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E-mobility, Power Control and Smart Systems (ICEMPS), International Conference on

作者： Sanjaikanth E Vadakkethil Somanathan Pillai Mayank Jindal Sushant Mimani Nasmin Jiwani T. Kiruthiga School of Electrical Engineering and Computer Science University Of North Dakota Grand Forks USA San Jose California USA GoDaddy Snohomish Washington USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Dept. of ECE Vetri Vinayaha College of Engineering and Technology Trichy INDIA

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

Cellular advert Hoc Networks (MANETs) is self-organizing wireless networks that lack the physical infrastructure and centralized manage of conventional networks. With a purpose to enable powerful communiqué, it's miles important to deal with the resource allocation problem in MANETs. Recently, Cloud Computing algorithms were used to evaluate aid allocation techniques in MANETs. Those algorithms contain sampling strategies, which include nearest neighbor, Monte Carlo, and hybrid strategies, to optimize the useful resource utilization levels. This allows for a more correct assessment of the to be had assets in MANETs. Moreover, these algorithms also can be carried out to autonomously control and time table communication flows with a view to improve standard network performance. This paper affords an outline of the existing aid allocation techniques and their evaluation in MANETs the usage of cloud computing algorithms. It also discusses the impacts of the useful resource control selections in MANETs and the challenges confronted in deploying cloud computing algorithms in these settings. Lastly, a fixed of future studies directions is proposed to in addition improve resource allocation techniques in MANETs.

关键词： Cloud computing Monte Carlo methods Wireless networks Power control Routing Resource management Mobile computing

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Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning

arXiv

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arXiv 2024年

作者： Wu, Man Zheng, Xin Zhang, Qin Shen, Xiao Luo, Xiong Zhu, Xingquan Pan, Shirui The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Monash University Melbourne Australia The College of Computer Science and Software Engineering Shenzhen University Guangdong China The School of Computer Science and Technology Hainan University Haikou China The Dept. of Electrical Engineering & Computer Science Florida Atlantic University United States Griffith University QLD Australia

Graph learning plays a pivotal role and has gained significant attention in various application scenarios, from social network analysis to recommendation systems, for its effectiveness in modeling complex data relations represented by graph structural data. In reality, the real-world graph data typically show dynamics over time, with changing node attributes and edge structure, leading to the severe graph data distribution shift issue. This issue is compounded by the diverse and complex nature of distribution shifts, which can significantly impact the performance of graph learning methods in degraded generalization and adaptation capabilities, posing a substantial challenge to their effectiveness. In this survey, we provide a comprehensive review and summary of the latest approaches, strategies, and insights that address distribution shifts within the context of graph learning. Concretely, according to the observability of distributions in the inference stage and the availability of sufficient supervision information in the training stage, we categorize existing graph learning methods into several essential scenarios, including graph domain adaptation learning, graph out-of-distribution learning, and graph continual learning. For each scenario, a detailed taxonomy is proposed, with specific descriptions and discussions of existing progress made in distribution-shifted graph learning. Additionally, we discuss the potential applications and future directions for graph learning under distribution shifts with a systematic analysis of the current state in this field. The survey is positioned to provide general guidance for the development of effective graph learning algorithms in handling graph distribution shifts, and to stimulate future research and advancements in this area. © 2024, CC BY.

关键词： Graph neural networks

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Self Organized Anchor Group based Routing Protocol for IoT Initial Setup

Self Organized Anchor Group based Routing Protocol for IoT I...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Naveen Kumar Gupta Apeksha Aggarwal Rama Shankar Yadav Rajendra Kumar Nagaria Dept. of Information Technology Center of Energy and Environment Dr B R Ambedkar National Institute of Technology Jalandhar Jalandhar India Dept. of CSE and IT Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj Department of Electronics and Comm. Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Geographical routing approach relies on local geographic information and performs multi-hop routing with the help of local decisions. The nodes are represented by 3D coordinates as some real IoT networks may be deployed in high buildings, hilly areas, underground or underwater. The geographical routing protocols follow the greedy path and suffer from void node problem (VNP). The traditional recovery policies follow a longer path around the boundary nodes of the void region which may go out of energy quickly. Thus, this paper proposes the concept of anchor group and presents an efficient anchor group routing protocol (EAGR) for IoT networks. The anchor group is treated as a sub-destination region where a group of nodes behaves like sub-destinations, which bypass the boundary nodes of the void to minimize the path length and divide the network traffic, in case of void in the network. The paper is also focused on considering the residual energy in the contention-based next-hop selection process. The experimental work has been carried out in the OMNET++ simulator’s INET framework and observed that the proposed method is outperforming existing protocols. Moreover, it has reported a longer network lifetime and is able to survive longer than other existing geographical routing protocols on high load.

