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检索条件"任意字段=3rd International Symposium on Information Processing in Sensor Networks"

共 1447 条记录，以下是171-180 订阅

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Virtual Modeling of Vision sensor on Smart Cars 3

Virtual Modeling of Vision Sensor on Smart Cars

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2020 3rd international symposium on Traffic Transportation and Civil Architecture, ISTTCA 2020

作者： Zhang, Ning-Ning Yang, Xiang Yang, Xu Xine-Yan Wang, Ya-Ou Li-Gao Wang, Xing- Ren Xi'An Traffic Engineering Institute Xi'an Shaanxi China Yunnan Science Research Institute of Communication Co. Ltd. Kunming Yunnan China

In order to build a visual sensor in a virtual environment and simulate a smart car obtaining information from external environment, the OpenGL development environment is established based on the Microsoft Visual C++6.0 language program;then the image acquisition unit and processing unit model is built, and finally the vision sensor model is installed on the car model to realize the virtual modeling of the vision sensor on smart cars, so that the smart car can obtain information from external environment in a virtual manner. © Published under licence by IOP Publishing Ltd.

关键词： Virtual reality

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Reinforcement Learning based Smart Energy Management System for Indoor Lighting

Reinforcement Learning based Smart Energy Management System ...

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international symposium on Advanced Computing and Communication (ISACC)

作者： Himanshu Ladhiya Rajib Malik Department of Electronics and Communication Engineering Indian Institute of Information Technology Guwahati Guwahati Assam India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The design of energy-efficient indoor lighting systems is a very interesting research challenge. Although this field has experienced significant developments, Reinforcement Learning (RL) remains relatively under explored. With reward-based optimization, RL seems to be the promising approach toward achieving sustainable and adaptive lighting solutions. The proposed approach introduces an RL-driven lighting control system that dynamically adjusts the brightness and orientation of LED lighting to optimize energy consumption while ensuring comfort for the users. The approach employs the Soft Actor-Critic (SAC) algorithm for processing of sensor data, related to light intensity, glare, and user response, to come up with lighting strategies at any point in time. The prototype system developed in simulation tests reveal that, compared to existing systems, energy usage can be reduced up to 25% without compromising the level of user comport. The results demonstrate the next generation of energy-efficient interior lighting solutions by providing a scalable, intelligent solution to smart building applications.

关键词： Technological innovation Smart buildings Energy conservation Lighting Reinforcement learning Pulse width modulation Light emitting diodes Energy efficiency Lighting control Testing

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IoT Based Coal Mine Safety and Health Monitoring System using LoRaWAN 3

IoT Based Coal Mine Safety and Health Monitoring System usin...

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3rd international Conference on Signal processing and Communication (ICPSC)

作者： Porselvi, T. Ganesh, Sai C. S. Janaki, B. Priyadarshini, K. Begam, Shajitha S. Sri Sai Ram Engn Coll Dept Elect & Elect Engn Chennai Tamil Nadu India Sri Sai Ram Engn Coll Dept EEE Chennai Tamil Nadu India

ISBN: (纸本)9781665428644

Today, the safety of miner workers is a significant challenge. Miner's health and life are helpless against a few basic issues, which incorporates the working environment, yet in addition its delayed consequence. To extent profitability and diminish the expense of mining alongside the thought of the safety of miners, a creative and innovative methodology is required. The proposed system comprises of two sections one to monitor the mineworker status and another one is the total monitoring section. In the mine laborer area, air contamination is primarily due to the outflows because of emissions of particulate matter and gases incorporate such as Sulphur dioxide (SO2), Nitrogen dioxide (NO2), Carbon monoxide (CO) and so forth we are utilizing two smoke sensors to monitor the diverse type of smoke level in the mine. To monitor the concentration level of unsafe gases, semiconductor gas sensors are utilized. If any smoke sensor value goes beyond the threshold range at that point the microcontroller will allow an alert to the person through a buzzer and sends the information to the monitoring section through the LoRaWAN module and in the monitor received data will be uploaded in the webpage through IoT. LoRaWAN is a media access control(MAC) and multi-point protocol for wide range networks. It enables lowpowered devices to communicate with Internet of Things (IoT) applications over long-run remote connections. LoRaWAN uses lower radio frequencies with a longer range. The LoRaWAN is a low-power, wide area networking (LPWAN) protocol based on LoRa Technology. LoRaWAN is highly considerable for its optimizing LPWAN for its range, battery capacity, durability, and cost-efficient. In underground mining, there are various factors due to which miners tumble down and lose consciousness. To overcome this issue, the system gives a crisis alarm to the supervisor using the LoRaWAN module if an individual falls somewhere by any reason.

