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Prior Alzehmiers detection using Deep Learning

Prior Alzehmiers detection using Deep Learning

引用

International Conference on Communications and signal processing

作者： M Dhanush M. Rajeswari J Jerlin Rajan K Jitendra Sai Department of DSCS Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Department of CSE Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Karunya school of Managemnt Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Alzheimer’s disease (AD) is the most common cause of dementia worldwide, and its incidence is rising. Although it usually affects those over 60, middle-aged people are more affected. However, the approval process for new drugs has been significantly delayed for more than ten years. The complex pathophysiology of AD presents obstacles for immunotherapy, despite its potential benefits. Existing Convolutional Neural Network (CNN) methods have drawbacks, regardless of whether they use region-or vertex-level input. While region-wise techniques find capturing minute spatial differences in cortical thickness difficult, vertex-wise methods are typically noisy. Using a Region with Convolutional Neural Network (RCNN) for more efficient feature extraction and classification is one suggested solution to these problems. This method expedites the process of generating trustworthy results by evaluating data simultaneously, which lowers the number of biomarkers required and related expenses for precise patient diagnoses. As such, it has potential in therapeutic contexts by enabling accurate and customized detection of AD.

关键词： Scalability signal processing algorithms signal processing Feature extraction Real-time systems Classification algorithms Convolutional neural networks

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Research on Level Monitoring System of Nuclear Power Plant Based on Split Guided Wave Radar

Research on Level Monitoring System of Nuclear Power Plant B...

引用

International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Hongwei Yu CGN Digital Technology Co. Ltd. Beijing China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

A liquid level measurement system based on HT1200M split guided wave radar is developed to meet the requirements of liquid level monitoring in nuclear power plants. Using guided wave radar technology, the system can realize non-contact liquid level measurement in the harsh environment such as high temperature, high pressure, and strong radiation, and has high measurement accuracy and stability. In order to further improve the system performance, Fast Independent Component Analysis (ICA) algorithm is used to process the data collected by guided wave radar. Fast ICA algorithm is an efficient blind source separation technology, which can quickly and accurately extract the independent components of mixed signals, effectively remove noise and interference, and improve the purity and identification of liquid level signals. After the system is built, the Fast ICA algorithm model is verified by simulation experiments. The experimental results show that compared with the traditional signal processing methods, Fast ICA algorithm has obvious advantages in improving the signal separation speed and enhancing the liquid level signal characteristics. The system shows excellent response ability and measurement accuracy in the simulation scenario of liquid level change in nuclear power plant, and provides a new and efficient solution for liquid level monitoring in nuclear power plant.

关键词： Liquids Accuracy Power measurement signal processing algorithms Radar Radar signal processing Level measurement Power generation Monitoring Nuclear measurements

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Enhancing Music Source Separation Using U-Net and Generative Adversarial Network

Enhancing Music Source Separation Using U-Net and Generative...

引用

IEEE Region 10 International Conference TENCON

作者： E Vinitha George S Abhiram Ambadi D Anantha Krishnan Fathima Farheen K R Ravishankar V P Devassia Model Engineering College Cochin University of Science and Technology Kochi Kerala India Model Engineering College APJ Abdul Kalam Technological University Kochi Kerala India St. Joseph's College of Engg and Tech. Palai APJ Abdul Kalam Technological University Kerala India

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

To achieve enhanced music source separation, the approach of combining the power of a U-Net architecture and Generative Adversarial Networks (GANs) is employed. The proposed system leverages the U-Net architecture for its ability to capture intricate patterns in spectrograms and effectively learn hidden features. Unlike traditional models, the U-Net is designed to process audio information using an encoder-decoder structure, providing a richer representation of the audio scene. Generative Adversarial Network is incorporated to refine the separated sources. The GAN introduces a discriminator network that guides the training process, encouraging the generator (U-Net) to produce source-separated audio that not only matches ground truth sources but also exhibits high-fidelity and perceptual quality. This paper aims to address common challenges in music source separation, such as dealing with overlapping sources and preserving the quality of the separated signals. By combining the strengths of the U-Net and GAN, this system stands at par with the state-of-the-art methods in music source separation, offering a promising solution for applications in audio restoration and immersive audio experiences.

关键词： Training Source separation signal processing algorithms Music Production Generative adversarial networks Machine listening Multiple signal classification Immersive audio Spectrogram

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Improving Sparse Dictionary Learning with Rejection of Fully Exposed Patches

Improving Sparse Dictionary Learning with Rejection of Fully...

