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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

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Cooperative Computation Offloading for Video Analysis in Ultra-Dense LEO Satellite-Terrestrial networks 5

Cooperative Computation Offloading for Video Analysis in Ult...

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5th international conference on Information Communication and Signal processing, ICICSP 2022

作者： Zhao, Qi Chen, Tianjiao Liu, Jiang Liu, Fangqi Zhou, Yuke Beijing Institute of Spacecraft Environment Engineering Beijing China Beijing University of Posts and Telecom State Key Laboratory of Networking and Switching Technology Beijing China Beijing University of Posts and Telecom State Key Laboratory of Networking and Switching Technology Purple Mountain Laboratories Nanjing Beijing China

ISBN: (纸本)9781665485890

Video analysis using artificial intelligence (AI) is widely adopted in various services. However, ground users with limited resources may not process such tasks locally. Fortunately, the ultra-dense low earth orbit (LEO) satellite networks allow multiple satellites to cooperatively handle these tasks to provide low-latency computing services. therefore, this paper considers a cooperative computation offloading scheme for video analysis in ultra-dense LEO satellite-terrestrial networks, allowing for flexible task scheduling and video quality selection. Considering the privacy of satellites and the dynamic network environment, the cooperative computation offloading problem is established as a distributed Markov decision process (MDP) to reduce the task delay while increasing the accuracy of video analysis. then, a multi-agent deep reinforcement learning (DRL) approach is proposed to obtain efficient offloading strategies. Finally, simulations are conducted to verify the feasibility and performance of the proposed scheme. © 2022 IEEE.

关键词： Reinforcement learning

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Sensitivity Enhancement of Kretschmann Configured Surface Plasmon Resonance sensor with 2D Nanomaterial: MXene 5th

Sensitivity Enhancement of Kretschmann Configured Surface Pl...

引用

5th international conference on VLSI, Communication and Signal processing, VCAS 2022

作者： Kumar, Rajeev Singh, Maneesh Kumar Pal, Sarika Verma, Alka National Institute of Technology Uttarakhand Srinagar246174 India Department of CSE Dev Bhoomi Uttarakhand University Uttarakhand Dehradun248007 India Institute of Engineering and Rural Technology U.P. Prayagraj211002 India

ISBN: (纸本)9789819909728

We present a SPR sensor consisting of CaF2 prism, Au metal layer and monolayer of MXene (Ti3C2Tx) for biomolecule sensing. the optimized SPR sensor configuration enhanced the sensitivity as well as other performance parameters such as FWHM, DA, and FoM. the theoretical analysis was carried out using the MATLAB and the COMSOL Multiphysics simulation software. the CaF2 prism is coated with metal layer for generation of surface plasmons (SPs), followed by MXene, for attachment of biochemical due to its larger surface area and (H, OH, F) functionalized groups. the simulation result shows that an enhanced maximum sensitivity of 292.55°/RIU for the variation in RIs of sensing medium (Δns = 0.005) at the 633 nm wavelength. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Prisms

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Analyzing Walking and Driving Behavior Across Different Age Groups Using Population Analysis and Correlation networks 5

Analyzing Walking and Driving Behavior Across Different Age ...

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5th international conference on Communications, Signal processing, and their Applications (ICCSPA)

作者： thelagathoti, Rama Krishna Malisetty, Saiteja Ali, Hesham H. Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

ISBN: (纸本)9781665482370

Altered mobility patterns are one of the early symptoms of aging. Several motor-related disorders such as Parkinson's disease are also associated with decreased or altered mobility. However, there is no standard clinical test to identify decreased mobility or diagnose movement variations. In recent years, wearable devices have become popular in measuring mobility parameters and quantifying physical activities such as walking and driving. Furthermore, wearable devices have been widely used in the collection and analysis of mobility data because they are small, affordable, and easy to use. the main objective of this research is to develop a data-driven computational model that can analyze mobility data collected from a group of individuals from different age groups and capture potential aging-related movement variabilities. Such a model would also allow computational tools to extract meaningful correlations between age groups and physical activity. In this study, we have analyzed the mobility data collected from 32 healthy adults from different age groups while they are walking outdoors, climbing stairs, and driving. We developed correlation network models and employed a population analysis-based approach to unravel the associations between aging and physical activities;mainly walking and driving. Although our analysis produced interesting results and identified key parameters that impacted the walking and driving patterns, it didn't significantly different between subjects belonging to different age groups, which we believe is mainly due to the limited use dataset in terms of size and variability. However, the proposed model and recommended approach pave the way for future studies that will further explore the relationships between mobility and aging using richer datasets.

