检索结果-内蒙古大学图书馆

SMART DRIVING LICENSING SYSTEM AND AUTHORIZATION OF AUTONOMOUS VEHICLE INTEGRATING WIRELESS SENSOR NETWORKS AND IoT

Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第9期83卷 69-85页

作者： Santhakumar, D. Sudhan, M.B. Prasanna, R. Manikandan, S.P. Prathaban, Banu Priya Kanna, Rajesh R. Saravanan, G. 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 SRM Institute of Science and Technology Chennai Ramapuram India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sree Sastha Institute of Engineering and Technology Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Tamil Nadu Chennai India

The advent of autonomous vehicles has revolutionized the automotive industry, offering promising advancements in safety, efficiency, and mobility. To integrate these autonomous vehicles into our society seamlessly, it is imperative to establish a robust licensing system that ensures the safe operation of automated vehicles on public roads. This article presents an effective smart Driving Licensing System (SDLS), a digital framework designed to regulate the existing licensing system and authorize licensing for autonomous vehicles. Through a comprehensive assessment process, the system assesses the drivers and the autonomous vehicle’s ability to navigate various road conditions, respond to dynamic environments, and prioritize safety for passengers and other road users. SDLS integrates high-level security through facial and fingerprint verification for identity verification to preserve licensing data. Hacking and unauthorized access to autonomous vehicles is prevented by robust cybersecurity protocols. The key benefits of SDLS include automated data collection, integration of wireless sensor networks (WSN) and Internet of Things (IoT), smart application processing, real-time license checks, cybersecurity measures, automated testing, and digital certification. The proposed system offers a comprehensive approach to safely integrating autonomous vehicles into our transportation ecosystem. The autonomous vehicles are integrated with antennas at rooftops or into bumpers, depending upon the vehicle design which can be connected with the SDLS for signal communication. Furthermore, the SDLS establishes a framework for continuous monitoring and updates, enabling ongoing assessments to keep pace with evolving technology and safety standards. © 2024 by Begell House, Inc.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Deep Learning & Computer Vision Integrated Smart Voting System 2

Deep Learning & Computer Vision Integrated Smart Voting Syst...

引用

2nd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Dube, Sanskruti Venkata Siva Sandeep, Mysore Challagundla, Hamsitha Nikhilesh Chand, Pulaparthi Vellore Institute of Technology School of Computing Science and Engineering Bhopal India Vellore Institute of Technology School of Information Technology Vellore India Vellore Institute of Technology School of Electronics Engineering Vellore India

ISBN: (纸本)9798350347456

In this paper, an online voting method for elections in India is initially suggested. The suggested model has higher security as the voter's raised secure password must be validated prior to the recording of the vote in the major database owned by our nation's Election Commission. The model's additional feature allows the voter to verify that the right candidate or party received their vote. In this arrangement, a voter has the option to cast a ballot from a place other than the one designated for them or from their favorite site. Vote counting will be made in an automated fashion under the proposed approach, saving a significant amount of period and allowing our nation's Commission set for Election to declare the results in a much fast succession. Alongside the password-based authentication, we have also utilized the face-based authentication along with the successful implementation of the OpenCV in addition to the password validation. We describe a model for a web voting entity for India in this application using the above said constituents. When contrasted with the conventional voting system, this version of voting systems seems to be considerably much secure and efficient. The delays and frauds occurring during counting of votes can be easily prevented. © 2023 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Comparing Deep Learning Object detection Methods for Real Time Cow Detection 38

Comparing Deep Learning Object detection Methods for Real Ti...

