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A Deep Learning-Based Algorithm for Energy and Performance Optimization of Computational Offloading in Mobile Edge Computing

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Khan, Israr Raza, Salman Rehman, Waheed Ur Khan, Razaullah Nahida, Kiran Tao, Xiaofeng National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science National Textile University Faisalabad Pakistan Department of Computer Science University of Peshawar Peshawar Pakistan Department of Computer Science University of Engineering and Technology Mardan Pakistan Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications China

Mobile edge computing (MEC) has produced incredible outcomes in the context of computationally intensive mobile applications by offloading computation to a neighboring server to limit the energy usage of user equipment (UE). However, choosing a pool of application components to offload in addition to the volume of data transfer along with the latency in communication is an intricate issue. In this article, we introduce a novel energy-efficient offloading scheme based on deep neural networks. The proposed scheme trains an intelligent decision-making model that picks a robust pool of application components. The selection is based on factors such as the remaining UE battery power, network conditions, the volume of data transfer, required energy by the application components, postponements in communication, and computational load. We have designed the cost function taking all the mentioned factors, get the cost for all conceivable combinations of component offloading decisions, pick the robust decisions over an extensive dataset, and train a deep neural network as a substitute for the exhaustive computations associated. Model outcomes illustrate that our proposed scheme is proficient in the context of accuracy, root mean square error (RMSE), mean absolute error (MAE), and energy usage of UE. © 2023 Israr Khan et al.

关键词： Data transfer

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Plant Disease Detection and Classification Using Transfer Learning

Plant Disease Detection and Classification Using Transfer Le...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S. Karthigaiveni Madhubala. C G. Avinash E. Saranya P. Rohini G. Yogapriya Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamilnadu India School of Environment and Architecture Design SRM Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science Engineering Sri Eshwar College of Engineering Chennai India Department of Computer Science and Engineering Saranathan College of Engineering Trichy Tamilnadu Chettinad School of Architecture Chettinad Academy of Research and Education Chennai Tamilnadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Agriculture is a fundamental component of human civilization. It contributes to the economy while also providing sustenance. Plant foliage or crops are susceptible to many illnesses during agricultural agriculture. The illnesses impede the development of their respective species. Timely and accurate identification and categorization of illnesses may mitigate the risk of further harm to the plants. The identification and categorization of these disorders have emerged as significant challenges. The conventional methods used by farmers to forecast and identify plant leaf diseases may be tedious and inaccurate. Challenges may occur while endeavoring to manually forecast illness kinds. The failure to promptly identify and categorize plant diseases may lead to the devastation of crops, causing a substantial reduction in yield. Farmers using automated image processing techniques in their fields may mitigate losses and enhance productivity. This study proposes using a deep learning framework utilizing the latest convolutional neural network model EfficientNet to identify tomato illnesses based on 18,161 plain and segmented photos of tomato leaves. The efficacy of two segmentation theories, namely U-net and Modified U-net, for leaf segmentation is shown. The proposed methodology for plant disease identification is straightforward and computationally efficient, necessitating less time for predictions compared to previous deep learning methodologies. This document presents the computed accuracies for several plant and leaf diseases.

关键词： Deep learning Productivity Plant diseases Image segmentation Accuracy Computational modeling Transfer learning Crops Agriculture Robustness

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Development of Low Cost Plasmonic Tracker Via a Microstepping with PC Based System

Development of Low Cost Plasmonic Tracker Via a Microsteppin...

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2023 International Conference on Engineering technology and Technopreneurship, ICE2T 2023

作者： Abdullah, Muhammad Rosli Harun, Noor Hasmiza Wahab, Azimah Abdul Ibrahim, S Noorjannah Jamilan, Mohd Azerulazree Rahim, Nur Aina Syuhada Universiti Kuala Lumpur British Malaysian Institute Medical Engineering Technology Section Selangor Malaysia Universiti Kuala Lumpur Institute of Medical Science Technology Selangor Malaysia Engineering International Islamic University Department Of Electrical & Computer Gombak Selangor 53100 Malaysia Ministry of Health Malaysia NMCRC Institute for Medical Research Selangor Shah Alam40170 Malaysia

