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image Denoising Using Feature Map based Convolutional Neural Networks

Image Denoising Using Feature Map Based Convolutional Neural...

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2023 IEEE international conference on Recent Trends in Electronics and Communication, ICRTEC 2023

作者： Ravi, A. Dupaguntla, Gnanasree Tadepalli, Devi Krishna Vissamsetti, Swetha Achanta, Radha Krishna Pscmr College of Engineering and Technology Department of Ece Vijayawada India

ISBN: (纸本)9798350396195

Noise corruption is inexorable in image recognition and it may heavily agitate the optical features of the capturing image. Convolutional Neural Networks (CNNs) have acquired good contemplation in the research of denoising. They possess various approaches to removing noise from a degraded image and to reconstruct an image with high visual quality. Because of its stratified learning and self-operating feature extraction capabilities, CNN has become useful for Machine Vision tasks. Furthermore, these methods mostly train a specified model for every noise level and desire multiple methods for denoising images with various noise levels. Large feature sets result in producing a noise effect, which causes an over-fitting of the network. A new method of Feature Map based Convolution Neural Network (FMBCNN) has been introduced for image denoising. The quantitative analysis is performed by calculating four metrics of MSE (Mean Square Error), PSNR (Peak signal to Noise Ratio), SSIM (Structural Similarity Index), and Entropy. They put forward a building block for Convolutional Neural Networks that tends to increase channel interdependencies at nearly no cost of computation. The weights accompanying a kernel also called a mask, are tuned to choose features dynamically. The performance of classification, segmentation, and detection modules is heavily influenced by feature selection. © 2023 IEEE.

关键词： Entropy

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Motor imagery EEG classification method: based on a novel BiLSTM-Attention-CNN hybrid neural network 2

Motor Imagery EEG classification method: based on a novel Bi...

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2nd international conference on signal image Processing and Communication, ICSIPC 2022

作者： Chu, Hongxing Zhang, Chaozhu School of Information and Automation Engineering Qilu University of Technology Shandong Academy of Sciences Jinan250353 China

ISBN: (纸本)9781510657694

Brain-computer interface (BCI) technology is a human-computer interaction technology that realizes the connection and communication between the human brain and external equipment by collecting and decoding electroencephalo-graph (EEG) signals. It is a major direction for the development of BCI to analyze and decode the incoming EEG signal in real time through online BCI systems to enable the control of external devices. In order to improve the efficiency of online BCI, it is important to further improve the recognition accuracy and reduce the classification time. Long Short-Term Memory (LSTM) network can improve the recognition accuracy by using the temporal characteristics of EEG signals through its temporal recursive structure. Therefore, the optimized Bi-directional Long Short-Term Memory (BiLSTM) can well express the long-term dependent information in the input. In the study, a new model was proposed, the Attention mechanism was introduced to the BiLSTM while integrating Convolutional Neural Networks (CNN). In the case of avoiding gradient disappearance or gradient explosion, the problem of ignoring correlation information between channels is solved, and learning high-dimensional features in EEG data. This study improves the accuracy of motor image (MI) EEG signal classification under the condition of saving the steps of feature extraction, which is of great significance for the development of online BCI systems. © 2022 SPIE.

关键词： Brain computer interface

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Research and Design of a Robot Controller based on MicroPython

Research and Design of a Robot Controller Based on MicroPyth...

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international Congress on image and signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Dafang Chen Shanghai Jinshan District Youth Activity Center Shanghai

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

With the rapid development of internet of Things technology and embedded systems, the application of robotics technology has become an important role in the field of modern technology. However, traditional robot control units often use C language to develop low-level drivers and application programs, which is difficult to develop and has a long cycle, making it a high threshold for non professional developers. Therefore, exploring a robot controller with high compatibility, rich library functions, and high development efficiency is of great significance. MicroPython is a scripting language, and as a lightweight Python implementation, its concise syntax and rich library functions provide new ideas for the development of robot controllers. This article aims to study the design and implementation of a robot controller based on MicroPython. It will comprehensively introduce the research methods and development process of the controller from three aspects: hardware development, firmware design and burning, and image processing applications, providing a new robot controller solution. With the rapid development of internet of Things technology and embedded systems, the application of robotics technology has become an important role in the field of modern technology. However, traditional robot control units often use C language to develop low-level drivers and application programs, which is difficult to develop and has a long cycle, making it a high threshold for non professional developers. Therefore, exploring a simple, easy-to-use, and highly efficient robot controller is of great significance.

关键词： Visualization image recognition Embedded systems Robot control C languages Libraries Hardware internet of Things Robots Microprogramming

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3D Ground-Penetrating Radar Void Detection Method based on Spatial Convergence 2

3D Ground-Penetrating Radar Void Detection Method Based on S...

