检索结果-内蒙古大学图书馆

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Makanyadevi, K. Avinash, M.V. Chandhrakiran, S.V. Kavitha, S. Bhalaram Krishna, S.A. Dhiyanesh, S. Department of Computer Science and Engineering M.Kumarasamy College of Engineering Thalavapalayam Tamilnadu Karur India Department of Electronics and Communication Engineering M.Kumarasamy College of Engineering Thalavapalayam Tamilnadu Karur India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

This project proposes a wearable assistive system aimed at improving navigation for visually impaired individuals. The system integrates AI-powered smart glasses and specially designed shoes, working together to enhance the user’s ability to detect and avoid obstacles in their surroundings. Central to the system is the YOLO v8 (You Only Look Once) algorithm, a state-of-the-art deep learning model that enables real-time object recognition. The smart glasses use this model to detect and classify objects such as people, vehicles, and potential obstacles, providing immediate feedback to the user regarding their environment. In addition to the object recognition capabilities of the glasses, the system employs ultrasonic sensors in both the glasses and shoes to detect nearby objects or obstacles. These sensors measure the distance to obstacles and provide critical information on the proximity of objects. When an obstacle is detected within a dangerous range, the system alerts the user using a buzzer and a vibration sensor, enhancing their spatial awareness. The smart glasses are responsible for detecting forward-facing objects, while the shoes focus on ground-level obstacles, such as uneven surfaces or stairs, which are crucial for safe navigation. The system is controlled by an Arduino Uno microcontroller that processes data from the sensors and manages the alerts in real-time. Designed for cost-effectiveness, energy efficiency, and ease of maintenance, this wearable assistive system offers an innovative solution for visually impaired individuals, providing them with greater independence and safety in everyday navigation. Ultimately, this project aims to improve the quality of life for users by fostering increased mobility and confidence in navigating their environment. © 2025 IEEE.

关键词： Ultrasonic sensors

来源：评论

学校读者我要写书评

暂无评论

Optimization of Memory Usage in High-Speed Cameras using FPGA 3

Optimization of Memory Usage in High-Speed Cameras using FPG...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavin Kumar, K. Kumar, Neelam Sanjeev Senthilkumar, C. Velmurugan, V. Sangar, G. Chandrasekaran, Gokul Kongu Engineering College Department of Electronics and Communication Engineering Erode638060 India Vadapalani Chennai India Simats University Simats Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai602 105 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Chennai India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331509675

High-speed cameras generate large amounts of data, making memory optimization difficult for real-time processing. This project minimizes data size by converting RGBA (Red, Green, Blue, Alpha) images to RGB, eliminating the Alpha channel to reduce memory usage. The captured images are provided as input to Verilog code in hexadecimal format, with the conversion done by MATLAB. Bilinear Interpolation is applied to reduce the potential quantization errors during the conversion, using the values of four surrounding pixels to smooth the image and maintain quality. After processing, the image is reconstructed using MATLAB, ensuring the integrity of the output. By utilizing the parallel processing capabilities of FPGAs, this method ensures real-time performance while optimizing memory usage. It is ideal for high-speed imaging applications where both efficiency and image quality are crucial. © 2025 IEEE.

关键词： MATLAB

来源：评论

学校读者我要写书评

暂无评论

Marine Robot for Underwater Surveillance

Marine Robot for Underwater Surveillance

引用

2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Vishnu Gowda, H.P. Reddy, Deepa N Gowda, Tejas B Shrikala, R. Yogitha, H.N. Bms Institute of Technology and Management Student of Department of Electronics and Communication Engineering Bengaluru560064 India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Bengaluru560064 India Bms Institute of Technology and Management Student of Department of Computer Science Engineering Bengaluru560064 India

