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Energy Efficient Spectrum Sharing and Resource Allocation for 6G Air-Ground Integrated Networks

IEEE Transactions on Network and Service Management 2025年

作者： Huang, Tao Liu, Jingyuan Chang, Zheng Wei, Yao Zhao, Xu Liang, Ying-Chang University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China University of Jyväskylä Faculty of Information Technology JyväskyläFIN-40014 Finland Chengdu611731 China Research Institute of China Telecom Guangzhou China

In this paper, we investigate the spectrum sharing and resource allocation scheme for air-ground integrated wireless network which consists of multiple unmanned aerial vehicles (UAVs) and a high altitude platform (HAP). We consider the UAVs are required to provide services or execute certain missions in the area that HAP owns the spectrum and other resources. Correspondingly, we propose an energy efficient spectrum sharing and resource allocation scheme so that the UAVs can flexibly utilize the radio resources within the area without degrading the quality of service (QoS) of the HAP. In the proposed scheme, we jointly optimize pricing of spectrum and transmit power to maximize the utility of both the HAP and UAVs in the considered system in an energy efficient manner. A game theoretic approach is then presented to find the spectrum sharing and resource allocation strategies for both HAP and UAVs and the problem has been addressed via convex optimization. Our extensive simulations demonstrate marked improvements in system utility, spectrum and energy efficiency, and also highlight the effectiveness of the proposed scheme. © 2004-2012 IEEE.

关键词： Convex optimization

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Design Objectives for Synthesis of Graphene PN Junction Circuits Based on Two-Level Representation

Design Objectives for Synthesis of Graphene PN Junction Circ...

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Euromicro Symposium on Digital System Design

作者： Subrata Das Arighna Deb Petr Fiser Debesh Kumar Das Dept of Computer Science & Engineering Supreme Knowledge Foundation Group of Institutions Hooghly India School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Faculty of Information Technology Czech Technical University Prague Czech Republic Dept. of Computer Science & Engineering Jadavpur University Kolkata India

ISBN: (数字)9798350380385

ISBN: (纸本)9798350380392

The development of electrostatically doped graphene PN-junctions shows promise for creating efficient low-power, high-speed circuits. In recent years, there has been a considerable interest in the synthesis of graphene PN junction logic circuits. However, existing synthesis methods lack assessment based on technology-specific cost metrics (e.g., the number of graphene PN junction gates, constant inputs), leading to insufficiently addressed design objectives. In this paper, we introduce synthesis approaches for graphene PN-junction circuits based on Sum-of-Products (SoP) and Exclusive Sum-of-Products (ESoP) function representations. Experimental results indicate that ESoP-based synthesis significantly reduces the number of graphene PN junction gates, constant inputs, and switching activity compared to SoP-based approaches. Overall, ESoP-based synthesis is deemed more suitable than SoP-based methods for designing graphene PN-junction logic circuits.

关键词： Measurement Costs Digital systems Logic circuits Graphene Switches Logic gates Circuit synthesis Logic Junctions

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Deep Learning Image Transfer by Simulation 1

Deep Learning Image Transfer by Simulation

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1st IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2021

作者： Ammar, Abdussalam Masaud Youssef Ellafi, Amira Bozed, Kenz A. Zerek, Amer R. Faculty of Electronic Technology Tripoli Libya Benghazi University Faculty of Information Technology Computer System Design Dept. Benghazi Libya Zawia University Faculty of Engineering Ee Department Zawia Libya

ISBN: (纸本)9781665418560

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning with Neural style transfer that has been a high risk application for deep learning, make attention from and advertising the effectiveness to both the academic prisons and the general public. However, we have found by removal experiments that optimizing an image in the way neural style transfer does, we can even factor out the deepness (multiple layers of exchange linear and nonlinear transformations) all together and have neural style transfer working to a certain range. We present the VGG-19 Artistic Type Neural Algorithm, which can convert and recombine the image quality and style of natural images. This algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous well known portrait. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks (CNN) and demonstrate their potential for high level image synthesis and manipulation. © 2021 IEEE.

关键词： Image classification

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An Effective Automated Framework for Oral Cancer Detection by Enhanced Convolutional Neural Networks

An Effective Automated Framework for Oral Cancer Detection b...