关键词： Coal Mining Safety Engineering Internet of Things (IoT) Smoke sensor LoRaWAN LPWAN

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Efficiently Training On-Policy Actor-Critic networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration 3

Efficiently Training On-Policy Actor-Critic Networks in Robo...

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3rd international symposium on Robotics and Intelligent Manufacturing Technology, ISRIMT 2021

作者： Chen, Zhaorun Chen, Binhao Xie, Shenghan Gong, Liang Liu, Chengliang Zhang, Zhengfeng Zhang, Junping Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering China Shanghai Jiao Tong University School of Mechanical Engineering China Fudan University Shanghai Key Lab of Intelligent Information Processing China

ISBN: (纸本)9781665437189

In complex environments with high dimension, training a reinforcement learning (RL) model from scratch often suffers from lengthy and tedious collection of agent-environment interactions. Instead, leveraging expert demonstration to guide RL agent can boost sample efficiency and improve final convergence. In order to better integrate expert prior with on-policy RL models, we propose a generic framework for Learning from Demonstration (LfD) based on actor-critic algorithms. Technically, we first employ K-Means clustering to evaluate the similarity of sampled exploration with demonstration data. Then we increase the likelihood of actions in similar frames by modifying the gradient update strategy to leverage demonstration. We conduct experiments on 4 standard benchmark environments in Mujoco and 2 self-designed robotic environments. Results show that, under certain condition, our algorithm can improve sample efficiency by 20% 40%. By combining our framework with on-policy algorithms, RL models can accelerate convergence and obtain better final mean episode rewards especially in complex robotic context where interactions are expensive. © 2021 IEEE.

关键词： Reinforcement learning

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Possibilities and Limitations of Object Detection Using Lidar

Possibilities and Limitations of Object Detection Using Lida...

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23rd international Stuttgart symposium, ISSYM 2023

作者： Trautmann, Toralf Blechschmidt, Fritz Friedrich, Matthias Mendt, Franziskus Friedrich-List-Platz 1 Dresden01069 Germany TraceTronic GmbH Stuttgarter Str. 3 Dresden01189 Germany ZAFT e. V. at HTW Dresden Friedrich-List-Platz 1 Dresden01069 Germany

ISBN: (纸本)9783658420475

Almost all manufacturers regard lidar sensors as a necessary extension of the environmental sensors already in use for the safe operation of automated driving functions from level 3 (highly automated). Recent developments provide a variety of information and also offer different detection patterns. This enables an extended and situation-dependent optimization of the environment recognition. However, both the different measuring principles and the physical limitations of the measuring principle pose enormous challenges for function development. The better the resolution of the systems, the more extensive the necessary algorithms with the corresponding requirements for real-time processing. Therefore, various examples should be used to explain how basic elements of the traffic infrastructure can be reliably and efficiently detected using lidar. In the field of object detection, the vehicle color is the limiting factor. The need to adapt the vehicle design to the sensor requirements is discussed. © Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2023.

关键词： Traffic signs

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Deep 3D-CNN using Resonance Imaging for Diagnosing Alzheimer’s

Deep 3D-CNN using Resonance Imaging for Diagnosing Alzheimer...