ISBN: (纸本)9798331529833

Underwater surveillance has become a crucial area of research, particularly for marine exploration and environmental monitoring, with marine robots playing a vital role. These robots, equipped with sensors and cameras, capture real-time data and images from underwater environments, reducing the need for human intervention in hazardous areas. However, a major challenge in this field is developing reliable communication networks, as underwater environments face limitations in bandwidth, range, and signal attenuation, hindering real-time data transmission. This paper presents a simple marine robot prototype based on the Raspberry Pi Model 4B, utilizing Electronic Speed Controllers (ESCs) and brushless motors for efficient movement. The robot's body is manually designed using 3D printing technology, with PETG (polyethylene terephthalate glycol) material chosen for its water resistance, durability, and buoyancy. The design ensures the robot's stability and functionality underwater, offering a cost-effective solution for marine robotics while underscoring the need for improved underwater communication technologies. © 2025 IEEE.

关键词： Marine communication

来源：评论

学校读者我要写书评

暂无评论

Denoising of MRI and Brain Tumor Classification using Local Binary Pattern and SVM Classifier 3

Denoising of MRI and Brain Tumor Classification using Local ...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavin Kumar, K. Kumar, Neelam Sanjeev Senthilkumar, C. Nanmaran, R. Sangar, G. Chandrasekaran, Gokul Kongu Engineering College Department of Electronics and Communication Engineering Erode India Vadapalani Chennai India Simats University Simats Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai602 105 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Chennai India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331509675

All data collection systems, including magnetic resonance imaging, are susceptible to noise. Modern high-field scanners can achieve high signal-to-noise ratios;however, noise becomes a significant issue that requires careful management when signals become extremely faint due to specific acquisition processes. The proposed scheme consists of steps which include denoising, feature extraction and classification. For denoising, the Non-Local Means filtering approach is used, and the performance of the denoising method is analyzed using PSNR values. The major objective here is to study texture classification methods to distinguish between various types of brain cancers, such as gliomas, meningiomas, and pituitary tumors. We then evaluate its performance on an additional dataset. The obtained photos serve both educational and evaluative purposes. The accuracy of two methods is computed and compared. The feature extraction performance is compared between two separate classifiers using standard deviation, variance, and mean as performance measures. Among the methods tested, SVM achieved the best accuracy rate of 96.67%. © 2025 IEEE.

关键词： KNN LBP MDC NLM SVM

来源：评论

学校读者我要写书评

暂无评论

Utilizing the Power of Residual and Attention Properties with Binary Focal Loss Optimization for Underwater Image Segmentation Using UNet Architecture 7th

Utilizing the Power of Residual and Attention Properties wit...

引用

7th International Conference on communication, Device and Networking, ICCDN 2024

作者： George, Geomol Anusuya, S. Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India

ISBN: (纸本)9789819764648

Recent developments in deep-sea exploration, environmental surveillance, and ocean research, accurate segmentation of underwater images is essential. This study pursues this goal by exploring underwater image segmentation from a deep learning perspective. It specifically looks at how well the Attention Residual UNet architecture, a more sophisticated version of the U-Net, works with the focal loss technique to achieve accuracy in this crucial task. Using attention mechanisms and residual connections, the Attention Residual UNet architecture, based on the U-Net framework, can capture fine details while maintaining contextual coherence. Model loss and accuracy measures are considered as this study carefully assesses the architecture’s performance. Notably, the model exhibits outstanding accuracy, obtaining 97.23% and 92.76% accuracy on the training and validation sets, respectively. The Jaccard coefficient further demonstrates the model’s efficiency, which measures the intersection between predicted and actual segments and has coefficients for the model and validation sets of 74.31% and 66.51%, respectively. The Mean Intersection over Union (MIoU) statistic, which boasts a value 91.24%, validates the model’s superiority. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Underwater imaging

来源：评论

学校读者我要写书评

暂无评论

Simultaneous Computation and communication Over MAC

引用

IEEE Transactions on Information Theory 2025年第5期71卷 3644-3665页

作者： Frey, Matthias Bjelakovic, Igor Gastpar, Michael C. Zhu, Jingge The University of Melbourne Department of Electrical and Electronic Engineering ParkvilleVIC3010 Australia Fraunhofer Heinrich Hertz Institute Berlin10587 Germany Technische Universität Berlin Department of Electrical Engineering and Computer Science Berlin10623 Germany École Polytechnique Fédérale de Lausanne School of Computer and Communication Sciences Lausanne1015 Switzerland

