检索结果-内蒙古大学图书馆

Parallel Implementation of the CCSDS Turbo Decoder on GPU

China Communications 2024年第10期21卷 70-77页

作者： Liu Zhanxian Liu Rongke Zhang Haijun Wang Ning Sun Lei Wang Jianquan School of Computer&Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial IntelligenceUniversity of Science and Technology BeijingBeijing 100083China School of Information Engineering Zhengzhou UniversityZhengzhou 450001China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China

This paper presents a software turbo decoder on graphics processing units(GPU).Unlike previous works,the proposed decoding architecture for turbo codes mainly focuses on the Consultative Committee for Space Data Systems(CCSDS)***,the information frame lengths of the CCSDS turbo codes are not suitable for flexible sub-frame parallelism *** mitigate this issue,we propose a padding method that inserts several bits before the information frame *** obtain low-latency performance and high resource utilization,two-level intra-frame parallelisms and an efficient data structure are *** presented Max-Log-Map decoder can be adopted to decode the Long Term Evolution(LTE)turbo codes with only small *** proposed CCSDS turbo decoder at 10 iterations on NVIDIA RTX3070 achieves about 150 Mbps and 50Mbps throughputs for the code rates 1/6 and 1/2,respectively.

关键词： CCSDS CUDA GPU parallel decoding turbo codes

来源：评论

学校读者我要写书评

暂无评论

A Review on the Role of Microrobots and Nanorobots used in Healthcare Domain 6

A Review on the Role of Microrobots and Nanorobots used in H...

引用

6th Parul International Conference on engineering and technology, PICET 2024

作者： Kumar, Shravan Kumar, Praveen Kumar, Rajan Kumar, Mukesh Kumar, Dhiraj Kumar, Gulshan Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India

ISBN: (纸本)9798350369748

Advanced technology such as microrobots and nanorobots have the potential to completely transform the healthcare industry. These tiny robotic devices provide fine control for a range of biological applications since they operate at the micro and nanoscales. This review explores the various healthcare uses, fabrication ideas, and design concepts of microrobots and nanorobots. It specifically looks at their works in tissue engineering, minimally invasive surgery, and targeted drug administration. Aiming to harness the transformative impact of these emerging technologies in healthcare, research and practitioners can recive significant insights from the discussion of challenges and future directions in the sector. Design-wise, microrobots and nanorobots display a range of configurations suited to particular uses, such as payload delivery systems, sensing capabilities, and propulsion mechanisms. © 2024 IEEE.

关键词： Microrobots

来源：评论

学校读者我要写书评

暂无评论

IOT-Enabled Smart Crop Recommendation System Using Machine Learning 5

IOT-Enabled Smart Crop Recommendation System Using Machine L...

引用

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

作者： Saranya, S.S. Dharani, A.S. Kavitha, M.N. Selya Dharsnee, M. Pragatheeswari, E. Selya Varsnee, M. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Technology - Ug Perundurai India Kongu Engineering College Department of Computer Science and Engineering Perundurai India Kongu Engineering College Department of Computer Technology Perundurai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

Agriculture, a foundation of India's economy, faces challenges in maximizing yield and resource efficiency. This research presents a machine learning-based system to recommend optimal crops and fertilizers based on soil quality and geographic location. By providing data-driven insights, the proposed system aims to enhance crop productivity, promote sustainable farming practices, and empower farmers to make informed decisions. © 2024 IEEE.

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

Automated Skin Cancer Classification and Detection Using Convolutional Neural Networks and Dermoscopy Images 4

Automated Skin Cancer Classification and Detection Using Con...

