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检索条件"机构=Microelectronics in the Electronics and Computer Engineering ECE Department"

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An Energy Minimization Scheme for NOMA-MEC

An Energy Minimization Scheme for NOMA-MEC

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Electrical Automation and Artificial Intelligence (ICEAAI), International Conference on

作者： Han Xiao Yitong Liu Xingcheng Liu Yi Xie Peiran Wu Yang Yi School of Electronics and Information Technology (SEIT) Sun Yat-sen University Guangzhou China Department of Electrical & Computer Engineering (ECE) University of Waterloo Waterloo Canada School of Information and Intelligent Engineering Guangzhou Xinhua University Guangzhou China School of of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computer Science Sun Yat-sen University Guangzhou China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

Non-Orthogonal multiple access (NOMA)-assisted mobile edge computing (MEC) has been widely studied due to its efficient utilization of communication resources. Existing works have demonstrated that the MEC system has the potential to provide higher quality of service (QoS) with lower cost by using NOMA transmission, but the impact of successive interference cancellation (SIC) decoding order in NOMA-MEC system has not been fully explored. This paper proposes an optimizing solution for power distribution and SIC decoding order in NOMA-MEC systems. And we prove that the energy consumption is minimized by using the proposed scheme. Our work explores the relationship between the SIC decoding order and power distribution in NOMA-MEC system, which are constrained by tasks and deadline. Computational experiments are conducted to explore the performance of the proposed solution. The results show the advantages of proposed solution and demonstrate that the importance of SIC decoding order becomes greater with increasing variance of channels and number of users.

关键词： NOMA Energy consumption Interference cancellation System performance Power distribution Quality of service Minimization Decoding Uplink Optimization

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Image Processing Based Early Breast Cancer Detection in Mammography Images Using GRU and XGBoost Approach

Image Processing Based Early Breast Cancer Detection in Mamm...

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electronics, Computing, Communication and Control Technology (ICECCC), International Conference on

作者： Abhishek Pandey V. Ramesh Ravi Mohan S. Kaliappan Swetha Reddy A K. Gurunathan Department of Computer Applications SCSVMV University Kanchipuram India Department of ECE Shri Ram Institute of Technology Jabalpur Madhya Pradesh India Division of Research and Development Lovely Professional University Punjab India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai India Department of Computer Science and Engineering Mount Zion College of Engineering and Technology Pudukkottai India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

One out of every eight women will be affected by breast cancer at some point in their lives, making it one of the most common malignancies in females. The most reliable method for finding breast cancer in its early stages is mammography. Due to the low visibility and weak contrast of mammographic photos, early identification of breast cancer is crucial for effective therapy. As a result of several computer-aided detection approaches, radiologists are now able to make more precise diagnoses. Operating Sequencing is crucial for several tasks, including morphology, preprocessing, segmentation, feature selection, and training models. The suggested method makes use of preprocessing technologies that can enhance mammography, remove noise, and identify pectoral muscles. Using image processing techniques, a digital image can be “segmented” into many layers. Acquiring a precise image of the mass is the goal of the morphological process. Geometric algorithms are employed by the feature selection technique. Training a GRU-XGBoost model requires careful consideration of which features to use. The proposed method outperforms the current gold standard algorithms, GRU and XGBoost, in every respect. The results showed a remarkable improvement, with an accuracy of 95.63%.

关键词： Training Image segmentation Sequential analysis Accuracy Computational modeling Noise Muscles

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Music Genre Classification Using Long Short-Term Memory with Gated Recurrent Unit

Music Genre Classification Using Long Short-Term Memory with...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： K Sumalatha Y Nagendar Jaya Sharma Padma C R Zaid Alsalami Department of ECE Malla reddy Engineering College for Women (Autonomous) Hyderabad Telangana India School of Computer Science and Engineering SR University Warangal Telangana India Department of CSE GITAM University Hyderabad India Department of Medical Electronics Engineering Dayananda College of Engineering Karnataka India The Islamic university Najaf Iraq

The music genre classification is an important part in data retrieval for the association of increasing music collections. It is one of the challenging tasks for classifying music with reliable accuracy. Numerous methods are utilized as handcrafted features for identifying individual patterns which are unable to define the actual characteristics of music. To overcome these issues, this paper proposed a Deep Learning (DL) based technique for classifying music genre. The Long Short-Term Memory with Gated Recurrent Unit (LSTM+GRU) is used to classifying the music genre. The GTZAN dataset is used in this paper which collects 1000 clips from 10 balanced genres music data. It is preprocessed by clip splitting procedure which is utilized to convert 30s music clip into 3s which helps to enhance the classification performance. Then, the Mel Frequency Cepstral Coefficients (MFCC) is used for extracting relevant features from input signal and LSTM+GRU is used for classifying the music genres. The accuracy, recall, precision and f1-score are used for calculating model performance. The LSTM+GRU attains accuracy 93.65%, recall 92.38%, precision 92.57% and 92.18% f1-score when compared to existing techniques like ResNet50 and Convolutional Neural Network with Bidirectional GRU (CNN+BiGRU).

