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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Ashutosh Ghimire Fathi Amsaad Tanvir Hossain Tamzidul Hoque Ahmed Sherif Department of Computer Science and Engineering Wright State University Department of Electrical Engineering and Computer Science University of Kansas School of Computing Sciences and Computer Engineering The University of Southern Mississippi

Ensuring trust in the semiconductor IC supply chain necessitates the critical detection of Hardware Trojans, yet current methods relying on side-channel analysis often require the use of golden chips for verification. This research paper presents a novel approach to detect Hardware Trojans in the semiconductor IC supply chain, addressing the need for trust and eliminating the use of golden chips. By combining unsuper-vised machine learning and side-channel analysis, the proposed technique leverages unique features from on-chip ring-oscillator networks to identify anomalies through unsupervised clustering. Evaluation on FPGA chips with Trojan insertion demonstrated exceptional accuracy, surpassing alternative methods with a 99 % accuracy rate. The centroid-based clustering model exhibited superior performance with a slight edge in false positive rate and an fl score. This research contributes to enhancing trust in semiconductor IC supply chains by offering a fresh perspective on Hardware Trojan detection.

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Cloud-Powered Patient Interaction: Humanoid Robots in Hospital Reception using NLP 10

Cloud-Powered Patient Interaction: Humanoid Robots in Hospit...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Naveenkumar, N. Priyadarshini, S. Swathy, R. Mishra, Nilamadhab Thirumaraiselvan, P. Renukadevi, S. Department of Computer Science and Engineering Tamil Nadu Namakkal India Vinayaka Mission's Kirupananda Variyar Arts & Science College Department of English Tamil Nadu Salem India Srm Institute of Science and Technology Department of Networking and Communications Chennai Tamil Nadu Kattankulathur India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India Department of Mathematics Tamil Nadu Chennai India

ISBN: (纸本)9798350353068

This research presents a novel method for improving patient engagement in hospital reception areas by using cloud computing and natural language processing (NLP) approaches to power humanoid robots. In order to improve the patient experience overall, the greeting procedure is being streamlined and personalized via the deployment of humanoid robots. To facilitate an effective connection between the hospital's computer systems and the humanoid robots, cloud computing enables the processing and analysis of data in real-time. The ability to comprehend and react to patient questions, provide pertinent information, and aid with navigation inside the hospital grounds are all made possible by the robots' natural language processing skills. Robots may access and update patient data using cloud resources, proving accurate and up-to-date information transmission. and since the system is cloud-based, it can learn and adapt on the go, so the robots may improve their communication abilities in response to human input. Results in decreasing wait times, improving information accuracy, and increasing overall patient happiness have been encouraging since the deployment of this cloud-powered patient contact system. To improve healthcare administration and patient care by integrating advanced technology into healthcare environments. Adding new features and improving the system in response to input from users (both patients and doctors) are potential directions for future growth. © 2024 IEEE.

关键词： Anthropomorphic robots

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Adversarial Attack and Defense for WiFi-based Apnea Detection System

Adversarial Attack and Defense for WiFi-based Apnea Detectio...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Ambalkar, Harshit Zhao, Tianya Wang, Xuyu Mao, Shiwen California State University Department of Computer Science SacramentoCA95819 United States Knight Foundation School of Computing and Information Sciences Florida International University MiamiFL33199 United States Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States

ISBN: (纸本)9781665494274

WiFi sensing systems have gained enormous interest in extensive areas, including vital sign monitoring. By using deep neural networks (DNNs), WiFi sensing systems can achieve high performance. However, the security and vulnerability of DNNs under adversarial attack would greatly affect the WiFi sensing performance. In this paper, we develop a DNN-based apnea detection system using WiFi channel state information (CSI) and evaluate its robustness under three different attacks. The experimental results show that adversarial attacks can significantly impact the model performance, and the defense scheme (i.e. adversarial training) can improve the system robustness. © 2023 IEEE.

