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2023 International Young Engineers Forum in Electrical and computer engineering, YEF-ECE 2023

作者： Santos, Ana Isabel Pedro Oliveira, Joao NOVA School of Science and Technology Department of Electrical and Computer Engineering Caparica Portugal Centre of Technology and Systems CTS-UNINOVA Department of Electrical and Computer Engineering Caparica Portugal

ISBN: (纸本)9798350300420

The paper presents the implementation of a Switched Capacitor Power Amplifier (SCPA) to be integrated into a Narrowband Internet of Things (NB-IoT) Transceiver. The SCPA is designed to operate at a frequency of 0.9GHz and aiming the maximum output power allowed by the standard of 23dBm. All the blocks within the SCPA were developed using RF components of a standard 130nm CMOS process, with a 1.2V/2.4V supply voltage. Results show that the SCPA is able to produce a maximum output power of 15.61dBm with a maximum Power-Added Efficiency (PAE) of 26.52% and a Total Harmonic Distortion (THD) of 0.68%. The measured HD2 and HD3 are-70.23dBc and-43.41dBc, respectively. Additional Process, Voltage and Temperature (PVT) corners and Monte Carlo simulations indicate the SCPA operates properly when subject to different conditions and mismatches. Besides the SCPA, a 16 QAM modulation stage is designed in VerilogA in order to evaluate the performance of the RF PA when a sequence of symbols is transmitted. © 2023 IEEE.

关键词： Power amplifiers

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Optimized Resource Allocation for IoT-D2D Communication Using WSN

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computer systems science & engineering 2023年第5期45卷 1789-1804页

作者： S.Arun Mozhi Selvi Roobaea Alroobaea Saeed Rubaiee Abdulkader S.Hanbazazah Department of Computer Science and Engineering Holy Cross Engineering CollegeVagaikulam628851Tamil NaduIndia Department of Computer Sceience College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21974Saudi Arabia Department of Industrial and Systems Engineering College of EngineeringUniversity of JeddahJeddah21959Saudi Arabia

The need for a strong system to access radio resources demands a change in operating frequency in wireless networks as a part of Radio Resource Management(RRM).In the fifth-generation(5G)wireless networks,the capacity of the system is expected to be enhanced by Device-to-Device(D2D)*** cooperation and Resources Allocation(RA)in the development of Internet of Things(IoT)enabled 5G wireless networks are investigated in this *** radio RA methods for D2D communication while not affecting any Mobile Users’(MU)communication is the main challenge of this *** performance goals such as practising equality in the rates of user data,increasing Network Throughput(NT),and reducing End-to-End Delay(EED)are achieved by *** study undertaken on optimising performance for various wireless networks is focused on in this research *** a polynomialtime Proportional Fair Resource Allocation Method(PFRAM),which considers the MU’s rate requirements,is the prime objective of this *** Resource Allocation Method(RAM)can be used by the proposed method for MU,and the time and differing location channel conditions are the factors to be adapted *** more than one resource block is allowed by our PFRAM to a D2D *** automatic maintenance of battery-less IoT wireless devices’energy level is done potentially using an Extensible Energy Management System(EEMS).Finally,the device’s Node Transmission Power(NTP)can be managed using an Energy-Saving Algorithm(ESA)designed in this work for Node Uplink Data Transmission(NUDT).The trade-off between the Packet Loss Rate(PLR)and NTP is balanced by the *** cost of NUDT’s average Energy Consumption(EC)is reduced by locating the optical *** order to free much bandwidth for wireless information,NUDT conserves the harvested energy for minimising Radio Frequency(RF)Energy Transmission(ET).MATLAB simulations are used to assess the proposed *** IoT device’s NTP is managed using

关键词： IoT energy transmission WSN mobility resource allocation

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Secure Channel Creation for Task Delivery in Vehicular Cloud Computing to Avoid Denial of Service (DoS) 3

Secure Channel Creation for Task Delivery in Vehicular Cloud...

