improving Distance Estimation in Sound Event Localization an...

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10th international conference on Signal Processing and Intelligent systems, ICSPIS 2024

作者： Abolfazli, Zahra Abutalebi, Hamid Reza Virtanen, Tuomas Yazd University Dept. of Electrical Engineering Yazd Iran Tampere University Faculty of Information Technology and Communication Sciences Tampere Finland

ISBN: (纸本)9798331532543

This paper introduces a novel enhancement to the Sound Event Localization and Detection (SELD) system, specifically focusing on improving distance estimation in Task 3 of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2024 challenge. The main innovation lies in the use of ResNet50 architecture instead of traditional CNNs, resulting in more precise and efficient feature extraction. Additionally, the Multi Activity-Coupled Cartesian Distance and Direction of Arrival (Multi-ACCDDOA) format is employed for simultaneous estimation of direction and distance. While the primary focus is on improving distance estimation, the system has maintained stable and acceptable performance in other aspects. These advancements hold significant potential for real-time applications in augmented reality and autonomous systems. © 2024 ieee.

关键词： real time systems

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Design of a Power-Efficient Static Random Access Memory Cell with Enhanced Stability for Internet of Things applications 13

Design of a Power-Efficient Static Random Access Memory Cell...

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13th ieee international conference on Communication systems and Network Technologies, CSNT 2024

作者： Sharma, Atharv Kahol, Venushree Sachdeva, Ashish Chitkara University Institute of Engineering and Technology Chitkara University Department of Ece Punjab India

ISBN: (纸本)9798350305463

In today's tech-driven era, where the need for swift data processing is paramount, memory emerges as a critical component, playing a key role in ensuring the high speed functionality of modern devices across diverse applications. From IoT devices and embedded systems to networking applications, SRAM (static random access memory) plays a pivotal role across a multitude of domains, illustrating its versatile and widespread use in diverse technological fields. In all these domains, high-speed processing is essential, underscoring the critical need for efficiently storing and swiftly accessing digital information. In this work, a bit-line enabled IT SRAM cell (BLE10T), designed for low power consumption is presented, that also shows improved access time and Static Noise Margin (SNM). The efficacy of low power BLE10T SRAM cell is also compared with conventional 6T SRAM cell. Comparing the BLE10T SRAM cell with the conventional 6T SRAM cell shows that the low power BLE10T SRAM cell consumes 1.52×, 1.05× and 2.35× times less power during hold, read and write operation. Moreover, it is 1.67× times faster during read operation and 1.15× times faster during write operation. The static noise margin during hold, read and write operation is improved by 1.01× 1.94× and 1.05× respectively, compared to conventional 6T bit-cell. The BLE10T cell and conventional 6T cell are simulated at 45nm technology node using cadence virtuoso simulator. © 2024 ieee.

关键词： embedded systems

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Harnessing Machine Learning for Enhanced Distributed computing in Wireless Network systems

Harnessing Machine Learning for Enhanced Distributed Computi...

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2024 international conference on Communication, Computer Sciences and Engineering, IC3SE 2024

作者： Rashmi, K. Anandhi, R.J. Singh, Navdeep Kumar, V. Surya Pavan Gupta, Krashnkant Albawi, Ali Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India New Horizon College Of Engineering Department Of Information Science Engineering Bangalore India Lovely Professional University Phagwara India Mlr Institute Of Technology Department Of CSE-AI&ML Telangana Hyderabad India Amity University Greater Noida Department Of Electronics & Communication Engineering India College Of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350366846