ISBN: (纸本)9798350304107

A plasmonic tracker had been developed for detecting a plasmonic energy at Total Internal Reflectance angle. It is crucial to determine thin film thicknesses, resonance angle and refractive index of a dielectric material. However, a low cost and flexible plasmonic tracker could offers a precise control for accurate measurement system. The main objective is to develop manual, automated and smart control of the plasmonic tracker at high resolution via a PC based system. The plasmonic tracker uses two 1.8° bipolar stepper motor as a prism and silicon photodiode stages. Both of the stepper motors were operated in microstepping that generate 3200 steps at 0.1125° pulse per step. A stable microstepping operation was observed at distance, period, phase angle, torque, RPM and power respectively at 1250 mm/ sec, 90 μS, 3.4°, 0.22 Nm, 200 RPM and 4.64 W. A plasmonic curve and resonance angle were plotted and displayed from a customized software interface which provides high sensitivity, throughput, performance and efficiency. © 2023 IEEE.

关键词： Plasmonics

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SAMOYE: ZERO-SHOT SINGING VOICE CONVERSION MODEL BASED ON FEATURE DISENTANGLEMENT AND ENHANCEMENT

arXiv

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arXiv 2024年

作者： Wang, Zihao Ma, Le Feng, Yongsheng Pan, Xin Jin, Yuhang Zhang, Kejun Department of Computer Science Zhejiang University Hangzhou China Department of AI Music and Music Information Technology Central Conservatory of Music Beijing China Institute of Remote Sensing and Applications Beijing University of Civil Engineering and Architecture Beijing China Department of Computer Science Zhejiang University Hangzhou310027 China Department of Computer Science Zhejiang Key Laboratory of Intelligent Systems and Equipment for Digital Creativity Innovation Center of Yangtze River Delta Zhejiang University Hangzhou China

Singing voice conversion (SVC) aims to convert a singer’s voice to another singer’s from a reference audio while keeping the original semantics. However, existing SVC methods can hardly perform zero-shot due to incomplete feature disentanglement or dependence on the speaker look-up table. We propose the first open-source high-quality zero-shot SVC model SaMoye that can convert singing to human and non-human timbre. SaMoye disentangles the singing voice’s features into content, timbre, and pitch features, where we combine multiple ASR models and compress the content features to reduce timbre leakages. Besides, we enhance the timbre features by unfreezing the speaker encoder and mixing the speaker embedding with top-3 similar speakers. We also establish an unparalleled large-scale dataset to guarantee zero-shot performance, which comprises more than 1,815 hours of pure singing voice and 6,367 speakers. We conduct objective and subjective experiments to find that SaMoye outperforms other models in zero-shot SVC tasks even under extreme conditions like converting singing to animals’ timbre. The weights, code, dataset, and documents of SaMoye are publicly available on https://***/CarlWangChina/*** Codes 68Txx(Primary)14F05, 91Fxx(Secondary) Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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An optimal control strategy for emergency vehicle priority system in smart cities using edge computing and IOT sensors

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Measurement: Sensors 2023年 26卷

作者： Rosayyan, Prakash Paul, Jasmine Subramaniam, Senthilkumar Ganesan, Saravana Ilango Intelligent Transportation and Networking Section of C-DAC Thiruvananthapuram Thiruvananthapuram695033 India Department of Computer Science and Engineering St. Thomas Institute for Science and Technology Thiruvananthapuram695584 India Department of Electrical and Electronics Engineering National Institute of Technology Tiruchirappalli620015 India

Smart city mission is a concept which improves economic growth and quality of life of people by using Information and Communication technology (ICT). Smart cities collect data from various Internet of Things (IoT) sensors and manage the resources and provide service to the public. Emergency Vehicle Priority/Pre-emption (EVP) is one of the major IoT components in smart cities which saves many lives by giving right of way green signal to the Emergency Vehicles (EVs) such as ambulances and fire engines. The existing EVP system gives green signal to the EVs immediate by stopping the conflicting movements. The sudden stoppage of normal traffic as well as the sequence change generates confusion to the road users and drivers. This needs an optimal control strategy which gives green signal to EVs as well as it should not affect the normal traffic flow. This paper proposes an optimal control strategy for EVP using edge computing and IoT sensor for smart cities. The experiment was conducted using a GPS based IoT sensor which keeps on sending the Location Information (LI) to the edge server. The edge server computes the optimum timings based on the proposed control strategy algorithm and clears the emergency vehicles. Waiting time of other road vehicles before and after implementing the system have been studied at a traffic junction at Thiruvananthapuram, India. The comparison result shows that the average waiting time of vehicles on the other road is reduced by 73.23% in the proposed system when compared with the existing system. Since, edge computing has been deployed the latency in communication has been reduced less than 100 ms. So, the proposed solution reduces the waiting time of other road vehicles and reduces the latency in communication simultaneously. © 2023