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3rd International Conference on Automation, Computing and Renewable systems, ICACRS 2024

作者： Meenambika, A. Pavalarajan, S. Lavanya, N. Anu Jerom, B. Ajith Divya, S. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Data Science PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9798331532420

Vehicular Cloud Computing (VCC) offers a promising platform for supporting various automotive applications and services. However, its distributed and dynamic nature makes it susceptible to Denial-of-Service (DoS) attacks, particularly during channel establishment. This research proposes a comprehensive approach to mitigate these risks by integrating a suite of security mechanisms. These include rate-limiting, resource management, traffic analysis, access control, authentication, encryption, intrusion detection, behavioral analysis, and blacklisting/whitelisting techniques. By incorporating these measures, the proposed approach enhances the security and reliability of VCC, ensuring the availability of critical automotive services and protecting vehicles from the impact of DoS attacks. © 2024 IEEE.

关键词： Cloud platforms

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Hybrid Deep Learning Models with Genetic Algorithm Optimization for Enhanced Kidney Tumor Detection 5

Hybrid Deep Learning Models with Genetic Algorithm Optimizat...

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5th International Conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Rajagopal, K. Kumari, V. Sheeja Saraswathy, S. Kumar, V. Suresh Ponmaniraj, S. Deepa, A. Saveetha Institute of Medical and Technical Sciences Simats University Computer Science and Engineering Chennai Thandalam India Saveetha Institute of Medical and Technical Sciences Simats University Department of Computer Science and Engineering Chennai Thandalam India Sri Sairam Engineering College Department of Computer Science and Business Systems Chennai India Institute of Ece Saveetha Institute of Medical and Technical Sciences Simats University Department of Computational Intelligence Chennai Thandalam India Saveetha Engineering College Department of Computer Science and Engineering Chennai Thandalam India

ISBN: (纸本)9798350379945

Accurate and timely segmentation of kidney tumors from medical images is crucial for effective treatment and prognosis. This research study proposes a novel hybrid deep learning framework incorporating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for enhanced kidney tumor segmentation. To optimize model performance, Genetic Algorithms (GA) are employed to fine-tune hyperparameters and network architecture. The proposed method effectively captures both local and global image features, leading to improved segmentation accuracy compared to existing techniques. Experimental results on a publicly available dataset demonstrate the superiority of our approach in terms of recall, F1-score, and precision. This research contributes to the advancement of computer-aided diagnosis for kidney cancer. © 2024 IEEE.

关键词： Convolutional neural networks

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Human Age Prediction Based on Brain MRI Using Density-Based Regression

Human Age Prediction Based on Brain MRI Using Density-Based ...

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2023 IEEE International Conference on Industrial Technology, ICIT 2023

作者： Bregu, Ornela Zamzami, Nuha Bouguila, Nizar Concordia Institute for Information Systems Engineering Concordia University Montreal Canada College of Computer Science and Engineering University of Jeddah Department of Computer Science and Ai Jeddah Saudi Arabia

ISBN: (纸本)9798350336504

In this paper, we have applied a density-based regression technique to predict the age of a human brain based on Magnetic Resonance Images. We propose four different multivariate discrete distributions to accommodate different mean-covariance and correlation structures, given the challenging nature of medical datasets. Then, we use a novel Minorization-Maximization framework to address the issue of high dimensional optimization. Finally, to handle the time and memory requirements of processing large amounts of high-resolution images, we have adopted a real-time framework, where the learning behavior is gradually improved as new data become available. © 2023 IEEE.

