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SSRN 2025年

作者： Sachdeva, Shelly Shankar, Saumil Soni, Kanika Wróblewska, Anna Paprzycki, Marcin Ganzha, Maria Department of Computer Science and Engineering National Institute of Technology Delhi Plot No. FA7Zone P1 GT Karnal Road Delhi110036 India Faculty of Mathematics and Information Science Warsaw University of Technology ul. Koszykowa 75 Warsaw00-662 Poland Systems Research Institute Polish Academy of Sciences ul.Newelska 6 Warsaw01-447 Poland

In the evolving landscape of healthcare, there is a growing need for advanced support for medical professionals, to deliver high-quality, personalized care. While traditional medical practices have served well, they are challenged by the complexities of modern medicine, from the vast amounts of medical data to the demand for faster, more accurate diagnoses and treatments. As the healthcare sector looks to innovate, one promising area is application of Large Language Models (LLMs), which have shown conversational capabilities across multiple domains. In this context, LLMs have demonstrated the ability to process complex medical data, generate treatment recommendations, and enhance communication between patients and healthcare providers. However, utilizing LLMs in healthcare confronts important challenges including hallucinations, accuracy of information, and handling of sensitive *** contribution introduces HCarePilot, a novel co-pilot system, designed to bridge the gap between traditional medical practices and the possibilities offered by LLMs. HCarePilot aims at developing comprehensive LLM-based medical support, capable of analyzing complex medical data, offering personalised treatment recommendations, and minimizing *** leverages transformers and attention mechanisms, while incorporating vector databases to enhance the retrieval of the relevant medical ***, the Gale Encyclopedia of Medicine was used as the primary dataset. This dataset served as the foundation for the RAG and for the evaluation of the performance of multiple retrieval and language models. Specifically, after processing 30 prompts of varying complexity, the results of HCarePilot, have been compared with other medical-based LLMs, i.e. *** and Ada Health. The comparisons were made across multiple metrics, including relevance score, F1 score, BLEU score, and METEOR score. Based on the relevance score, HCarePilot demonstrated superior performance, outperforming

关键词： Theranostics

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Segmentation of breast cancer using fuzzy C means and classification by SVM based on LBP features

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AIP Conference Proceedings 2022年第1期2405卷

作者： M. Malathi P. Sinthia G. Aloy Anuja Mary M. Nalini Fareen Farzana Wahed 1Department of Electronics and Communication Rajalakshmi Institute of Technology Chennai 600 124 India 2Department of Biomedical Engineering Saveetha Engineering College Chennai 602105 India 3Department of Electronics and Communication Engineering Vel Tech RangarajanDrSagunthala R&D Institute of Science and Technology Chennai 600 062 India 4Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India 5Department of Electronics and Communication Engineering Saveetha Engineering College Chennai 602105 India

Recent years many women are affected by breast cancer. Mammogram is one of the early breast cancer diagnosis techniques used to identify the abnormal regions of breast. The recommended research work uses Fuzzy C-means segmentation algorithm to locate the wound area of mammogram breast image. Further the features of abnormal regions were extracted using Local Binary pattern (LBP) techniques. The statistical features are Entropy, Mean, RMS (Root Mean Square), correlation helps to train the neural network. Finally SVM (Support Vector Machine) classifier is utilized to categorize the abnormal regions of mammogram images with the accuracy rates of 86%.

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Model-Free Optimization Scheme for Efficiency Improvement of Wind Farm Using Decentralized Reinforcement Learning ⁎

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IFAC-PapersOnLine 2020年第2期53卷 12103-12108页

作者： Zhiwei Xu Hua Geng Bing Chu Menghao Qian Ni Tan Department of Automation Tsinghua University and Beijing National Research Center for Information Science and Technology Beijing 100190 China School of Electronics and Computer Science Faculty of Physical Sciences and Engineering University of Southampton Southampton SO17 1BJ U.K. Department of Electrical Engineering Tsinghua University Beijing 100190 China

Wake interactions caused by the complex wakes between the turbines within a wind farm have significant adverse effect on the total power generation of the wind farm. To mitigate the effect of wake interactions and optimize the total power output of wind farm, this paper proposes a model-free control scheme using reinforcement learning by developing a decentralized Q learning method. The proposed approach guarantees that the output power of wind farm converges to the optimal total power under different wind conditions, and further ensures the gradual changes of control variables of wind turbines and thus avoids the unexpected sharp drop of the power generation performance of wind farm. Simulation results are provided to demonstrate the effectiveness of the proposed method.

