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Joint Spectrum Sensing and Resource Allocation for OFDMA-based Underwater Acoustic Communications

IEEE Communications Letters 2025年第6期29卷 1300-1304页

作者： Kim, Minwoo Choi, Youngchol Kim, Yeongjun Seo, Eojin Yang, Hyun Jong Dept. of Electrical Engineering Pohang Korea Republic of Daejeon Korea Republic of Samsung Electronics System LSI Modem Development Team Device Solutions Hwaseong Korea Republic of Seoul National University Dept. of Electrical and Computer Engineering Seoul Korea Republic of

Underwater acoustic (UWA) communications generally rely on cognitive radio (CR)-based ad-hoc networks due to challenges such as long propagation delay, limited channel resources, and high attenuation. To address the constraints of limited frequency resources, UWA communications have recently incorporated orthogonal frequency division multiple access (OFDMA), significantly enhancing spectral efficiency (SE) through multiplexing gains. Still, the low propagation speed of UWA signals, combined with the dynamic underwater environment, creates asynchrony in multiple access scenarios. This causes inaccurate spectrum sensing as inter-carrier interference (ICI) increases, which leads to difficulties in resource allocation. As efficient resource allocation is essential for achieving high-quality communication in OFDMA-based CR networks, these challenges degrade communication reliability in UWA systems. To resolve the issue, we propose an end-to-end sensing and resource optimization method using deep reinforcement learning (DRL) in an OFDMA-based UWA-CR network. Through extensive simulations, we confirm that the proposed method is superior to baseline schemes, outperforming other methods by 42.9 % in SE and 4.4 % in communication success rate. © 1997-2012 IEEE.

关键词： Deep reinforcement learning

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Automated Bengali Sign Language Character Classification with Deep Learning Techniques 15

Automated Bengali Sign Language Character Classification wit...

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

作者： Akter, Tahmina Mahmud, Tanjim Barua, Tikle Naher, Sultana Rokeya Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh University of Information Technology and Sciences Dept. of Computer Science and Engineering Dhaka Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

Recognition of Bengali sign language characters is crucial for facilitating communication for the deaf and hard-of-hearing population in Bengali-speaking regions, which encompass approximately 430 million people worldwide. Despite the significant number of individuals requiring this support, research on Bengali sign language character recognition remains underdeveloped. This article presents a novel approach to categorize Bengali sign language characters using the Ishara-Lipi dataset, based on convolutional neural networks (CNNs) and pretrained models. We evaluated our approach using metrics such as accuracy, precision, recall, F1-score, and confusion matrices. Our findings indicate that the CNN model achieved the highest performance with an accuracy of 98%, followed by VGG19 with 94% and ResNet variants achieving around 88%. The proposed model demonstrates robust and efficient classification capabilities, significantly bridging the gap in existing literature. This study holds substantial promise for enhancing assistive technology, thereby improving social inclusion and quality of life for Bengalispeaking deaf and hard-of-hearing individuals. © 2024 IEEE.

关键词： Bengali sign language CNNs Deaf and hard-of-hearing community Image classification

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IoT Based Poultry Farm Smart Management System

IoT Based Poultry Farm Smart Management System

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Krishnamurthy, K.T. Managuli, Manjunath Malipatil, Sushma Bagyalakshmi, K. Salake, Sudha V. Kadalagi, Pankaja S. Patil, Sheetal B. Bms College of Engineering Vtu Dept. of Electronics & Instrumentation Belagavi India Kls Gogte Institute of Technology Vtu Dept. of Electronics & Communication Belagavi India Amc College of Engineering Vtu Dept. of Computer Science Belagavi India Sri Ranganathar Institute of Engineering & Technology Dept. of Electrical & Electronics Coimbatore India Kls Gogte Institute of Technology Vtu Dept. of Computer Science Belagavi India Sgb Institute of Technology Vtu Dept. of Computer Science Belagavi India

ISBN: (纸本)9798350359688

One of the greatest significant and fastest-growing financial sectors of India's agriculture industry at the moment is poultry. Chicken output has been steadily rising globally due to superior manufacturing processes and uniform farming management. Automation is essential in today's environment, and the Internet of Things (IoT) is a rapidly developing idea. Traditional systems are being transformed into automated systems. The project's main goal is to automate chicken farms by utilizing Internet of Things technology to carry out several management-related tasks. This study examines how a poultry farm's environmental factors, similar to humidity, temperature, and ammonia gas, are automatically observed and managed to promote chicken growth. The water position is also controlled and covered with the help of a detector module. All detector values are acquired by connecting all the detector modules to the microcontroller, and also, using the WIFI module, they will be uploaded to the webpage. Being in the care of the loud ranch can get the inner eco-friendly condition over a PC using the internet. This system will control temperature, moisture, ammonia gas and water situation with the help of a cooling addict, exhaust addict, ventilation window and DC motor without any moral interface. Grounded on the threshold values, it'll switch on the bias. therefore, this system design provides automated flesh, reduces the pool, and increases the product of healthy cravens. © 2024 IEEE.