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Enhancing Healthcare Delivery Through IoT Applications in Remote Patient Monitoring and Telemedicine

Enhancing Healthcare Delivery Through IoT Applications in Re...

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Kottala Sri Yogi Dankan Gowda V Manoj Kumar S B R.Nithya A.Yasmine Begum Suganthi.N Department of Operations Symbiosis Institute of Business Management Hyderabad Symbiosis International University Pune India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Dept. of ECE Faculty of Engineering Management & Technology BGSIT Adichunchanagiri University Karnataka India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Namakkal Tamilnadu India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Tirupati India Department of Electrical and Electronics Engineering Dayananda Sagar College of Engineering Bangalore Karnataka India

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364576

This paper presents how IoT plays a vital role in improving delivery of healthcare services especially through remote patient monitoring and telemedicine. The proposed system also employs IoT devices aimed at passing various health indicators like; pulse rate, blood glucose levels, blood pressure, and physical activity to continually and objectively provide real-time data in the body. This particular study, the participants with chronic diseases, normal population and elderly people also participated in the test of the system. Some of the findings include; it is possible to measure the daily steps corresponding to the a calorie expenditure, the sleep score ranging between 60-80 and oxygen saturation percentages ranging from 96%-99% in various postures. The system also proved the correlation between the percentage of variation in ambient temperature and the percentage of variation in the pulse rate which was quite sensitive to the changes in temperatures. Based on these observations, it is possible to conclude that IoT-based monitoring can improve patient’s conditions as long as underlying issues will be detected at an early stage and necessary actions will be taken in time. The study also also demonstrates the application of telemedicine in making healthcare more accessible especially for the people from rural settings or in developing countries. The results show a good direction regarding the future of the system; however, security issues, privacy relating to data as well as integration are key hurdles in this connection.

关键词： Temperature sensors Temperature measurement Patient monitoring Telemedicine Medical services Real-time systems Internet of Things Security Biomedical monitoring Older adults

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The Future of Multimedia: Micro Facial Recognition in Advanced Systems 9

The Future of Multimedia: Micro Facial Recognition in Advanc...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Preethi, Thakkalapally Choudalla, Saila Ram Govathoti, Sudeepthi Rajasri, K. Mohsen, Karrar Shareef Prakash, Dudi Bhanu Sai Kumar, K. And Ai&ds India Andhra Pradesh Surampalem India Gitam Deemed-to-be-University Dept of Cse Gitam School of Technology Telangana Hyderabad India Mlr Institute of Technology Department of Electrical And Electronics Engineering Telangana Hyderabad India Al-Ayen University Information And Communication Technology Research Group Scientific Research Center Thi-Qar Iraq Cvr College of Engineering Department of Ece Hyderabad India Koneru Lakshmaiah Education Foundation Computer Science Green Fields Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350394474

Paper attendance systems are still widely used at many universities, consuming significant amounts of valuable classroom time. Furthermore, the system is likely to fail due to the possibility of human mistake and the rising chance of records being misplaced or lost altogether. The authors overcame this issue by automatically posting attendance information using a facial recognition software. This strategy also helps the school take a look at the student's financial situation. Attendance records and a list of slackers might be generated by the system. They can notify students and guardians via email of attendance and financial obligations. Using KNN deep learning methodology, photos taken by a CC camera or cell phone mounted at the door are used to identify students as they enter the classroom. This study incorporates ResNet and KNN-based Deep-learning technologies to extract useful information from low-quality photos. They might also use a video taken using a cell phone to keep track of attendance. The suggested method improves upon the state-of-the-art to the extent that it achieves an accuracy of 97.34 percent, sensitivity of 98.34 percent, recall of 97.23 percent, and an F1 score of 98.23 percent. © 2024 IEEE.

关键词： Face recognition

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Task Offloading in Edge Computing System with Deep Reinforcement Learning and Reward Shaping

Task Offloading in Edge Computing System with Deep Reinforce...

