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

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FPGA Interfaced ECG Signal processing Based on Machine Learning Classifier

FPGA Interfaced ECG Signal Processing Based on Machine Learn...

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2023 International conference on Energy, Materials and Communication Engineering, ICEMCE 2023

作者： Jeya Malar, R. Mary Mettilda, J. Mageshbabu, M. Siva Nagini, R. Grace, Lily Saron Gireesha, B. Kings Engineering College Department of Information Technology Chennai India Ppg Institute of Technology Department of Electronics and Communication Engineering Coimbatore India SIMATS Saveetha School of Engineering Department of Electronics and Communication Engineering Thandalam Chennai India Universal College of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Vallioor India Kings Engineering College Department of Electronics and Communication Engineering Chennai India University College of Science Tumkur University Department of Electronics Karnataka572103 India

ISBN: (纸本)9798350393378

This study aims to propose an FPGA-oriented ECG signal processing system architecture. The method identifies FPGA complexes and classifies beats as either preterm or typical contractions of the ventricular wall. For pulse classification, an element of the Open Source ECG Analysis Software is used, and for FPGA complexity verification, a method based on a phase-space image of an ECG signal is used. Artificial intelligence (AI) is increasingly serving as the basis for autonomous and independent systems. One of the most popular machine learning techniques among many categorization strategies is the Support Vector Machine (SVM) classification. We may further improve the SVM classifier's accuracy and classification process by using convex optimization techniques to identify the best solution. Numerous machine learning techniques, such as SVM classification, need a lot of processing power since they are compute-and data-intensive. Additionally, a number of machine learning algorithms have made it into incorporated and handheld gadgets, which have very particular demands. © 2023 IEEE.

关键词： Electrocardiograms

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Optimizing FPGA Hardware Design for Temporal Filtering of High-Definition Video

Optimizing FPGA Hardware Design for Temporal Filtering of Hi...

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International conference on Computers, Communications, and systems (ICCCS)

作者： Xingjian Fu College of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

In daily life, noise reduction is an inevitable aspect of video or image processing because noise can affect the subjective quality of viewing. Therefore, video denoising filtering is a crucial operation in video image processing. Among various denoising methods, motion-compensated temporal filtering is a typical and effective filtering technique. However, there is still room for further research in the hardware implementation of this method. Hence, this paper investigates the Multi-hypothesis Motion-Compensated Filter algorithm and proposes an efficient hardware structure for the filtering algorithm. By parallelizing or pipelining various modules, hardware efficiency is effectively improved. By FPGA evaluation, the final experiments demonstrate that the hardware structure for motion-compensated temporal weighted filtering presented in this paper can meet the requirements of video denoising filtering with a resolution of 1920xl080@60fps.

关键词： Filtering Communication systems image processing Noise reduction Noise Filtering algorithms Hardware

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Liver Disease Prediction using various image processing Techniques and Machine Learning algorithms

Liver Disease Prediction using various Image Processing Tech...

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Software, systems and Information Technology (SSITCON), International conference on

作者： M. S Saravanan Siva Rama Krishna Prasad S Dhanalakshmi Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of IT Guru Nanak Institutions Technical Campus Ibrahimpatnam India Department of Bioinformatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Predicting liver disease usually entails estimating a person’s chance of contracting ailments related to the liver using a variety of techniques. The prediction is made using the Novel Decision Tree algorithm and the Naive Bayes algorithm. From CHAOS Grand challenge webpage, data collected for liver images. Decision tree classifiers are compared to Naive Bayes classifiers for their accuracy and precision in diagnosing liver disease in patients. When it comes to predicting liver illness, the Naive Bayes classifier is $75.15 \%$ accurate, whereas the Novel Decision tree classifier is $80.03 \%$ accurate. 0.022 is the most significant number. g-power is used to calculate the sample size for two group at 16. The 95 percent alpha value for the g power is 0.05. The prediction of liver disease at early stage achieved by comparing Novel Decision Tree to Naive Bayes, Novel Decision Tree outperforms the latter in terms of precision and accuracy.

关键词： Accuracy Machine learning algorithms Liver diseases image processing Software algorithms Prediction algorithms Software systems Naive Bayes methods Decision trees Information technology

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Calculation of a phase diffractive optical element that forms a given set of spheroidal functions 8

Calculation of a phase diffractive optical element that form...

