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SHIS: secure healthcare intelligent scheme in internet of multimedia vehicular environment

Multimedia Tools and Applications 2024年第40期83卷 87781-87800页

作者： Mangla, Cherry Rani, Shalli Dhiman, Gaurav Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Bikram Government College Punjab Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Division of Research and Development Lovely Professional University Phagwara144411 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India MEU research Unit Middle East University Amman Jordan

For providing a safe and secure healthcare environment in vehicular multimedia networks has been emerged as a promising area because of data exchange in multimedia format. Because of properties of Long Term Evolution (LTE) with low delay, high data rate, high mobility, an efficient safe environment can be provided to the in healthcare environment for multimedia content (images, audio, text, video). Despite these benefits, due to the high mobility and data transmission using an open channel, i.e., Internet, multimedia vehicular networks are facing many security breaches in recent years. The Internet of Multimedia Vehicular Environment calls for the establishment of a safe and intelligent healthcare system, and the proposed work problem lays the groundwork for those requirements. One of the security breaches is the Denial of Service (DoS) attack in healthcare. To mitigate this issue, in this paper, we propose, SHIS, a model for detection of Imposter location using two parameters (i) packet counter and (ii) average ratio of packet delivery. It helps to detect attacks faster in comparison to the existing approach (MEC-DoS) for healthcare data. We proposed SHIS to detect denial of service attack to find the location of malicious nodes in the multimedia network. Simulation results showed that SHIS outperformed the existing techniques as it has 20% improvement in detection of attacks as compared to the existing techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Denial-of-service attack

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Radar Probing Optimization for Joint Beamforming and UAV Trajectory Design in UAV-Enabled Integrated Sensing and Communication

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IEEE Transactions on Communications 2024年第6期73卷 4469-4485页

作者： Liu, Yaxi Mao, Wencan He, Boxin Huangfu, Wei Huang, Tianyao Zhang, Haijun Long, Keping University Of Science And Technology Beijing Beijing Advanced Innovation Center For Materials Genome Engineering Beijing Engineering And Technology Research Center For Convergence Networks And Ubiquitous Services School Of Computer And Communication Engineering Beijing100083 China University Of Science And Technology Shunde Innovation School Beijing China National Institute Of Informatics Information Systems Architecture Science Research Division Tokyo101-8430 Japan

Unmanned aerial vehicle (UAV)-enabled massive multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) is an emerging platform to perform communication and sensing efficiently and flexibly. However, the existing works barely consider the radar probing tasks and neglect the benefits of the dedicated sensing signal. In this paper, we focus on joint optimizations in radar probing tasks, and a novel indicator is introduced, namely radar probing error. Two optimizations in radar probing tasks are established: i) joint transmit beamforming design for large-scale regional radar probing and communication task;ii) joint transmit beamforming and UAV trajectory design for communication enhancement and radar probing task. For the former task, we adopt both communication and novel sensing precoders to further support the MIMO radar. A semidefinite relaxation is utilized to relax the original non-convex problem, which is proven to be tight. For the latter task, we adopt block coordinate descent to alternately optimize the precoders and UAV trajectory where the fractional programming approach and successive convex approximation are further adopted. Experiment results testify the validation of the proposed methods for radar probing tasks in UAV-enabled MIMO ISAC. Moreover, results show the fundamental trade-off between the dual functions and reveal the effectiveness of the introduced sensing precoder. © 1972-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Dermoscopic Skin Lesion Classification using Color Features Based on Fine-Tuning of Resnet50 and Densenet121 Transfer Learning Models

Dermoscopic Skin Lesion Classification using Color Features ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence systems, IACIS 2024

作者： Kujani, T. Kumar, V. Dhilip Bhuvanya, R. Kavitha, A. Sathya, T. Sathiya Priya, S. Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Sri Ramachandra Institute of Higher Education and Research Sri Ramachandra Faculty of Engineering and Technology Chennai India Government Polytechnic College Computer Engineering Karur India Sri Sairam Engineering College Computer Science and Business Systems Chennai India

ISBN: (纸本)9798350360660

Skin cancer, which stands as the most prevalent form of human malignancy, is primarily identified through visual examination. The procedure for identifying skin lesions typically starts with an initial clinical assessment, which might be followed by dermoscopic analysis, a biopsy, and a subsequent histopathological checkup. Image-based Skin lesion classification presents a significant challenge due to the subtle and intricate variations in their appearance. Deep Convolutional Neural Network (DCNN) show promising results in managing diverse and intricate tasks involving a wide array of finely detailed object categories. Skin lesions Classification is shown in this work initially using a six-layered CNN performed on the skin cancer dataset to classify seven classes of lesions with 10015 images. Exploratory data analysis of the dataset is performed to identify any outliers and to verify the balance of samples in each class. It was observed after 50 epochs, the training accuracy was 98.03% and the testing accuracy was 67.40%. To avoid the overfitting observed in the CNN approach, the pre-trained transfer learning models, Resnet50 and Densenet121 were finetuned with data augmentation to handle the imbalance of samples present in seven classes. The results obtained prove that the Densenet121 model achieves high performance compared with the other Resnet50 model after augmentation. © 2024 IEEE.