引用

European signal processing Conference (EUSIPCO)

作者： Matthieu Chancel Hacheme Ayasso Michel Desvignes Jean-Michel Vignolle Eric Lespessailles GIPSA-Lab Univ. Grenoble Alpes CNRS Grenoble INP Grenoble France Image Group TRIXELL Moirans France Department of Rheumatology Centre Hospitalier Universitaire Régional d'Orléans Orléans France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Sparse dictionary learning is widely used in image processing tasks such as denoising, classification and image enhancement. However, learning features from X-ray images can be challenging since some parts of the image contains no or very little clinical information which makes them totally exposed to X-rays. These areas are irrelevant for medical content reconstruction and waste the space in the dictionary. Moreover, they slow down the convergence of the learning methods. In this paper, we present a prior knowledge about these fully exposed areas based on their physical distribution and propose a specialized dictionary learning algorithm to improve convergence speed and the quality of the learned dictionary while keeping the same quality of reconstruction. We compare our solution to a variance based approach and show that our solution is robust to the X-ray dose and then more efficient than the variance-based approach.

关键词： Learning systems Dictionaries Noise reduction signal processing algorithms Machine learning signal processing X-ray imaging Image reconstruction Image enhancement Convergence

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AI-Powered Pneumonia Detection: Enhanced Chest X-ray Interpretation with CNNs

AI-Powered Pneumonia Detection: Enhanced Chest X-ray Interpr...

引用

Electronics, Communication and signal processing (ICECSP), International Conference on

作者： Mohit Beri Kanwarpartap Singh Gill Deepak Upadhyay Swati Devliyal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

The nearly lethal bacterial disease known as pneumonia, caused by the streptococcus pneumoniae bacterium, can infect any individual at any time. It often affects one or both lungs. One out of every three deaths in India is attributed to pneumonia, according to the World Health Organisation (WHO). In order to diagnose pneumonia, chest X-rays must be reviewed by trained radiologists. In order to treat pneumonia as quickly as possible, especially in faraway regions, it would be beneficial to have an automated method to detect the disease. The success of deep learning algorithms in analysing medical images has piqued a lot of people's interest in using Convolutional Neural Networks (CNNs) for illness detection. The proposed CNN-based method has several advantages over more traditional approaches, including greater accuracy, scalability, and the possibility of automation. Our proposal aims to improve the accuracy of pneumonia diagnoses by using deep learning techniques to identify complex patterns and traits in medical imaging data. Furthermore, features extracted from large datasets using pre-trained convolutional neural network (CNN) models perform quite well in image classification tasks. In this study, we evaluate these models’ capacity to distinguish between normal and pathological chest X-rays by employing pre-trained convolutional neural network (CNN) models as feature extractors and a number of classifiers thereafter. Analytical methods are employed to determine which CNN model is most suitable for the job at hand.

关键词： Deep learning Pneumonia Accuracy Scalability signal processing algorithms signal processing Feature extraction Convolutional neural networks X-ray imaging Biomedical imaging

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GGBDCA:Scene graph generation based on Global Gradient Balanced Distribution and Compound Attention

GGBDCA:Scene graph generation based on Global Gradient Balan...

引用

International Conference on signal processing Proceedings (ICSP)

作者： Jiajia Liu Linan Zhao Guoqing Zhang Linna Zhang Yigang Cen School of Computer Science and Technology Beijing Jiaotong University Beijing China Beijing Fast Hand Technology Co. LTD School of Mechanical Engineering Guizhou University Guiyang China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Scene Graph Generation (SGG) is a key task in computer vision, aimed at automatically extracting objects and their relationships from images. Despite significant advances in SGG, the challenge of long-tail distribution continues to impede accurate prediction of rare relationships. To address this, we propose a novel SGG method based on Global Gradient Balanced Distribution and Compound Attention (GGBDCA). First, we introduce a Transformer-based framework that leverages a compound attention mechanism to extract detailed features. To tackle the long-tail identification problem, we propose a Global Gradient Balanced Distribution (GGBD) algorithm, which converts the long-tail issue into a multi-objective optimization problem. Through Gradient Balance Grouping (GBG), similar categories are clustered to enhance attention on rare classes. Then, the Multi-Gradient Descent Algorithm (MGDA) is employed to solve the multi-objective optimization, while the Adaptive Calibration Function (ACF) dynamically adjusts classification scores to improve the model’s generalization. These three core modules—GBG, MGDA, and ACF—work together to balance learning between head and tail classes, focusing more on rare categories and boosting overall performance.