关键词： Deep neural networks

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Enhanced Robotic Vision System Based on Deep Learning and Image Fusion

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computers, Materials & Continua 2022年第10期73卷 1845-1861页

作者： E.A.Alabdulkreem Ahmed Sedik Abeer D.Algarni Ghada M.El Banby Fathi E.Abd El-Samie Naglaa F.Soliman Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelSheikh UniversityKafrelsheikh33511Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt

Image fusion has become one of the interesting fields that attract researchers to integrate information from different image *** is involved in several *** of the recent applications is the robotic *** application necessitates image enhancement of both infrared(IR)and visible *** paper presents a Robot Human Interaction System(RHIS)based on image fusion and deep *** basic objective of this system is to fuse visual and IR images for efficient feature extraction from the captured ***,an enhancement model is carried out on the fused image to increase its *** image enhancement models such as fuzzy logic,Convolutional Neural Network(CNN)and residual network(ResNet)pre-trained model are utilized on the fusion results and they are compared with each other and with the state-of-the-art *** results prove that the fuzzy logic enhancement gives the best results from the image quality ***,the proposed system can be considered as an efficient solution for the robotic vision problem with multi-modality images.

关键词： Deep learning fuzzy logic image fusion IR images

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Analysis of software-defined radio for optical networking

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Journal of Optical Communications 2025年

作者： Kumar, Arun Sarveswara Rao, T.D.N.S.S. Chandrashekhara, K.T. Chakravarty, Sumit Gaur, Nishant Nanthaamornphong, Aziz Department of Electronics and Communication Engineering New Horizon College of Engineering Bengaluru India ECE Department Sri Vasavi Engineering College AP Tadepalligudem534101 India Department of CSE/ISE BMS Institute of Technology & Management Bangalore India Department of Electrical Engineering and Computer Engineering Kennesaw State University KennesawGA United States Department of Physics JECRC University Jaipur India College of Computing Prince of Songkla University Phuket Thailand

Software defined networking (SDN) is a communications methodology that centralizes network management and programmability resulting in more flexible network administration. This project aims to configure and simulate a software-defined network to implement customized, dynamically-assigned flow control rules between network nodes - replacing static routes. We consider virtual network embedding (VNE) in development of network routing algorithms. Through this project, we seek to demonstrate the feasibility and benefits of SDN-based network control for diverse systems, enhancing adaptability and efficiency in managing communication between diverse network nodes. The proposed work is crucial for advancing adaptive, efficient, and scalable communication systems, enabling dynamic resource management, enhanced interoperability, and supporting next-generation technologies like six generation (6G) and IoT seamlessly. © 2024 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Internet of things

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Applications of Quantum Cascade Lasers in Spectroscopy and Trace Gas Analysis 4

Applications of Quantum Cascade Lasers in Spectroscopy and T...

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4th International Conference on Advances in electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Anandaram, Harishchander Velpula, Srikanth Ramkumar, M. Ramya, G. Lakshmisridevi, S. Ashok, P. Amrita Vishwa Vidyapeetham Amrita School of Artificial Intelligence Coimbatore India Sr University Department of Electrical and Electronics Engineering Telangana Warangal India Sri Krishna College of Engineering and Technology Electronics and Communication Engineering Tamilnadu Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Bs Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Maharashtra Pune India

