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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems IV"

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A Deep Neural Network for image Classification Using Mixed Analog and Digital Infrastructure 3rd

A Deep Neural Network for Image Classification Using Mixed A...

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3rd International conference on Emergent Converging Technologies and Biomedical systems, ETBS 2023

作者： Kala, R. Punitha, M. Poomani Alias Banupriya, P.G. Veerasamy, B. Bharathi, B. Alzubi, Jafar Ahmad Abed Associate professor Department of Computer Science and Engineering Karpagam College of Engineering Tamil Nadu Coimbatore641032 India Department of Information Technology Sethu Institute of Technology Kariapatti Puloor India Department of Computer Science and Engineering Karpagam Institute of Technology Tamil Nadu Coimbatore641032 India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil626126 India Department of Information Technology Coimbatore Institute of Engineering and Technology Tamil Nadu Coimbatore641109 India School of Engineering Al-Balqa Applied University Salt Jordan

ISBN: (纸本)9789819986453

Recently, Deep Learning (DL) methods are a well-known solution for image and object detection in computer vision applications. The software-based implementation of DL algorithms requires huge resources and consumes more power. To overcome these challenges, a hardware implementation of Deep Neural Network (DNN) using the integrated analog–digital architecture was proposed in this article. The Very Large-Scale Integration (VLSI)-based DNN implementation utilizes Pulse-Width Modulation (PWM), and analog–digital circuits. The integration of analog and digital architecture integrates the benefits of both circuits, which reduces power consumption and enhances processing speed and classification accuracy. The fabrication of the VLSI chip includes the DNN neuron circuit, PWM circuit, digital adders-subtracters, and SRAM memory. The experimental result verifies that the presented DNN VLSI architecture reduces the power consumption of the neural system, and enhances the image classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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Model with NLP and Machine Learning for Predicting Symptoms Based on Textual Conversations with COVID-19 Infected Individuals 13

Model with NLP and Machine Learning for Predicting Symptoms ...

引用

Decima Tercera Conferencia Iberoamericana de Complejidad, Informatica y Cibernetica, CICIC 2023 en el contexto de the 14th International Multi-conference on Complexity, Informatics, and Cybernetics, IMCIC 2023 - 13th Ibero-American conference on Complexity, Informatics and Cybernetics, CICIC 2023 in the context of the 14th International Multi-conference on Complexity, Informatics, and Cybernetics, IMCIC 2023

作者： Varela-Tapia, Eleanor A. Sangacha-Tapia, Lady M. Acosta-Guzmán, iván L. Ordoñez-Chávez, Frank E. Acosta-Varela, Christopher I. Facultad de Ciencias Matemáticas y Físicas Universidad de Guayaquil Guayaquil Ecuador Instituto Superior Universitario Tecnológico del Azuay Cuenca Ecuador Facultad de Ingeniería Industrial Universidad de Guayaquil Guayaquil Ecuador Facultad de Ingeniería en Electricidad y Computación Escuela Superior del Politécnica del Litoral Guayaquil Ecuador

ISBN: (纸本)9781950492695

AI is a science that has influenced humanity in recent years, with natural language processing being a field within it used in the development of systems that understand unstructured texts and extract essential information from them. This project aims to design a model using natural language processing techniques and supervised machine learning algorithms for the classification and prediction of text based on textual conversations with individuals infected with Covid-19. In the methodology, data was collected through a survey of a population residing in zone 8 of the Guayas province, corresponding to the cantons of Guayaquil, Durán, and Samborondón in Ecuador, resulting in 5000 surveys. Prior to model training, a manual cleaning process was performed, removing irrelevant and semantically meaningless information, including the correction of texts with spelling errors, emojis, and information unrelated to the topic. This process resulted in 4140 records. Subsequently, preprocessing and processing techniques were applied, resulting in 3925 valid records. The modeling utilized a multi-output classifier with the best logistic regression classifier for the classification of multiple labels with binary classes. The results obtained showed metrics including an accuracy of 0.845860, precision of 0.975319, sensitivity of 0.934876, and an F1 Score of 0.953337. © 2023 *** Rights Reserved.

关键词： Natural language processing systems

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Sclera Segmentation on the Half-UNet Neural Network

Sclera Segmentation on the Half-UNet Neural Network

引用

Digital Signal processing and its Applications (DSPA), International conference on

作者： Sergei Chernyadyev Natalya Kharina Department of the Radio Electronic Means Vyatka State University Kirov Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Nowadays, technology for biometric authentication technologies is actively implemented, users can usually authenticate themselves by their face, voice, fingerprint, vascular bed of the finger, palm, iris. Using images of sclera vessels for authentication is mathematically and technically more complex, so it is used less often. In addition, existing sclera segmentation algorithms often do not allow ensuring the required quality of authentication systems. The paper proposes an algorithm for sclera segmentation based on the lightweight convolutional neural network Half-UNet, and compares the obtained results with known algorithms for sclera segmentation. Analysis of the obtained results shows that the developed algorithm effectively highlights the eye sclera region, surpassing known modern algorithms, and can be used in implementation of biometric authentication system as a stage of processing scanner images before the sclera vessels segmentation stage.