关键词： Wind farm power optimization model-free approach decentralized control Q learning method

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Intrinsic skyrmions in monolayer Janus magnets

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Physical Review B 2020年第9期101卷 094420-094420页

作者： Jiaren Yuan Yumeng Yang Yongqing Cai Yihong Wu Yuanping Chen Xiaohong Yan Lei Shen (沈雷) Faculty of Science and School of Material Science and Engineering Jiangsu University Zhenjiang 212013 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 School of Information Science and Technology ShanghaiTech University Shanghai 201210 China Joint Key Laboratory of the Ministry of Education Institute of Applied Physics and Materials Engineering University of Macau Macau 999078 China Department of Mechanical Engineering and Engineering Science National University of Singapore 9 Engineering Drive 1 Singapore 117542

Skyrmions are localized solitonic spin textures with protected topology, which are promising as information carriers in ultradense and energy-efficient logic and memory devices. Recently, magnetic skyrmions have been observed in magnetic thin films, and are stabilized by the extrinsic interfacial Dzyaloshinskii-Moriya interaction (DMI) and/or external magnetic fields. The specific effects in magnetic monolayer materials have not been thoroughly studied. Here, we investigate the intrinsic magnetic skyrmions in a family of monolayer Janus van der Waals magnets, MnSTe, MnSeTe, VSeTe, and MnSSe, by the first-principles calculations combined with the micromagnetic simulations. The monolayer Janus MnSTe, MnSeTe, and VSeTe with out-of-plane geometric asymmetry and strong spin-orbit coupling have a large intrinsic DMI, which could stabilize a sub-50-nm intrinsic skyrmions in monolayer MnSTe and MnSeTe at zero magnetic field. Whereas a monolayer VSeTe with an in-plane easy axis forms a magnetic domain rather than skyrmions. Moreover, the size and shape of skyrmions can be tuned by an external magnetic field. Therefore, our work motivates a vista for seeking intrinsic skyrmions in atomic-scale magnets.

关键词： Dzyaloshinskii-Moriya interaction Skyrmions Monolayer films Van der Waals systems Density functional calculations First-principles calculations

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3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video

arXiv

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arXiv 2021年

作者： Wang, Guangming Zhong, Jiquan Zhao, Shijie Wu, Wenhua Liu, Zhe Wang, Hesheng Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Engineering Mechanics Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom

Depth and ego-motion estimations are essential for the localization and navigation of autonomous robots and autonomous driving. Recent studies make it possible to learn the per-pixel depth and ego-motion from the unlabeled monocular video. A novel unsupervised training framework is proposed with 3D hierarchical refinement and augmentation using explicit 3D geometry. In this framework, the depth and pose estimations are hierarchically and mutually coupled to refine the estimated pose layer by layer. The intermediate view image is proposed and synthesized by warping the pixels in an image with the estimated depth and coarse pose. Then, the residual pose transformation can be estimated from the new view image and the image of the adjacent frame to refine the coarse pose. The iterative refinement is implemented in a differentiable manner in this paper, making the whole framework optimized uniformly. Meanwhile, a new image augmentation method is proposed for the pose estimation by synthesizing a new view image, which creatively augments the pose in 3D space but gets a new augmented 2D image. The experiments on KITTI demonstrate that our depth estimation achieves state-of-the-art performance and even surpasses recent approaches that utilize other auxiliary tasks. Our visual odometry outperforms all recent unsupervised monocular learning-based methods and achieves competitive performance to the geometry-based method, ORB-SLAM2 with back-end optimization. Copyright © 2021, The Authors. All rights reserved.