ISBN: (纸本)9798350343670

The primary emphasis of this research paper lies in advancing versatile quantum cascade laser-based spectroscopic methods tailored for highly sensitive gas detection. These techniques hold promise for a range of applications including atmospheric sensing, biomedical science, and high-resolution fundamental molecular spectroscopy. Quantum Cascade Lasers (QCLs) have revolutionized spectroscopic techniques and trace gas analysis due to their unique properties. In spectroscopy, QCLs offer widely tunable, high-powered, and narrow-linewidth radiation across the mid-infrared (MIR) region, enabling precise identification and quantification of molecular species. Utilizing a continuous wave cavity ring-down spectrometer (CW-CRDS) with high resolution, coupled with an external cavity continuous-wave quantum cascade laser (CW-EC-QCL) functioning within the wavenumber interval from 1250 cm1 to 1333 cm1 enables spectroscopic examination. This has found applications in various fields namely, Atmospheric Sensing, Biomedical Science, Security and Defense, Environmental Monitoring, Industrial Process Control, Astrophysics and Astronomy. In trace gas analysis, QCLs offer unmatched sensitivity and selectivity. They can detect minuscule concentrations of gases, down to parts per trillion or even lower, making them indispensable in fields where precise gas measurements are paramount. This includes environmental monitoring, medical diagnostics, industrial safety, and scientific research. The versatility and precision of QCL-based spectroscopic techniques continue to drive innovation and advancements in these diverse fields. © 2024 IEEE.

关键词： Free Space Optics Gain Switching Quantum Cascade Lasers Rate Equations Spectroscopic Techniques Trace Gas Analysis

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Optimized viewport-adaptive 360-degree video streaming

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CAAI Transactions on Intelligence Technology 2021年第3期6卷 347-359页

作者： Xiaolei Chen Di Wu Ishfaq Ahmad College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhouChina Department of Computer Science and Engineering University of Texas at ArlingtonArlingtonTexasUSA

Because of the rapid growth of head-mounted displays and 5G networking deployment,360-degree video has become increasingly *** generate the real experience of a virtual environment,360-degree videos require an ultrahigh resolution and frame rate to cover an omnidirectional *** two prerequisites impose challenges for the transmission bandwidth and storage capacity of 360-degree video *** reduce bandwidth and storage waste while providing a good immersive experience,we propose an optimized viewport-adaptive 360-degree video streaming method using high-efficiency video coding tiling,motion-constrained tile sets and MPEG dynamic adaptive streaming over HTTP spatial relationship *** paper describes the rigorous design of the optimized system,which can assign different bitrates to different tiles in the *** experimental results show that the proposed streaming system compares favourably with existing methods in terms of bitrate savings and storage capacity reduction.

关键词： video streaming system

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Development of Resistive-type IoT-Based Methane Gas Concentration Transmitter Using SnO2 Material

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Journal of The Institution of Engineers (India): Series D 2023年第2期104卷 515-529页

作者： Ghosh, Animesh Mondal, Bikas Mondal, Santu Hazra, Tanmoy Biswas, Arindam Department of Electrical Engineering Techno Polytechnic Durgapur West Bengal Panagarh India Department of Applied Electronics and Instrumentation Engineering Asansol Engineering College West Bengal Asansol India Department of Computer Science and Engineering NSHM Knowledge Campus West Bengal Durgapur India Department of Mining Engineering Kazi Nazrul University West Bengal Asansol India Centre of IoT and AI Integration with Education-Industry-Agriculture and School of Mines and Metallurgy Kazi Nazrul University West Bengal Asansol India

Many conventional sensors are used in industries since industrial methane sensing is critical. In the present case, a SnO2 layer is the sensing medium for methane that decreases its electrical resistance with increase in methane concentration. A Wheatstone bridge detects the decrease in resistance, thereby converting it to a suitable voltage. Since the environment where methane gas would be present is hazardous, electrical communication is impossible through a physical network. Instead, the authors have taken the help of the Internet of Things (IoT) for data communication purpose which has become much more feasible nowadays for communicating data in a hazardous environment. The mathematical relationship of the methane concentration with the transmitted current and voltage is found by theoretically analyzing the proposed methane sensor. The authors have tested the proposed system of methane concentration measurement experimentally. The paper presents the experimental results with reasonable accuracy, sensitivity, and linearity. [Figure not available: see fulltext.]. © 2022, The Institution of Engineers (India).

