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A Fast Phase-Preserving Imaging Algorithm based on Segmented Pulse Compression for GNSS-InSAR System

A Fast Phase-Preserving Imaging Algorithm based on Segmented...

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2022 IEEE international conference on signal Processing, Communications and Computing, ICSPCC 2022

作者： Zhou, Jingtian Liu, Feifeng Wang, Zhanze Xu, Zhixiang Zhang, Shuyao Zeng, Zhangfan Beijing Institute of Technology School of Information and Electronics Beijing100081 China Key Laboratory of Electronic and Information Technology In Satellite Navigation Beijing Institute of Technology Ministry of Education Beijing100081 China Hubei University School of Computer Science and Information Engineering Wuhan China

ISBN: (数字)9781665469722

ISBN: (纸本)9781665469722

In GNSS-based InSAR systems, in order to obtain high azimuth resolution, more than 600s data need to be acquired, which results in large data amount. In this paper, a fast phase-preserving imaging algorithm based on segmented pulse compression is proposed to reduce calculation of GNSS-InSAR system. First, a segment echo signal processing model is established based on bistatic configuration. Then, calculation complex of pulse compression is reduced through data segments along range direction. Finally, pulse compression result for imaging is obtained by summing the segment result and image result is obtained by back projection algorithm. Raw data results suggest the effectiveness of the fast phase-preserving imaging algorithm. © 2022 IEEE.

关键词： image segmentation

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Cardiac image Analysis for Accurate Heart Disease Diagnosis Using Deep Learning Techniques: A Comprehensive Review 1

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6th international conference on Soft Computing and signal Processing, ICSCSP 2023

作者： David Samuel Azariya, S. Mohanraj, V. Jothi, G. Jeba Emilyn, J. Akilandeswari, J. Department of Information Technology Sona College of Technology Tamil Nadu Salem India Department of Computer Application Sona College of Arts and Science Tamil Nadu Salem India

ISBN: (数字)9789819986286

ISBN: (纸本)9789819986279

Coronary illness is a leading cause of mortality worldwide. Prompt discovery and precise forecasts of coronary disease can help in forestalling and dealing with the sickness. Lately, profound learning strategies, e.g., convolution neural networks (CNNs) have shown promising outcomes in different clinical imaging errands, including coronary illness expectation. This review proposes a CNN-based approach for coronary illness expectation utilizing cardiovascular pictures. The proposed model comprises numerous convolution layers, pooling, and completely associated layers. The model is prepared on an enormous dataset of cardiovascular pictures and related marks for coronary illness presence or non-attendance. The prepared model is assessed on a different test dataset to evaluate its exactness, responsiveness, explicitness, and other execution measurements. Our outcomes show that the proposed CNN-based approach accomplishes high exactness and outflanks customary machine learning strategies for coronary illness expectation. The proposed model can be utilized as a device to help doctors in the early determination and treatment of coronary illness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electrocardiograms

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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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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 Method for Correction and Enhancement of Non-Uniformly Illuminated images

A Method for Correction and Enhancement of Non-Uniformly Ill...

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2022 IEEE international conference on Knowledge Engineering and Communication systems, ICKES 2022

作者： Li, Yang-Yu Li, Yong-Gao Zhang, Qian-Bo Wang, Jia-Lin Li, Da-Cheng Anhui Institute of Optics and Fine Mechanics Hfips Chinese Academy of Sciences Key Laboratory of Optical Calibration and Characterization Hefei230031 China Special Police of China Beijing102211 China

ISBN: (纸本)9781665456371

Non-contact imaging detection of explosives and other hazardous materials is currently a hot spot for research in the field of explosives detection technology, and near-infrared hyperspectral detection technology based on multivariate optical calculations is one of the feasible means. However, in practical applications, the application of this technology is limited due to uneven illumination, which is prone to false alarms and missed alarms. In this paper, we first briefly introduce the explosive imaging detection system based on multivariate optical computation and analyze the characteristics of the scored image;then we adopt the multi-scale top-hat transformation to calculate the background illumination and correct the light inhomogeneity of the scored image;finally, we convert the scored image into a signal-to-noise ratio image through the steps of image segmentation and calculation of signal-to-noise ratio, which further solves the problem of threshold drift caused by light inhomogeneity and thus greatly improves the accuracy of recognition results is greatly improved. The experimental results show that the recognition accuracy and stability of the method in this paper are higher compared with the previous recognition results, which provides technical support for practical applications. © 2022 IEEE.