关键词： Convolutional neural networks

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A Two-Stage Stochastic Techno-Economic Optimal ESS Sizing Model to Enable Maximum Exploitation of RESs

A Two-Stage Stochastic Techno-Economic Optimal ESS Sizing Mo...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Mohseni-Bonab, Seyed Masoud Alizadeh, Ali Kamwa, Innocent Rabiee, Abbas Digital Systems Hydro-Quebec Research Institute School of Electrical and Computer Engineering College of Engineering Laval University QC Canada School of Electrical and Computer Engineering College of Engineering Laval University QC Canada University of Zanjan Iran

ISBN: (纸本)9798350396782

Renewable Energy Sources (RESs) are gaining traction in active distribution systems due to their cost-effectiveness and the imperative for energy transition. However, their integration poses technical hurdles, including reverse power flow (RPF) and power balance issues, potentially resulting in energy curtailment. Energy Storage systems (ESSs) have emerged as a pivotal technology to mitigate these challenges by storing energy from RESs and harmonizing their output power with technical constraints. This paper introduces a two-stage techno-economic stochastic model for optimizing ESS sizing, allowing for the maximization of installed RESs capacity while considering their stochastic behavior. Investors can leverage this model to enhance profits by factoring in the economic aspects of ESSs and the potential for renewable energy installation, leading to an increase in revenue. © 2023 IEEE computer Society. All rights reserved.

关键词： energy storage systems monetary value analysis Renewable energy sources two-stage stochastic model

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An Underwater AUV Charging Platform Based on a Novel Hybrid Energy System and Its Energy Scheduling Strategy

An Underwater AUV Charging Platform Based on a Novel Hybrid ...

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2023 IEEE International Conference on Power Science and Technology, ICPST 2023

作者： Zhang, Zhijie Yang, Jie Zhou, Yuying Cai, Zongju Liu, Yuming Yue, Xiaofang CSSC Systems Engineering Research Institute Haidian District Beijing China Beijing Renji Real Estate Development Co. Ltd Beijing China

ISBN: (纸本)9798350311358

To meet the automatic energy replenishment requirements of underwater AUVs, This paper presents a novel underwater self-service charging platform for AUVs. The platform adopts a new integrated architecture of power generation, energy storage, and power charging. For the requirements of the underwater environment, a closed hydrogen oxygen fuel cell system is used on the power generation side. Furthermore, to improve the power generation capacity of the platform, the ocean current power generation system is introduced into the system. On the energy storage side, high specific energy lithium-ion batteries are used. In the power charging subsystem, two sets of charging devices are designed to provide energy for two AUVs simultaneously. And wireless energy transmission method are adopted into the charging devices which can effectively improve charging reliability. DC power supply network is designed to improve platform charging efficiency, and master slave control strategy is utilized to achieve DC bus stability, while each power generation source connecting to the DC bus. For the proposed new charging platform, a new energy scheduling strategy is proposed. The proposed scheduling strategy effectively improves the energy utilization efficiency of the platform while maintaining DC bus voltage stability. The new platform is introduced and modeled in the paper while the energy scheduling algorithm is derived. The simulation of the platform and the energy scheduling strategy is carried out which verifies the effectiveness of the underwater charging platform and scheduling strategy. © 2023 IEEE.

关键词： Energy storage

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Black-hole optimisation algorithm with FOPID-based automation intelligence photovoltaic system for voltage and power issues

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Australian Journal of Electrical and Electronics engineering 2024年第2期21卷 115-127页

作者： Basil, Noorulden Marhoon, Hamzah M. Hayal, Mohammed R. Elsayed, Ebrahim E. Nurhidayat, Irfan Shah, Mohd Asif Department of Electrical Engineering College of Engineering Mustansiriyah University Baghdad Iraq Department of Systems Engineering College of Information Engineering Al-Nahrain University Baghdad Iraq Faculty of Engineering Mansoura University Mansoura Egypt Peneliti Matematika Terapan Jawa Barat Majalengka Indonesia Department of Economics Kabridahar University Kabridahar Ethiopia Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Division of Research and Development Lovely Professional University Punjab Phagwara India