引用

4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sara, Umme Das, Utpol Kanti Sikder, Juel Chowdhury, Sudipta Roy Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research Savar Dhaka Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Sadar Rangamati4500 Bangladesh

The uncontrolled growth of skin cells in the epidermis producing the creation of a mass termed a tumor is a dangerous condition known as skin cancer. Current developments in deep learning artificial intelligence have greatly improved image-based diagnosis. In this study, we included a Skin Lesion Cancer feature extractor Convolutional Neural Network (SLC-CNN) model, which is used for both classification with the SVM classifier and segmentation with XGBoost for skin cancer. In our proposed system, a test image of skin cancer is taken and pre-processed for both classification and segmentation purposes. After applying pre-processing, the test image features are extracted using the SLC-CNN feature extractor, which features are used in SVM to classify the types of skin cancer (Benign and Malignant), and based on the classification result, a trained XGBoost model is called to segment the cancer region. We have tested our system using the dermoscopy image collection from the International Skin Imaging Collaboration (ISIC) and built it in Google Colab to best use the GPU. Our suggested approach has gained a segmentation accuracy of 95.25% and a classification accuracy of 99.6%. © 2025 The Authors. Published by Elsevier B.V.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Non-planar dielectrics derived thermal and electrostatic field inhomogeneity for boosted weather-adaptive energy harvesting

引用

National science Review 2023年第9期10卷 248-257页

作者： Yi Zhou Tianpeng Ding Yin Cheng Yi Huang Wu Wang Jianmin Yang Lin Xie Ghim Wei Ho Jiaqing He Shenzhen Key Laboratory of Thermoelectric Materials and Department of Physics Southern University of Science and Technology Department of Electrical and Computer Engineering National University of Singapore School of Electronic Science and Engineering State Key Laboratory of Electronic Thin Film and Integrated Devices University of Electronic Science and Technology of China Department of Materials Science and Engineering National University of Singapore Institute of Materials Research and Engineering Agency for ScienceTechnology and Research (A*STAR) GuangdongHong Kong-Macao Joint Laboratory for Photonic-Thermal-Electrical Energy Materials and Devices Southern University of Science and Technology

Weather-adaptive energy harvesting of omnipresent waste heat and rain droplets,though promising in the field of environmental energy sustainability,is still far from practice due to its low electrical output owing to diele ctric structure irrationality and *** we present atypical upcycling of ambient heat and raindrop energy via an all-in-one non-planar energy harvester,simultaneously increasing solar pyroelectricity and droplet-based triboelectricity by two-fold,in contrast to conventional *** delivered non-planar dielectric with high transmittance confines the solar irradiance onto a focal hotspot,offering transverse thermal field propagation towards boosted inhomogeneous polarization with a generated power density of 6.1 mW m-2at 0.2 ***,the enlarged lateral surface area of curved architecture promotes droplet spreading/separation,thus travelling the electrostatic field towards increased *** enhanced pyroelectric and triboelectric outputs,upgraded with advanced manufacturing,demonstrate applicability in adaptive sustainable energy harvesting on sunny,cloudy,night,and rainy *** findings highlight a facile yet efficient strategy,not only for weather-adaptive environmental energy recovery but also in providing key insights for spatial thermal/electrostatic field manipulation in thermo electrics and ferroelectrics.

关键词： non-planar dielectrics heat manipulation pyroelectrics weather-adaptive energy harvesting

来源：评论

学校读者我要写书评

暂无评论

Enhancing Automatic Image Captioning: A Systematic Analysis of Models, Datasets, and Evaluation Metrics 4

Enhancing Automatic Image Captioning: A Systematic Analysis ...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Ganesan, Kothai Amutha, S. Saketh, G. Venkata Reddy, G. Vishnu Vardhan Chowdary, G. Harinath Sai, J. Ajay Kpr institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331529635

The fusion of computer vision and natural language processing (NLP) has given rise to the interdisciplinary field of automatic image captioning, which aims to generate descriptive text for images without human intervention. This area of research has attracted considerable interest due to its potential uses in improving accessibility, managing content, and enhancing search engine optimization. Over the past years, there has been a drastic increase in the accuracy and detail of captions thanks to recent advances in deep learning, especially with convolutional neural networks (CNNs), long short-term memory (LSTMs), and recurrent neural networks (RNNs). This research examines cutting-edge approaches to automatic image captioning, including attention mechanisms, models based on transformers, and techniques utilizing reinforcement learning. The effectiveness of these methods was evaluated using benchmark datasets, and their advantages and limitations were assessed. Additionally, the obstacles faced in producing contextually appropriate and linguistically precise captions were explored, such as dealing with intricate scenes, uncommon objects, and abstract ideas. The paper concludes by highlighting potential areas for future research, including learning across multiple modalities, captioning across languages, and incorporating common-sense knowledge to improve caption generation. © 2024 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Knowledge-enriched joint-learning model for implicit emotion cause extraction