关键词： Robots

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Enhancing effectivity of automated irrigation system using fuzzy logic

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AIP Conference Proceedings 2021年第1期2336卷

作者： S. Krishna Bhat Sukanya S. Kumar Keerthana Krishnakumar Sherin Shaju Pankaj Kumar G. 1Department of Computer Science and Engineering Federal Institute of Science And Technology (FISAT) Angamaly Kerala-683 577 India 2Department of Electrical and Electronics Engineering Mets School of Engineering Mala Kerala-680 732 India

Irrigation in agricultural lands is critical and time-consuming and its automation possess huge challenges. A smart irri- gation system needs real-time values of moisture, humidity, temperature etc. Capturing the real-time values of moisture, humidity, temperature etc. requires the deployment of multiple sensors at multiple locations. The occurrence of localized phenomena can affect the proper functioning of the irrigation system. Determining the parameters and deciding how much irrigation is to be done, is a daunting task. Moreover, these values can be altered or falsely measured due to highly localized situations or interference, like the proximity of a generator when it switches on can alter the temperature measured by the nearest sensor, or that of a water container or bucket placed near the sensor can alter humidity values. All these factors can affect the judgement of how irrigation is done by the automation system and hence affect the crops, as these readings would cause either over-irrigation or dehydration of certain areas of the crop, and can result in damaged or reduced productivity. Our approach consists of deploying a large number of sensors throughout a large agricultural land and aggregating the data. The localized errors can be weeded out through proper analysis of sensor data. We used a fuzzy logic-based system to make decisions on irrigation. Certain instances may require human intervention to resolve, such as intentional tampering of sensors or destruction of sensors due to pests or natural phenomenon. The system detects and alerts the farmer or worker to check in at that particular site.

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Exploiting deep learning for secure transmission in an underlay cognitive radio network

arXiv

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arXiv 2021年

作者： Zhang, Miao Cumanan, Kanapathippillai Thiyagalingam, Jeyarajan Tang, Yanqun Wang, Wei Ding, Zhiguo Dobre, Octavia A. School of Information Science and Engineering Chongqing Jiaotong University Chongqing China Department of Electronic Engineering University of York YorkYO10 5DD United Kingdom Scientific Computing Department of Rutherford Appleton Laboratory Science and Technology Facilities Council Harwell Campus Ditcot United Kingdom School of Electronics and Communication Engineering Sun Yat-Sen University Shenzhen510006 China School of Information Science and Technology Nantong University Nantong China Nantong Research Institute for Advanced Communication Technologies Nantong China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China School of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom Department of Electrical and Computer Engineering Memorial University St. John’sNLA1B 3X5 Canada

This paper investigates a machine learning-based power allocation design for secure transmission in a cognitive radio (CR) network. In particular, a neural network (NN)based approach is proposed to maximize the secrecy rate of the secondary receiver under the constraints of total transmit power of secondary transmitter, and the interference leakage to the primary receiver, within which three different regularization schemes are developed. The key advantage of the proposed algorithm over conventional approaches is the capability to solve the power allocation problem with both perfect and imperfect channel state information. In a conventional setting, two completely different optimization frameworks have to be designed, namely the robust and non-robust designs. Furthermore, conventional algorithms are often based on iterative techniques, and hence, they require a considerable number of iterations, rendering them less suitable in future wireless networks where there are very stringent delay constraints. To meet the unprecedented requirements of future ultra-reliable low-latency networks, we propose an NN-based approach that can determine the power allocation in a CR network with significantly reduced computational time and complexity. As this trained NN only requires a small number of linear operations to yield the required power allocations, the approach can also be extended to different delay sensitive applications and services in future wireless networks. When evaluate the proposed method versus conventional approaches, using a suitable test set, the proposed approach can achieve more than 94% of the secrecy rate performance with less than 1% computation time and more than 93% satisfaction of interference leakage constraints. These results are obtained with significant reduction in computational time, which we believe that it is suitable for future real-time wireless applications. © 2021, CC0.

关键词： Cognitive radio

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Intuitionistic fuzzy clustering and natural exponential inertia weight with Artificial Bee Colony based scheduling approach for mobile grid

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AIP Conference Proceedings 2021年第1期2387卷

作者： Kavitha Bharathi Shanmugam Dhavamani Mohanasundaram 1Department of Computer Technology –UG Kongu Engineering College Perundurai Erode Tamilnadu India - 638 060 2Department of Mathematics Kongu Engineering College Perundurai Erode Tamilnadu India - 638 060