A complete wireless network system for flexible resource sharing, spectrum management, security, and cross-layer optimization is described in this paper. Five algorithms comprise the system. These are ADS, QoS-ML, CLO, and DSM. Wireless networks adapt and grow, and these applications solve issues. real-time resource allocation using predictive models enhances network performance, latency, and efficiency with the PRA algorithm. DSM uses adaptive spectrum utilization to reduce interference. ADS uses machine learning to detect unusual activity and implement flexible safety measures to make networks safer. QoS-ML improves routing options using real-time QoS data to ensure network-wide QoS. CLO alters layer connections, utilizes metrics across layers, and is continually searching for network improvements. A detailed analysis of the research examined each program's distinct qualities and how they function together. The discussion focused on how well the framework performed in various settings and was adaptable. In the conclusion, the algorithms' strengths and potential in distributed computing environments were highlighted. The recommended technique outperformed others in speed, latency, security, energy efficiency, scalability, and customer satisfaction. Performance trends, parameter distributions, and trade-offs were shown, making the structure more dependable. By offering a full, adaptive, and successful method, this study advances wireless network management. Optimizations, scalability, and real-world applications might be studied to ensure the system works in various conditions. In challenging and changing situations, the proposed architecture supports wireless network expansion. © 2024 ieee.

关键词： Machine learning

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From Detection to Action: Implementing Deep Learning Inference in PLC systems via Docker Services 18

From Detection to Action: Implementing Deep Learning Inferen...

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18th IFAC conference on Programmable Devices and embedded systems (PDES)

作者： Korosi, L. Kajan, S. Berki, M. Skirkanic, J. Lucny, M. Melichar, A. Mihalik, J. Slovak Univ Technol Bratislava Inst Robot & Cybernet Ilkovicova3 Bratislava 81219 Slovakia

This paper focuses on the implementation of recognition and detection algorithms for programmable logic controllers (PLCs) based on artificial intelligence (AI). AI methods based on deep neural networks (DNN) are characterized by a large number of hidden layers and neurons in hidden layers, which increases the computational complexity of computing the outputs of artificial neural networks (ANNs). Popular convolutional neural networks (CNN) are among such ResNet, AlexNet, GoogLeNet and others. A remote cloud solution is proposed in this paper to compute CNN outputs. The CNN implementation was tested on object recognition and detection applications. Training and testing of the CNN and YOLO detector was carried out in the Python environment. Copyright (c) 2024 The Authors.

关键词： Programmable logic controllers (PLC) Convolutional Neural Network (CNN) YOLO detector Cloud computing Docker

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Research on Optimization Algorithm and Simulation Model of On-Board Battery Energy Management 5

Research on Optimization Algorithm and Simulation Model of O...

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5th ieee international conference on Power, Electronics and Computer applications, ICPECA 2025

作者： Hu, Yinquan Chongqing Vocational Institute of Engineering Chongqing402260 China

ISBN: (纸本)9798331533694

This paper proposes an on-board battery energy management optimization algorithm based on deep reinforcement learning (DRL). By adjusting the battery charging and discharging strategy in real time, it optimizes energy usage and prolongs battery life. The algorithm models the battery charging and discharging process as a Markov decision process (MDP) and designs an adaptive reward function, which can dynamically optimize battery performance under different driving environments. Experimental simulation results show that the system using this optimization algorithm has significant advantages in energy efficiency, charging cycle and battery life compared with the traditional PID control algorithm. In various driving scenarios such as simulated urban roads and highways, the optimization algorithm improves energy efficiency by 12.6%, reduces charging cycles by 15%, and prolongs battery life by 18%. These experimental results verify the application potential of deep reinforcement learning in on-board battery energy management and demonstrate its advantages and feasibility in practical applications. © 2025 ieee.

关键词： Battery management systems

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Recognition of Handwritten Punjabi Characters Using Machine Learning Techniques 1

Recognition of Handwritten Punjabi Characters Using Machine ...