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international symposium on Artificial Intelligence and Signal processing (AISP)

作者： Nadish Ayub Syed Zubair Ahmad Shah Assif Assad Nusrat Mohi Ud Din Department of Computer Science and Engineering Islamic University of Science and Technology Awantipora India

Alzheimer’s Disease (AD), the most prevalent type of dementia, is an incurable neurological disorder that leads to progressive mental decline. The majority of an AD diagnosis in practise is based on the patient’s clinical history and neu- ropsychological information, such as magnetic resource imaging, despite the fact that an exact diagnosis of AD is challenging (MRI). Deep learning’s use in recognising AD is the subject of growing research in recent few years. It enables for automatic AD identification using 3D brain MRI images. For AD recognition, the network makes use of a three-dimensional convolutional neural network (3DCNN). It is distinctive in that AD recognition accurately reflects the three-dimensional structure of the brain as a whole. Due to the fact that MRIs are 3D objects and utilise 3D Convolutional Neural networks (CNNs) since using 3D CNNs the spatial integrity is maintained as compared to other methods that only employ a section of the MRI, such as ROI-based CNNs which works on some specific ROI, 2D slices that uses 2D slices, and 3D Patch-level that use 3D pathes. Four successive processing layers, two fully connected layers, and a classification layer is employed to design the CNN in this study. Each of the three groups in the structure is composed of three layers: a convolutional layer, a pooling layer, and a normalisation layer. The AD Neuroimaging Initiative’s MRI data is used to train and evaluate the algorithm. The data used in this work is comprised of 1656 MRI scans of 382 patients. According to the results of the trial, the suggested algorithm had a 99% training accuracy of AD recognition with a validation accuracy of 73%.

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Hybrid K-Mean PSO Clustering Algorithm for Energy-Efficient of Object Tracking in WSNs

Hybrid K-Mean PSO Clustering Algorithm for Energy-Efficient ...

引用

Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), international Conference on

作者： Sahar Hamad Ahmed Ahmed Noori Rashid University Headquarter University of Anbar Anbar Iraq College Of Computer Science and Information Technology University of Anbar Anbar Iraq

Target tracking can be a time-consuming and resource-hungry process due to the limitations in resource, power, sensing capability, communication, memory, and computational capability, it is a challenge to implement traditional target track solutions in wireless sensor networks (WSNs) applications. As any of the operations of data collection, processing, and dissemination leads to an increase in resource consumption, the target tracking algorithms in WSN should effectively utilize the resource and also reduce the computational complexity. in this paper, a new and effective multiple objects tracking approach was proposed by combining dynamic and static clustering techniques with the predictive tracking technique using the Long Short-Term Memory (LSTM) which is the extension of Recurrent Neural networks (RNNs). In this work two models from the LSTM were used, where the first model will classify the objects' motion path and then discover the object's new location, and the second model was used to predict the three nodes that will track the objects, and computing the energy consumption for this approach and proposed anew algorithm called Hyper K_Mean PSO Clustering algorithm (HKM_PSO) to improve the energy consumption and increasing the lifetime of the network. The approaches and the algorithm that proposed in this work were very effective. The most important contribution was optimizing the energy consumed in the process of tracking multiple objects and increasing the life of the network by improve the energy through the process of selecting the head cluster and increasing the life of the network.

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Routing Protocols in Wireless sensor networks

Routing Protocols in Wireless Sensor Networks

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Computing, Analytics and networks (Indo-Taiwan ICAN), Indo – Taiwan 2nd international Conference on

作者： Vanshit Gupta Vansh Verma Yogita Yogita Monika Sethi Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665499453

Wireless sensor networks (WSNs) emerging as a powerful technique for maintaining habitats in green spaces, getting information about actions in danger zones, keeping a close watch on infrastructure, including keeping a eye on suspicious behavior on the battleground. Power, space, and processing power constraints are quite significant for nodes in a sensor network. The adoption of appropriate routing algorithms is vital for lengthening the longevity of the sensor nodes. A common routing protocol could not be appropriate for sensor networks through all tasks, despite the fact that sensors are designed exclusively for surveillance and delivering activities. And the proto- cols elucidate the interaction of nodes with one another and the spreading of data among them throughout the network. The wireless communication sector has developed dozens of novel routing algorithms over the past 15 years that were aimed at the various designing domain possibilities. This paper demonstrates variety of routing protocols, which includes data centric, hierarchical, location based and Quality of Service (QoS) based protocols, employed in WSNs. Our framework’s objective is to inspect the WSNs and throw light on the classification of protocols. The paper is concluded with the future possibilities of such protocols in WSNs.