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International Conference on Advanced Computing, ICAC

作者： M Santhiya M Sindhuja R Jegatha J Manikandan Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai Department of Computational Intelligence Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Department of Information Technology Sri Sai Ram institute of technology Chennai Department of Information Technology St. Joseph’s College of Engineering OMR Chennai

Oral cancer, an aggressive form of cancer affecting the mouth and throat, has been on an alarming worldwide increase due to tobacco use and smoking. Oral cancer presents various challenges such as high incidence rates, delayed diagnosis, inadequate treatment plans and late prognoses and outcomes; early detection is crucial in improving prognosis outcomes and survival prospects. Machine learning techniques have gained prominence for improving cancer diagnosis while decreasing mortality and morbidity; in recent years they were used to detect oral cancer, providing highly accurate classification tasks yet limited capabilities in terms of feature extraction capabilities. To address these limitations, we are currently employing a deep-learning approach. To this end, we have devised an automated framework encompassing feature extraction, preprocessing and classification. Contrast-limited adaptive Histogram Equalization (CLACHE), used for effective pre-processing purposes to increase image contrast and resolution, is one form of pre-processing while Gray Level Cooccurrence Matrix extracts statistical texture-based features; finally, a Modified Convolutional Neural Net (MCNN) was employed for accurate classification of oral cancer cases. Utilizing Kaggle Repository dataset, we used it to assess the performance of our framework, while comparing it against other high-performance methods. Our proposed framework had an accuracy rating of 99.13% which surpasses current state-of-the art approaches.

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Real-Time Driver Sleepiness Detection and Classification Using Fusion Deep Learning Algorithm 5th

Real-Time Driver Sleepiness Detection and Classification Usi...

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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Rajawat, Anand Singh Goyal, S.B. Bhaladhare, Pawan Bedi, Pradeep Verma, Chaman Florin-Emilian, Țurcanu Candin, Mihaltan Traian School of Computer Science and Engineering Sandip University Nashik India Faculty of Information Technology City University Malaysia Petaling Jaya Malaysia Galgotias University Greater Noida India Faculty of Informatics University of Eötvös Loránd Budapest1053 Hungary Department of Building Services Faculty of Civil Engineering and Building Services Gheorghe Asachi Technical University of Iasi Jassy700050 Romania Faculty of Building Services Cluj-Napoca Technical University of Cluj-Napoca Cluj-Napoca400114 Romania

ISBN: (纸本)9789811998751

In the past few years, driving while tired has become one of the main causes of car accidents. This kind of dangerous driving can cause serious injuries or even death and huge financial losses. The face, a significant part of the body, can send many different messages. When a person is tired, their facial expressions, like how often they blink their eyes, differ from when they are alert. This is very clear when it comes to drivers. In this study, a fusion deep learning algorithm (FDLA) is used to determine if a driver is tired and how tired they are in real time. This algorithm uses video footage to determine how tired a driver is based on how often they blink and how long their eyes are closed. We are making a new face-tracking algorithm because the ones that are already out there aren’t good enough. This will make face tracking more accurate. Also, a new way to find problems in facility areas has been made, and it is based on seventy essential spots. After that, these parts of the driver’s face can be used to determine how he or she is feeling. When the eyes and lips are used together, a driver care solution can tell if the driver is getting tired. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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An Integration Of Wireless Communications And Artificial Intelligence For Autonomous Vehicles 3

An Integration Of Wireless Communications And Artificial Int...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Raghuram, Kadambari Altarawni, Ibrahim Vijayalakshmi, K. Kumar, Ajay Jadeja, Abhijeetsinh Chandra, Pankaj Jntuk Department of Computer Science and Engineering Andhra Pradesh Kakinada India Aqaba University of Technology Applied Computing and Artificial Intelligent Department Aqaba Jordan Ramapuram Campus Srmist India INurture Education Solution Pvt. Ltd It Academics Delhi India Sankalchand Patel University Faculty of Computer Science Shri C.J. Patel College of Computer Studies India Guru Ghasi das Viswavidyalaya Dept of Information Technology Chattisgarh Bilaspur India

ISBN: (纸本)9798350399264

The pinnacle of transportation is the development of autonomous driving, which, with the help of CAVs and related traffic management systems, can eventually lead to congestion- and accident-free driving. This vision has as of late prodded extraordinary examination interest in fields including IoV, LTE-V2X, and 5G. In any case, the huge volume of traffic information that CAVs produce makes issues for both the current organizations and the approaching 5G correspondence organizations. For outside network innovations, the VMBS fills in as both a client hub and an edge processing hub. For CAVs, it fills in as a base station and a data caching hub, melding correspondence and calculation. People offer both the VMBS-empowered handset and figuring for CAVs as well as the VMBS-helped wireless innovation for other wireless gadgets to achieve this. It is underlined and addressed that there are a number of research obstacles and open questions. Last but not least, the results of the simulation show that the planned VMBS-CCNA may significantly enhance throughput, latency, and the average amount of links. © 2023 IEEE.