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1st ieee international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, P. Kaushik, Manas Manglik Chauhan, Yuvraj Singh Kumar, Narendra Kumar, Ashwani Rathore, Rachna Chandigarh University Dept. of CSE Chandigarh India Manipal University Dept. of Mechanical and Industrial Eng Karnataka India Chandigarh University Dept. of Computer Science Engineering Chandigarh India Usha Martin University Dept. of Computing and IT Jharkhand India Mangalayatan University Dept. of Computer Eng. and Applications Aligarh India Shrimant Madhav Rao University Dept. of Mathematics Gwalior India

ISBN: (纸本)9798350356816

Detecting handwritten Punjabi alphabets (PbAD) presents significant challenges for text detection systems due to the similarity among many characters and their complex curves and edges. Existing automatic detection systems often struggle to deliver high accuracy with real-world data. In this study, we propose an approach for PbAD that synthesizes various techniques reviewed from existing literature to optimize accuracy. Our methodology involves evaluating and combining multiple classification and feature extraction techniques, as identified through an extensive literature review. Unlike many studies that rely on manually created datasets, our approach will leverage both available online datasets and those developed in previous research. Previous research has reported accuracies ranging from 80% to 99%, and our goal is to advance these results by integrating and refining the best practices from the literature. © 2024 ieee.

关键词： Adversarial machine learning

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Consensus in Second-Order Discrete-time MAS With Distinct and Multiple Coupling Matrices 10

Consensus in Second-Order Discrete-Time MAS With Distinct an...

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10th international conference on systems and Informatics, ICSAI 2024

作者： Sun, Chenggong Liu, Xiwei Tongji University Department of Computer Science and Technology Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China

ISBN: (纸本)9798331510138

In this paper, we explore and establish several necessary and sufficient conditions that are instrumental in achieving consensus within second-order discrete-time multi-agent systems (SDMAS). Different with previous research, this study addresses the more intricate scenario where the velocity coupling matrix is distinct from the position coupling matrix. We introduce a novel eigenvector condition tailored to this challenge. Furthermore, in response to the burgeoning interest in multiweighted networks, this study is extended to SDMAS with multiple weights. We employ a technique, that rearranges the order of variables, to effectively simplify the analysis of multiweighted networks by transforming them into single-weighted equivalents. The efficacy of our method is substantiated through three numerical examples, which illustrate the practical value in elucidating consensus dynamics within SDMAS featuring diverse coupling matrices. © 2024 ieee.

关键词： Multi agent systems

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OMN-IBUS Type of Networking System for the Communication System in 6G 4

OMN-IBUS Type of Networking System for the Communication Sys...

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4th ieee international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Devi, A. Sangeetha Bhatt, Deepanshu Bansal, Saloni Keerthika, A. Mohanraj, T. Santhosh, S. Tamil Nadu T.M.Palayam India Ies Institute of Technology and Management Ies University Department of Mechanical Engineering M.P Bhopal India Gla University Department of Computer Engineering and Applications Mathura India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India TN India Einstein College of Engineering Department of Electronic and Communication Engineering Tamil nadu India

ISBN: (纸本)9798350360165

The scientific community and industry have been working together in recent years to shift computational resources off centralized cloud systems to decentralized computing, or 'edge' computing. This change is a result of the cloud system's inability to keep up with the growing demands of emerging relationships, which are marked by the spread of time-sensitive applications like sophisticated robotics, augmented reality/virtual the truth technologies, self-driving cars, and vital remote control of Internet of Things applications. Decentralized computing at the border is still in its infancy and lacks a full platform, despite the critical role that it will play in future distributed systems. This article presents OMNIBUS, a novel decentralized edge architecture. This approach uses moving vehicles as resources for processing and storage, enabling the continuous delivery of computational capability to end-devices across many locations. Scalability and flexibility are two characteristics that set OMNIBUS distinct from other edge computing solutions. The suggested method, which is based on mathematical models of prediction, determines computational capacity for particular locations by learning the direction and time of vehicle movements. It also presents a theoretical foundation for learning, improvement, and caching amid dynamic conditions caused by moving vehicles, specifically for sequential concurrent conversion. OMNIBUS's scalability allows for almost infinite growth, which might result in a significant increase in network speed. © 2024 ieee.

关键词： Scalability

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