关键词： Forecasting

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An Ensembled Real-Time Hand-Gesture Recognition using CNN 15

An Ensembled Real-Time Hand-Gesture Recognition using CNN

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kavitha, M.N. Saranya, S.S. Prasad, M. Kaviyarasu, S. Ragunath, N. Rahul, P. Kongu Engineering College Department of Computer Science and Design Tamilnadu Erode India PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Tamilnadu Coimbatore India Controller of Examinations Tamilnadu Namakkal India Kongu Engineering College Bachelor of Science - Information Systems Tamilnadu Erode India

ISBN: (纸本)9798350370249

Hand sign recognition is a vital technology in the human-computer interaction, enabling individuals to communicate with machines naturally and effectively. An innovative approach for real-time hand sign identification with the help of CNN and OpenCV is introduced with the fusion of computer vision and deep learning that can accurately interpret and classify an extensive range of hand signs and gestures. This research contributes significantly to the fields of computer vision and human-computer interaction, offering a practical and efficient solution for hand sign recognition. The combination of CNN and OpenCV presents a promising avenue for enhancing accessibility and communication, especially in environments where verbal communication is limited or non-existent. The model is trained with multiple data so that the system can recognize the hand gestures more precisely. Pre-trained architectures like ResNet and MobileNet are combined with the CNN model using ensemble learning and the performance is improved when compared to all the three CNN architectures individually. The ensemble model provides better accuracy of 96 %. The potential applications of this technology are vast, from assisting the hearing-impaired in understanding sign language to more immersive and intuitive interactions. Overall, the approach holds the promise of bridging the gap between human gestures and machine understanding, opening new doors for meaningful interactions between individuals and intelligent systems. © 2024 IEEE.

关键词： Intelligent systems

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Revolutionizing Brain Stroke Classification with Neuro-Xception and ELM Integration in Deep Learning 2

Revolutionizing Brain Stroke Classification with Neuro-Xcept...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Jagadeesan, S. Ramalingam, Venkadeshan Abinash, P. Ilayaraja, V. Masood, Jafar Ali Ibrahim Syed Tamil Priya, D. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India UTAS-Shinas branch Department of Computing and Information Sciences Oman School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331542573

Stroke and related neurological disorders are difficult to classify accurately, hence new computational approaches are needed to enhance diagnosis. In order to transform the categorization of brain strokes, this study presents Neuro-Xception, a novel framework that combines the Xception architecture with Extreme Learning Machines (ELM). The rapid learning speed and enhanced generalisation capability of ELMs are combined with the feature extraction expertise of Xception to create Neuro-Xception. Through training on large neuroimaging datasets, Neuro-Xception learns complex patterns that are essential for differentiating between different types and intensities of stroke. By means of extensive testing on various datasets, Neuro-Xception exhibits notable gains in performance compared to traditional techniques, indicating its potential to revolutionise stroke detection in medical environments. Furthermore, Neuro-Xception's scalability and flexibility are enhanced by the combination of Xception and ELMs, which makes it a viable tool for real-time clinical decision support systems and personalised medicine. This study highlights how merging Xception and ELMs can revolutionise neurological healthcare and this development could lead to more accurate and efficient diagnostic techniques for treating stroke patients. Neuro-Xception offers a potential path for increasing diagnosis accuracy and expediting timely medical intervention. It is a major step forward in the use of artificial intelligence to enhance patient outcomes and advance the profession of stroke care. © 2024 IEEE.

关键词： Personalized medicine

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Type 2 Diabetes Mellitus Monitoring Through Non-invasive IoT-Based System 18th

Type 2 Diabetes Mellitus Monitoring Through Non-invasive IoT...

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18th EAI International Conference on Body Area Networks, BODYNETS 2024

作者： Hussain, Aamir ur Rehman, Attique Hussain, Altaf Li, Qimeng Gravina, Raffaele Fortino, Giancarlo Department of Computer Science Muhammad Nawaz Sharif Agriculture University Multan Multan Pakistan Department of Computer Engineering Modeling Electronics and Systems University of Calabria CS Rende87036 Italy