关键词： Three-dimensional displays Buildings Telecommunication traffic Routing Routing protocols

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Breast Cancer Prediction Using Machine Learning

Breast Cancer Prediction Using Machine Learning

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Shiv Ashish Dhondiyal Pooja Luthra Bhupender Sharma Rajni Rajni Ishita Uniyal Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Chandigarh University Mohali Punjab Department of Computer Science and Application Coer University Roorkee Uttarakhand Department Of Computer Science & Engineering Guru Jambheshwar University of Science and Technology Hisar Haryana India Department of Electrical Engineering Graphic Era Hill University

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

The number of newly diagnosed cases of breast cancer exceeds 2.3 million annually worldwide. There is a severe lack of valid prognostic and predictive indicators for the clinical treatment of breast cancer patients at the present time. In order to decrease the mortality rate and increase the survival time of breast cancer patients, early detection of this deadly disease is crucial. Improving breast cancer prognosis relies on the analysis and processing of mammography data, the gold standard for screening and early detection. In order to detect breast cancer in mammograms, the Fuzzy C-means algorithm is employed for image segmentation. Next, we train it to identify the characteristics of the segmented regions, and then we use an effective classifier to categorise the trained pictures into their respective mammography classes. This research used the Breast Cancer Wisconsin Diagnostic dataset and two ML algorithms: K-Nearest Neighbours (KNN) and Random Forest. After getting the results, we compared and evaluated the performance of these two classifiers. Potentially useful tools for both early diagnosis and treatment planning include breast cancer prediction models that account for a wide range of risk factors. Effective disease management involves the collection, storage, and administration of many data sets, as well as intelligent systems based on several aspects for the prediction of breast cancer.

关键词： Image segmentation Accuracy Machine learning algorithms Sensitivity Databases Nearest neighbor methods Prediction algorithms Breast cancer Classification algorithms Random forests

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A Novel Cloud-Based Framework for Leaf Disease Detection and Classification: Enhancing Plant Health Using Android Applications

A Novel Cloud-Based Framework for Leaf Disease Detection and...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Ajmeera Kiran Arpit Namdev Radha Raman Chandan Pravin B. Waghmare Dharmesh Dhabliya Ankur Gupta Dept. of CSE MLR Institute of Technology Dundigal Hyderabad Telangana India Dept. of IT University Institute of Technology RGPV Bhopal MP India Dept. of Computer Science School of Management Sciences Varanasi UP India Dept. of Civil Engineering Acharya Shrimannarayan Polytechnic MSBTE Mumbai India Dept. of IT Vishwakarma Institute of Information Technology Pune Maharashtra India Dept. of CSE Vaish College of Engineering Rohtak Haryana India

The difficult challenge of identifying the leaf disease in agriculture is employed to stop the dangerous epidemic. The majority of plant diseases are brought on by fungus, bacteria, and viruses, with the fungi often attached to the plant's reproductive organs. As a result, bacteria have simpler life cycles and are more dangerous than fungi. Similar to bacteria, viruses are microscopic particles that contain genetic information and protein but no additional protein. Therefore, it is crucial to spot illness in plant leaves. Therefore, the goal of this research is to identify leaf disease in a cloudy environment. The use of smartphones for many applications has increased recently. A brand-new operating system called Android is freely accessible in the Android Market. Thus, this study uses an Android application to develop a cloud-based illness categorization system.

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Developing Advanced Security Frameworks for IoT Devices in Connected Healthcare Ecosystems

Developing Advanced Security Frameworks for IoT Devices in C...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Karthik Vivek Veeraiah G. Nageswara Rao Priyanka Torne Aradhana Sahu Arpit Namdev Dept. of ECE Nitte (Deemed to be University) NMAMIT Nitte Udupi India Dept. of Computer Science Sri Siddhartha Academy of Higher Education Karnataka India Dept. of EEE Lakireddy Bali Reddy College of Engineering Mylavaram JNTUK Kakinada AP India Dept. of Electronics SAVPM's Sancheti College of Arts Commerce & Science Pune Maharashtra India Dept. of CSE Rungta College of Engineering and Technology Bhilai Chhattisgarh India Dept. of IT University Institute of Technology RGPV Bhopal MP India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

The quick integration of IoT devices in connected healthcare ecosystems has transformed patient care. These gadgets improve health outcomes, tailored treatment plans, along with real-time monitoring. But the great connectivity and data exchange among these gadgets creates serious security concerns. Suggest the development of improved security systems especially meant for IoT devices in medical environments. Important issues such data privacy, appliance authentication, safe communication, and anomaly detection would be covered by these frameworks. Modern encryption techniques, blockchain technology, along with ML algorithms help our system to achieve strong protection against cyber attacks. Simulations along with real world case studies help to evaluate the proposed framework in order to show its value in lowering risks along with enhancing security posture of linked healthcare environments, presents a road map in case of future developments in field of specialist security solutions in IoT-enabled healthcare industry.