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8th International conference on Information Technology and Nanotechnology, ITNT 2022

作者： Krupnikov, Andrey Kirilenko, Mikhail Samara National Research University Samara Russia Samara National Research University Samara Russia Image Processing Systems Institute RAS Branch of the FSRC 'Crystallography and Photonics' RAS Samara Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

We consider spheroidal functions calculated by the method of solving the equation for eigenvalues and eigenvectors of the finite Fourier transform. With the help of iterative algorithms, a phase-only diffractive optical element was calculated to form a superposition of eigenmodes of the optical system, which are spheroidal functions. © 2022 IEEE.

关键词： Optical systems

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Advancements in Hyperspectral image processing Using Machine Learning

Advancements in Hyperspectral Image Processing Using Machine...

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Information Technology, Electronics and Intelligent Communication systems (ICITEICS), IEEE International conference on

作者： Kasarapu Ramani P Yukitha Sake Karthik Karapakula Aravind Chowdary Mandapalli Akash Dept of Data Science Mohan Babu University(Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Department of CSSE Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

In several domains, such as remote sensing, agriculture, and environmental monitoring, hyperspectral image processing is essential. In this work, the Indian Pines dataset is used to investigate hyperspectral picture classification. Five machine learning algorithms—Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Extreme Gradient Boosting (XGBoost) are assessed for performance. It improves classification accuracy for preprocessing methods such as feature extraction, PCA and noise reduction. The effectiveness of each method is evaluated using performance indicators such as kappa coefficient, average accuracy, and overall accuracy. Our results highlight how well XG Boost performs in obtaining the maximum accuracy of 96.12% when compared to other models.

关键词： Support vector machines Accuracy Machine learning algorithms Vectors Classification algorithms Convolutional neural networks Environmental monitoring

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image Captioning Based on Convolutional Neural Network and Transformer 2

Image Captioning Based on Convolutional Neural Network and T...

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2022 2nd International conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2022

作者： Haidarh, Mosa Zhang, Shuang College of Software Northeastern University Shenyang Liaoning 110819 China

ISBN: (纸本)9783800760268

image captioning refers to the automatic description of images using words, and the task has sparked the interest of researchers in the fields of computer vision and NLP. In recent years, most works on image captioning used the encoder-decoder framework where the encoder is used to get the image features vectors, and the decoder takes these features vectors to generate the description of the image. By combining CNN and LSTM, the encoder-decoder framework for image captioning has achieved significant progress;Additionally, by including attention mechanisms into this framework, the performance of the captioning models was significantly enhanced. Furthermore, the Transformer, which depends on attention in its work, is better in terms of performance and efficiency in NLP missions compared with LSTM. Based on the previous ideas, we proposed a model based on CNN and Transformer that achieved accurate image captions. With the proposed model, the Transformer-Encoder is used to get a new representation of the image features to help the Transformer-Decoder focus on the most relevant part of the image when generating a new word. We also employ adaptive attention in the Transformer-Decoder to determine when and where the decoder uses the image information. The experiments were done on the combined datasets of Flickr30K, Flickr8K, and 1K images collected by us, and the testing results prove that the work is effective and valuable. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Natural language processing systems

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Real Rainy Scene Analysis: A Dual-Module Benchmark for image Deraining and Segmentation

Real Rainy Scene Analysis: A Dual-Module Benchmark for Image...

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2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Zhao, Shilei Huang, Wenxin Yang, Mingxuan Liu, Wenxuan Wuhan Dashuiyun Technology Co. Ltd China School of Computer Science and Information Engineering Hubei University China School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798350313154

Scene understanding in adverse weather conditions (e.g., rainy and foggy days) has drawn increasing attention, raising some specific benchmarks and algorithms. However, rain streaks in images and videos can significantly degrade visual quality and reduce the effectiveness of computer vision algorithms. Concerning on lack of rainy-clean pairs training samples in real rainy scenes, we propose an unsupervised deraining method that does not require any explicit rain-clean image pairs for training. Instead, our approach leverages the statistical properties of rain streaks and clean regions to learn a rain removal model. Specifically, we choose contrastive learning that encourages the generator to produce derained images that are indistinguishable from clean images while the discriminator distinguishes between real clean images and rain streak patterns. Furthermore, we incorporate a semi-supervised mechanism during segmentation module that exploits low-confidence Pseudo-label to be treated as negative samples for certain classes to facilitate contrastive learning. Our experiments demonstrate that our integrated framework achieve better performance on the given challenging scenarios such as heavy rain and dynamic scenes. © 2023 IEEE.