关键词： Internet of things

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Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond

arXiv

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arXiv 2024年

作者： Wang, Zhiyuan Duan, Jinhao Yuan, Chenxi Chen, Qingyu Chen, Tianlong Zhang, Yue Wang, Ren Shi, Xiaoshuang Xu, Kaidi Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Computer Science Drexel University PhiladelphiaPA19104 United States Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Section of Biomedical Informatics & Data Science Yale School of Medicine Yale University New HavenCT06510 United States Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence (AI) interaction systems, particularly in the domain of healthcare engineering. However, a robust and general uncertainty measure for free-form answers has not been well-established in open-ended medical question-answering (QA) tasks, where generative inequality introduces a large number of irrelevant words and sequences within the generated set for uncertainty quantification (UQ), which can lead to biases. This paper introduces Word-Sequence Entropy (WSE), a method that calibrates uncertainty at both the word and sequence levels, considering semantic relevance. WSE quantifies uncertainty in a way that is more closely aligned with the reliability of LLMs during uncertainty quantification (UQ). We compare WSE with six baseline methods on five free-form medical QA datasets, utilizing seven popular large language models (LLMs). Experimental results demonstrate that WSE exhibits superior performance in UQ under two standard criteria for correctness evaluation. Additionally, in terms of real-world medical QA applications, the performance of LLMs is significantly enhanced (e.g., a 6.36% improvement in model accuracy on the COVID-QA dataset) by employing responses with lower uncertainty that are identified by WSE as final answers, without any additional task-specific fine-tuning or architectural modifications. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Brain Cortical Surface Can Predict Fiber Trajectories

Brain Cortical Surface Can Predict Fiber Trajectories

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IEEE International Symposium on Biomedical Imaging

作者： Tianle Han Yifan Lv Mengshen He Yiheng Liu Tianming Liu Bao Ge School of Physics & Information Technology Shaanxi Normal University Xi'an China Department of Computer Science and Bioimaging Research Center Cortical Architecture Imaging and Discovery Lab The University of Georgia GA USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Gray matter and white matter are fundamental constituents of the brain, and exploring the relationship between them is for gaining insights into the intricate architecture of the brain. This study utilizes information extracted from the gray matter surface of the brain to predict the trajectories of white matter fibers. Specifically, it predicts the coordinates of fiber trajectories by utilizing both fiber endpoints and reference points on the cortical surface. Both qualitative analysis and extensive quantitative experiments confirm that information derived from the brain's cortical surface can be employed to predict fiber trajectories. Furthermore, this conclusion remains robust across diverse fiber tracking and surface reconstruction techniques. The study introduces a novel perspective, elucidating the fundamental principles of fiber connectivity and the intricate relationship between gray matter and white matter.

关键词： Surface reconstruction Brain Pathology Nerve fibers Magnetic resonance imaging Grey matter Streaming media

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TransNILM: A Transformer-based Deep Learning Model for Non-intrusive Load Monitoring 4

TransNILM: A Transformer-based Deep Learning Model for Non-i...