关键词： Wireless communication Wireless sensor networks Surveillance Quality of service Routing Routing protocols Behavioral sciences

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Concentration Detection of Continuous-Flow Magnetic Nanoparticles Using Giant Magnetoresistance sensor 3

Concentration Detection of Continuous-Flow Magnetic Nanopart...

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3rd international symposium on Material and Electrical Engineering Conference, ISMEE 2021

作者： Xuan, Manh Vu Ngoc, Thao Pham Quoc, Tuan Vu Minh, Hieu Nguyen Hoang, Nam Nguyen Quang, Loc Do Duc, Trinh Chu Thanh, Tung Bui National Center for Technological Progress Ministry of Science and Technology Center for Microelectronics and Information Technology Hanoi Viet Nam Vnu University of Engineering and Technology Hanoi Faculty of Electronics and Telecommunications Hanoi Viet Nam Institute of Physics Vietnam Academy of Science and Technology Hanoi Viet Nam Vnu University of Science Vietnam National University Nano and Energy Center Hanoi Viet Nam Vnu University of Science Vietnam National University The Faculty of Physics Hanoi Viet Nam

ISBN: (纸本)9781665423625

Giant magnetoresistance (GMR) sensors offer great sensitivity and integration capabilities on devices, especially microfluidic systems. This approach allows fast identification and real-time analysis of the detection of the biomolecules conjugated with the magnetic nanoparticles (MNPs), which are essential requirements for biomedical applications. This study integrated the GMR sensor with microchannel and signal processing circuits to determine MNPs concentration in a continuous flow using Helmholtz magnetic field source. The most significant advantage of this technique consists of modifying and integrating the GMR sensor to enhance the output signal and the system's sensitivity at a shallow concentration of MNPs. The high sensitivity was experimentally determined as 3.018V/(***-1) with a low detection limit (LOD) of about 19 μ***-1. This proposed system is appropriate to integrate into point-of-care microfluidic devices to detect biomolecule concentrations in real-time diagnostics. © 2021 IEEE.

关键词： Microchannels

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Accelerating Quadratic Transform and WMMSE

Accelerating Quadratic Transform and WMMSE

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IEEE international symposium on information Theory

作者： Kaiming Shen Ziping Zhao Yannan Chen Zepeng Zhang Hei Victor Cheng School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) China School of Information Science and Technology ShanghaiTech University China Department of Electrical and Computer Engineering Aarhus Universrity Denmark

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Fractional programming (FP) arises in various communications and signal processing problems because several key quantities in the field are fractionally structured, e.g., the Cramér-Rao bound, the Fisher information, and the signal-to-interference-plus-noise ratio (SINR). A recently proposed method called the quadratic transform has been applied to the FP problems extensively. The main contributions of the present paper are two-fold. First, we investigate how fast the quadratic transform converges. To the best of our knowledge, this is the first work that analyzes the convergence rate for the quadratic transform as well as its special case the weighted minimum mean square error (WMMSE) algorithm. Second, we accelerate the existing quadratic transform via a novel use of Nesterov's extrapolation scheme [2]. Specifically, by generalizing the minorization-maximization (MM) approach in [3], we establish a nontrivial connection between the quadratic transform and the gradient projection, thereby further incorporating the gradient extrapolation into the quadratic transform to make it converge more rapidly. Moreover, the paper showcases the practical use of the accelerated quadratic transform with two frontier wireless applications: integrated sensing and communication (ISAC) and massive multiple-input multiple-output (MIMO).

关键词： Extrapolation Wireless sensor networks Wireless networks Signal processing algorithms Transforms Programming Integrated sensing and communication

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