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2nd international conference on Mechatronics and Automation technology, ICMAT 2023

作者： Zhou, Nanjie Tang, Jiaming Huang, Zhiyong Li, Weixiong Zhang, Xiaoning Guangzhou Beihuan Intelligent Transportation Technology Co. Ltd. China Xiaoning Institute of Roadway Engineering China School of Civil Engineering and Transportation South China University of Technology China

ISBN: (纸本)9781643684826

In order to solve the problem of void feature detection of 3D ground-penetrating radar data, the test of void detection in polystyrene cubic simulation road is carried out in this paper. The B-scanning image was processed by binarization, corrosion, expansion and thin length, and the suspected void region was obtained, According to the characteristics of multi-channel detection of 3D ground-penetrating radar, a method is proposed to divide the B-scanning image of suspected void into several clusters and calculate the convergence of each cluster to identify and determine the void signal. The detection method has been verified in the engineering, The results show that in the process of the field test and verification, the algorithm of 3D ground-penetrating radar data void characteristic detection accurately recognizes 4 sites with void potential. Also, the 3D matching test successfully eliminated 8 suspected void regions, indicating that the 3D ground-penetrating void feature detection algorithm has excellent robustness in the task of void segmentation of radar images. The technique can detect the voids inside city roads accurately and fast which have great significance in reducing casualties and property damage. © 2024 The Authors.

关键词： Ground penetrating radar systems

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Machine learning-based intelligent accident detection and notification system in IoT network 11

Machine learning-based intelligent accident detection and no...

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11th international conference on Emerging Trends in Engineering and technology - signal and Information Processing, ICETET - SIP 2023

作者： Rathod, Gopal Sunil Jajulwar, Kapil Kubde, Umesh G H Raisoni College of Engineering Department of Electronics and Telecommunication Engineering Maharashtra Nagpur India LGPS Hybrid Energy Pvt. Ltd. Narendra Nagar Maharashtra Nagpur442001 India

ISBN: (纸本)9798350348422

The number of traffic accidents is increasing every day as the number of cars increases. It is said that 3 million people die each year worldwide and 55 million people are injured. Lack of emergency medical care at the scene of an accident or long post-accident response times are the leading causes of death. Cognitive aids can be used to detect incidents and alert traffic watchers or rescue teams, reducing waiting times for lifesaving operations. Transportation systems have received considerable attention from industry and academia due to the growing demand for urban exploration and have been seen as a way to improve road safety in these areas. In this study, an IoT and machine learning-based intelligent rescue team accident detection model was presented. An IoT module has been developed that can recognize an accident, collect all accident data such as position, pressure, force and speed, and transmit all relevant data to the cloud. Machine learning models are used in the cloud to validate IoT module data and activate emergency modules after a failure is detected. © 2023 IEEE.

关键词： internet of things

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A Survey on Attention-based image Captioning: Taxonomy, Challenges, and Future Perspectives 4th

A Survey on Attention-Based Image Captioning: Taxonomy, Chal...

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4th international conference on Machine Intelligence and signal Processing, MISP 2022

作者： Sharma, Himanshu Padha, Devanand Selwal, Arvind Department of Computer Science and Information Technology Central University of Jammu Samba181143 India

ISBN: (纸本)9789819900466

Generating textual descriptions for images is called image captioning (IC). IC is an innovative concept of Computer Vision (CV) with several applications such as visual assistance, multi-modal search, machine vision, and many more. Enabling a system to generate image captions has witnessed an excess of progress aided by various state-of-the-art deep learning techniques. Recently, the attention-based image captioning (AIC) approaches have amplified the core field of IC with their promising results. The exponential growth of AIC approaches demands a thorough examination of its literature. There have been relatively lesser contributions in extensively surveying AIC systems up to this point. We review a comprehensive and analytical survey of AIC approaches reported in the literature and propose an alternative taxonomy for diverse AIC techniques. We also highlight a list of open challenges and directions for future work. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recurrent neural networks

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Design of Visual Management System for Underground Cable Pipeline based on GIS

Design of Visual Management System for Underground Cable Pip...

引用

2022 international conference on image, signal Processing, and Pattern Recognition, ISPP 2022

作者： Song, Zhe Wang, Min State Grid Shanghai Cable Company Shanghai200072 China

ISBN: (纸本)9781510654846

The conventional underground cable pipeline visual management system has problems such as too small signal coverage, resulting in too long system response time. A visual management system for underground cable pipeline based on GIS is designed. The Visual DSP++ environment linker is utilized to access source files. And then the characteristics of underground cable piping is extracted. The temperature control equation of heat source area is calculated and the signal coverage by GIS is adjusted. Through reading the layer vector data and different laying legend, different laying modes of cables are displayed. Finally, the visual management function of system software is designed. The experiments show that the average response time of the underground cable pipeline visual management system in this paper is 5.475s, 8.027s and 7.938s, respectively. Compared with the other two types of underground cable pipeline visual management systems, it takes less time, which proves that the underground cable pipeline visual management system integrated with GIS technology is more reliable. © 2022 SPIE.