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4th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022

作者： Cheng, Xu Zhao, Meng Zhang, Jianhua Wang, Jinghao Pan, Xueping Liu, Xiufeng Section of Energy Markets Smart Innovation Norway Halden Norway Tianjin University of Technology School of Computer Science and Engineering Tianjin China Norwegian University of Science and Technology Department of Electric Power Engineering Trondheim Norway College of Energy and Electrical Engineering HoHai University Nanjing China Technical University of Denmark Lyngby Department of Technology Management and Economics Denmark

ISBN: (数字)9781665491440

ISBN: (纸本)9781665491440

Non-intrusive load monitoring (NILM) is crucial for energy efficiency in demand-side management. The conventional methods, such as combination optimization or the factorial hidden Markov model, are not effective to obtain the energy consumption information of each appliance. To overcome the limitations of these conventional methods, deep learning-based methods are widely developed. Inspired by recent advances in deep learning models, this paper proposes a novel transformer-based deep learning model for NILM. In this proposed model, transformer-layer enhanced convolutional neural networks (CNN) are firstly utilized for feature learning, a temporal scaling module is then developed for multi-scale information learning. Finally, these learned features are fed into a decoder which consists of three residual GRU modules and a transformer-based CNN module. To evaluate the proposed method, this paper conducts experiments on one residential dataset and one commercial dataset and compares our proposed model with several state-of-The-Art methods. The experimental results demonstrate that the proposed method achieves better performance and has good generalization capability. © 2022 IEEE.

关键词： Hidden Markov models

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Cox-ResNet: A Survival Analysis Model Based on Residual Neural Networks for Gene Expression Data

Cox-ResNet: A Survival Analysis Model Based on Residual Neur...

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IEEE International Conference on Networking, Sensing and Control

作者： Qingyan Yin Wangwang Chen Ruiping Wu Zhi Wei School of Science Xi'an University of Architecture and Technology Xi'an China Department of Computer Science New Jersey Institute of Technology Newark USA

ISBN: (纸本)9781665472449

Survival analysis with genomics data provides a deep understanding of biological processes related to prognosis and disease progression at the molecular level. However, high-dimensional small sample genome data causes computational challenges in survival analysis. To address this problem of overfitting and poor interpretation of existing models, we applied the deep learning technology to genome data and proposed a survival analysis model based on an image-based residual neural network model, called Cox-ResNet. High-dimensional gene expression data was embedded into 2D images according to gene positions on chromosomes, and then a residual network model based on Cox proportional hazards was introduced to perform survival analysis. We demonstrated the performance of Cox-ResNet on five datasets of different cancer types from TCGA, comparing it with the cutting-edge survival analysis methods. The Cox-ResNet model not only shows better performance in prediction accuracy, but also biologically interpretable, by generating heat-maps and prognostic genes for high-risk groups with the guided Grad-Cam visualization method. By performing protein-protein interaction network analysis, we examined hub genes and their biological functions for the bladder cancer. These findings confirm that Cox-ResNet model provides a new solution for discovering the driver genes of poor cancer prognosis.

关键词： Analytical models Biological system modeling Computational modeling Genomics Data models Gene expression Bioinformatics

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3D visualization of hurricane storm surge impact on urban infrastructure

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Urban Informatics 2024年第1期3卷 1-14页

作者： Ye, Xinyue Li, Shoujia Gao, Ge Retchless, David Cai, Zhenhang Newman, Galen Du, Jiaxin Duffield, Nick Department of Landscape Architecture & Urban Planning Texas A&M University College Station USA Department of Computer Science & Engineering Texas A&M University College Station USA Center for Geospatial Sciences Applications and Technology Texas A&M University College Station USA Department of Marine and Coastal Environmental Science Texas A&M University Galveston USA Department of Electrical and Computer Engineering Texas A&M University College Station USA

As climate change intensifies, resulting in more severe rainfall events, coastal cities globally are witnessing significant life and property losses. A growingly crucial component for flood prevention and relief are urban storm flood simulations, which aid in informed decision-making for emergency management. The vastness of data and the intricacies of 3D computations can make visualizing the urban flood effects on infrastructure daunting. This study offers a 3D visualization of the repercussions of hurricane storm surge flooding on Galveston, TX residences, illustrating the impact on each structure and road across varied storm conditions. We employ target detection to pinpoint house door locations, using door inundation as a metric to gauge potential flood damage. Within a GIS-based framework, we model the damage scope for residences exposed to varying storm intensities. Our research achieves three core goals: 1) Estimating the storm inundation levels on homes across different storm conditions; 2) Assessing first-floor elevations to categorize housing damages into three distinct groups; and 3) Through visualization, showcasing the efficacy of a proposed dike designed to shield Galveston Island from future storm surge and flood events.

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