关键词： Internet of things

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Automated Tumor Detection using Convolution Neural Network 5

Automated Tumor Detection using Convolution Neural Network

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Raturi, Ayush Joshi, Kireet Jain, Paras Gupta, Vishan Kumar Gupta, Avdhesh Meena, Jaishree Graphic Era Deemed to be University School of Computer Science & Engineering Dehradun India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Vit Bhopal University School of Computing Engineering and Artificial Intelligence MP Bhopal India Amity University Punjab Amity School of Engineering & Technology Punjab Mohali India Akg Engineering College Department of Cse UP Ghaziabad India Amity School of Biological Sciences Amity School Punjab Mohali India

ISBN: (纸本)9798350376685

Brain tumors are quite formidable primarily because they can occur at any place within the brain and may also exhibit dissimilar behavior patterning. The disease in its initial stage is not very dangerous, if it is not treated then the result is a lot of damage to the brain. To overcome these challenges, further research and development is needed and more specifically the implementation of new technologies. This paper seeks to contribute to these goals by developing a deep learning convolutional neural network with transfer learning approach, that can help in detection of brain tumors with high accuracy and efficiency. In the long run, it should help in the early diagnosis and timely appropriate treatment of patients which should somehow contribute to the development of neurooncology. This study proposes a model that involves MRI for detection of brain tumors through the application of deep learning. The high performance of this model is attributed to the transfer learning technique that has been applied on VGG16 trained on 3000 medical MRI images of the human brain. The proposed model provided quite satisfactory accuracy of 99%. 33% on this dataset. Using this automated detection model, it is possible for the healthcare professionals to be able to predict the presence of brain tumors quite well using this system. © 2024 IEEE.

关键词： Convolutional neural networks

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Flexible and Secure EV Charging System Using Blockchain Technology with Namib Beetle Optimization and Continual Spatio-Temporal Graph Convolutional Networks 4

Flexible and Secure EV Charging System Using Blockchain Tech...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Kaushal, Rajanish Kumar Shashibhushan, G. Hemantha, C. Chellaiah, Pandeeswaran Bordoloi, Dibyhash Natrayan, L. Chandigarh University Department of Electrical Engineering Punjab Mohali India Sir M Visvesvaraya Institute of Technology Department of Electronics and Communication Karnataka Bengaluru562157 India Bgs Institute of Technology Department of Mechanical Engineering Karnataka Mandya India St. Joseph's College of Engineering Department of Computer Science and Engineering Tamil Nandu Chennai600119 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331540364

The advancement in electric vehicles (EVs) has heightened the need to have better, safer, and smart charging solutions. There may be a problem with the implementing blockchain technology which may be a problem of scalability that can result in narrow space in providing services for multiple similar activities at different EV places at anyone time thus slowing down the rate of charging. This paper proposes a hybrid method for a flexible and secure smart charging system that combines blockchain technology with advanced techniques. The proposed method is the combined execution of together Namib beetle optimization algorithm (NBOA) and Continual Spatio- Temporal Graph Convolutional Networks (CSTGCN). Henceforth, it is called as NBOA-CSTGCN method. The objective of the proposed technique is to improve the efficiency and stability of EV charging systems. The NBOA is utilized to optimize EV charging schedules, ensuring a balanced load on the power grid. The CSTGCN is employed to predict and adapt to dynamic energy demand, facilitating efficient energy distribution across charging stations. By then, the proposed method is realized in MATLAB platform and associated to various existing approaches for example Particle Swarm Optimization (PSO), Adaptive Non-dominated Sorting Genetic Algorithm II (ANSGA-II), Krill Herd Algorithm (KHA). The proposed NBOA-CSTGCN method achieves the highest efficiency of 92.1 %, outperforming PSO (70.5%), ANSGA-II (78.2%), and KHA (86.7%). It also shows superior utility, with the Aggregator increasing from 2 in PSO to 7 in NBOA-CSTGCN, and the EV utility rising from 3.1 in PSO to 5.9 in NBOA-CSTGCN. These results demonstrate that NBOA-CSTGCN offers the best performance and effectiveness compared to existing methods. © 2024 IEEE.

关键词： Charging stations

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