引用

38th IEEE Region 10 Conference, TENCON 2023

作者： Martha, Gichuki Wambui Waweru Mwangi, Ronald Aramvith, Supavadee Rimiru, Richard Jomo Kenyatta University of Agriculture & Technology Computing Department School of Computing & Information Technology Nairobi Kenya Chulalongkorn University Department of Electrical Engineering Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9798350302196

Deep learning algorithms particularly Convolutional Neural Networks (CNNs) are the state-of-the-art techniques for object detection, classification, segmentation and behaviour classification. These algorithms have extensive application across various domains including agriculture. However, cow identification in dairy farming still relies on methods like direct visual monitoring which are time consuming, costly and inaccurate;or use of invasive contact devices such as sensors which can cause discomfort during attachment or removal. This research compared three deep learning object detection models i.e. YOLOv5, YOLOv7 and YOLOv8, which were selected based on their performance in object detection tasks. We generated cow images from videos captured from a housed dairy cattle barn. The dataset had 11,828 cow images, augmented to depict different illumination conditions and using makesense AI tool, we annotated the images in YOLO format, trained and validated the three models to visualize cow bounding boxes. Our approach demonstrates efficiency of the YOLOv8 model, achieving an accuracy of 94.7% and 93 % before and after data augmentation respectively. YOLOv8 baseline model was finetuned using the Ray Tune library achieving a mAP@O.50 score of 92. 9%. This research makes a significant contribution in the future research direction of the YOLO algorithm and highlights the practical implementation of deep learning models for cow detection, applicable in livestock management. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

A Creative Research in the Field of Urban Fire Detection 5

A Creative Research in the Field of Urban Fire Detection

引用

5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Feng, Junming Wang, Ruixiang Xi, Chenyang Peng, Jiayu Harbin Institute of Technology Faculty of Computing Harbin China Harbin Institute of Technology School of Information Science and Engineering Harbin China Harbin Institute of Technology School of Software Harbin China

ISBN: (纸本)9798350373820

As the most common disaster in human daily production and life, fire is very likely to threaten people's life and property safety. In order to avoid fire, this paper designs and implements a fire detection system based on video and YOLOv5 model, and describes the improvement of YOLOv5 compared with the previous generation YOLO model. Collect fire images through the network, use image processing technology to enhance the data, make fire data set and divide the data set by setting aside method. Then the self-made fire data set is used to train the fire detection model based on YOLOv5 model, and the model is improved by adding transfer learning, multi-scale training and other technologies to improve the fire detection rate, and the corresponding weight files are obtained. Finally, the training method of the model and the ability of the model to detect fire are expounded, and the innovative application of the model in the field of fire detection is realized. © 2024 IEEE.

关键词： Fires

来源：评论

学校读者我要写书评

暂无评论

PALCT: vehicle-to-vehicle communication based on pseudonym assignment and encryption scheme using delay minimisation cover tree algorithm

引用

International Journal of Vehicle Information and Communication Systems 2025年第2期10卷 132-147页

作者： Tandon, Righa Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Jaypee University of Information Technology HP Solan173 234 India Department of Data Science School of Computing Mohan Babu University AP Tirupati517 102 India

Vehicle-to-vehicle communication is one of the new paradigms of networking, which should be secure, fast, and efficient. In this paper, we propose a framework that implements the pseudonym-based authentication scheme in which communication among vehicles is encrypted by using a matrix array symmetric key (MASK), digital signature algorithm (DSA), and intelligent water drop (IWD). The proposed security scheme also ensures the handling of many security attacks like key-guessing, non-repudiation, replay, and modification. In the proposed scheme, to preserve the identity of the vehicle, we have provided the different pseudonyms for each vehicle in the network that ensure secure communication among vehicles. Furthermore, the proposed delay minimisation cover tree algorithm ensures the issue of timed delay during vehicle-to-vehicle communication. In this algorithm, we have used Dijkstra’s algorithm for finding the optimal shortest path during vehicular communication. The results obtained show that the proposed scheme is effective and efficient, as it reduces the time delay by 4% for 140 vehicle nodes and 28.4% for 1000 vehicle nodes. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Encryption algorithms

来源：评论

学校读者我要写书评

暂无评论

Extraction of Unstructured Electronic Healthcare Records using Natural Language Processing 1

Extraction of Unstructured Electronic Healthcare Records usi...