ISBN: (纸本)9783031725234

Type 2 diabetes (T2d), also known as diabetes mellitus or adult-onset diabetes, is a chronic condition that disrupts the body’s insulin regulation, a crucial hormone for controlling blood glucose levels. Glucose, a form of sugar, is the primary energy source for cells in the human bloodstream. Regulating glucose metabolism is vital for the body’s proper functioning. The primary challenges in managing T2d include blood sampling, timely analysis, risk of skin infections, and blood-borne diseases. This paper presents the design of an IoT-based, non-invasive system for monitoring blood glucose levels. Our system utilizes medical sensors to gather data, which is then cleaned and transformed using data mining algorithms. Subsequent analysis compares the accuracy and sensitivity of this data with results from pathology labs and glucometers. The system generates specific warning notifications and provides standard medical advice in response to these alerts, aiding T2d patients in painlessly and effortlessly monitoring and controlling their blood sugar levels. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Glucose sensors

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Deduplicating and Ranking Solution Programs for Suggesting Reference Solutions 4

Deduplicating and Ranking Solution Programs for Suggesting R...

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4th Asia Service sciences and Software engineering Conference, ASSE 2023

作者： Shirafuji, Atsushi Watanobe, Yutaka Department of Computer and Information Systems University of Aizu Aizu-Wakamatsu Japan Department of Computer Science and Engineering University of Aizu Aizu-Wakamatsu Japan

ISBN: (纸本)9798400708534

Referring to solution programs written by other users is helpful for learners in programming education. However, current online judge systems just list all solution programs submitted by users for references, and the programs are sorted based on the submission date and time, execution time, or user rating, ignoring to what extent the programs can be helpful to be referenced. In addition, users struggle to refer to a variety of solution approaches since there are too many duplicated and near-duplicated programs. To motivate learners to refer to various solutions to learn better solution approaches, in this paper, we propose an approach to deduplicate and rank common solution programs in each programming problem. Inspired by the nature that the many-duplicated program adopts a more common approach and can be a general reference, we remove the near-duplicated solution programs and rank the unique programs based on the duplicate count. The experiments on the solution programs submitted to a real-world online judge system demonstrate that the number of programs is reduced by 60.20%, whereas the baseline only reduces by 29.59% after the deduplication, meaning that users only need to refer to 39.80% of programs on average. Furthermore, our analysis shows that top-10 ranked programs cover 29.95% of programs on average, indicating that users can grasp 29.95% of solution approaches by referring to only 10 programs. The proposed approach shows the potential of reducing the learners' burden of referring to too many solutions and motivating them to learn a variety of solution approaches. © 2023 ACM.

关键词： Online systems

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Image Caption Generation Related to Object Detection and Colour Recognition Using Transformer-Decoder 4

Image Caption Generation Related to Object Detection and Col...

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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Kamangar, Zainab Umair Shaikh, Ghulam Mutjaba Hassan, Saif Mughal, Nimra Kamangar, Umair Ayaz Sukkur Iba University Craib Computer Science Department Sukkur Pakistan Sukkur Iba University Computer Systems Engineering Department Sukkur Pakistan

ISBN: (纸本)9798350335316

The dependence on digital images is increasing in different fields. i.e, education, business, medicine, or defense, as they are shifting towards the online paradigm. So, there is a dire need for computers and other similar machines to interpret information related to these images and help the users understand the meaning of it. This has been achieved with the help of automatic Image captioning using different prediction models, such as machine learning and deep learning models. However, the problem with the traditional models, especially machine learning models, is that they may not generate a caption that accurately represents that Image. Although deep learning methods are better for generating captions of an image, it is still an open research area that requires a lot of work. Therefore, a model proposed in this research uses transformers with the help of attention layers to encode and decode the image token. Finally, it generates the image caption by identifying the objects along with their colours. The fliker8k and Conceptual Captions datasets are used to train this model, which contains images and captions. The fliker8k contains 8,092 images, each with five captions, and Conceptual Captions contains more than 3 million images, each with one caption. The contribution of this presented work is that it can be utilized by different companies, which require the interpretation of diverse images automatically and the naming of the images to describe some scenario or descriptions related to the images. In the future, the accuracy can be increased by increasing the number of images and captions or incorporating different deep-learning techniques. © 2023 IEEE.

关键词： Object detection

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