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检索条件"任意字段=IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

共 36766 条记录，以下是121-130 订阅

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Fortifying the 5G Core: Analysing and Detecting DoS Attack from Compromised NFs using HTM 17

Fortifying the 5G Core: Analysing and Detecting DoS Attack f...

引用

17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Dey, Meenu Rani Bharti, Ashish Patra, Moumita Indian Institute of Technology Assam Guwahati India

ISBN: (纸本)9798331531195

The rapid increase in mobile data traffic, the growing number of connected devices, and the variety of applications are creating significant demands on the core networks of 5G and beyond (5GB). While 5G and 5GB technologies are effectively meeting these demands through strategies like network slicing, they also introduce vulnerabilities to specific attacks, such as session mirroring and resource overallocation, which arise when core functions like the SMF (Session Management Function) are compromised. These attacks can deplete network resources, resulting in Denial of Service (DoS). In this work, we address these attacks by analyzing their impact using various traffic metrics. We propose an anomaly detection approach based on Hierarchical Temporal Memory (HTM), which can effectively manage real-time traffic anomalies by leveraging previous learning states. Compared to other neural network models (Recurrent Neural Network, Long Short Term Memory, Multi-Layer Perception), our results show that the HTM-based detection method delivers superior performance in real-time traffic, particularly in terms of anomaly score, accuracy, precision, recall, and F1 score metrics. © 2025 ieee.

关键词： 5G mobile communication systems

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An Efficient RISC-V Vector Coprocessor for Heart Rate Variability Detection on Edge 38

An Efficient RISC-V Vector Coprocessor for Heart Rate Variab...

引用

38th international conference on VLSI Design, VLSID 2025

作者： Pedada, Uday Kiran Sharma, Tarun Grover, Deepank Deb, Sujay School of VLSI Design and Embedded Systems NIT Kurukshetra Haryana India IIIT Delhi Dept. of Electronics and Communication Engineering New Delhi India

ISBN: (纸本)9798331522445

Given the emergence of modern wearable devices, there is a need for devices with efficient, low-power processing capabilities to perform continuous health monitoring. Heart Rate Variability (HRV) is a crucial health parameter indicating cardiac health by measuring the time variation between heartbeats. Monitoring HRV continuously is challenging due to the need for efficient and low-power processing on edge devices. While conventional methods rely on general-purpose processors, these are not optimized for the specific needs of continuous HRV detection, especially in terms of power and area efficiency. This work addresses the issue by proposing an efficient vector coprocessor based on the open-source RISC-V Instruction Set Architecture (ISA) for HRV detection on edge devices. The vector coprocessor reduces power consumption and enhances performance by taking vector operations from the main processor. We also implement a vector-aware HRV calculation algorithm optimized for edge computing, which processes data in smaller chunks to fit the limited memory of edge devices. The proposed work shows an 80% improvement in power density compared to the Ibex processor. © 2025 ieee.

关键词： Coprocessor

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Patch-Based 3D CNN-Transformer Approach for Non-Uniformly Compressed Voxel Classification 29

Patch-Based 3D CNN-Transformer Approach for Non-Uniformly Co...

引用

29th international conference on Information Technology, IT 2025

作者： Koba, Felix Marin, Aleksandr Čakić, Stevan University of Donja Gorica Faculty for Information Systems and Technologies Oktoih 1 Podgorica81000 Montenegro

ISBN: (纸本)9798331517649

Three-dimensional (3D) voxel models have wide applications in fields such as robotics, medical imaging, autonomous navigation, and augmented reality. To address challenges of spatial sparsity and computational efficiency, this research proposes a voxel-based 3D convolutional neural network (3D CNN) integrated with a Transformer encoder for object classification on the ModelNet10 dataset. The 3D models are non-uniformly compressed into a 16×16×16 voxel grid, with compression factors incorporated as inputs to reduce sparsity. An in-house augmentation tool further enhances the dataset size and diversity by performing real-time voxel editing and labeling. The proposed method achieves an accuracy of 92.15% while utilizing only 184k trainable parameters, demonstrating an efficient and lightweight approach to object classification. © 2025 ieee.

关键词： Convolutional neural networks

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Laser frequency locking for quantum sensing applications using AI/ML techniques 17

Laser frequency locking for quantum sensing applications usi...

引用

17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Mangipudi, Srikanth Avvaru, Rama Theja Chaganti, Gopika Rao Barthwal, Sachin Quantum AI Global Hyderabad India

ISBN: (纸本)9798331531195

The development of narrow-linewidth and highly stable lasers is one of the key tasks of cutting-edge technologies. Such lasers provide unique opportunities in a variety of progressive areas such as quantum sensing, quantum communication, ultrafast optical ranging, atomic clocks, astronomy, and others. This paper presents a hybrid approach for laser frequency stabilization by integrating Saturated Absorption Spectroscopy (SAS) locking/injection locking using artificial intelligence (AI) and machine learning (ML). Both the SAS and injection locking modules employ a support vector machine (SVM) to automatically lock and relock the laser frequency based on real-time pattern recognition of absorption spectra. Experimental results show that the system can achieve rapid relocking and maintain high spectral purity with minimal user intervention. © 2025 ieee.