关键词： DC motors

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Advancements in Animal Tracking: Assessing Deep Learning Algorithms 15

Advancements in Animal Tracking: Assessing Deep Learning Alg...

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

作者： Akter, Tahmina Majumder, Avishek Mahmud, Tanjim Habib, Israt Binteh Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh International Islamic University Chittagong Dept. of Computer Science and Engineering Chittagong Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

In the rapidly advancing field of computer vision, object detection has become crucial for various applications, including animal tracking, face detection, and surveillance systems. This study investigates the efficacy of contemporary object detection methodologies by evaluating the performance of the You Only Look Once (YOLO) models and TensorFlow Model Zoo architectures for animal tracking. YOLO models, known for their ability to process entire images in real-time and predict bounding boxes and class probabilities simultaneously, offer significant advantages over traditional methods such as Convolutional Neural Networks (CNNs) and Fast R-CNNs. This paper compares the performance of YOLOv5 and YOLOv7, alongside TensorFlow-based models like Faster R-CNN ResNetv152 and SSD ResNet101, using a dataset of animal images. Our findings reveal that YOLOv5 outperforms other models with a mean average precision (mAP) of 9 7.5%, demonstrating superior accuracy and efficiency in object detection tasks. YOLOv7 also shows strong performance with an mAP of 96.7%, while TensorFlow Model Zoo's Faster R-CNN and SSD models lag behind with mAPs of 81.9% and 81.6%, respectively. The results highlight the significant advancements in deep learning and object detection algorithms, particularly the advantages of YOLO's architecture in handling complex detection tasks in real-world scenarios. © 2024 IEEE.

关键词： Convolutional neural networks

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Recognition of Fingerprint Images using CNN for Cybercrime Detection System 2

Recognition of Fingerprint Images using CNN for Cybercrime D...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Kavitha, T. Patil, Bhimaraya Saraswathi, S. Rajarajeswari, S. Patil, Anita New Horizon College of engineering Dept. of Computer Engineering Bangalore India Sri Sivasubramaniya Nadar College of Engineering Dept. of Computer Science Engineering Chennai India Vellore Institute of Technology School of computer science and engineering Vellore India City Engineering College Dept. of Information Science Bengaluru India

ISBN: (纸本)9798350372892

Fingerprint identification plays a crucial role in crime detection, but manual analysis is time-consuming and error-prone. This research aims to develop an automated system for accurate and efficient fingerprint matching using machine learning. The proposed approach involves image acquisition, preprocessing, feature extraction, and classification. Fingerprint images are enhanced, binarized, and segmented. Minutiae (ridge endings and bifurcations) are extracted as features. A convolutional neural network (CNN) is trained on a dataset of fingerprint images for classification. By comparing algorithms, Random Forest achieved over 8 1 % accuracy, outperforming Decision Trees (nearly 75% accuracy). The trained CNN model can accurately match fingerprints from crime scenes with potential suspects, providing reliable evidence. The developed system offers law enforcement agencies an automated and accurate fingerprint identification tool, improving crime-solving efficiency and reducing manual effort. The proposed approach showcases the potential of machine learning in forensic applications. © 2024 IEEE.

关键词： Random forests

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Comparative analysis of Machine Learning Techniques for the Detection of Heart Disease

Comparative analysis of Machine Learning Techniques for the ...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adeniyi, Jide Kehinde Adeniyi, Tunde Taiwo Adeniyi, Emmanuel Abidemi Abiodun, Moses Kazeem Awotunde, Joseph Bamidele Landmark University SDG 9: Industry Innovation and Infrastructure Dept. of Computer Science Nigeria University of Ilorin Dept. of Computer Science Nigeria Bowen University Dept. of Computer Science Iwo Nigeria Landmark University SDG 10: Reduced Inequality Dept. of Computer Science Nigeria

ISBN: (纸本)9798350358155

The World Health Organization stated that an alarming 32 percent of worldwide fatalities was from heart related challenges. This percentage is even more escalated in poor and moderate countries. Most heart related issues can be ameliorated or managed if they are detected on time. Hence, with the advent of machine learning techniques, prediction of heart related issues can be made. Several machine learning methods have been proposed for heart related issues prediction, and each with varying performance level. A few comparisons have been made amongst these machine learning techniques with little details. In this study, a detailed comparison of four machine learning techniques was considered. The machine learning methods considered were selected based on their performance from literatures. The examined methods include the decision tree, random forest, support vector machines, and logistic regression. Prior to training and testing, the dataset was preprocessed by encoding the categorical data and scaling the dataset features. The outcome of the testing showed that the accuracy was obtained for logistic regression and support vector machine;with both having an accuracy of 0.882. © 2024 IEEE.

