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2022 International conference on ENTERprise Information systems, CENTERIS 2022 - International conference on Project MANagement, ProjMAN 2022 and International conference on Health and Social Care Information systems and Technologies, HCist 2022

作者： Jardim, Sandra António, Joao Mora, Carlos Smart Cities Research Center - Ci2 Polytechnic Institute of Tomar Quinta Do Contador Tomar2300-313 Portugal Techframe - Sistemas de Informaçao SA Rua Almeida Garrett 83A Buzano Sao Domingos de Rana2785-338 Portugal

Medical image analysis is an invaluable tool in medicine. Different imaging modalities provide an effective means for mapping images that can feed machine and deep learning models which can significantly contribute to increase knowledge of diseased anatomy for medical research, being an important component in diagnosis and treatment planning. Accurate segmentation of medical images is a key step in the use of Artificial Intelligence as a tool to support physicians. Although there are many approaches to medical image segmentation, thresholding is one of the simplest methods that can be used, being of great interest to the scientific community. Over the years, several approaches to segmentation of medical images based on threshold methods have been proposed, which have evolved both in computational complexity, with shorter processing times, and in efficiency and accuracy of results. This manuscript summarizes the most common threshold-based approaches for medical image segmentation and discuss some of the methods and algorithms proposed in recent years, analyzing their advantages, disadvantages, and limitations. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Medical imaging

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Effective in-the-Moment image Compression on FPGA: A Combinatorial Method Including DWT and IWT Techniques

Effective in-the-Moment Image Compression on FPGA: A Combina...

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International conference on Advanced Computing and Communication systems (ICACCS)

作者： U. Dinesh V. Deepak Rajan S. Navaneethan Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

This paper presents an innovative way of image compression using Field-Programmable Gate Array (FPGA) implementation of the Integer Wavelet Transform (IWT) and Discrete Wavelet Transform (DWT) algorithms. For situations where resources are limited, the flexibility and adaptability offered by the FPGA architecture are ideal. Our technique strikes a compromise between compression effectiveness and image quality by utilizing DWT for multi-resolution analysis and IWT for spatial redundancy reduction. Real-time processing and resource optimization are ensured by the FPGA implementation. FPGA-optimized algorithms that tackle resource constraints are among the contributions. Evaluations demonstrate enhanced signal-to-noise ratios, compression ratios, and execution times. This study highlights how fast FPGA can compress images, especially for embedded systems and space missions. The study not only improves image compression but also highlights how FPGA can be used to increase the effectiveness of signal processing algorithms.

关键词： image coding Space missions Redundancy Signal processing algorithms Parallel processing Real-time systems Discrete wavelet transforms Field programmable gate arrays Optimization Signal to noise ratio

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CHARACTERIZATION OF TUNABLE REBOUND PROPERTIES OF MICROSTRUCTURED MAGNETOACTIVE ELASTOMERS 17

CHARACTERIZATION OF TUNABLE REBOUND PROPERTIES OF MICROSTRUC...

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ASME conference on Smart Materials, Adaptive Structures and Intelligent systems (SMASIS)

作者： Kriegl, Raphael Jezersek, Matija Kravanja, Gaia Hribar, Luka Kokot, Gasper Drevensek-Olenik, Irena Shamonin, Mikhail Ostbayerische Tech Hsch Regensburg Regensburg Germany Univ Ljubljana Fac Mech Engn Ljubljana Slovenia J Stefan Inst Ljubljana Slovenia Univ Ljubljana Fac Math & Phys Ljubljana Slovenia

ISBN: (纸本)9780791888322

We present a novel method to control the rebounding behavior of small mm-sized solid balls by employing magnetoactive elastomers (MAEs) with microstructured surfaces. An MAE is a composite material consisting of mu m-sized ferromagnetic particles dispersed in a soft elastomer (e.g., polydimethylsiloxane) matrix. In the act of rebounding, the ball hits an MAE surface and bounces back. The MAE samples contained 75 wt.% of iron. This composite material is known to respond to an applied magnetic field with increased stiffness (due to the magnetorheological effect) and plasticity. To adjust the rebound properties, the top layer of the MAE material was additionally modified by micromachining lamellar structures with different dimensions on the 100 mu m scale via laser ablation. Due to the resulting high aspect ratio, these surface structures were sensitive to the magnetic field direction. The lamellas could stand up straight or lay down flat. The rebound behavior was evaluated by using a custom build apparatus that facilitates dropping of the balls in a precise and repeatable manner. A ball was dropped from different heights. The ball trajectory was captured with a high-speed camera to investigate the rebound properties. The recorded video was processed using a custom software written in Python. The experimental procedure and data processing algorithms are presented in detail. The results for the samples with different geometrical dimensions are provided as examples. It is made evident that the magnetic field influences the rebound properties of small non-magnetic balls impinging microstructured MAE surfaces. The change in surface topography is an effective way to control the ball rebound. The fabrication flexibility in geometrical dimensions of surface microstructures opens a convenient way to tune the desired response to magnetic fields. The presented idea may find applications in impact mitigation or small-scale sorting machinery, e.g. for recycling.