关键词： Support vector machines

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Research on Optimization Algorithm of embedded AI real-time Deep Learning Model in IoT Devices

Research on Optimization Algorithm of Embedded AI Real-Time ...

引用

Power Electronics, Computer applications (ICPECA), ieee international conference on

作者： Yifan Zheng School of Big Data Fuzhou University of International Studies and Trade Fuzhou Fujian China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

This paper focuses on the optimization algorithm of embedded AI real-time deep learning models in IoT devices, aiming to solve the problem of efficient and low-latency inference in resource-constrained environments. In order to solve the problems of high computational complexity and large memory occupation of existing models when running on embedded devices, this paper proposes a multi-stage optimization framework, which combines model compression, quantization and acceleration techniques to significantly improve the real-time performance of the model. We have introduced a structured pruning approach that reduces the amount of model parameters and computational overhead by removing redundant neurons and channels. Secondly, converting the floating-point number into a low-bit integer representation, which further reduces the memory occupation and computational complexity. In addition, an adaptive inference scheduling algorithm was designed to dynamically adjust the inference path according to the characteristics of the input data, which improved the response speed and energy efficiency ratio of the model. The optimized model in this paper achieves more than 50% improvement in inference speed in multiple IoT application scenarios, while maintaining a high accuracy rate.

关键词： Deep learning Adaptation models Accuracy Scheduling algorithms Computational modeling Optimization models real-time systems Internet of Things Artificial intelligence Optimization

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Dynamic Equilibrium in Robotics:Techniques and applications for Self Balancing Robots

Dynamic Equilibrium in Robotics:Techniques and Applications ...

引用

2025 ieee international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Kumar, Nitin Arora, Ujjawal Chaudhary, Neelu Manav Rachna University Computer Science & Technology Faridabad India

ISBN: (纸本)9798331529833

Self-balancing robots have become a key focus in robotics and control system studies since they are capable of standing on two wheels as an upright system. In the implementation of these systems, PID control, Linear Quadratic Regulation, fuzzy logic as well as reinforcement learning in machine learning is applied to control the balance and motion of the robots in the robots in different environmental conditions. This article reviews literature on self-balanced robots over the last half century moving beyond mechanical and electrical designs to control strategies including how double loop control incorporated PID control and how this has now shifted to machine learning. It also describes sensor fusion techniques where a Kalman filter or complementary role filters is employed sensor's accelerometer data and gyroscope data towards the assistance of estimating how tilted the robot is to the ground in degrees. Practical examples are also analyzed from personal devices such as the Segue, to education and robotics research. The paper also investigates the current challenges in the field including sensor drift , battery life, and especially the need for real time adjustment to varying environmental conditions such as bumpy terrain or perturbations. A number of ways are suggested such as using better sensors, control strategies and low power actuators which all improve balance robot systems. The study finishes with the discussion of the optimal developments, particularly focusing on the use of overcome strategies of controls using artificial intelligence for bringing about more flexibility and independency to self-balancing robots. © 2025 ieee.

关键词： Three term control systems

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real-time Indoor Target Tracking Using IoT and Wireless Sensor Networks 4

Real-Time Indoor Target Tracking Using IoT and Wireless Sens...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Katakwar, Sachin R. Jajulwar, Kapil K. Tadas, Surekha M. Tech Communication Engineering G H Raisoni College of Engineering Nagpur India G H Raisoni College of Engineering Department of Electronics and Telecommunication Engineering Nagpur India

ISBN: (纸本)9798331523923

Indoor target tracking presents significant challenges across various fields such as security surveillance, industrial automation, and healthcare. This research paper outlines the design and implementation of a reliable direction monitoring system aimed at achieving accurate indoor target tracking using IoT and wireless sensor networks. By utilizing advanced sensor technologies and sophisticated data processing algorithms, the proposed system enables real-time monitoring and precise localization of targets within indoor spaces. The combination of IoT devices and wireless sensor networks facilitates effective data collection, analysis, and visualization, thereby improving the overall performance of the monitoring system. The results indicate that the developed system is effective in accurately tracking and localizing targets, fulfilling the essential requirement for efficient indoor direction monitoring solutions. The paper concludes with a discussion on the practical implications and potential applications of this system, emphasizing its importance in enhancing indoor target localization across diverse sectors. © 2025 ieee.

