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Radio spectrum awareness using deep learning: Identification of fading channels, signal distortions, medium access control protocols, and cellular systems

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Intelligent and Converged Networks 2021年第1期2卷 16-29页

作者： Yu Zhou Hatim Alhazmi Mohsen H.Alhazmi Alhussain Almarhabi Mofadal Alymani Mingju He Shengliang Peng Abdullah Samarkandi Zikang Sheng Huaxia Wang Yu-Dong Yao Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA the College of Information Science and Engineering Huaqiao UniversityXiamen 361021China the College of Engineering Architecture and TechnologyOklahoma State UniversityStillwaterOK 74078-1010USA

Radio spectrum awareness,including understanding radio signal activities,is crucial for improving spectrum utilization,detecting security vulnerabilities,and supporting adaptive *** tasks include spectrum sensing,identifying systems and terminals,and understanding various protocol *** this paper,we investigate various identification and classification tasks related to fading channel parameters,signal distortions,Medium Access Control(MAC)protocols,radio signal types,and cellular ***,we utilize deep learning methods in those identification and classification *** evaluations demonstrate the effectiveness of deep learning in those radio spectrum awareness tasks.

关键词： cellular system deep learning signal classification spectrum awareness Convolutional Neural Network(CNN)

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The Importance of Being Earnest: Multiple Heterogeneous Container Loading with a Simple Genetic Algorithm 28th

The Importance of Being Earnest: Multiple Heterogeneous Con...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Rusin, Francesco Fiala, Jan Sanker, Julian Bhattarai, Suyesh Ekárt, Anikó Department of Engineering and Architecture University of Trieste Trieste Italy Institute of Automation and Computer Science Brno University of Technology Brno Czech Republic Yale College Yale University New Haven United States Mechatherm International Limited Cradley Heath United Kingdom Aston Centre for Artificial Intelligence Research and Application Aston University Birmingham United Kingdom

ISBN: (纸本)9783031900617

In this study we address the complex practical problem of multiple heterogeneous container loading with a simple genetic algorithm. We demonstrate that with a well-chosen representation including a heuristic and a suitable fitness function the other aspects of the genetic algorithm do not need extensive work for good results. Following systematic study of our method on synthetically generated data, we visually showcase the solution for a company-based problem instance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic algorithms

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A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection

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computers, Materials & Continua 2022年第10期73卷 719-733页

作者： Jie Chen Chengsheng Yuan Chen Cui Zhihua Xia Xingming Sun Thangarajah Akilan School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310053China Jiangsu Yuchi Blockchain Research Institute Nanjing210044China Department of Software Engineering Lakehead UniversityThunder BayON P7B 5E1Canada

Fingerprint identification systems have been widely deployed in many occasions of our daily ***,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit *** address challenges from PA,fingerprint liveness detection(FLD)technology has been proposed and gradually attracted people’s *** vast majority of the FLD methods directly employ convolutional neural network(CNN),and rarely pay attention to the problem of overparameterization and over-fitting of models,resulting in large calculation force of model deployment and poor model *** at filling this gap,this paper designs a lightweight multi-scale convolutional neural network method,and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features,so that the number of model parameters is greatly reduced,and support multi-scale true/fake fingerprint ***,the representation self-challenge(RSC)method is used to train the model,and the attention mechanism is also adopted for optimization during execution,which alleviates the problem of model over-fitting and enhances generalization of detection ***,experimental results on two publicly benchmarks:LivDet2011 and LivDet2013 sets,show that our method achieves outstanding detection results for blind materials and *** size of the model parameters is only 548 KB,and the average detection error of cross-sensors and cross-materials are 15.22 and 1 respectively,reaching the highest level currently available.

关键词： FLD lightweight multi-scale RSC blind materials

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Multi-Target Classification Using Deep Learning Models for Automotive Applications

Multi-Target Classification Using Deep Learning Models for A...

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International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Soumya. A Linga Reddy Cenkeramaddi Chalavadi Vishnu Yash Vinod Lanjewar Krishna Mohan C Department of Computer Science and Engineering Indian Institute of Technology Hyderabad India Department of Information and Communication Technology University of Agder Grimstad Norway Department of Architecture and Regional Planning Indian Institute of Technology Kharagpur India

This paper presents a multi-perspective convolutional neural network (CNN) that extracts the class of objects supporting intelligent transportation systems. The proposed model is the visual geometry group (VGG) backbone network with custom feature extraction blocks, that use multilayer prediction heads. The model addresses both multi-class and multi-object classification tasks utilizing the automotive object detection dataset. The model is designed with multiple prediction heads to classify different objects in an image and enable object count prediction. A publicly available automotive object detection dataset with 19800 images and labels has been utilized. The dataset consists of five primary types of objects: Persons, Trucks, Motorbikes, Cars, and Cyclists. On the dataset, pre-trained models such as VGG, Resenet, EfficientNet, and DenseNet were tested and their classification performance was evaluated. The experimental results illustrate the superiority of the proposed VGG backbone deep learning CNN model in comparison to other pre-trained models.