引用

CAAI Transactions on Intelligence technology 2023年第1期8卷 118-128页

作者： Chenghao Wu Shumin Shi Jiaxing Hu Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention ***,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application *** lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local ***,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event *** address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different *** on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is *** authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause *** on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.

关键词： emotion cause extraction external knowledge fusion implicit emotion recognition joint learning

来源：评论

学校读者我要写书评

暂无评论

Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

引用

IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Automatic Ship Detection and Classification from Imagery Dataset MASATI Using Deep Learning Framework 5th

Automatic Ship Detection and Classification from Imagery Dat...

引用

5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Patel, Krishna Bhatt, Chintan Department of Computer Science & Engineering Devang Patel Institute of Advance Technology and Research Charotar University of Science and Technology Changa India Gandhinagar India

ISBN: (纸本)9789819761029

A significant challenge is the automatic detection and classification of ships from aerial images for maritime safety and surveillance. There are two techniques, image processing and computer vision, used before the traditional supervised classifiers to extract the features from the images and classify them. The framework proposed in this paper is You Only Look Once (YOLOv8), which was recently developed and implemented for object detection and classification on the COCO dataset. The authors used the framework with some preprocessing and augmentation on the satellite image to detect and classify ships, which helps maritime organizations locate the objects. The MAritime SATellite Imagery dataset (MASATI) of more than 7000 images is used in this paper. The proposed method is tested with two different preprocessing steps, auto-orient and mosaic augmentation, in version 1 and version 2;the preprocessing step includes auto-orient and grayscale along with the augmentation as crop, shear, blur, and mosaic. The framework generates mAP of 57.8% and 63.7% for version 1 and version 2, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Satellite imagery

来源：评论

学校读者我要写书评

暂无评论

Robust intrusion detection for network communication on the Internet of Things: a hybrid machine learning approach

引用

Cluster Computing 2024年第7期27卷 9975-9991页

作者： Soltani, Nasim Rahmani, Amir Masoud Bohlouli, Mahdi Hosseinzadeh, Mehdi Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliu Taiwan Research and Innovation Department Petanux GmbH Bonn Germany Institute of Research and Development Duy Tan University Da Nang Viet Nam Department of Computer Science University of Human Development Sulaymaniyah Iraq

The importance and growth of the Internet of Things (IoT) in computer networks and applications have been increasing. Additionally, many of these applications generate large volumes of data, which are critical and require protection against attacks. Various techniques have been proposed to identify and counteract these threats. In this paper, we offer a hybrid machine learning approach (using the k-nearest neighbors and random forests as supervised classifiers) to enhance the accuracy of intrusion detection systems and minimize the risk of potential attacks. Also, we employ backward elimination and linear discriminant analysis algorithms for feature reduction and to lower computational costs. Following the training phase, when discrepancies arose between the decisions of the classifiers, the ultimate determination was supported by ISO/IEC 27001 regulations. The performance of the proposed model was assessed within a Python programming framework, utilizing the CICIDS 2017, NSL-KDD, and TON-IoT datasets. The outcomes illustrated that the proposed approach attained a noteworthy accuracy of 99.96% in the multi-class classification of CICIDS 2017, 99.37% in the binary classification of the NSL-KDD dataset, and 99.96% in the multi-class classification of TON-IoT dataset. Furthermore, the attack success rate for each dataset stands at 0.05%, 0.24%, and 0% respectively, demonstrating a significant reduction compared to other methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Intrusion detection

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：