关键词： Indoor positioning systems

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A Comprehensive Survey and Implementation of Health Kiosk Innovations: AI Integration, IoT applications, and Predictive Healthcare Solutions 3

A Comprehensive Survey and Implementation of Health Kiosk In...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Amrutesh, Aniverthy Bharadwaj, Shreelakshmi Darshan Kumar, T. Asha Rani, K.P. Gowrishankar, S. Dr. Ambedkar Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru560056 India

ISBN: (纸本)9798331515911

Kiosks, initially designed for administrative purposes, have rapidly evolved into vital tools in healthcare, offering a range of services such as diagnostics, telemedicine, and health data management. This paper presents a comprehensive survey of health kiosks, emphasizing their application in healthcare environments, particularly with the integration of AI, IoT, and predictive analytics. Key technologies, including advanced biometric sensors and AI-driven health insights, have significantly enhanced their functionality. The economic impact of deploying health kiosks, their integration with existing healthcare systems, and the importance of adhering to international medical standards are thoroughly examined. A review of academic research, patents, and industry innovations sheds light on the latest advancements, while case studies of real-world implementations demonstrate their growing adoption across different sectors. The paper also provides a comparative analysis of kiosk models globally, addressing challenges in deployment. Future trends such as AI and wearable technology integration, blockchain for secure health data management, and personalized healthcare services are explored, highlighting the potential for health kiosks to further revolutionize healthcare delivery. Considering the Literature available a practically feasible Health Kiosk is implemented with a custom ML model deployed with an accuracy of 95.3% is achieved. © 2025 ieee.

关键词： Electronic health record

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Indigenous Design and Development of Interface Board for TMS320F28335 DSP and Programming via MATLAB Simulink embedded Coder

Indigenous Design and Development of Interface Board for TMS...

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Smart and Sustainable Developments in Electrical Engineering (SSDEE), ieee international conference on

作者： Urbesh Sarkar Prasun Mishra Smart Power Electronics & Electrical Drives Lab Advanced Technology Development Centre Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

This paper presents an indigenous design and development of an interface board, interfacing different micro-controller peripherals and sensors, for the Texas Instruments TMS320F28335 Digital Signal Processor (DSP), with a focus on its integration with MATLAB Simulink embedded Coder to do efficient programming and code deployment. The developed interface board, having essential hardware, provides a reliable and seamless connection between the TMS320F28335 DSP and the development environment, enabling real-time control, signal and data processing tasks for various embedded control systems. MATLAB Simulink embedded Coder is employed to generate C code for the TMS320F28335 through automatic code generation and direct deployment of algorithms to the DSP streamlining the development process for embedded applications. This paper discusses the hardware components of the interface board along with communication protocols and configuration of various C2000 microcontroller blocks for software integration with MATLAB, highlighting its versatility and ease of use for real-time embedded system development. A case study of real-time implementation of closed-loop v/f control of induction motor is presented and the obtained results demonstrate the efficacy of the interface board while enabling its efficient programming and operation. This work contributes a viable, customizable solution for the students, researchers, and engineers started working with TMS320F28335 DSPs and embedded Coder, promoting enhanced productivity and flexibility in embedded system development.

关键词： Motor drives Codes Induction motors embedded systems Software packages Programming real-time systems Hardware Sensors MATLAB

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Multimodal Learning in Natural Language Processing: Fact-Checking, Ethics and Misinformation Detection

Multimodal Learning in Natural Language Processing: Fact-Che...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Joshi, Himanshu Sanjay Taherdoost, Hamed College of Business Westcliff University CA United States College of Technology & Engineering Westcliff University CA United States University Canada West Vancouver Canada Gus Institute Global University Systems London United Kingdom Q Minded Quark Minded Technology Inc. Vancouver Canada

ISBN: (纸本)9798331509828

Multimodal learning (MML) is a subtype of deep learning that improves the capabilities of machine learning models by integrating modalities from multiple data sources such as text, images, audio, and video. This integration of modalities produces a more comprehensive understanding of complex data and nuanced outputs. applications of this approach in real-time decision-making, information retrieval, and knowledge representation can improve the insights derived from the natural language processing (NLP) system. Moreover, in a time of information overload, there are concerns regarding the credibility of the content. The role of NLP systems is central to processing textual data in MML systems. In this environment, the ethical development of multimodal systems for fact-checking is crucial. This paper addresses an existing gap in use of multimodal learning in the context of fact-checking by investigating issues related to integration of NLP, Question-Answering (QA) systems, and knowledge graphs to combat misinformation. This research presents multimodal data based frameworks based on knowledge graph integration. The exploration of the frameworks enables to enhance the accuracy, reliability, and ethical soundness of fact-checking mechanisms. This study also highlights new directions for overcoming challenges related to modality alignment, computational efficiency, and ethical transparency. © 2025 ieee.

关键词： Knowledge graph

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