引用

1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Patil, Snehal Sameer Moorthy, Vaishnavi SRM Institute of Technology School of Computing Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur India

ISBN: (纸本)9798350336009

Artificial Intelligence in the healthcare sector is becoming increasingly essential to extract huge texts for decision-making. Extraction of clinical data is a fundamental task in Medical Natural language processing. This process is still challenging through deep learning due to critical medical data, lack of interpretability, and limited availability. Text extraction from Electronic Healthcare records is crucial for improving patient care and understanding clinical decision-making. It also supports analysing the patients' feedback and physician notes to identify areas for improvement in patients' satisfaction and care quality. This helps in drug discovery and development through clinical data patterns. The proposed research focuses on implementing Natural language processing methods for data processing like classification and prediction, Word Sense Disambiguation, Segmentation, and word Embedding. These methods can process vast amounts of medical text data for decision support, research, and drug discovery. It can increase the possibility of identifying the patients who may at risk for certain conditions and diseases related to cancer and comparing it with their medical history. The chief aim is to provide improvised data analyses that could further improve their treatment. © 2023 IEEE.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

DHD: Double Hard Decision Decoding Scheme for NAND Flash Memory

DHD: Double Hard Decision Decoding Scheme for NAND Flash Mem...

引用

2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Cui, Lanlan Wang, Yichuan Xiao, Renzhi Li, Miao Liu, Xiaoxue Hei, Xinhong School of Computer Science and Engineering XI'AN University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China School of Software Engineering Jiangxi University of Science and Technology Nanchang China School of Information Engineering Zhongnan University of Economics and Law Wuhan China

ISBN: (纸本)9783982674100

With the advancement of NAND flash technology, the increased storage density leads to intensified interference, which in turn raises the error rate during data retrieval. To ensure data reliability, low-density parity-check (LDPC) codes are extensively employed for error correction in NAND flash memory. Although LDPC soft decision decoding offers high error correction capability, it comes with a significant latency. Conversely, hard-decision decoding, although faster, lacks sufficient error correction strength. Consequently, flash memory typically initiates with hard-decision decoding and resorts to multiple soft decision decoding upon failure. To minimize decoding latency, this paper proposes a decoding mechanism based on the double hard decision, called DHD. This DHD scheme improves the Log-Likelihood Ratio (LLR) in the hard decision process. After the first hard decision fails, the read reference voltage (RRV) is adjusted to perform the second hard decision decoding. If the second hard decision also fails, soft decision decoding is then employed. Experimental results demonstrate that when the Raw Bit Error Rate (RBER) is 8.5 × 10-3, DHD reduces the Frame Error Rate (FER) by 86.4% compared to the traditional method. © 2025 EDAA.

关键词： Error correction

来源：评论

学校读者我要写书评

暂无评论

ML assisted enhanced recommendation system for reliable volunteering environment 2

ML assisted enhanced recommendation system for reliable volu...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Prabakaran, N. Meenakshi, K. Kumarnath, J. Rajasekaran, P. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Mathematics Presidency University Bengaluru560064 India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul624005 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu603203 India

The coronavirus pandemic has negatively impacted everyone around the globe. The need to follow social distancing has increased the demand for technology that makes remote volunteering accessible. Alerting, aiding, and educating individuals at risk in a privacy-preserving manner are the need of the hour. Several NGOs have taken up this responsibility but are lacking the resources to reach willing volunteers and donations. This project attempts to make a novel on-demand service application using ID3, CART, and C4.5 decision tree classification algorithms and compare their accuracies. Algorithm C4.5 here we use as a Decision Tree classifier that can be make decisions based on univariate or multivariate predictor. C4.5 Algorithm builds decision trees based on the conception of information gain, with the decision of each classification associated with the target classification. The best way to determine uncertainty is to use entropy that reflects the basis of decision. We aim to classify volunteer data based on predicted volunteer reliability. C4.5 algorithm shows 11.63 higher accuracy when compared to other algorithm and the value of 0.36 Gini index for the C4.5 indicates that there is an adequate equality. It is conferred that the C4.5 algorithm will best solve this problem based on the required dataset that we have created. © 2024 Elsevier B.V.. All rights reserved.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Video Compression through Advanced Video Saliency Aware Spatial-Temporal Integration and Attention Mechanisms