The article discusses the challenges related to voltage and power management in automated intelligent photovoltaic (PV) systems. It suggests that Fractional Order Proportional Integral Derivative (FOPID) controllers can help address these challenges effectively. The research aims to create a highly adaptable and precise automated intelligent PV system that focuses on managing voltage and power issues. It introduces a novel approach using the Black Hole Optimization (BHO) algorithm and leverages the advantages of FOPID controllers for automation. The study also considers thermal models for batteries within the automated intelligent PV system and explores different options to handle voltage and power issues stemming from PV panels. It involves fine-tuning 15 parameters using the BHO algorithm and concludes that one configuration (Instance 1) is preferable due to its superior accuracy and scalability. This choice is supported by objective validation. In essence, your abstract is about improving the performance and automation of PV systems, particularly in dealing with voltage and power challenges, through the application of FOPID controllers and the innovative BHO algorithm. ©, Engineers Australia.

关键词： Two term control systems

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Connection Between Damping Torque Analysis and Energy Flow Analysis in Damping Performance Evaluation for Electromechanical Oscillations in Power systems

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Journal of Modern Power systems and Clean Energy 2022年第1期10卷 19-28页

作者： Yong Hu Siqi Bu Xin Zhang Chi Yung Chung Hui Cai Department of Electrical Engineering and Research Institute for Smart Energy The Hong Kong Polytechnic UniversityHong KongChina Shenzhen Research Institute The Hong Kong Polytechnic UniversityShenzhenChina Centre for Energy Systems and Strategy Cranfield UniversityCranfieldUK Department of Electrical and Computer Engineering University of SaskatchewanSaskatoonCanada State Grid Jiangsu Economic Research Institute NanjingChina

The damping performance evaluation for electromechanical oscillations in power systems is crucial for the stable operation of modern power *** this paper,the connection between two commonly-used damping performance evaluation methods,i.e.,the damping torque analysis(DTA)and energy flow analysis(EFA),are systematically examined and revealed for the better understanding of the oscillatory damping ***,a concept of the aggregated damping torque coefficient is proposed and derived based on DTA of multi-machine power systems,which can characterize the integration effect of the damping contribution from the whole power ***,the pre-processing of measurements at the terminal of a local generator is conducted for EFA,and a concept of the frequency-decomposed energy attenuation coefficient is defined to screen the damping contribution with respect to the interested *** this basis,the frequency spectrum analysis of the energy attenuation coefficient is employed to rigorously prove that the results of DTA and EFA are essentially equivalent,which is valid for arbitrary types of synchronous generator models in multi-machine power ***,the consistency between the aggregated damping torque coefficient and frequency-decomposed energy attenuation coefficient is further verified by the numerical calculation in case *** relationship between the proposed coefficients and the eigenvalue(or damping ratio)is finally revealed,which consolidates the application of the proposed concepts in the damping performance evaluation.

关键词： Electromechanical oscillation damping performance damping torque analysis(DTA) energy flow analysis(EFA) frequency spectrum

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Deep Attention Driven Reinforcement Learning (DAD-RL) for Autonomous Decision-Making in Dynamic Environment

Deep Attention Driven Reinforcement Learning (DAD-RL) for Au...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Chowdhury, Jayabrata Shivaraman, Venkataramanan Dangi, Sumit Sundaram, Suresh Sujit, P.B. Indian Institute of Science Robert Bosch Centre for Cyber-Physical Systems Bangalore India Indian Institute of Science Department of Aerospace Engineering Bangalore India Indian Institute of Science Education and Research Department of Data Science & Engineering and Electrical Engineering & Computer Sci-ence Bhopal India

ISBN: (纸本)9781665410205

Autonomous Vehicle (AV) decision-making in ur-ban environments is inherently challenging due to the dynamic interactions with surrounding vehicles. For safe planning, AV/ego must understand the weightage of various spatiotemporal interactions in a scene. Contemporary works use colos-sal transformer architectures to encode interactions mainly for trajectory prediction, resulting in increased computational complexity. To address this issue without compromising spatiotemporal understanding and performance, we propose the simple Deep Attention Driven Reinforcement Learning (DAD-RL) framework, which dynamically assigns and incorporates the significance of surrounding vehicles into the ego's RL-driven decision-making process. We introduce an AV-centric spatiotemporal attention encoding (STAE) mechanism for learning the dynamic interactions with different surrounding vehicles. To understand map and route context, we employ a context encoder to extract features from context maps. The spatiotemporal representations combined with contextual encoding provide a comprehensive state representation. The resulting model is trained using the Soft-Actor Critic (SAC) algorithm. We evaluate the proposed framework on the SMARTS urban benchmarking scenarios without traffic signals to demonstrate that DAD-RL outperforms recent state-of-the-art methods. Furthermore, an ablation study underscores the importance of the context-encoder and spatiotemporal attention encoder in achieving superior performance. © 2024 IEEE.

