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Multi-objective and Seagull Optimization Enabled Traffic signal Controlling for Traffic Management in Cities 6th

Multi-objective and Seagull Optimization Enabled Traffic Sig...

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6th international conference on Information and Communication technology for Intelligent systems, ICTIS 2021

作者： Reddy, Seelam Meghana Sai Satyanarayana Reddy, S. Sanjana, Seelam Vardhaman College of Engineering Hyderabad India Sreyas Institute of Engineering and Technology Hyderabad India Kennesaw State University KennesawGA United States

ISBN: (纸本)9789811935701

Traffic signal control is significant to solve the real world issues such as the fuel wastage, time wastage, environmental pollution, accidents due to traffic congestion and several other factors. Hence, this research introduces a novel traffic management system based on the multi-objective function. Initially, the smart city map is taken from the real satellite image and is then segmented to gather more detailed information. Then, using the network simulation through MATLAB, the information regarding the traffic is gathered and then the paths are identified for efficient routing to avoid the traffic congestion. From, the identified path, the traffic signal control is employed optimally based on the solution encoding, multi-objective function and the seagull optimization algorithm (SOA). Finally, the performance of the proposed method is evaluated based on the performance metrics, like travel time, distance and average traffic density. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Traffic congestion

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Gas Tank Air Leakage Identification in Power Plant based on Subdivided Self-encoding Features 3

Gas Tank Air Leakage Identification in Power Plant Based on ...

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3rd international conference on Energy Engineering and Power systems, EEPS 2023

作者： Hu, Liehao Liao, Meiying Deng, Wenyang Qin, Shaoji Zhou, Junhuang South China University of Technology School of Electrical Power Guangzhou China School of Internet of Things Engineering Guangdong Polytechnic of Science and Technology Guangzhou China Guangzhou Power Electrical Technology Co. Ltd. Electric Power Artificial Intelligence Application Division Guangzhou China

ISBN: (纸本)9798350313857

The equipment of hydropower plant is widely distributed underground, and it is difficult to achieve full-time and full-cycle coverage only by manual inspection. With the continuous development of intelligent and intelligent technology, unattended power plants have become possible. The pressurized gas tank in the production area of the power plant has a relatively complex distribution of working conditions: normal operation, normal deflation, and normal shutdown. Due to the similarity in the sound scale between the leakage fault of the pressurized gas tank and the normal gas leakage, this paper proposes a long short-term memory-convolution sound recognition network based on stacked autoencoder features. First, the distribution characteristics of the sound of the pressurized gas tank are analyzed. Secondly, the pre-processed voice is subdivided and encoded by the stack autoencoder. Finally, a long short-term memory(LSTM)-convolution parallel network is constructed to identify the air leakage sound. The test results show the high accuracy and effectiveness of the method. © 2023 IEEE.

关键词： signal encoding

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Study on Hyperspectral Remote Sensing images of GF-5 De-Blurring based on Sparse Representation 2023

Study on Hyperspectral Remote Sensing Images of GF-5 De-Blur...

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5th international Symposium on signal Processing systems, SSPS 2023

作者： Wang, Hong School of Electronic Information Sichuan Modern Vocational College Chengdu China

ISBN: (纸本)9798400700040

Deblurring high resolution remote sensing image is a very important problem in remote sensing research. In this paper, we propose a new deblurring algorithm for high-resolution remote sensing images (HSI) based on sparse representation. The purpose of this study is to apply compressed sensing measurement and reconstruction technology to realize the processing of remote sensing image, and discuss the Under what circumstances can CS achieve better results in remote sensing image processing. The algorithm uses fast gradient projection algorithm to achieve deblurring and retain the important ground information of the original image. Experiments on remote sensing images obtained by GF-5 show that the algorithm can filter the blurring of remote sensing images well and improve the peak-to-noise ratio (PSNR) of images. The algorithm has better performance than other sparse representation algorithms. This paper explores the application of dictionary learning theory and sparse decomposition in remote sensing image processing. By further extending the algorithm proposed in this paper and adding new constraints, remote sensing image restoration, target recognition, deblurring, fusion and so on can be carried out. © 2023 ACM.

关键词： image reconstruction

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A mechanical arm based on image recognition and remote control

A mechanical arm based on image recognition and remote contr...

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2023 international conference on Mechatronics and Smart systems, CONF-MSS 2023

作者： Li, Kangmin Zhongfang Yachen Planning and Architectural Design Co. LTD Beijing China

Mechanical arm, image recognition and wireless transmission have been a hot topic and technology in recent years. This paper mainly introduces the object recognition mechanical arm based on the mechanical arm and wireless transmission technology. The system is modular, and the whole equipment system is divided into remote control system and mechanical arm system according to function. The remote control system is an upper computer software which integrates image recognition, related data processing module and wireless transmission module. The remote end includes wireless transmission module, steering gear control module and image acquisition module. The image is collected and sent to the remote control system, and the motion trajectory of the mechanical arm is obtained after calculation, and the control signal of the stepping motor on the mechanical arm is further calculated, and finally sent to the mechanical arm system to achieve the purpose of remote control. At the same time, during the movement of the mechanical arm, the relevant data is collected by the sensor on the mechanical arm and sent back to the remote control terminal for display and judgment. In this project, the mechanical arm system can automatically recognize the target on a two-dimensional plane and move the mechanical arm according to the object position. Meanwhile, the upper computer can display relevant data information. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： image recognition

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Implementation of Digital Filter Functions in Edge Device Framework for IoT Application System 6

Implementation of Digital Filter Functions in Edge Device Fr...