关键词： Aging Mobility parameters Wearable Devices Network Models population analysis

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AoI-Based Priority-Aware Transmission Scheduling for State Estimation in Industrial IoT Systems 5

AoI-Based Priority-Aware Transmission Scheduling for State E...

引用

5th international conference on Communications, Signal processing, and their Applications (ICCSPA)

作者： Lyu, Ling Zhao, Lihong Dai, Yanpeng Dalian Maritime Univ Sch Informat Sci & Technol Dalian 116026 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781665482370

In industrial Internet of things (IoT) systems, many sensors are deployed in the field to sense the running state of equipments. Moreover, the more the sensory information collected and transmitted by sensors, the better the state estimation performance. However, the limited spectrum resources make it challenging to support the simultaneous transmission of all sensors, which will significantly reduce the estimation accuracy. Moreover, due to the heterogeneous sensing abilities of sensors, scheduling different sensors will have distinct contributions on improving the estimation performance. To address this issue, this paper proposes a priority-aware transmission scheduling scheme for state estimation. In particular, the priority is firstly given based on Age of Information (AoI), where AoI characterizes the freshness of sensory information and is related to the historical scheduling situations. then, we investigate the relationship between the AoI of sensory information and the error of state estimation, based on which a priority-aware transmission scheduling algorithm is designed by considering the spectrum resource limitation and the sensing ability heterogeneity. To further improve the estimation accuracy, a constrained optimization problem is formulated to jointly design the transmission scheme and transmit power. Besides, a low-complexity heuristic algorithm is proposed to solve the formulated mixed integer nonlinear problem efficiently. Finally, the simulation results show the effectiveness and superiorities of the proposed transmission scheduling scheme.

关键词： Industrial IoT systems AoI priority transmission scheduling

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New Optimization: Design of a Neural Network Cat and Dog Recognition Prediction Model 5

New Optimization: Design of a Neural Network Cat and Dog Rec...

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2023 5th international conference on Artificial Intelligence and Computer Applications, ICAICA 2023

作者： Zhang, Xinwei Zhang, Longqing Song, Zi'ang Guangdong University of Science and Technology Dongguan China Hebei University of Environmental Engineering Qinhuangdao China

ISBN: (纸本)9798350323313

With the advent of the big data era, there has been a surge in research focused on the application of convolutional neural networks (CNNs) and image processing. Similar to how humans effortlessly identify cats and dogs in our everyday lives, modern machines are also capable of performing this task. One effective approach to achieving this is through leveraging Kaggle's automatic cat and dog identification technology. this study aims to tackle the challenge of recognizing cats and dogs in images with complex appearances by developing a Sequential object-based CNN model. To accomplish this, the cutting-edge deep learning framework PyTorch is utilized, along with high-performance GPUs for computational power. the network is trained and tested separately on images of dogs and cats. Experimental results demonstrate that the CNN model achieves remarkably high recognition accuracy and exhibits exceptional performance in distinguishing between different breeds of dogs and cats. © 2023 IEEE.

关键词： Convolutional neural networks

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Research on Monitoring System of Ocean Observation Buoy Based on Multi-sensor 5th

Research on Monitoring System of Ocean Observation Buoy Base...

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5th EAI international conference on Advanced Hybrid Information processing (ADHIP)

作者： Yan, Xing-kui Zhao, Huan-Yu Qilu Univ Technol Shandong Acad Sci Inst Oceanog Instrumentat Qingdao Peoples R China Shandong Prov Key Lab Ocean Environm Monitoring T Qingdao Peoples R China Natl Engn & Technol Res Ctr Marine Monitoring Equ Qingdao Peoples R China

ISBN: (纸本)9783030945541;9783030945534

With the continuous development of my country's marine three-dimensional monitoring business, it has put forward higher requirements for the stability and reliability of marine monitoring buoy communication equipment. In order to meet the above requirements, this study designed a multi-sensor-based marine observation buoy monitoring system. Optimize the system hardware structure by optimizing the multi-sensor function framework, and then improve the system software operation process to improve the system positioning accuracy and precision. Experimental results show that the multi-sensor-based marine observation buoy monitoring system is highly effective in practical applications and can provide a better reference for the three-dimensional monitoring business of my country's marine environment.