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Problems Analysis and Countermeasure Suggestions for Project-Based Industry-university-Research Collaborative Innovation 2

Problems Analysis and Countermeasure Suggestions for Project...

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2nd International Conference on Engineering Education and Information technology, EEIT 2022

作者： Bao, Wei Li, Zhihao Li, Ruiyan Jilin University of Architecture and Technology College of Computer Science and Engineering Changchun China Wayou Jilin Education Technology Co. Ltd. Industrial Research Department Changchun China Jilin Jiaheng Geological Environment Surveying and Mapping Co. Ltd. Technology Department Changchun China

ISBN: (数字)9781665488426

ISBN: (纸本)9781665488426

With the development of industry-university-research collaborative innovation, the depth and breadth of collaborative cooperation have been continuously improved. Based on the analysis and research of the existing literatures and theories of industry-university-research collaborative innovation, this paper investigates the examples of cooperation in industry-university-research, takes the 'Research and product development of key technologies for in situ monitoring of surface water quality in Jilin province' project cooperated by a university and Jilin Jiaheng Geological Environment Surveying and Mapping Co., Ltd. as an example, combines the existing theories and research conclusions. From the four directions of object selection, technology transfer, knowledge transfer and benefit distribution, this paper analyzes the problems encountered in the practice of project-based collaborative innovation model in industry-university-research, fills the theoretical blanks of collaborative innovation in industry-university-research, puts forward solutions based on the platform, personnel, system, benefits and other aspects, improves the development of collaborative innovation in industry-university-research, and looks forward to industry-university-research collaborative innovation. © 2022 IEEE.

关键词： Surface waters

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Autonomous Artificial Intelligence Systems for Fraud Detection and Forensics in Dark Web Environments

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第9期47卷 51-62页

作者： Rawat, Romil Oki, Olukayode A. Chakrawarti, Rajesh Kumar Adekunle, Temitope Samson Lukose, Jose Manappattukunnel Ajagbe, Sunday Adeola Department of Computer Architecture and Communications University of Extremadura Badajoz06006 Spain Department of Information Technology East London Walter Sisulu University South Africa Department of Computer Science and Engineering Sushila Devi Bansal College Bansal Group of Institutions Indore India Colorado State University United States Department of Information Technology Walter Sisulu University South Africa Department of Computer & Industrial Production Engineering First Technical University Ibadan200255 Nigeria

Artificial Intelligence (AI) influenced technical aspects of research for generating automated intelligent behaviors covering divergent domains but has shown appreciable results when used in cyber forensic technology for crime analysis and detection. AI experts warned about possible security risks associated with algorithms and training data, as AI inherits computing features dealing with IoT-based Smart applications and autonomous transportation, and may be found to be susceptible to vulnerability and threats. The present work discusses models for analyzing terrorists related information and categorizing malicious events by covering the literature review on security risks and AI-related criminality by presenting a taxonomy of criminal behavior and signatures covering tools and criminal targets used in fraudulent activities using AI features by digital forensic techniques. We've also shown how AI may make existing crimes more potent, and that new sorts of crimes could emerge that haven't been identified previously. This study has presented a systematic structure for AI crime and dealing strategies. Furthermore, we have proposed AI forensics, a unique strategy for combating AI crime. We discovered that several concepts of DF are still not preferred in AI-based forensics after conducting a comparative examination of forensics. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Artificial intelligence

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Analysis and Design of Mobile Model for DTN

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Chinese Journal of Electronics 2023年第4期18卷 635-638页

作者： Zhiting Lin Yugui Qu Yusheng Ji Baohua Zhao Department of Computer Science University of Science and Technology of China Hefei China Province Key Laboratory of Software in Computing and Communication Hefei China Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

Delay tolerant networks may become unexpectedly partitioned due to node mobility or variation in signal strength. However, most widely used models in some relative works are generally very simplistic. In order to exploit intelligent forwarding algorithms, a novel nodal mobile model of delay tolerant networks is presented to map reality with more accuracy. And several approaches are introduced to analyze the network structure, such as n-cliques, n-clans, degree, closeness and betweenness. Our research results showed that the centralizations became smaller when the wireless connections were concerned. It meant that quite a number of nodes became potential relays because of the new structure of networks.