Research in mobile grid computing is inspired by the broad benefits of intensive scientific and enterprise applications by the scientific community in grid and mobile computing. The primary aim of grid computing is to combine the power of wide-ranging resources that are very diverse in nature and provide users with essential services to users in the future. Resources are complex and varying in a mobile grid system and the availability is limited for a period of time. With resource mobility in mind, classification is the most challenging and primitive challenge in the mobile grid environment. The previous paradigm outlined the Quantum based approach on Gravitational search Algorithm (QBGSA) for scheduling the task (job) on a mobile computing grid. In any case, it has problem with computational difficulty and execution time. To solve this problem, the proposed system designed an Intuitionistic Fuzzy C-Means (IFCM) clustering and Natural Exponential Inertia Weight with Artificial Bee Colony (NEIWABC) based scheduling approaches. In this proposed research work, grid server has all the information about mobile grid networks. Once it receiving the task request from user, it verifies the available resource providers. The nodes are clustered by using Intuitionistic Fuzzy C-Means (IFCM) clustering algorithm and Cluster Head (CH) selection is done using mobile capacity such as storage capacity, computational power. Then the task request is promoting to the selected CH in the cluster. Then the CH schedules the jobs to the appropriate mobiles to respond user. The task scheduling is performed by using Natural Exponential Inertia Weight with Artificial Bee Colony (NEIWABC) algorithm. The results of this experiment depicts that the proposed system attains good efficiency on comparison with previous system.

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On the effective capacity of an underwater acoustic channel under impersonation attack

arXiv

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arXiv 2020年

作者： Aman, Waqas Haider, Zeeshan Waqas H. Shah, S. Mahboob Ur Rahman, M. Dobre, Octavia A. Electrical engineering department Information Technology University Lahore54000 Pakistan Computer Laboratory University of Cambridge CambridgeCB3 0FD United Kingdom Department of Electrical and Computer engineering Memorial University St. John'sNLA1B 3X5 Canada

This paper investigates the impact of authentication on effective capacity (EC) of an underwater acoustic (UWA) channel. Specifically, the UWA channel is under impersonation attack by a malicious node (Eve) present in the close vicinity of the legitimate node pair (Alice and Bob);Eve tries to inject its malicious data into the system by making Bob believe that she is indeed Alice. To thwart the impersonation attack by Eve, Bob utilizes the distance of the transmit node as the feature/fingerprint to carry out feature-based authentication at the physical layer. Due to authentication at Bob, due to lack of channel knowledge at the transmit node (Alice or Eve), and due to the threshold-based decoding error model, the relevant dynamics of the considered system could be modelled by a Markov chain (MC). Thus, we compute the state-transition probabilities of the MC, and the moment generating function for the service process corresponding to each state. This enables us to derive a closed-form expression of the EC in terms of authentication parameters. Furthermore, we compute the optimal transmission rate (at Alice) through gradient-descent (GD) technique and artificial neural network (ANN) method. Simulation results show that the EC decreases under severe authentication constraints (i.e., more false alarms and more transmissions by Eve). Simulation results also reveal that the (optimal transmission rate) performance of the ANN technique is quite close to that of the GD method. Copyright © 2020, The Authors. All rights reserved.

关键词： Quality of service

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Error analysis of the numerical method for correcting the propagation of EM waves in the troposphere

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Journal of Microwaves, Optoelectronics and Electromagnetic Applications 2020年第3期19卷 407-414页

作者： Liu, Yuanzhi Tang, Tao Liu, Guo Olaimat, M. Science and Technology on Electronic Information Control Laboratory Southwest China Research Institute of Electronic Equipment Chengdu610036 China College of Electronic Engineering Chengdu University of Information Technology Chengdu610025 China Department of Electrical and Computer Engineering University of Waterloo WaterlooN2L 3G1 Canada School of Electrical Engineering and Computer Science University of Ottawa OttawaK1N 6N5 Canada Key laboratory of Atmospheric Sounding China Meteorological Administration Chengdu610225 China

Electromagnetic (EM) waves do not travel in straight lines in the troposphere due to refraction. This leads to errors in many engineering applications. In order to correct these errors, a numerical solution based on the difference method has been proposed, which can reveal the degree of EM wave bending owing to atmospheric refraction, moreover, it can predict the actual path of the EM wave propagating in the troposphere. The error of the numerical method is studied here, which reveals the source of the error. Based on this, two improvement methods are proposed. The computational accuracy of the numerical methods is verified by comparison with the analytical solution. The proposed method ensures the computational efficiency and accuracy of the problem of refraction of EM waves in the troposphere. © 2020 SBMO/SBMag

关键词： Error analysis

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