We study communication over a Gaussian multiple-access channel (MAC) with two types of transmitters: Digital transmitters hold a message from a discrete set that needs to be communicated to the receiver with vanishing error probability. Analog transmitters hold sequences of analog values. Some functions of these distributed values (but not the values themselves) need to be conveyed to the receiver, subject to a fidelity criterion such as mean squared error (MSE) or a certain maximum error with given confidence. For the case in which the computed function for the analog transmitters is a sum of values in [-1,1] , we derive inner and outer bounds for the tradeoff of digital and analog rates of communication under peak and average power constraints for digital transmitters and a peak power constraint for analog transmitters. We then extend the achievability result to a class of functions that includes all linear and some non-linear functions. This extended scheme works over fading channels as long as full channel state information is available at the transmitter. The practicality of our proposed communication scheme is shown in channel simulations that use a version of the scheme based on low density parity check (LDPC) coding. We evaluate the system performance for different block lengths and Gaussian as well as non-Gaussian noise distributions. © 1963-2012 IEEE.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Innovative Approach for Early Heart Disease Prediction: Gaussian Mixture Model and K-Nearest Neighbour 3

Innovative Approach for Early Heart Disease Prediction: Gaus...

引用

3rd International Conference on Intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Surendran, R. Anandaraj, B. Deepa, P. Mageshwari, M. Balamurugan, K.S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Madnapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer science and engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Department of Electronics and communication engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

An Electrocardiogram (ECG) contributes significantly to early diagnosis and classification of heart diseases, arrhythmia which means irregular heart rate. Regrettably, the process became difficult due to asymmetric and minimal amplitude from recordings of ECG and cannot do the extraction from multiple signals. By using machine learning technology, it can classify and extract multiple samples, but cannot extract the data features from complex data structures. To overcome the limitations, previously introduced metaheuristic optimization (MHO) to improve the classification and extraction. Proposed a method which supports and K-Nearest Neighbour (KNN) Gaussian Mixture Model (GMM) algorithms for determine the abnormalities, predicting cardiac events, and improve diagnostic accuracy. As a result, this can help us to get results more quickly and accurately with sensitivity 99.7% and accuracy 99.2%, for helping healthcare professionals in diagnosing arrythmias, myocardial infarction and any other heart conditions. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Deep-EERA:DRL-Based Energy-Efficient Resource Allocation in UAV-Empowered Beyond 5G Networks

引用

Tsinghua Science and Technology 2025年第1期30卷 418-432页

作者： Shabeer Ahmad Jinling Zhang Ali Nauman Adil Khan Khizar Abbas Babar Hayat School of Electronic Engineering Beijing University of Posts and TelecommunicationBeijing 1000876China Department of Information and Communication Engineering Yeungnam UniversityGyeongsan 38541Republic of Korea School of Information Engineering Xi’an Eurasia UniversityXi’an 710065China Department of Computer Science Hanyang UniversitySeoul 04763Republic of Korea

The rise of innovative applications,like online gaming,smart healthcare,and Internet of Things(IoT)services,has increased demand for high data rates and seamless connectivity,posing challenges for Beyond 5G(B5G)*** is a need for cost-effective solutions to enhance spectral efficiency in densely populated areas,ensuring higher data rates and uninterrupted connectivity while minimizing *** Aerial Vehicles(UAVs)as Aerial Base Stations(ABSs)offer a promising and cost-effective solution to boost network capacity,especially during emergencies and high-data-rate ***,integrating UAVs into the B5G networks presents new challenges,including resource scarcity,energy efficiency,resource allocation,optimal power transmission control,and maximizing overall *** paper presents a UAV-assisted B5G communication system where UAVs act as ABSs,and introduces the Deep Reinforcement Learning(DRL)based Energy Efficient Resource Allocation(Deep-EERA)*** efficient DRL-based Deep Deterministic Policy Gradient(DDPG)mechanism is introduced for optimal resource allocation with the twin goals of energy efficiency and average throughput *** proposed Deep-EERA method learns optimal policies to conserve energy and enhance throughput within the dynamic and complex UAV-empowered B5G *** extensive simulations,we validate the performance of the proposed approach,demonstrating that it outperforms other baseline methods in energy efficiency and throughput maximization.