关键词： Industries Roads Ecosystems Data protection Medical services Real-time systems Encryption Security Internet of Things Protection

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On the Connection Between Magnetic-Field Odometry Aided Inertial Navigation and Magnetic-Field SLAM

arXiv

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arXiv 2025年

作者： Skog, Isaac Kok, Manon Hendeby, Gustaf Huang, Chuan Edridge, Thomas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Delft Center for Systems and Control Delft University of Technology Delft Netherlands Dept. of Electrical Engineering Linköping University Linköping Sweden

Magnetic-field simultaneous localization and mapping (SLAM) using consumer-grade inertial and magnetometer sensors offers a scalable, cost-effective solution for indoor localization. However, the rapid error accumulation in the inertial navigation process limits the feasible exploratory phases of these systems. Advances in magnetometer array processing have demonstrated that odometry information, i.e., displacement and rotation information, can be extracted from local magnetic field variations and used to create magnetic-field odometry-aided inertial navigation systems. The error growth rate of these systems is significantly lower than that of standalone inertial navigation systems. This study seeks an answer to whether a magnetic-field SLAM system fed with measurements from a magnetometer array can indirectly extract odometry information — without requiring algorithmic modifications — and thus sustain longer exploratory phases. The theoretical analysis and simulation results show that such a system can extract odometry information and indirectly create a magnetic field odometry-aided inertial navigation system during the exploration phases. However, practical challenges related to map resolution and computational complexity remain significant. © 2025, CC BY.

关键词： Inertial navigation systems

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Enhanced integral imaging 3D dept. map by using SRCNN 13

Enhanced integral imaging 3D depth map by using SRCNN

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13th International Conference on Information and Communication technology Convergence, ICTC 2022

作者： Uchino, Hideaki Lee, Jaehoon Kim, Hyun-Woo Honda, Kazuaki Cho, Myungjin Lee, Min-Chul Kyusyu Institute of Technology Dept. of Computer Science and Networks Fukuoka Iizuka Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Anseong Kyonggi-do Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

In this paper, we propose a method that can generate a three-dimensional (3D) dept. map accurately by using an integral imaging technique through low-resolution elemental images. The conventional method may produce an accurate 3D dept. map when it utilizes high-resolution images. However, if the elemental image has low resolution, it can cause the shifting pixel error when it reconstructs the 3D image by using volumetric computational reconstruction (VCR). Therefore, it may not provide a high-quality 3D dept. map through the reconstructed image. To solve this problem, our proposed method utilizes a machine learning technique that can enhance the resolution of the elemental image. Our proposed method enlarged the low-resolution elemental image. Then, it generates the high-resolution image through the super-resolution convolution neural network (SRCNN). Therefore, it can reduce the shifting pixel error and it can generate various reconstructed images according to the dept.. Finally, it can generate an accurate 3D dept. map through the error-reduced 3D image. To prove our proposed dept. map accuracy, we implement the simulation experiment and evaluate the image through the image quality metric. © 2022 IEEE.

关键词： Errors

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Optimization of EV Scheduling in a Microgrid Integrated with a Rooftop Solar System Using Forward Dynamic Programming

Optimization of EV Scheduling in a Microgrid Integrated with...

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Power and Energy Systems engineering (CPESE), International Conference on

作者： Le The Cuong Hoang Nhat Tuyen Nguyen Duc Hirotaka Takano School of Electronic and Electrical Engineering Hanoi University of Science and Technology Hanoi Vietnam Dept. of Electrical Electronic and Computer Engineering Faculty of Engineering Gifu University Japan

Electric vehicles (EVs) have been a trend in terms of transportation for a decade because of the energy transition. However, as the number of EVs increases, various problems related to the power quality, such as voltage and load profile, arise. To reduce the negative impact of EV charging, several solutions are suggested, with the integration of renewable energy sources into the charging station combined with Vehicle-to-Grid technology. This paper presents an algorithm to minimize the charging cost of all EVs and flatten the load curve, considering the travel schedule of EV owners and voltage constraints in the power grid.

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Tri-Directional Decoder for Edge Discontinuity Classification

Tri-Directional Decoder for Edge Discontinuity Classificatio...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Jiayue Wang Hyuk-Jae Lee Hansang Cho Byungsoo Kang Hyunmin Jung Dept. of Electrical and Computer Engineering Seoul National University Seoul Korea Samsung Electro-Mechanics. CO. LTD. Suwon Korea Dept. of Smart ICT Convergence Engineering Seoul National University of Science and Technology Seoul Korea

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Extracting and exploiting edge information is important for various computer vision tasks. According to the physical characteristics, edges are further categorized into reflectance, illumination, normal, and dept. discontinuities. Previous studies for edge discontinuity classification have achieved impressive performance in extracting discontinuities, but the capacity of classification remains limited. This paper proposes TriDecTr, a novel network comprising a transformer-based encoder to improve semantic understanding and a tri-directional decoder to explore relationships among categories. Extensive experiments demonstrate that TriDecTr achieves state-of-the-art performance on the BSDS-RIND dataset with 0.531 in ODS, 0.571 in OIS, and 0.461 in AP. Moreover, TriDecTr significantly narrows the performance gap between illumination edges and the other categories from 0.191 to 0.118 in ODS.

关键词： Reflectivity computer vision Circuits and systems Image edge detection Semantics Lighting Transformers

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