关键词： magnetoactive elastomer image processing rebound mm-sized ball lamellar array magnetic field microstructured surface

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Students' Course Results Prediction Based on Data processing and Machine Learning Methods

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JOURNAL OF SIGNAL processing systems FOR SIGNAL image AND VIDEO TECHNOLOGY 2022年第11期94卷 1199-1211页

作者： Liu, Jinyang Yin, Chuantao Wang, Kunyang Guan, Minghui Wang, xi Zhou, Hong Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China Beihang Univ Sino French Engineer Sch Beijing 100191 Peoples R China

Smart learning has been concerned in the domain of education, and how to correctly predict students' performance is one of meaningful issues. Mature performance prediction methods are promising to be applied to education systems to make educators know more about students' learning states and provide students with academic help in time. Based on students' historical course results and courses' basic information, our work proposes a student's course results prediction model using reasonable data processing and machine learning methods, which can well achieve the goal of out-of-sample results prediction. Our model framework mainly has two parts: numeric and non-numeric features vector embedding algorithms;model optimization based on data augmentation and integration. Firstly, we generate respectively feature embedding vectors for non-numeric and numeric data;Secondly by applying relative statistical methods we combine the previous embedding vectors to form a synthetic feature matrix, then use the Gramian Angular Field and Markov Transition Field to augment the data information and extend the feature embedding by matrix integration. To verify the validity of our model in experiments, we implement multilayer perceptron as training model to realize prediction task. Our model has quite good interpretability so that it can well be understood what happens in the model framework and how the data is processed throughout the model pipeline. This can well help us make relative adjustments and tunes according to different problems and scenarios, thus get better prediction results.

关键词： Machine learning Data optimization and processing Vector embedding Data augmentation Feature integration

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Pumpkin Leaf Disease Detection: Convenience of CNN Over Traditional Machine Learning in Terms of image Classification 3rd

Pumpkin Leaf Disease Detection: Convenience of CNN Over Trad...

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3rd International conference on Smart systems - Innovations in Computing (SSIC)

作者： Mithu, Mosaddek Ali Momo, Shampa Islam Hasan, Md Mehedi Rahman, Kazi Motiour Sattar, Abdus Daffodil Int Univ 102 Mirpur Rd Dhanmondi Dhaka 1207 Bangladesh

ISBN: (纸本)9789811628771;9789811628764

Bangladesh having a growing agricultural economy quick detection of plant leaf diseases is a primary requirement. Disease discovery with the existing diagnostic procedures demands a longer time. Therefore, growers frequently miss the best time for stopping and treating diseases. Further, early identification and classification of pumpkin leaf diseases extremely needed. This paper proposes to discover the pumpkin leaf diseases by utilizing a modern image processing procedure convolutional neural network (CNN). CNN applied for image classification and recognition because of its high accuracy. Besides, a comparison of traditional machine learning algorithms like support vector machines (SVM), K-nearest neighbor (KNN), decision tree, and Naive Bayes with the performance of CNN is demonstrated in our work. Tensorflow library was adopted to implement the CNN algorithm and Scikit-learn used in terms of utilizing the above-mentioned traditional machine learning algorithms. Finally, we detected the pumpkin leaf diseases by the algorithm that exhibits an assuring accuracy to our suggested approach.

关键词： Classification Convolutional neural network (CNN) Data analysis Detection image processing Leaf disease Machine learning

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IMDL-BenCo: A Comprehensive Benchmark and Codebase for image Manipulation Detection & Localization 38

IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Ma, xiaochen Zhu, Xuekang Su, Lei Du, Bo Jiang, Zhuohang Tong, Bingkui Lei, Zeyu Yang, xinyu Pun, Chi-Man Lv, Jiancheng Zhou, Jizhe College of Computer Science Sichuan University China Department of Computer and Information Science University of Macao China Engineering Research Center of Machine Learning and Industry Intelligence MOE China

A comprehensive benchmark is yet to be established in the image Manipulation Detection & Localization (IMDL) field. The absence of such a benchmark leads to insufficient and misleading model evaluations, severely undermining the development of this field. However, the scarcity of open-sourced baseline models and inconsistent training and evaluation protocols make conducting rigorous experiments and faithful comparisons among IMDL models challenging. To address these challenges, we introduce IMDL-BenCo, the first comprehensive IMDL benchmark and modular codebase. IMDL-BenCo: i) decomposes the IMDL framework into standardized, reusable components and revises the model construction pipeline, improving coding efficiency and customization flexibility;ii) fully implements or incorporates training code for state-of-the-art models to establish a comprehensive IMDL benchmark;and iii) conducts deep analysis based on the established benchmark and codebase, offering new insights into IMDL model architecture, dataset characteristics, and evaluation standards. Specifically, IMDL-BenCo includes common processing algorithms, 8 state-of-the-art IMDL models (1 of which are reproduced from scratch), 2 sets of standard training and evaluation protocols, 15 GPU-accelerated evaluation metrics, and 3 kinds of robustness evaluation. This benchmark and codebase represent a significant leap forward in calibrating the current progress in the IMDL field and inspiring future breakthroughs. Code is available at: https://***/scu-zjz/IMDLBenCo. © 2024 Neural information processing systems foundation. All rights reserved.