关键词： Geographic information systems

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Mangrove Species Classification in Qi’ao Island based on Gaofen-2 image and UAV LiDAR 1

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international conference on Communications, signal Processing, and systems, CSPS 2023

作者： Sun, Yuchao Wei, Zheng Gao, Yang Ren, Hongkai Chen, Qidong Dong, Di Hu, Ping South China Sea Institute of Planning and Environmental Research State Oceanic Administration Guangzhou510300 China Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources Guangzhou510300 China Zhuhai519082 China

ISBN: (数字)9789819975051

ISBN: (纸本)9789819975396

Mangrove species classification is of great significance to the study of mangrove community structure and biodiversity. Most researches use foreign high-resolution remote sensing images or UAV images for mangrove species classification. In order to improve classification accuracy, LiDAR and hyper-spectral data are often used to assist classification. In this paper, based on the Gaofen-2 image and the CHM data obtained by the UAV Lidar, the mangroves in Qi'ao Island, Zhuhai are classified among species by using the random forest classification method. The classified species include 5 types of true mangroves, 3 types of semi mangroves, Phragmites australis and non-vegetation. The results show that the use of Gaofen-2 image can only effectively distinguish the Sonneratia apetala, Acrostichum aureum and non-vegetation, the accuracy of distinguishing other mangrove species is not ideal;After Gaofen-2 image fusion of CHM data, the classification accuracy of each mangrove species has been significantly improved, with the overall classification accuracy reaching 91.44%, which verifies the effectiveness of Gaofen-2 image fusion of external data in mangrove species classification research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image fusion

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Design of Factory Environmental Monitoring System based on ESP32 5

Design of Factory Environmental Monitoring System based on E...

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5th international conference on Frontiers technology of Information and Computer, ICFTIC 2023

作者： Song, Wenze Wang, Xuezhen Wang, Tianqi Li, Zhixuan Zhang, Zening Yang, Lishan Liaocheng University School of Physics Science and Information Engineering Liaocheng China

ISBN: (纸本)9798350309034

With the rapid population of embedded devices, Industrial internet of Things (IIoT) has provided a promising opportunity to enhance intelligent industrial systems by leveraging the growing ubiquity of wireless sensor and optical devices, which is crucial to ensure the reliable operation of the traditional industrial system and benefit the manufacturing process. In this paper, an efficient factory environmental monitoring system is designed for production data acquisition, which is based on ESP32-S3 and network database, enabling autonomous data collection, analysis and visualization via graph signal processing method, which reduces transmission volume and conserves network bandwidth. The experimental results show that the designed monitoring system with low cost, convenient operation and strong safety, which provides a practical solution to collect and analyze the data obtained by the industrial wireless sector. © 2023 IEEE.

关键词： data collection ESP32 factory environmental monitoring

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Oct image Blind Despeckling based on Gradient Guided Filter with Speckle Statistical Prior 48

Oct Image Blind Despeckling Based on Gradient Guided Filter ...

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48th IEEE international conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Li, Sanqian Xiong, Muxing Yang, Bing Zhang, Xiaoqing Higashita, Risa Liu, Jiang Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Shenzhen China Tomey Corporation Japan

ISBN: (纸本)9781728163277

Optical coherence tomography (OCT) imaging technique has been widely used for ocular disease diagnosis. However, speckles occur in OCT images due to the property of coherent imaging, inevitably affecting the visual quality and clinical analysis. To alleviate this problem, we propose a novel gradient-guided speckle image filtering method (GGSF) with structure enhancement for directly removing speckles in OCT images. Specifically, the multiplicative characteristic of speckle noise is incorporated into the guided filtering processing for modeling raw OCT images. To avoid getting trapped in image distortions, we further employ gradient regularization to integrate the structure prior information into the guided speckle image filtering procedure. Additionally, we introduce the statistical property of speckle noise obeying a gamma distribution into the least square method solver for the resulting non-convex GGSF model. Experimental results on the AS-OCT dataset demonstrate the effectiveness of GGSF for OCT image despeckling compared with competitive methods. Furthermore, we validate the benefits of GGSF for subsequent clinical analysis with the CM-OCT dataset. © 2023 IEEE.

关键词： Optical tomography

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