关键词： Deep Reinforcement Learning(DRL) Unmanned Aerial Vehicles(UAVs) resource allocation energy efficiency 5G and beyond network

来源：评论

学校读者我要写书评

暂无评论

Distributed adaptive differential evolution algorithm for mathematical functions optimization and development of optical band pass filter

引用

Optical and Quantum Electronics 2025年第1期57卷 1-25页

作者： Ghosh, Arup Kumar Garai, Gautam Bhattacharjee, Subhankar Department of Electronics and Communication Engineering Techno International New Town Kolkata India Computer Section Saha Institute of Nuclear Physics 1/AF Bidhannagar Kolkata700064 India

In this paper, we have discussed an innovative method for crafting a one-dimensional photonic crystal (1D PhC) based Optical Band Pass Filter (OBPF) using a Differential Evolution (DE) algorithm. 1D PhC-based OBPF plays a vital role in optical communication systems due to its ability to control light propagation. The proposed technique optimizes the photonic crystal structure to achieve the desired optical bandpass characteristics efficiently. The DE algorithm streamlines the design process and enables fast exploration of the design space. The effectiveness of this approach is validated through simulations and comparisons with the existing well-known methods. The OBPF comprises multiple dielectric layers with varying thicknesses and refractive indices to enable high transmission at specific wavelengths and low transmission elsewhere. The Optimization techniques are employed to adjust the parameters like thickness, refractive index, and number of layers. The DE method, thus, enhances the performance of OBPF. MATLAB implementation showcases significant outcomes with high transmittance (99.9727%), low reflectance (0.0273%), and a narrow Full Width at Half Maximum of 0.5 nm that affirms its efficacy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crystal filters

来源：评论

学校读者我要写书评

暂无评论

SwinSegFormer: Advancing Aerial Image Semantic Segmentation for Flood Detection

IEEE Open Journal of the Computer Society

引用

IEEE Open Journal of the computer Society 2025年第1期6卷 645-657页

作者： Shaheen, Muhammad Tariq Iqbal, Hafsa Khurshid, Numan Sadia, Haleema Saeed, Nasir School of Electrical Engineering and Computer Science Islamabad Pakistan Madrid28911 Spain United Arab Emirates University Department of Electrical and Communication Engineering Al Ain United Arab Emirates

Semantic segmentation of aerial images is essential for unmanned aerial vehicle (UAV) applications in disaster management, particularly for identifying the flood-affected areas. Traditional techniques face challenges in capturing global semantic information due to their limited receptive fields, and high computational requirement. To address these issues, we propose a novel transformer-based model named SwinSegFormer, which feature a hierarchical encoder that efficiently generates multi-scale high-resolution features along with a lightweight decoder to reduce computational overhead. The proposed model is trained on FloodNet dataset and demonstrates efficient performance on challenging classes such as vehicles, pools, and flooded and non-flooded roads, which are crucial for effective disaster management. Additionally, we developed a post-processing module to categorize areas into flooded and non-flooded. The model achieves a validation mIoU of 75.1%, mDice of 85.4%, and mACC of 87.1%, representing a 10-12% improvement over state-of-the-art vision transformer-based methods. The effectiveness of model is further evaluated on real-world unlabeled flood imagery, highlighting its potential for supporting first aid activities during floods. Relevant codes are available at:https://***/Shaheen1998/SwinSegFormer. © 2020 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：