关键词： Rain

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The Hybrid Model of LSB—Technique in image Steganography Using AES and RSA algorithms 6th

The Hybrid Model of LSB—Technique in Image Steganography Us...

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6th International conference on Soft Computing and Signal processing, ICSCSP 2023

作者： Talasila, Srinivas Vijaya Kumar, Gurrala Vijaya Babu, E. Nainika, K. Veda Sahithi, M. Mohan, Pranay VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9789819984503

The process of embedding a data file or information inside another data file or picture is known as steganography. The goal of steganography is to conceal the presence of a message or data so as to avoid it being seen by an observer who does not know how or what to look for. Steganalysis techniques are capable of helping hide details in electronic data such as pictures, audio files, and video files. For instance, a message might be hidden in the unimportant portions of a picture or in the sparse components of an audio transmission. Data encryption can additionally be employed to safeguard the data on printed sheets by using magic markers or other approaches. The least significant bit (LSB) approach, an edge detection technique, has the disadvantage of poorer security and imperceptibility compared to the classic steganography techniques already in use. We propose a paradigm that integrates steganography and cryptography to increase security. To add a cryptographic flavour, we use the Advance Encryption Standard (AES) algorithm, which protects the security of the secret message bits. In this study, we compare the performance of the LSB approach using AES and the LSB technique using AES and RSA (Rivest–Shamir–Adleman) algorithms to that of the Pixel Locator Sequence (PLS) technique. To test the performance of the suggested approaches, we compute the metrics Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) and compare the findings with previously published PLS techniques that show good enough values for our proposed model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mean square error

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A Hybrid Approximate Multiplier for Energy Efficient image processing

A Hybrid Approximate Multiplier for Energy Efficient Image P...

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Smart systems for applications in Electrical Sciences (ICSSES), International conference on

作者： V Sathiyanarayanan K Sanjaiyan J Rahul J Ram Guhanesh S Marirajan C Saravanakumar Electronics and Communication Department SRM Valliammai Engineering College Chennai India

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

In the field of Very Large-Scale Integration (VLSI) circuits, low power and energy-efficient solutions are desperately needed. “Urdhva Tiryagbhyam“ is one of the Sutras of Vedic Mathematics used as multiplication method in this paper. This study deals with the 8-bit Approximate Vedic Multiplier (AVM) based on the Vertical and Crosswise technique of Vedic mathematics is introduced in this project as a solution to the significant time and hardware resources consumed by multiplication operations, particularly in processors handling image processing applications. The Xilinx Vivado Design Suite simulator (EDA) tool is used for synthesis and simulation, and Verilog is used for implementation. The main goal of this research implements the approximate multiplier in real-time image processing applications. In comparison to traditional precise multipliers, this study intends to illustrate its energy-efficient capabilities by drastically cutting the power and space consumption of image processing circuits. In the age of energyefficient computing, the research findings demonstrate the significance of approximation multipliers in attaining optimal performance and compact device designs.

关键词： Power demand Program processors image processing Signal processing algorithms Computer architecture Very large scale integration Energy efficiency

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Learning-based fast image enhancement for driving scenes 6

Learning-based fast image enhancement for driving scenes

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6th CAA International conference on Vehicular Control and Intelligence, CVCI 2022

作者： Dong, Mingwei Liu, Runwu Kong, Huifang School of Electrical Engineering and Automation Hefei University of Technology Hefei China

ISBN: (数字)9781665453745

ISBN: (纸本)9781665453745

Visual sensors such as cameras can obtain rich image and video information, which is one of the most effective and lowest cost perception sensors for autonomous driving. However, when the environmental conditions change rapidly, the quality of acquired images decreases, resulting in various deep learning algorithms embedded in the camera may be difficult to complete object detection and other tasks. Although some existing image enhancement methods can mitigate the effects of environmental factors, these methods often involve complex calculations that ignore the importance of processing speed in the autonomous driving field. To solve these problems, double-branch image enhancement network is proposed in this paper, which includes low resolution branch and full resolution branch. With low resolution branch, local features of image can be obtained at a small computational cost. With full resolution branch, the details which are lost due to down-sampling are obtained. The enhanced image is obtained by fusing the feature graphs of two branches. The test result on BDD100K shows that our method has higher image processing speed on the premise of ensuring image quality. © 2022 IEEE.

关键词： image enhancement

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