关键词： Performance evaluation image segmentation Neural networks Memory management Signal processing algorithms Authentication Biometric authentication Mobile handsets System implementation Iris recognition

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Multiple Transform Selection in Versatile Video Coding: a Review 8

Multiple Transform Selection in Versatile Video Coding: a Re...

引用

8th International conference on Advanced Computing and Communication systems, ICACCS 2022

作者： Chowdary, T. Anil Nalluri, Purnachand VIT-AP University School of Electronics Engineering Inavolu Andhra Pradesh 522237 India

ISBN: (纸本)9781665408165

Tools for Transform Coding in coding of video relied on DCT-II traditionally for mapping residuals of image/video signals. Residual mapping can be done to a domain where quantizing and encoding tools give better efficiency in coding than domain of spatial. Nonetheless, DCT-II cannot model available types of residual image/video signals effectively. DCT-II modeling all types of residuals is not possible because of non-stationary behaviour of contents of video. So, to improve performance in compression of 4 X 4 blocks, DST-VII was instigated into VVC. In this paper This Paper presents survey of different algorithms for selection of optimal transform for each block (4X4, 8X8, 16X16, 32X32 and 64X64) of residual image/video for VVC. © 2022 IEEE.

关键词： Video signal processing

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Hyperspectral image processing with Machine Learning and Mathematical Models 7th

Hyperspectral Image Processing with Machine Learning and Ma...

引用

7th International conference on Soft Computing and Signal processing, ICSCSP 2024

作者： Augustin, Anju Kiliroor, Cinu C. Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam India

ISBN: (纸本)9789819799251

Hyperspectral imaging is a fast-growing imaging technique in many fields, like remote sensing, fruit analysis, clinical images, etc. The spectral image consists of two parts: spectral data and spatial data. The processing of spectral images includes the processing of the spatial part as well as the spectral part. This paper analyzes various methods and algorithms that are used for spectral image processing in existing systems and proposes a diagram for hyperspectral image processing. There are five important steps for processing the spectral image and analyzing the region selection and dimensionality reduction methods in detail. Comparing the performance of region selection and dimensionality reduction techniques, the Rembg algorithm and Principal Component Analysis (PCA), respectively, show very good results. A well processed image has better performance in the classification and regression steps, and it reduces the computational time and cost. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Principal component analysis

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OCTA-Based Biomarker Characterization in nAMD

OCTA-Based Biomarker Characterization in nAMD

引用

International image processing, Applications and systems conference (IPAS)

作者： Maria Simona Tivadar Ioana Damian Adrian Petru Groza Simona Delia Nicoara Department of Computer Science Technical University of Cluj-Napoca Romania European University of Technology European Union Department of Ophthalmology "Iuliu Hatieganu" University of Medicine and Pharmacy Emergency County Hospital Cluj-Napoca Romania

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

We aim to enhance ophthalmologists’ decision-making when diagnosing the Neovascular Age-Related Macular Degeneration (nAMD). We developed three tools to analyze Optical Coherence Tomography Angiography images: (1) extracting biomarkers such as mCNV area and vessel density using image processing; (2) generating a 3D visualization of the neovascularization for a better view of the affected regions; and (3) applying an ensemble of three white box machine learning algorithms (decision tree, support vector machines and DL-Learner) for nAMD diagnosis. The learned expressions reached 100% accuracy for the training data and 68% accuracy in testing. The main advantage is that all the learned models white-box, which ensures explainability and transparency, allowing clinicians to better understand the decision-making process.

关键词： Three-dimensional displays Accuracy Optical coherence tomography image processing Angiography Decision making Medical services Machine learning Biomarkers Glass box

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The Implementation and Optimization of High-Performance image processing algorithms Based on Artificial Intelligence

The Implementation and Optimization of High-Performance Imag...