关键词： Adversarial machine learning

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Reducing the Computational Complexity of Learning with Random Convolutional Features 48

Reducing the Computational Complexity of Learning with Rando...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Omidi, M.A. Seyfe, B. Valaee, S. Dept. of Electrical Engineering Tehran Iran University of Toronto Dept. of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9781728163277

In the last decade, there has been a surge of research interest in feature extraction using random sampling. These techniques are fast and scalable and, at the same time, have practical favorability in low-sample size and high-dimensional training data. Convolutional Kitchen Sinks-based methods are promising random feature extractors for time series data. Since these methods are data-independent, many of the extracted features are redundant. To address this problem, we propose a simple and efficient feature selection method based on knee/elbow detection in the curve of ordered coefficients in linear regression. Our empirical studies show that without significant loss in accuracy, the proposed feature selector, on average, prunes more than 84 percent of randomly generated features. © 2023 IEEE.

关键词： Feature selection random convolution the curvature of the discrete curves time series data

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A Pulse Width Modulation-Based Spike Generator to Eliminate Timing Errors in Spiking Neural Networks

A Pulse Width Modulation-Based Spike Generator to Eliminate ...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Jo, Jungkook Sim, Keonhee Sim, Sangwoong Jun, Jaehoon Inha University Dept. of electrical and computer engineering and program in semiconductor convergence Incheon Korea Republic of Inha University Dept. of electrical engineering Incheon Korea Republic of

ISBN: (纸本)9798331510756

This paper proposes a novel spike generator for processing in memory (PIM) technology. Most of the electronics today utilize a von Neumann architecture. The von Neumann architecture suffers from the separation of memory and processor. This architecture delays data transfer between memory and processor. To overcome the issues, we utilize the spiking neural network (SNN) that combines memory and processor. SNN can be classified into voltage-based, current-based, and time-based architectures, each with its own pros and cons. Time-based SNN suffers from timing issues. To address the timing sensitivity of time-based neuron SNN, this paper proposes a PWM-based SNN. The PWM-based SNN utilizes pulse width-based logic to overcome timing sensitivity. © 2025 IEEE.

关键词： Pulse width modulation

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Financial Viability of PV Grid for an Intelligent System of Residential Buildings in sub-urban Areas of Punjab, Pakistan 16

Financial Viability of PV Grid for an Intelligent System of ...

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16th Seminar on Power Electronics and Control, SEPOC 2024

作者： Shehzad, Moazzam Jamal, Nasir Muqeet, Hafiz Abdul Qureshi, Noman Sohaib Lincoln University College Dept. of Electrical Engineering Petaling Jaya Malaysia Punjab Tianjin University of Technology Dept. of Electrical Engineering Lahore Pakistan University of Engineering and Technology RCET Campus Dept. of Computer Science Lahore Pakistan

ISBN: (纸本)9798350379419

With fuel prices rising and environmental concerns intensifying, the financial burden on consumers and businesses has shown an upward trajectory, prompting a search for sustainable alternatives sources. This study investigates the financial feasibility of installing a grid-tied solar photovoltaic (PV) system in residential buildings in suburban areas of Punjab, Pakistan. The research uses a complete intelligent financial model to assess the feasibility of a grid-connected solar PV system, incorporating parameters such as solar irradiance levels, system size, capital costs, operating expenses, energy rates, its incentives, and payback periods in a systematic computational optimization methodology. The shift to solar photovoltaic systems provides an affordable way to lower energy bills that can also lessen the dependency on fossil fuels. Additionally, solar PV systems help significantly to reduce greenhouse gas emissions and environmental damages by utilizing the clean energy of the sun. This is in line with worldwide efforts to battle climate change and protect the planet from environmental concerns. This research uses RET Screen software to evaluate the financial feasibility with GHG reduction analysis as well. The study's findings are useful for homeowners, policymakers, and stakeholders interested in encouraging renewable energy adoption and sustainable development in Punjab, Pakistan's suburban region. © 2024 IEEE.

关键词： Computational Optimization Feed-In-Tariff Net Energy Metering Net Present Value Net-Metering

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Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration 1

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

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1st International Conference on Advances in computer science, electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Saklani, Sumit Manchanda, Mahesh Sharma, Raman Singh, Devendra Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Application Dehradun India

ISBN: (纸本)9798331518578

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments. © 2025 IEEE.

关键词： Decision making

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