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6th international conference on Vocational Education and Electrical Engineering, ICVEE 2023

作者： Panduman, Yohanes Yohanie Fridelin Funabiki, Nobuo Sukaridhoto, Sritrusta Okayama University Department of Information and Communication Systems Okayama Japan Politeknik Elektronika Negeri Surabaya Department of Informatic and Computer Surabaya Indonesia

ISBN: (纸本)9798350326642

Currently, we are developing an edge device framework to help the setup of the edge device remotely through an internet of Things (IoT) application server platform called SEMAR (Smart Environmental Monitoring and Analytical in Real-Time). The framework allows the connection of different sensor interfaces, standardizes data, and utilizes it through multiple output components. However, it lacks capabilities for sensor data processing at the edge device. In this paper, we implement filtering functions using digital signal processing techniques in the edge device framework. The techniques include low, high, and band-pass filters based on Butterworth and Chebyshev-I, as well as Kalman and Savitzky-Golay filters. By identifying these techniques and parameters in the edge configuration file through SEMAR, the user can utilize them without writing codes. It also provides the cascading filter to create a series of filters for data processing in sequences. For evaluations, we apply the proposed functions to the fingerprint-based indoor localization system (FILS15.4). The evaluation results confirmed the effectiveness of the implemented filtering functions in the edge device framework. © 2023 IEEE.

关键词： internet of things

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Electrocardiogram signal Classification Using Lightweight DNN for Mobile Devices 18

Electrocardiogram Signal Classification Using Lightweight DN...

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18th international conference on Future Networks and Communications (FNC) / 20th international conference on Mobile systems and Pervasive Computing (MobiSPC) / 13th international conference on Sustainable Energy Information technology (SEIT)

作者： Mewada, Hiren Pires, Ivan Miguel Prince Mohammad Bin Fahd Univ Elect Engn Dept POB 1664 Al Khobar 31952 Saudi Arabia Inst Telecomunicacoes P-6201001 Covilha Portugal Politecn Santarem Escola Super Gestao & Tecnol Complexo AndaluzApartado 295 P-2001904 Santarem Portugal

Mobile users can use a mobile sensor to record ECG data, and on -device ECG classification can provide a more efficient diagnosis than standard care. Deep Neural Networks (DNNs) have excelled in artificial intelligence (AI) -based applications to extract patterns from the complex waveform to analyze the ECG waveform. Despite their superior performance, their high computational complexity severely limits their use in resource-constrained mobile phones. This study aimed to analyze various lightweight deep learning models for ECG classification on edge devices while maintaining comparable performance. Five DNNs, including SqueezeNet, ShuffleNet, MobileNet, EfficientNet, and NASNet, designed explicitly for resource-constrained mobile, are tested and compared for ECG classification. Time-series ECG signals are converted to a 2D scalogram using the continuous wavelet transform. These generated scalograms are classified into three categories: cardiac arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythms (NSR). A comprehensive comparison is presented using accuracy analysis and the required trainable parameters among these models. The experimental analysis of PhysioNet's dataset shows that ShuffleNet has the fewest learnable parameters, with a validation accuracy of 87.50%. In contrast, SqueezeNet performed better, with 93.75% validation accuracy and 724K learnable parameters.

关键词： Lightweight Deep Learning ECG image Classification

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UAVs-Borne MIMO Radars Layout Optimization based on Clustered signal Integration 8

UAVs-Borne MIMO Radars Layout Optimization Based on Clustere...

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8th international conference on signal and image Processing, ICSIP 2023

作者： Zhang, Zhao Li, Xiaolong Chen, Zhe Wang, Mingxing Gao, Longji Sun, Zhi University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China

ISBN: (纸本)9798350397932

This paper considers the layout problem for the unmanned aerial vehicles (UAVs)-borne multi-input multi-output (MIMO) radar system in the situation of signal integration to improve the detection probability and radar effective coverage area (ECA). Because of the coherent constraints between UAV node echo signals, the signals can only be integrated coherently when the distances between nodes are relatively short;otherwise, the signals must be integrated incoherently. Thus, we propose a system model of the clustered UAV MIMO radar based on hybrid integration for the varying distances between the nodes. The model groups the system's nodes into clusters, where the distance between nodes within the same cluster is small and the distance between nodes from different clusters is larger. A clustering criterion and an approximation of the ECA solution calculation are also included in the model. Then, we propose an optimization solution for radar node placement based on Cuckoo search-particle swarm optimization (CS-PSO). Finally, compared to CS-based, PSO-based, and random deployment methods, the simulation results examine the feasibility and effectiveness of the proposed method. © 2023 IEEE.