关键词： Deep reinforcement learning

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Generational Genetic Mechanism of Space Mutagenesis in Rice Based on Multi-Omics

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Rice Science 2025年第3期32卷 400-425页

作者： ZENG Deyong CUI Jie YIN Yishu DAI Cuihong YU Wencheng ZHAO Haitian GUAN Shuanghong CHENG Dayou SUN Yeqing LU Weihong Department of Food Science and Engineering School of Medicine and Health Harbin Institute of Technology National and Local Joint Engineering Laboratory for Synthesis Transformation and Separation of Extreme Environmental NutrientsHarbin Institute of Technology The Intelligent Equipment Research Center for the Development of Special Medicine and Food Resources Chongqing Research Institute of Harbin Institute of Technology Environmental Systems Biology Institute Dalian Maritime University

Intergenerational inheritance of stress memory plays a crucial role in plant adaptation to environmental changes, particularly in the context of spaceflight, where plants may serve as a food source for humans on long-duration missions. However, the intergenerational genetic effects of spaceflightinduced stress memory in plants remain unclear. In this study, we assessed the cross-generational genetic effects of spaceflight stress memory using the rice mutant B10, identified during the SJ-10 return satellite mission. Our results showed that the oxidative stress effects induced by spaceflight persisted until the M5 generation in rice. We found that the rice genome remained unstable post-spaceflight, leading to alterations in genome methylation levels. Additionally, we observed significant changes in the methylation levels of transposons, suggesting their involvement in the intergenerational inheritance of spaceflightinduced stress memory. Furthermore, we identified thousands of differentially expressed genes(DEGs) and differentially alternatively spliced(DAS) genes induced by spaceflight stress memory across multiple rice generations. Notably, differentially methylated cytosines were more abundant in non-expressed genes than in DEGs or DAS genes. A substantial number of DEGs and DASs related to oxidative stress were identified, primarily involved in the generation and scavenging of reactive oxygen species. This study also presented report on the response of alternative splicing events in rice to spaceflight stress. Moreover, our findings revealed that genome methylation was associated with gene expression but not with DAS. In conclusion, our study provides comprehensive insights into the intergenerational inheritance of spaceflight-induced stress in rice and may contribute to uncovering novel mechanisms of oxidative stress-induced genomic instability and epigenetic regulation in plant stress inheritance.

关键词： DNA methylation intergenerational inheritance rice SJ-10 return satellite spaceflight stress memory

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Speech Enhancement Using U-Net-Based Progressive Learning with Squeeze-TCN 5th

Speech Enhancement Using U-Net-Based Progressive Learning wi...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Vanambathina, Sunny Dayal Nandyala, Sivaprasad Jannu, Chaitanya Sirisha Devi, J. Yechuri, Sivaramakrishna Parisae, Veeraswamy School of Electronics Engineering VIT-AP University Amaravati India Secure Systems Research Center Technology Innovation Institute Masdar City United Arab Emirates NRI Institute of Technology Vijayawada Agiripalli India Computer Science and Engineering Institute of Aeronautical Engineering Telangana Hyderabad India

ISBN: (纸本)9789819735228

Speech enhancement is crucial in many speech processing applications. Recently, researchers have been exploring ways to improve performance by effectively capturing the long-term contextual relationships within speech signals. Nevertheless, these investigations often neglect the significance of the time-frequency (T-F) distribution details of speech spectral elements, which are also important for improving speech quality. Moreover, accurately mapping spectral information in supervised methods requires both global and local information. This paper presents a novel speech enhancement model that utilizes long-term and time-frequency (T-F) information of speech spectral elements for enhancing target speakers. The system is composed of a feature extractor and reconstructor. The feature extractor introduces the U-Net block to enable feature recalibration from multiple scales. Furthermore, the time-frequency attention (TFA) within the U-net’s feature extractor delineates the prominent time-frequency distribution of speech through the simultaneous utilization of two branches: one for time-frame attention and another for frequency channel attention. TFA involves the assignment of distinct attention weights to individual time-frequency spectral components, empowering the models to concentrate on both the "when" (time frames) and the "what" (frequency-wise channels). In the reconstructor, several filtering modules (FMs) are stacked to facilitate the reconstruction of a spectrum in a progressive manner to obtain a coarse estimation by reducing noise in the magnitude domain. Furthermore, the intermediate outcome can be progressively improved across stages by unfolding the filtering modules repeatedly, ultimately resulting in the estimation of the spectrum. The proposed approach is tested on the Librispeech and Voicebank datasets. The findings indicate that this method surpasses earlier models and attains the state-of-the-art performance on these datasets. © The Author(s), under

关键词： Deep neural networks

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