引用

Integrated Intelligence and Communication systems (ICIICS), International conference on

作者： Caizeng Ye Peng Wang Shandong Institute of Commerce and Technology Jinan China Jinan Engineering Polytechnic Jinan China

image processing (IP) technology has emerged on the basis of AI, digital imaging technology, and multimedia technology, and people began to use computers to process images to improve image quality and improve human vision. With the advancement of computer vision and AI technology, people want to achieve high performance IP. In the field of IP, techniques such as image segmentation, image compression, and image restoration are active research topics in computer vision. In this paper, we propose IP algorithms such as Artificial Bee Colony(ABC) Optimization Algorithm and Searcher Optimization Algorithm (SOA) for this block of image compression, analyze the image compression effect of these two algorithms, and compare the image compression quality by comparing the PSNR of each algorithm, and the results get that the image compression effect based on artificial bee colony(ABC) Algorithm is better and the image compression code book quality is better with the same compression ratio.

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CHARACTERIZATION OF WETTING PROPERTIES OF MAGNETOACTivE ELASTOMER SURFACES 16

CHARACTERIZATION OF WETTING PROPERTIES OF MAGNETOACTIVE ELAS...

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ASME 16th Annual conference of the Smart Materials, Adaptive Structures and Intelligent systems (SMASIS)

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

ISBN: (纸本)9780791887523

Commercially available contact angle (CA) measuring devices usually do not allow for the application of magnetic fields to the sample under test. A setup for measuring the CA of liquids on magnetosensitive surfaces has been developed specifically for investigating the surfaces of magnetoactive elastomers (MAEs). The addition of a programmable linear stage, which moves a permanent magnet, allows for fine control of the magnetic field applied to the MAE without the need for large and power-consuming electromagnets. Paired with a custom control and evaluation software, this measurement setup operates semiautomatically, limiting operator error and increasing precision, speed, as well as repeatability of static and dynamic CA measurements for different magnetoactive materials. The software is equipped with robust droplet fitting algorithms to avoid experimental challenges arising with soft magnetoactive materials, such as the curling of sample edges or diffuse non-reflective surfaces. Several application examples on MAE surfaces, both processed and unprocessed, are presented.

关键词： magnetoactive elastomers image processing droplet contour fitting contact angle

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Research on image Enhancement Technology and Signal processing Methods in Teaching Hydraulic systems

Research on Image Enhancement Technology and Signal Processi...

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Digital Analysis and processing, Intelligent Computation (DAPIC), International conference on

作者： Shijie Guan Pengpeng Liu Zhengyi Zhang Weiwei Dong Xiangtong Kong High-tech Institute QingZhou Shandong China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

Hydraulic system as an important part of industrial automation, its teaching effect directly affects the quality of related technical personnel training. Hydraulic transmission course is a theoretical and practical combination of the post course offered by our non-commissioned officers' vocational and technical education in mechanical manufacturing technology, because the hydraulic system involves complex images and signal data, students often face difficulties in understanding and analyzing in the learning process. Through the course, students can systematically master the basic theoretical knowledge of the hydraulic transmission system, familiar with the structural composition and working principle of commonly used hydraulic components, and master the working principle and characteristics of common basic circuits. This paper proposes to combine image enhancement technology with signal processing methods to optimize the teaching of hydraulic system. By analyzing the application of image enhancement technology in image denoising, edge detection and brightness adjustment, as well as the practical effect of signal processing methods in frequency domain analysis, time-frequency analysis and intelligent algorithms, we explore how to enhance students' theoretical understanding and practical ability of hydraulic system. The study shows that the integration of image and signal processing technology can not only significantly improve the teaching efficiency, but also provide technical support for the construction of intelligent teaching mode in the future.

关键词： Training Time-frequency analysis image edge detection Education Brightness Hydraulic systems Virtual reality Signal processing image enhancement image denoising

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Binary Stochastic Flip Optimization for Training Binary Neural Networks

Binary Stochastic Flip Optimization for Training Binary Neur...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Tatsukichi Shibuya Nakamasa Inoue Rei Kawakami Ikuro Sato Department of Computer Science Institute of Science Tokyo Tokyo Japan Department of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

For deploying deep neural networks on edge devices with limited resources, binary neural networks (BNNs) have attracted significant attention, due to their computational and memory efficiency. However, once a neural network is binarized, finetuning it on edge devices becomes challenging because most conventional training algorithms for BNNs are designed for use on centralized servers and require storing real-valued parameters during training. To address this limitation, this paper introduces binary stochastic flip optimization (BinSFO), a novel training algorithm for BNNs. BinSFO employs a parameter update rule based on Boolean operations, eliminating the need to store real-valued parameters and thereby reducing memory requirements and computational overhead. In experiments, we demonstrated the effectiveness and memory efficiency of BinSFO in fine-tuning scenarios on six image classification datasets. BinSFO performed comparably to conventional training algorithms with a 70.7% smaller memory requirement. Code is released at https://***/TatsukichiShibuya/ICASSP2025_BinSFO

关键词： Training Speech coding Memory management Signal processing algorithms Signal processing Classification algorithms Servers Speech processing Optimization image classification

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