关键词： MIMO systems

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internet of Medical Things: A CT image Denoising in Tetrolet Domain 5

Internet of Medical Things: A CT Image Denoising in Tetrolet...

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5th international conference on Information systems and Computer Networks, ISCON 2021

作者： Diwakar, Manoj Pandey, Richa Sharma, Ritik Saun, Sapna Singh, Prabhishek Pandey, Neeraj Kumar Graphic Era Deemed to Be University Department of Computer Science Engineering Uttarakhand Dehradun India Graphic Era Hill University Campus-Haldwani Department of Computer Science Engineering Uttarakhand India Amity University Uttar Pradesh Amity School of Engineering Technology Department of Computer Science Engineering Noida India DIT University School of Computing Dehradun India

ISBN: (纸本)9781665403412

The internet of Medical Things (IoMT) is a huge community of linked medical equipment and technologies that communicate with a large number of Servers over the internet to provide a variety of services such as medical image denoising. Projection-based reconstructed images in computed tomography (CT) are noisy due to thermal noise and electrical noise, both of which are approximately AWGN in magnitude. Because of the variety of clinical features and textures included in CT reconstructed images, denoising is a difficult process. It is possible to estimate the true noise of a single image, and then to minimize both the real noise and the additional noise by reconstructing CT images. A unique modified approach is proposed in this work, in which the noise will be calculated actual as well as added, followed by a proposed method for denoising, which is based on thresholding in tetrolet domain. Different denoising schemes are used in the experiments, and the results are compared to one another. Experimentation has revealed that both in terms of PSNR and visual quality, the suggested technique yields promising results that are favorable to both. © 2021 IEEE.

关键词： image denoising

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MSTCNet: Parallel Multi-Scale Network for Medical image Segmentation 23

MSTCNet: Parallel Multi-Scale Network for Medical Image Segm...

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2023 4th international conference on Control, Robotics and Intelligent System, CCRIS 2023

作者： Xiao, Hongzhao Tang, Jie Song, Hongjian Hu, Juncheng South China University of Technology Guangdong Guangzhou China Guilin Medical University Guangxi Guilin China

ISBN: (纸本)9798400708190

Transformer-like architectures, which are the model of choice in the field of natural language processing, have recently been adapted to computer vision (CV) fields and demonstrated remarkable effectiveness on various CV tasks. However, current transformer-based methods require large-scale datasets, which are usually unavailable in medical image analysis, thus resulting in adverse achievement. To this end, we propose a novel segmentation model, named MSTCNet, which constructs a parallel multi-scale transformer (MST) encoder in U-Net. In MST, we devise multi-scale patch partition and multi-scale mix attention to perform multi-scale long-range dependencies modeling. The U-Net encoder paralleled with MST alleviates the burden of large-scale datasets and extract local features supplementarily. We also propose Feature Fusion Head to narrow the gap between convolutional features and transformer features. Sufficient experiments demonstrate that our MSTCNet outperforms state-of-the-art methods on GlaS and ISIC18 datasets and is more suitable for medical image segmentation with small-scale datasets. © 2023 ACM.

关键词： signal encoding

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Multispectral Pedestrian Detection Via Two-Stream YOLO With Complementarity Fusion For Autonomous Driving 3

Multispectral Pedestrian Detection Via Two-Stream YOLO With ...

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3rd IEEE international conference on Electronic Communications, internet of Things and Big Data, ICEIB 2023

作者： Chan, Hung-Tse Tsai, Po-Ting Hsia, Chih-Hsien Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Kang Chiao International School Xiugang Campus New Taipei Taiwan Department of Computer Science and Information Engineering National Ilan University Yilan Taiwan

ISBN: (纸本)9798350333862

With the booming development of autonomous cars, the core technologies of vehicle-to-everything (V2X), automated driving systems (ADS), shared mobilities, and electrified vehicles (EV) have gained much attention for research. These technologies use sensors to obtain information about the vehicle's surroundings and algorithms and analyze or make decisions. For example, pedestrian detection tasks use images taken by visible cameras. However, visible image information does not perform well in harsh environments (e.g., light confusion, insufficient illumination value), and thermal cameras rely on temperature for imaging, so they can be used to compensate for visible information and complementarity between visible and thermal image information. Due to the diversity of scenes and unstable illumination, the task of pedestrian detection using multispectral information is challenging. To solve the problem, we propose a multispectral two-stream model based on one-stage architecture to capture multispectral information simultaneously and then enhance the multispectral complementarity by an adaptive multispectral weight adjustment method. This model has better accuracy than the recent ones and effectively uses the information between different modalities. © 2023 IEEE.

关键词： Autonomous vehicles

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