引用

SN Computer Science 2024年第7期5卷 926页

作者： Ravishankar, H. AnithaKumari, R.D. Sarvamangala, D.R. Rashmi, C. Deepa, K.R. School of Computing and Information Technology REVA University Karnataka Bangalore India School of Electronics and Communication Engineering REVA University Karnataka Bangalore India School of Electrical and Electronics Engineering REVA University Karnataka Bangalore India

In the field of computer vision, video saliency detection is a critical task that involves identifying and highlighting areas within video frames that are most likely to attract human attention. Despite its importance, this task poses significant challenges due to the dynamic nature of video content, which encompasses varying spatial and temporal features. Addressing these challenges, the Dynamic Attentive Integration for Spatial-Temporal Saliency (DAISTS), a novel methodology that significantly enhances the precision of video saliency detection is designed. DAISTS introduces a dual-path spatial-temporal feature hierarchy, effectively merging deep and shallow learning attributes to improve saliency map accuracy. Additionally, spatial and channel attention aided integration is designed, which adaptively refine feature fusion based on scene context and training data. Moreover, DAISTS incorporates a frame-based attention model, a breakthrough in temporal analysis that selectively prioritizes frames for improved saliency prediction. The methodology is further supported by a comprehensive loss function that integrates Relative Entropy, linear correlation coefficient, and normalized scan-way saliency metrics, ensuring a balanced and effective approach to training. This encapsulates the approach to advancing video saliency detection, highlighting DAISTS as a robust, efficient, and adaptable solution, to make a significant impact in the realm of computer vision. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computer vision DAISTS Relative entropy Temporal analysis

来源：评论

学校读者我要写书评

暂无评论

Dynamic Data Placement Strategy with Network Security Issues in Distributed Cloud Environment for Medical Issues: An Overview

引用

Recent Advances in Computer Science and Communications 2024年第6期17卷 25-38页

作者： Pradhan, Devasis Behera, Majusha Gheisari, Mehdi Department of Electronics and Communication Engineering Acharya Institute of Technology Karnataka Bangalore560107 India School of Electronics Engineering Campus 12 KIIT deemed to be University Odisha Bhubaneswar India Department of Cognitive Computing Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

The rapid integration of distributed cloud systems in the healthcare industry has profoundly impacted the management of valuable medical data. While this advancement has significantly improved data handling, protecting sensitive healthcare information in such a complex environment remains daunting. This comprehensive study explores the crucial intersection between dynamic data placement strategies and network security concerns in distributed cloud environments, particularly healthcare. After establishing the significance and context of this research, the survey delves into the growing need to safeguard medical data within the everevolving landscape of cloud-based healthcare systems. It lays out fundamental concepts, such as dynamic data placement and network security, highlighting their unique implications in the medical domain. Ultimately, this survey sheds light on the most effective approaches for balancing dynamic data placement and network security in the healthcare sector. This research delves into examining many tactics, evaluating their effectiveness in handling delicate medical information, and presenting tangible use cases. A key focus of this investigation is the fusion of data organization and network safety within the healthcare industry. It investigates the adaptability of dynamic data positioning techniques in fortifying network security and safeguarding against potential threats unique to the healthcare sector. Case studies of the successful implementation of these strategies in healthcare establishments are also included. © 2024, Bentham Science Publishers. All rights reserved.

关键词： Electronic health record

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：