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Thinking and Moving: An Efficient computing Approach for Integrated Task and Motion Planning in Cooperative Embodied AI systems (Invited Paper) 24

Thinking and Moving: An Efficient Computing Approach for Int...

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43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Wan, Zishen Du, Yuhang Ibrahim, Mohamed Zhao, Yang Krishna, Tushar Raychowdhury, Arijit Georgia Institute of Technology AtlantaGA United States University of Minnesota Twin Cities MinneapolisMN United States

ISBN: (纸本)9798400710773

Cooperative embodied AI systems, where multiple agents collaborate to accomplish complex, long-horizon tasks, show significant promise for real-world applications. These systems integrate perception, cognition, and action through integrated task and motion planning (TAMP), leveraging the advanced reasoning and communication capabilities of large language models (LLMs). However, their efficiency is often hindered by challenges such as high computational latency and redundant communication, largely due to the reliance on LLMs for sequential planning decisions. In this paper, we aim to identify the inherent characteristics and optimization opportunities in cooperative embodied AI systems. We first present a cognitive-inspired modular framework encompassing perception, memory, communication, planning, and execution. We then conduct a detailed profiling analysis of two state-of-the-art cooperative embodied systems, revealing the significance of each module and identifying critical bottlenecks such as redundant message pre-generation and excessive LLM usage in decision-making processes. Based on these insights, we propose several model- and system-level optimizations, including a planning-first communication strategy, selective multi-agent communication, and planning-guided multi-step execution. Evaluated across long-horizon cooperative tasks, these optimizations reduce the frequency of LLM inference runs, achieving an average 3.93× speedup in end-to-end task execution. Finally, we discuss the challenges and potential directions for embodied AI computing, to enhance system flexibility, efficiency, and scalability. © 2024 Copyright is held by the owner/author(s).

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How Lightweight Deep Learning Enhances Performance in DPU-Accelerated Autonomous Driving on Zynq SoC 11

How Lightweight Deep Learning Enhances Performance in DPU-Ac...

引用

11th international conference on Mechatronics and Robotics Engineering, ICMRE 2025

作者： Hwang, Gyu Hyeon Oh, HoBin Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Sungkyunkwan University Department of Computer Science and Engineering Suwon Korea Republic of

ISBN: (纸本)9798331509293

This study presents a lightweight deep learning model developed for DPU-accelerated systems. It aims to provide real-time autonomous driving on resource-constrained systems such as the Ultra96v2. A customized kids electric car served as the platform. Custom power supply and steering control systems were set up in the car to enable real-world testing. To enhance inference performance, various methods were used. These included input size reduction, channel-pruning, and quantization. As a consequence, the pruned and quantized YOLOv3-Tiny model produced a frame rate of 67.592 FPS. This is roughly a 25x increase over the original YOLOv3's 2.715 FPS on Ultra96v2's PL domain. These results show that real-time deployment is feasible on FPGA-based platforms. The work offers insights for creating efficient and scalable embedded systems for self-driving vehicle system. © 2025 ieee.

关键词： Direct digital control systems

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A Paradigm Shift in real-time Detection: The YOLOv7 Approach to Trainable Freebie Optimization 3

A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Vinitha, A. Jeevitha, S. Kumar, N.B. Mahesh Sathish Kumar, L. Saravanan, D. Karpagam Academy of Higher Education Department of Computer Applications Tamilnadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil Tamilnadu Virudhunagar India Hindusthan Institute of Technology Computer Science and Engineering Tamil Nadu Coimbatore India Madhya Pradesh Sehore India

ISBN: (纸本)9798331509675

Significant progress has been made in real-time object detection, with YOLOv7 emerging as a state-of-the-art model that achieves the highest levels of accuracy and speed. This study combines Bag-of-Freebies (BoF) strategies with YOLOv7 to improve performance without incurring extra computational expenses. Our tests show that adding BoF preserves real-time inference capabilities while increasing mAP@0.5 by 3.5%. The accuracy and FPS of YOLOv7 with BoF are superior to those of the current YOLO-based models (YOLOv4, YOLOv5, YOLOv6). According to the results, YOLOv7 with BoF sets a new standard for real-time object detection by achieving a mAP@0.5 of 55.3% and 83 FPS on the COCO dataset. © 2025 ieee.

关键词： Contrastive Learning

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Enhancing Cybersecurity in Medical Cyber-Physical systems Using Blockchain and Deep Learning

Enhancing Cybersecurity in Medical Cyber-Physical Systems Us...