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IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Huishi Zhao Yitong Liu Xingcheng Liu School of Electronics and Information Technology (SEIT) Sun Yat-sen University Guangzhou China Department of Electrical & Computer Engineering (ECE) University of Waterloo Waterloo Canada School of Information and Intelligent Engineering Guangzhou Xinhua University Guangzhou China

ISBN: (数字)9798350373554

ISBN: (纸本)9798350373561

The evolution of mobile internet has brought challenges to smart devices such as smartphones and VR headsets, only having limited computational power to handle intensive tasks. The advent of edge computing provides a solution, in which the smart devices are enabled to offload tasks to nearby servers, thus facilitating the necessary computations with reduced latency and energy consumption. This study focuses on the challenge of task offloading. In our proposed scheme, edge computing is considered as a Markov decision process (MDP). Hence, the deep Q-networks with reward shaping (DQN-RS) can be utilized to improve training effectiveness for sparse reward conditions. Experimental results show that this method DQN-RS significantly enhances DRL algorithm performance compared to a well hand-crafted greedy algorithm, demonstrating the efficacy of reinforcement learning in discovering optimal offloading strategies.

关键词： Greedy algorithms Training Energy consumption Real-time systems Servers Task analysis Smart devices

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Enhancing Cybersecurity in Rural Areas - A Multilayered AI Approach to Combat Phishing Threats using Quantum Computing 7

Enhancing Cybersecurity in Rural Areas - A Multilayered AI A...

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7th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2025

作者： Rani, A. Jaya Mabel Sethi, V. Manoj Vuppala, Sashi Kiran Thilagam, K. Manimegalai, L. Manjunath, T.C. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Applications Analyst Avaya LLC 6021 Connection Dr Irving 75039 TX United States Java Technical Architect Telangana Hyderabad India Department of ECE Velammal Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Tamil Nadu Chennai India Computer Science & Engineering Department Dean Research (R & D) Rajarajeswari College of Engineering Karnataka Bangalore India

ISBN: (纸本)9798331515683

Rapid advances in quantum computing have raised worries about the security of classical encryption systems, as quantum computers can break many cryptographic techniques. This research strengthens encryption algorithms against quantum-based cryptographic assaults at the convergence of cybersecurity and quantum computing. Quantum computers, using quantum parallelism and quantum algorithms like Shor's and Grover's, threaten the security of widely used cryptographic systems like RSA and ECC, which use integer factorization and discrete logarithms. Post-quantum cryptography (PQC) is needed because quantum assaults can break classical encryption systems. This work emphasizes the need for quantum-resistant encryption techniques by examining the theoretical and practical effects of quantum computing on cryptography. It discusses quantum-safe algorithms like lattice-based, hash-based, and code-based cryptography that could replace or improve encryption standards. The research also examines quantum key distribution (QKD), a new secure communication method that uses quantum physics to share keys. The computational expense, implementation difficulty, and cybersecurity infrastructure integration difficulties of switching to quantum-resistant encryption methods are also examined. Quantum computing is evolving, hence safe encryption algorithms that can withstand quantum-based cryptographic assaults are needed. This paper concludes by emphasizing the need to standardize quantum-resistant algorithms, accelerate quantum cryptography research, and promote international collaboration to protect sensitive data from quantum threats. Future communication systems will depend on post-quantum cryptography to protect digital data. © 2025 IEEE.

关键词： Elliptic Curve Cryptography (ECC) Grover's Algorithm Post-Quantum Cryptography (PQC) Quantum-Based Cryptographic Attacks Quantum-Resistant Encryption RSA Encryption Shor's Algorithm

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Video Based Car Parking Management and Monitoring Using computer Vision and Machine Learning

Video Based Car Parking Management and Monitoring Using Comp...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Bulla Sujitha Anitha Ponraj Chakkarapani V Sriram Parabrahmachari T. V. Hyma Lakshmi T. Annamani Department of ECE V. R. Siddhartha Engineering College Vijayawada AP India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Kalasalingam University Krishnankoil Tamil Nadu India School of Electrical and Electronics Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of CSE Guru Nanak institutions Technical Campus Ibrahimpatnam Telangana India Department of ECE S.R.K.R. Engineering College Bhimavaram A.P India Department of ECE Anurag University Hyderabad Telangana India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Each day, countless individuals around the globe are affected by the extensive and complex problem of traffic congestion. This raising challenge is becoming increasingly substantial across the country, driven by factors such as rising population numbers, speedy urban development, insufficient infrastructure, and changing socioeconomic requirements. This paper depicts a novel global video-based parking approach designed to boost the supervising and management of parking spaces using real-time aerial statistics. Utilizing advanced image processing and computer vision procedures, the system essentially detects and examines the occupancy position of parking slots. In our realization, we assessed a total of 48 parking spaces, positively identifying 7 as unused while 41 were occupied. The application of Python, OpenCV, and machine learning assisted real-time monitoring, allowing precise counting and expose of parking availability. The procedure contained critical procedures, including Gaussian blur filtering to minimize noise and improve edge detection, followed by binary thresholding to describe vehicles and parking spots from the background. Furthermore, a median filter was utilized to boost the clarity of vehicle contours. These image enhancement methods significantly expanded detection accuracy and facilitated precise recognition of the Region of Interest (ROI). The results show the system's ability to provide reliable, real-time data for dynamic parking administration, thereby focusing the challenges of urban parking.