关键词： Wireless communication Analytical models Wireless sensor networks Protocols Social networking (online) Sensitivity analysis Social network theory

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Influence of 3D laparoscopic surgery on surgeon’s visual pattern and mental workload

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Journal of Medical Engineering and technology 2021年第5期45卷 375-379页

作者： Zhang, Jian-Yang Shen, Zhi-Hao Wang, Bao-Ping Liu, Feng Li, Juan School of Computer and Information Technology Nanyang Normal University Nanyang China Department of Medical Engineering Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China School of Civil Engineering and Architecture Nanyang Normal University Nanyang China

Previous studies have found that surgeons perform better in three-dimensional (3D) surgery than in two-dimensional (2D) surgery. However, no comparative studies have revealed the impact of 3D laparoscopic surgery on the surgeon’s vision. To explore the effect of laparoscopic surgeons’ depth perception during 3D laparoscopic surgery, 10 participants were recruited and performed 4 sets comparative simulated laparoscopic procedures in a virtual simulator, and eye movement signals were acquired, which were used to characteristics the visual differences. Fixation rate and saccade speed were used to characterise the influence of moderating variables for visual characteristics. The results from the data showed significant differences in eye movement behaviour. Compared with 2D laparoscopic surgery, surgeons have more average fixation rate (p-values = 0.001, 0.000, 0.003 and 0.015, respectively) and faster saccade speed (p-values = 0.037, 0.003, 0.073 and 0.105, respectively) in 3D laparoscopic surgery. The results of this study showed that surgeons had more efficient visual search in 3D laparoscopic surgery. At the same time, the results also indicated that surgeon’s mental workload in 3D laparoscopic surgery was low. The relevant conclusions of this paper revealed the advantages of 3D laparoscopic surgery through visual efficiency. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Eye movements

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HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction

arXiv

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arXiv 2023年

作者： Wang, Yijun Sun, Changzhi Wu, Yuanbin Li, Lei Yan, Junchi Zhou, Hao WeChat Technical Architecture Department Tencent Inc China Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Computer Science and Technology East China Normal University China ByteDance AI Lab China University of California Santa Barbara United States

Entity relation extraction consists of two sub-tasks: entity recognition and relation extraction. Existing methods either tackle these two tasks separately or unify them with word-byword interactions. In this paper, we propose HIORE, a new method for unified entity relation extraction. The key insight is to leverage the high-order interactions, i.e., the complex association among word pairs, which contains richer information than the first-order word-by-word interactions. For this purpose, we first devise a W-shape DNN (WNet) to capture coarse-level high-order connections. Then, we build a heuristic high-order graph and further calibrate the representations with a graph neural network (GNN). Experiments on three benchmarks (ACE04, ACE05, SciERC) show that HIORE achieves the state-of-the-art performance on relation extraction and an improvement of 1.1 ∼ 1.8 F1 points over the prior best unified model. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach

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Environmental science and Pollution Research 2024年第22期31卷 32449-32463页

作者： Rajak, Upendra Chaurasiya, Prem Kumar Verma, Tikendra Nath Dasore, Abhishek Ağbulut, Ümit Meshram, Kundan Saleel, CAhamed Saboor, Shaik Cuce, Erdem Mian, Zhibao Department of Mechanical Engineering RGM College of Engineering and Technology Nandyal518501 India Department of Mechanical Engineering National Institute of Technology Raipur492010 India Department of Mechanical Engineering Maulana Azad National Institute of Technology Vellore462003 India Department of Biological and Agricultural Engineering Faculty of Engineering Universiti Putra Malaysia Selangor UPM Serdang43400 Malaysia Department of Mechanical Engineering Faculty of Mechanical Engineering Yildiz Technical University Istanbul34349 Turkey Department of Civil Engineering Guru Ghasidas Vishwavidyalaya Bilaspur India Department of Mechanical Engineering College of Engineering King Khalid University Abha61421 Saudi Arabia School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu Vellore632014 India Department of Mechanical Engineering Faculty of Engineering and Architecture Recep Tayyip Erdogan University Zihni Derin Campus Rize53100 Turkey School of Engineering and the Built Environment Birmingham City University BirminghamB4 7XG United Kingdom Faculty of Science and Engineering School of Computer Science University of Hull Cottingham Road HullHU6 7RX United Kingdom Center for Research Impact & amp Outcome Chitkara University Punjab Rajpura140401 India

This article presents the outcomes of a research study focused on optimizing the performance of soybean biofuel blends derived from soybean seeds specifically for urban medium-duty commercial vehicles. The study took into consideration elements such as production capacity, economics and assumed engine characteristics. For the purpose of predicting performance, combustion and emission characteristics, an artificial intelligence approach that has been trained using experimental data is used. At full load, the brake thermal efficiency (BTE) dropped as engine speed increased for biofuel and diesel fuel mixes, but brake-specific fuel consumption (BSFC) increased. The BSFC increased by 11.9% when diesel compared to using biofuel with diesel blends. The mixes cut both maximum cylinder pressure and NOx emissions. The biofuel-diesel fuel proved more successful, with maximum reduction of 9.8% and 22.2 at rpm, respectively. The biofuel and diesel blend significantly improved carbon dioxide (CO2) and smoke emissions. The biofuel blends offer significant advantages by decreeing exhaust pollutants and enhancing engine performance. Graphical Abstract: (Figure presented.) © The Author(s) 2024.

关键词： Diesel engines

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