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

作者： Pimple, Jagdish Sharma, Avinash Madhyanchal Professional University Department of Computer Sci. & Engineering Bhopal India

ISBN: (纸本)9798331529833

Medical Cyber-Physical systems (MCPS) are essential in modern healthcare, enabling real-time patient monitoring, diagnosis, and treatment. However, their integration of digital and physical elements exposes them to cyber-attacks, threatening both patient safety and data integrity. This paper shows a novel cybersecurity framework that combines blockchain technology and deep learning-based intrusion detection to protect MCPS. The proposed model uses a deep DNN-based intrusion detection system (IDS) to categorize medical data as either normal or malicious. Blockchain technology is incorporated to ensure secure, tamper-proof, and decentralized storage of patient information. To improve detection accuracy and reduce false positives, feature fusion techniques, processed through convolutional neural networks (CNNs), are applied. Ethereum-based blockchain ensures that medical data remains confidential and resistant to unauthorized alterations. The system's effectiveness is validated using a healthcare dataset, showing a marked improvement in detection accuracy, precision, and recall over traditional methods. This integrated solution strengthens the safety and reliability of MCPS, protecting patient data while maintaining efficient data shared between medical devices and healthcare networks. Future research will focus on real-time applications and scaling the model for larger healthcare systems. © 2025 ieee.

关键词： Cybersecurity

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Securing the internet of things communication and data transmission by using Blockchain based Architecture 3

Securing the internet of things communication and data trans...

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

作者： Sheetal Kumari, Vasantha Badiger, Veena S Presidency College Department of Computer Applications Bengaluru India

ISBN: (纸本)9798331515911

Due to the rapid growth of technology usage of Internet of Things devices has revolutionized in multiple domains and areas, but still there are few challenges to address like security and performance issues. Most of the traditional approaches are not sufficient in solving those issues related to IoT ecosystems. This paper aims to present a block chain technology-based solution to the problems of security challenges and improvement in integrity, confidentiality, and availability for IoT systems. The features of Block chain in the sense of an immutable nature help secure the IoT system to share data with others, enhance authentication, and transparency. The integration of block chain with IoT devices can also ensure data provenance thus creating an environment for trust with users and stakeholders. To build this blockchain-based IoT system, the proposed architecture ensures a framework of not only ensuring the security of the IoT ecosystem but also sustainability. Future work will be based on designing prototypes and applications within real-world environments to validate the effectiveness of these proposed solutions in various IoT scenarios. © 2025 ieee.

关键词： Blockchain Technology challenges IoT architecture security

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Deep Learning Techniques for Intrusion Detection systems in Healthcare Environments 14

Deep Learning Techniques for Intrusion Detection Systems in ...

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14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Alam, Mahfooz Ashraf, Zubair Singh, Priya Pandey, Bishwajeet Rehman, Kaleemur Aldasheva, Laura Department of Master of Computer Applications G. L. Bajaj Institute of Technology and Management Greater Noida201306 India College of Computing and Information Technology University of Bisha Bisha67714 Saudi Arabia Department of Computer Science Institute of Engineering & Technology Dr. B. R. Ambedkar University Khandri Agra India Department of CSE-AI G. L. Bajaj Institute of Technology and Management Greater Noida201306 India Department of IS and CS Astana IT University Astana Kazakhstan

ISBN: (纸本)9798331531935

Nowadays, healthcare systems face ever-increasing cyber security threats due to the sensitive nature of patient data and the proliferation of IoT-enabled medical devices. Traditional Intrusion Detection systems (IDS) struggle to adapt to healthcare environments' dynamic and heterogeneous data. Deep learning techniques, with their ability to analyze complex and high-dimensional data, have emerged as a promising approach to enhancing IDS performance. This paper explores the application of deep learning methods for intrusion detection in healthcare, focusing on their advantages, challenges, and practical implementations. Topics include neural network architectures, feature engineering for medical and network data, and real-time intrusion detection in resource-constrained environments. The goal is to provide a roadmap for leveraging deep learning to ensure robust and scalable security for healthcare systems. © 2025 ieee.

关键词： Network intrusion

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IoT based Thermal Management and State of Charge Estimation of LFP battery 3

IoT based Thermal Management and State of Charge Estimation ...

引用

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

作者： Anand, Deepthi Paraag, V. Naik, Sahana Kavana, T.M. Vasudha, Karanam Shubha Rao, K. Bnm Institute of Technology Dept. of Eee Bangalore India

ISBN: (纸本)9798331515911

Efficient storage of electricity is pivotal in modern applications, necessitating the use of batteries. However, enclosing batteries in compact spaces, such as power plants or electric vehicles, often leads to excessive heat generation, diminishing efficiency and longevity. Heat generation primarily stems from the charging and discharging processes. A liquid cooling system, powered by a motor connected to the battery system, circulates fluid externally around the battery layers to manage temperature, optimizing the battery's performance within the ideal range of 25°C to 35°C. Thermal Modelling of the battery is implemented in systems to understand how temperature changes affect their performance, efficiency, and reliability. IoT-based Battery Thermal Management System (IoT-BTMS) is introduced resulting in a sophisticated solution utilizing sensors, communication technologies, and data analytics. This system monitors and regulates battery temperature in real-time, based on sensed data a relay will enable cooling system if temperature values exceed the limit values or when the system becomes unstable, the cooling system continues until the system stabilizes and temperatures fall within limits. © 2025 ieee.