关键词： Multi-sensor Ocean observation Buoy monitoring Positioning accuracy Precision

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A Review on Optimal Deep Learning Based Prediction Model for Multi Disease Prediction 1

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5th international conference on Smart Technologies in Data Science and Communication, SMART-DSC 2022

作者： Minda, Aneel Kumar Ganesan, Vithya International SOS Dubai United Arab Emirates CSE Koneru Lakshmiah Education Foundation Guntur India

ISBN: (数字)9789811968808

ISBN: (纸本)9789811968792

Healthcare data collecting and processing is one of the most worrisome troublesome to optimize the methodology. With the advent of the digital era and technological advancements, a vast quantity of multidimensional data on patients is created, including clinical factors, hospital resources, illness diagnostic information, patients’ records, and medical equipment. the enormous, dense, and complex data must be processed and evaluated to extract knowledge for effective decision-making. Medical data mining offers a lot of potential for uncovering hidden patterns in medical data sets. By identifying significant patterns and detecting correlations and relationships among many variables in huge databases, the use of various data mining tools and machine learning approaches has changed healthcare organizations. this review paper identifies an importation in the medical data, providing and comparing existing data for the future course of action. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data science for healthcare Disease predication Health care Health prediction Medical care data analysis

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Secure IBS Scheme for Vehicular Ad Hoc networks 5th

Secure IBS Scheme for Vehicular Ad Hoc Networks

引用

5th international conference on Soft Computing and Signal processing, ICSCSP 2022

作者： Jenefa, J. Sajini, S. Anita, E. A. Mary Bangalore India SRM Institute of Science and Technology Tamil Nadu Ramapuram India

ISBN: (纸本)9789811986680

Vehicular Ad hoc networks (VANET) have drastically grown in recent years since they provide a better and more secure driving experience. Due to its characteristics, it is vulnerable to many security attacks. Even though many authentication schemes are proposed, their overheads are high. Hence, this study proposes a new Identity-Based Signature (IBS) for authentication with privacy-preservation. It supports secure communications with additional security features. It requires less overhead since it uses XOR operations and one-way hash functions for the signing and verification process. When the proposed scheme’s performance is compared to the recent schemes, it is observed that the proposed approach is more efficient in computation and communication. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Authentication

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Plant Leaves Disease Detection Using Integrated Convolutional Neural networks: A Case Study on Potato 5th

Plant Leaves Disease Detection Using Integrated Convolutiona...

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5th international conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Kumar, Anuj Upreti, Abha Pandey, Vandana School of Computing Graphic Era Hill University Bareilly Road Uttarakhand Haldwani263139 India

ISBN: (纸本)9789819773701

Agriculture is very important for India, making up a big part (17–18%) of the country’s economy. Many people rely on farming for their livelihood. India has a lot of farmland, even more than the U.S. and China. But keeping crops safe from pests and diseases is crucial for getting good amount of produce. the research focuses on using technology like image processing and machine learning to help with this. Specifically, we looked at potato plants and their leaves. the model uses convolutional neural network (CNN) and got really good results. Our model could tell with 99.78% accuracy if a potato leaf was healthy or had diseases like early blight or late blight. the cool thing about our research is that it’s not just for the lab—it can be super helpful for farmers with big fields. the technology we used can quickly check large areas of farmland for any issues. this means we can find and deal with problems early, keeping crops healthy. Using technology like image processing, machine learning, and deep learning makes it easier and more accurate to identify diseases. this is a big deal for farmers with large farms, as it helps them keep an eye on everything without spending too much time. Our research is like giving a boost to India’s farming strength. the high accuracy of the model and its ability to measure the affected leaf area are big steps forward in making farming more precise. As India stays on top in global farming, adopting these advanced technologies is crucial for making sure the future of agriculture is strong and sustainable. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Resource Allocation Strategy for Virtualized Wireless sensor networks Based on Actor-Critic 21

Resource Allocation Strategy for Virtualized Wireless Sensor...

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21st IEEE international conference on Ubiquitous Computing and Communications / 5th IEEE international conference on Data Science and Computational Intelligence / 21st IEEE international conference on Computer and Information Technology

作者： Li, Changjin Zhou, Guangxu Guo, Yue Hou, Junfeng Zhang, Hongjun Li, Song Henan Prov Tobacco Co Xuchang Branch Xuchang 461000 Henan Peoples R China

ISBN: (纸本)9781665477260

Aiming at the problem of effective utilization of virtualized WSN resources, a reinforcement learning-based resource allocation strategy for virtualized WSNs is proposed from the perspective of service requests of virtualized WSNs. A virtual sensor network request deployment utility maximization model is established based on the quality requirements of service sensing information and resource capacity constraints. Considering the randomness of service requests and the dynamic changes in the network environment, the resource optimization problem is transformed into MDP and solved using Actor-Critic reinforcement learning methods, and the best resource allocation strategy is obtained. Finally, a large number of simulation experiments are carried out to verify the effectiveness of the proposed method.

关键词： virtual sensing network virtual network mapping markov decision process resource allocation

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