关键词： computer vision Thresholding (Imaging) Accuracy Filtering Streaming media Real-time systems Car parking Vehicle dynamics Monitoring Traffic congestion

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Design and Development of a Microcontroller Based Solar PV Cooker

Design and Development of a Microcontroller Based Solar PV C...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Anita Morake Ravi Samikannu L. Akila R. Karpagam R. Rohini Abid Yahya Department of Electrical and Communications Systems Engineering Botswana International University of Science and Technology Palapye Botswana Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Electrical and Electronics Engineering Periyar Maniammai Institute of Science and Technology Thanjavur India Department of Electrical and Electronics Engineering Easwari Engineering College Ramapuram India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Tiruchengode India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

This study proposes an innovative approach to improve solar cooking systems by combining microcontroller technology and deep-learning techniques. The Solar-PV Microcontroller-based Cooker concept aims to provide a sustainable and eco-friendly cooking method by harnessing the sun's power. This innovative approach addresses global energy conservation and access to clean cooking options, particularly in areas where conventional methods contribute to deforestation and indoor air pollution. The system includes inputs for cooking duration and temperature monitoring, enhancing solar energy efficiency and user experience. The study investigates how deep learning algorithms can be incorporated into the microcontroller-based solar cooker system to enhance functionality and versatility. Cooking settings can be automatically adjusted to maximize energy consumption and cooking results by analyzing real-time data from sensors and human inputs. The hybrid approach combines the best features of deep learning with microcontroller technology, demonstrating the significance of renewable energy in a cleaner, more efficient future. The Solar-Pv cooker demonstrates the possibilities of fusing culinary technology with computer algorithms, showcasing the potential of renewable energy in a cleaner, more efficient future.

关键词： Temperature sensors Deep learning Temperature measurement Renewable energy sources Microcontrollers Solar energy Ubiquitous computing User experience Sensors Temperature control

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Deep Learning Applications in Microscopy and Holography for near-field Signal Processing

Deep Learning Applications in Microscopy and Holography for ...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Milad Mohseni K T Thilagham Aravinda K B Santhosh Kumar Amandeep Nagpal B. T. Geetha Department of computer and engineering Islamic Azad University Tabriz Branch Tabriz Iran Metallurgical Engineering Government College of Engineering. salem Tamilnadu Department of Electronics and Communication Engineering New Horizon College of Engineering Bangalore Institute of Aeronautical Engineering Dundigal Hyderabad Lovely Professional University Phagwara Department of ECE Saveetha School of Engineering SIMATS TamilNadu India

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

Focusing on near-field signal processing, we provide a novel and comprehensive strategy for improving picture quality, obtaining super-resolution, and performing quantitative analysis in the disciplines of microscopy and holography. Three deep learning algorithms—Enhanced ImageNet (EINet), HoloReconGAN, and QuantSegNet—are combined in this method to maximize their potential. In order to improve images, super-resolve them, and analyze them quantitatively, many algorithms have been developed. In order to better comprehend each method, mathematical equations are provided to describe the main steps involved. With the use of convolutional neural networks, noise is reduced and finer features are brought into focus with the help of the Enhanced ImageNet (EINet) method. To accomplish super-resolution and 3D reconstruction from holographic data, HoloReconGAN combines generative adversarial networks (GANs) with variational autoencoders (VAEs). Label-free segmentation and quantitative analysis of structures in microscopy and holography pictures are the focus of QuantSegNet, a semantic segmentation network. Our suggested technique has been shown to outperform six established methods over a broad variety of assessment criteria. Image quality, noise suppression, feature recognition, computational efficiency, and resilience are just few of the areas where it shines. It's also faster, uses less memory, is more accurate, easier to use, cheaper, and faster. Applications in several scientific fields might be facilitated, and the area of microscopy and holography as a whole could see significant advancements as a result.

关键词： Deep learning Technological innovation Three-dimensional displays Statistical analysis Microscopy Superresolution Noise reduction

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