关键词： Cooling systems

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AI Enhanced Anesthesia Dosing: Evaluating ML Models 5

AI Enhanced Anesthesia Dosing: Evaluating ML Models

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5th international conference on Emerging systems and Intelligent computing, ESIC 2025

作者： Patangia, Deepshikha Panda, Amrita Dash, Satya Ranjan Barra, Paola Disha, M. Kalinga Institute of Medical Sciences Dept. of Anaesthesiology Bhubaneswar India Kiit University School of Computer Applications India University of Naples 'Parthenope' Department of Science and Technology Italy

ISBN: (纸本)9798331522100

Artificial intelligence (AI) is revolutionizing health-care, particularly in the field of anesthesiology. This paper evaluates the application of AI-enhanced models in optimizing anesthesia dosing to improve patient outcomes and operational efficiency. By integrating machine learning (ML) techniques, we analyze structured and unstructured patient data to predict anesthesia needs, automate drug delivery, and monitor vital signs in real time. The study examines several ML models, such as Support Vector Machines, Decision Trees, and Gradient Boosting, assessing their accuracy in predicting anesthetic requirements. The results show that AI can effectively reduce human error, enhance decision-making, and improve patient safety during perioperative care. The paper discusses the system architecture, data collection methods, and model performance, emphasizing the future potential of AI in automating anesthesia processes. © 2025 ieee.

关键词： Anesthesiology

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Object Detection in real-time Surveillance Using Deep Learning-Based YOLO Framework

Object Detection in Real-Time Surveillance Using Deep Learni...

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2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Kukreti, Sanjeev Praveen, R.V.S. Bansal, Saloni Raju, Hemanth Singh, Navdeep Begum, Rowsonara Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India LTIMindtree Limited Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

The continual growth of the computer vision and artificial intelligence fields overtime have provided the basis for more efficient and accurate surveillance. This paper examines the use of YOLO, a deep learning model that is used in the detection of objects, for use in real-time surveillance systems. The added benefits of identifying and labeling multiple objects in a single pass forward makes YOLO ideal for real-time surveillance. Unlike other approaches to object detection such as region-based methods or methods with multiple stages of detection, YOLO cast object detection as a regression problem where boxes and class probabilities are predicted jointly. This leads to a far improved performance when compared with their slower counterparts without the sacrifice of precision. In this work, the YOLO model is finetuned on a large database of surveillance videos to improve object detection at low lighting and in the presence of occlusion and crowding. The developed system has a high detection speed and accuracy;the application areas will include uses such as security inspection, traffic regulation, and anomaly detection. This is done to affirm that using YOLO is more efficient and faster than the other object detection algorithms in real-time surveillance tasks. The results indicate directions for further improvements to guarantee public safety and improve efficient surveillance systems. © 2025 ieee.

关键词： Deep learning

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A Hybrid Deep Learning Model for Optimizing Electric Vehicle Battery and Navigation systems

A Hybrid Deep Learning Model for Optimizing Electric Vehicle...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Arul Jose, R. Ebby Darney, P. Santhana Krishnan, R. Relin Francis Raj, J. Jansi Rani, D. Sundaravadivel, P. Rajarajeswari College of Engineering Department of Electrical and Electronics Engineering Bengaluru India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331505745

This paper proposes a deep learning-based optimization framework for Electic Vehicle that leverages LSTM, CNN, and DRL to enhance battery management, autonomous navigation, and energy efficiency. The LSTM model predicts the State of Charge (SoC) and State of Health (SoH) of the EV battery, utilizing temporal patterns in sensor data to improve predictive maintenance. The CNN-based model focuses on real-time obstacle detection and fault recognition in autonomous driving systems, processing visual data from onboard cameras, LiDAR, and radar sensors. Finally, the DRL model optimizes energy consumption and route planning by training an agent to navigate traffic and road conditions efficiently while minimizing power usage. Data for the system is collected from various sensors embedded in the vehicle, including battery data, traffic information, GPS, and driver behavior. The data is preprocessed and augmented to ensure the models are robust and generalizable. Training involves GPU-accelerated computing platforms to enhance model efficiency. The performance of the models is evaluated through metrics such as R2 score, accuracy, and energy efficiency improvements, with results showing a 20-30% enhancement in energy consumption and battery life. The proposed system has practical applications in battery management, autonomous driving, and real-time energy optimization, contributing to the advancement of intelligent EV technologies. The work presents a step toward integrating deep learning techniques into EVs for improved performance and operational efficiency. © 2025 ieee.

关键词： State of charge

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