关键词： signal to noise ratio

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End-to-end Multi-source image Phase Retrieval 3

End-to-end Multi-source Image Phase Retrieval

引用

3rd IEEE international conference on Computer systems, ICCS 2023

作者： Zhao, Nan Song, Tianyang Guo, Yina Taiyuan University of Science and Technology Shanxi China

ISBN: (纸本)9798350313666

The phase information of multi-source hybrid images is prone to confusion during image propagation. Since traditional phase retrieval algorithms require a large number of assumptions that cannot be executed by obtaining amplitude, which makes phase retrieval of multi-source mixed images with phase distortion challenging. To address these phase retrieval problems, inspired by dual learning, we propose an end-to-end phase retrieval algorithm based on Multi-source image Phase Retrieval via dual Generative Adversarial Networks (MPRDGAN). By introducing the Least Square Error in the loss function of the network, the algorithm improves the performance of the model to prevent overfitting and ensures the phase retrieval of multi-source mixed images. Experimental results show that the MPRDGAN algorithm reduces the image restoration error, so that the correlation of restored images can reach more than 94.5%, which is widely used in various images such as face and remote sensing. © 2023 IEEE.

关键词： Generative adversarial networks

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An Application of Quantum Mechanics to Attention Methods in Computer Vision 48

An Application of Quantum Mechanics to Attention Methods in ...

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

作者： Zhang, Juntao Luo, Yihao Cheng, Peng Li, Zehan Wu, Hao Yu, Kun An, Wenbo Zhou, Jun Ams Institute of System Engineering Beijing China Yichang Testing Technique R&d Institute Yichang China Coolanyp L.L.C. Wuxi China University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781728163277

This work proposes the quantum-state-based mapping (QSM) for machine learning. QSM uses wave functions that describe microscopic particle systems as mappings. By QSM, original inputs or features extracted by neural networks are processed as quantum states to train wave function parameters. QSM has a low computational cost, almost no additional parameters, and is easy to integrate with other modules. We demonstrate the simplest form of the wave function as a mapping, that is, when a one-dimensional particle is in an infinitely deep potential well, in combination with advanced attention modules. Experiments show that QSM significantly improves the feature recalibration ability of attention module in transfer learning tasks. Then, we tried to analyze the effectiveness of QSM. This work indicates that QSM has an important application value in interdisciplinary machine learning. © 2023 IEEE.

关键词： Attention method image classification Quantum mechanics Transfer Learning

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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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Research and Design of a Robot Controller based on MicroPython

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

引用

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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2nd international conference on Science, Engineering Management and Information technology, SEMIT 2023

2nd International Conference on Science, Engineering Managem...

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2nd international conference on Science, Engineering Management and Information technology, SEMIT 2023

ISBN: (纸本)9783031722837

The proceedings contain 46 papers. The special focus in this conference is on Science, Engineering Management and Information technology. The topics include: Verification and Validation of Knowledge Engineering systems: A Life Cycle Framework;The Critical Factors of Success of Gamification in Digital Banking Services: Using Analytic Hierarchy Process (AHP) Approach;e-Shopping Sites Preference Analysis with Multi-criteria Decision-Making Methods;advancing Anemia Diagnosis: Harnessing Machine Learning Methods for Accurate Detection;monkeypox Detection with K-mer Using Machine Learning Algorithms;cloud Computing Model for Handling Medical Big Data: A Mobile Hospital Pervasive Healthcare Application;Harnessing Advanced AI Techniques: An In-Depth Analysis of Machine Learning Models for Improved Diabetes Prediction;image Processing in Toxicology: A Systematic Review;augmented Reality Immersive World with Hologram Special Effect in Early Childhood Education;improving the Visual Ergonomics of Computerised Workplaces Through the Use of Specialised Eye-Rest Software;a Diagnosis Model based on Federated Learning for Lung Cancer Classification;Arrhythmia Detection from ECG Traces images Using Transfer Learning Approach;enhancing Traffic Flow Prediction in Urban Areas Through Deep Learning and Probe Information: A Comparative Study;an Approach to Multi-agent Deep Q-Network Optimization of signal Control in Multi-intersection Road Environments to Enhance Urban Traffic Flow;blockchain-Driven Smart Contracts: An Overview of Application Areas and Gap Identification in Construction Management Literature;stochastic Optimization Methodology for Production Planning with Uncertain Demand and Lead Time based on the Digital Twin;role of Top Management Commitment and Information technology Investment in Digital Transformation;antecedents of Mobile Banking Apps Adoption Among Consumers in Ghana.

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