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A Comprehensive Analysis of Modern Generative Models for Echocardiography image Synthesis

A Comprehensive Analysis of Modern Generative Models for Ech...

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2024 International conference on Artificial Intelligence, Metaverse and Cybersecurity, ICAMAC 2024

作者： Rakhmetulayeva, Sabina Bolshibayeva, Aigerim Department of Cybersecurity Information Processing and Storage Satbayev University Almaty Kazakhstan Department of Information systems International IT University Almaty Kazakhstan

ISBN: (纸本)9798350353488

The use of machine learning (ML) in the medical field is hindered by the scarcity of high-quality data. This work tackles the deficiency of echocardiogram pictures (echoCG) by using advanced generative models for synthetic creation. We provide a comprehensive practical assessment of these approaches and ascertain their limits. Our study examines the efficacy of a cycle-consistent generative adversarial network (CycleGAN), a contrastive unpaired translation (CUT) approach, and a latent diffusion model (Stable Diffusion 1.5). We outline our data generating approach, exhibit picture examples, describe our assessment strategy, and offer findings from a user study measuring the perceived quality and medical validity of the produced samples, done with licensed cardiologists and surgeons. © 2024 IEEE.

关键词： Generative adversarial networks

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A Prediction Model of Credit Risk in SCF Based on IG-GA-SVM 2

A Prediction Model of Credit Risk in SCF Based on IG-GA-SVM

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2nd International conference on Advanced algorithms and Signal image processing, AASIP 2022

作者： Fu, YingJun Li, MeiYan Sun, xiaoNi Shang, BoChao School of Energy and Mining Shandong University of Science and Technology Shandong China School of Computer Science and Engineering Xi'an Technological University Shanxi China

ISBN: (纸本)9781510660571

To manage the credit risk in supply chain financing (SCF) more effectively and maintain the healthy operation of the supply chain, this paper proposes a prediction model of credit risk in SCF based on IG-GA-SVM. Firstly, the information gain (IG) is used to extract the feature indexes of the original index system, and then the genetic algorithm (GA) is used to optimize the parameters of the support vector machine (SVM). Finally, the feature indexes selected by the information gain are input into the GA-SVM model for training, and the final prediction model is established. The test results show that the performance of the prediction model based on IG-GA-SVM is better than that of BP, SVM, and GA-SVM models. It has good generalization ability and can provide strong decision support for financial institutions to provide financing for small and medium-sized enterprises (SMEs). © 2022 SPIE.

关键词： Genetic algorithms

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RESEARCH ON BROADBAND UNDERSAMPLING ISAR IMAGING METHOD FOR VEHICLE TARGETS

RESEARCH ON BROADBAND UNDERSAMPLING ISAR IMAGING METHOD FOR ...

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IET International Radar conference 2023, IRC 2023

作者： Huang, Meng Tang, Shiyang Zhang, Linrang xin, Rou Kang, Hailong Hangzhou Institute of Technology Xidian University Hangzhou China National Key Lab of Radar Signal Processing Xidian University Xi'an China

ISBN: (纸本)9781839539954

ISAR imaging of vehicle targets often faces complex multi-target scenes and wide band undersampling problem. In this case, the performance of traditional imaging algorithms is degraded or even invalid. This paper proposes an imaging method that combines UAMP-SBL algorithm, image domain line detection, minimum entropy criterion, and particle swarm optimization algorithm to address the issues of broadband undersampling and multi target imaging in vehicle target ISAR imaging. Firstly, the compressed sensing model of wide band undersampling signal is established, and then the complete echo of multiple targets is reconstructed by using UAMP-SBL algorithm;Based on the line detection in image domain, the first order phase term coefficient of each sub target are estimated to achieve coarse alignment of sub target envelope. Then, the joint parameter estimation of the remaining first-order and second-order phase terms of the sub target is completed based on the minimum entropy criterion and particle swarm optimization algorithm to achieve accurate alignment of the envelope of the sub target. Finally, the effectiveness of the proposed method is demonstrated by the simulations. © The Institution of Engineering & Technology 2023.

关键词： Particle swarm optimization (PSO)

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Valluvan: processing Name Board images to Enhance Communication for Native Tamil Speakers 1

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5th International conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Ramesh, Sriramachandran Prasanna Kumar, N. Murali Krishnan, D.A. Parveen Sultana, H. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9789819718412

ISBN: (纸本)9789819718405

The Valluvan app is a language solution for native Tamil speakers. The system emphasizes the recognition of name boards, translation, and speech output to enhance communication and access to information. The app utilizes advanced technology to analyze images and text on name boards, transforming it into readable text for machines using sophisticated algorithms, the text is then translated to Tamil. The Valluvan app utilizes text-to-speech conversion methods to deliver speech output in Tamil, enhancing users’ understanding of translated content. The paper presents an overview of the app, discussing its methodology, implementation details, experimental results, and analysis. An app with potential to empower native Tamil speakers by facilitating access to and comprehension of information from diverse language sources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image enhancement

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