关键词： Autonomous vehicles

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Path optimization method for the sign problem caused by fermion determinant

arXiv

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arXiv 2025年

作者： Hisayoshi, Kazuki Kashiwa, Kouji Namekawa, Yusuke Takase, Hayato Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka811-0295 Japan Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima730-0053 Japan

The path optimization method with machine learning is applied to the one-dimensional massive lattice Thirring model, which has the sign problem caused by the fermion determinant. This study aims to investigate how the path optimization method works for the sign problem. We show that the path optimization method successfully reduces statistical errors and reproduces the analytic results. We also examine an approximation of the Jacobian calculation in the learning process and show that it gives consistent results with those without an approximation. Copyright © 2025, The Authors. All rights reserved.

关键词： Fermions

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Social-enhanced recommendation using graph-based contrastive learning 25

Social-enhanced recommendation using graph-based contrastive...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Xue, Peng Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models. © 2023 IEEE.

关键词： Recommender systems

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Development of Innovative Model for Waste Management System Using Internet of Things (IoT) and Machine Learning

Development of Innovative Model for Waste Management System ...

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International Conference on Informatics and Computing (ICIC)

作者： Dimas Akmarul Putera Ansarullah Lawi Filmada Ocky Saputra Sri Handayani Yun Arifathul Fatimah Ivan Muhammad Reza Sholikun Zainal Arifin Hasibuan Alvendo Wahyu Aranski Faculty of Industrial Technology Institut Teknologi Batam Batam Indonesia Faculty of Computer Science Universitas Dian Nuswantoro Semarang Indonesia Faculty of Health Sciences Universitas Dian Nuswantoro Semarang Indonesia Faculty of Engineering Universitas Muhammadiyah Magelang Magelang Indonesia Semarang City Health Office Semarang Indonesia Faculty of Engineering and Computer Science Universitas Komputer Indonesia Bandung Indonesia Faculty of Information Technology Institut Teknologi Batam Batam Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Effective and efficient waste management system is a significant challenge in coastal areas. This research proposes the development of innovative model for waste management system using IoT and machine learning. The system consists of hardware such as cameras and image sensors installed in coastal locations to monitor the volume and types of waste. The image data collected from these devices is stored and analyzed using image processing techniques. The YOLO (You Only Look Once) algorithm is used to identify and classify the types of waste. The identified data is then used to train a machine learning model, which allows for the prediction of future waste volume and types. These predictions are used to optimize waste collection schedules, reduce operational costs, plan the types of waste processing, and minimize environmental impact. With this holistic approach, the system is expected to enhance the efficiency and sustainability of waste management, providing an innovative solution to waste management issues in coastal areas.

关键词： Waste management YOLO Solid modeling Schedules Image processing Sea measurements Machine learning Predictive models Internet of Things Sustainable development

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Enhancing Speech Emotion Recognition With Conditional Emotion Feature Diffusion and Progressive Interleaved Learning Strategy

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1787-1800页

作者： Yang Liu Xin Chen Zhichao Peng Yongwei Li Xingfeng Li Peng Song Masashi Unoki Zhen Zhao School of Information Science and Technology Qingdao University of Science and Technology Qingdao China School of Information Hunan University of Humanities Science and Technology Loudi China CAS Key Laboratory of Behavioral Science Institute of Psychology Chinese Academy of Sciences Beijing China Faculty of Data Science City University of Macau Macau China School of Computer and Control Engineering Yantai University Yantai China School of Information Science Japan Advanced Institute of Science and Technology Nomi Japan

Speech emotion recognition (SER) aims to identify the speaker's emotional states in specific utterances accurately. However, existing methods still face feature confusion when attempting to recognize certain emotions because traditional acoustic feature extraction methods fail to capture dynamic emotional changes, blurring emotional boundaries. Additionally, existing classification networks (CNs) are constrained by fixed learning strategies, hindering their ability to capture subtle emotional nuances and resulting in label confusion. To address these two issues, we introduce 3D multiresolution modulation filtered cochleogram (MMCG) features by computing the deltas and delta-deltas of MMCG features to enhance the dynamic emotional changes and produce distinct emotional boundaries. We then customize a conditional emotion feature diffusion (CEFD) module, which progressively diffuses features based on emotional context to retain emotional nuances effectively and reduce reliance on conditioned information. In addition, a confidence filtering module is used to filter diffused features based on confidence-based posterior probabilities to ensure enhanced feature discrimination. We design a flexible training strategy named the progressive interleaved learning strategy (PILS) to learn further complex emotional nuances, which consists of two alternating stages: fine-tuning the CN parameters and supervising the CEFD output. Testing on the IEMOCAP, CASIA, and EMODB corpora demonstrates significant performance improvements in SER.

关键词： Feature extraction Emotion recognition Training Three-dimensional displays Diffusion models Speech enhancement Electronic mail Speech recognition Semantics Face recognition

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