关键词： Heavily-tailed distribution Head Heuristic algorithms signal processing algorithms Clustering algorithms Tail Feature extraction Classification algorithms Compounds Optimization

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Research of RSSI Indoor Ranging Algorithm Based on Sparrow Search Algorithm and BP Neural Network

Research of RSSI Indoor Ranging Algorithm Based on Sparrow S...

引用

IEEE International Conference on signal processing, Communications and Computing (ICSPCC)

作者： Zuo Yin Kun Ye Haixin Sun School of Informatics Xiamen University Xiamen China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

Traditional methods for location estimation using Received signal Strength Indication (RSSI) rely on the log-normal shadow model to formulate the range measurement model. However, the parameter selection in this method is usually based on empirical data, which is easily affected by environmental factors, resulting in a decrease in the accuracy of ranging. In order to improve the ranging accuracy and reduce the impact of RSSI fluctuation, we proposed a new ranging method to develop a powerful ranging model using sparrow search algorithm and back propagation neural network (SSA-BP). In this method, the RSSI value of the target node is first initially normalized and then input into the SSA-BP ranging model to output the distance between the target node and the anchor node. Experimental results show that compared with the traditional BP algorithm and genetic algorithm (GA), the SSA-BP algorithm has faster convergence speed and higher ranging accuracy.

关键词： Location awareness Accuracy Fluctuations Neural networks signal processing algorithms Distance measurement Robustness Indoor environment Received signal strength indicator Genetic algorithms

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Structured Compressive Sensing Based Joint User Activity and Data Detection for Grant-Free mMTC

Structured Compressive Sensing Based Joint User Activity and...

引用

6th International Conference on Intelligent Computing and signal processing (ICSP)

作者： Yuanyuan Zhang Yongtian Luo Jin Xu Weihua Liu Lixun Huang Zhe Zhang College of Electronic Information Engineering Zhengzhou University of Light Industry Zhengzhou China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

This work deals with the active user and data detection problem in the uplink of grant-free massive machine-type communication (mMTC) systems. We formulate joint active user and data detection as a problem of equivalent sparse signal recovery problem represented by the multiple measurement vector (MMV) model. To address this issue, we develop a Sparse Pre-estimation Subspace Pursuit (SPeSP) algorithm based on greedy search. First, the algorithm employs variable step-size matching pursuit to estimate the signal sparsity, setting the initial selection step size to a small value and gradually increasing it to approximate the true sparsity. Then, by utilizing subspace tracking, the algorithm reduces reconstruction errors and progressively updates the number of atoms to the estimated sparsity, thereby enhancing the reconstruction performance of sparse signals. Simulation results demonstrate that the SPeSP algorithm outperforms traditional Sparse Adaptive Matching Pursuit algorithms in terms of active user detection rate and signal reconstruction performance.

关键词： Simulation signal processing algorithms Matching pursuit algorithms Massive machine type communications Approximation algorithms Vectors signal reconstruction Data models Sensors Uplink

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Procedural Content Generation for Racing Games

Procedural Content Generation for Racing Games

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signal processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International Conference on

作者： M. Raghavendra Ram Amey Jadhav V. Uma Maheshwar Reddy Asesh Kumar Tripathy K. Yogiswar Kancharla Ramya Puppala Department of Computer science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

Procedural Content Generation (PCG) is a critical element of contemporary game design, facilitating the creation of dynamic and diverse game worlds. This paper explores the application of PCG in racing games, focusing on algorithms, techniques, and design principles that contribute to the creation of immersive and challenging racing experiences. It also discusses challenges and ethical considerations associated with PCG in racing games and explores future research directions in this field.

关键词： Industries Ethics Technological innovation Procedural generation signal processing algorithms Games Manuals

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A New Extrapolation Solution for MU-MISO Symbol-Level Precoding

A New Extrapolation Solution for MU-MISO Symbol-Level Precod...

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International Conference on Wireless Communications and signal processing (WCSP)

作者： Mu Liang Ang Li Xiaoyan Hu Christos Masouros School of Information and Communications Engineering Xi'an Jiaotong University Xi'an China Department of Electronic and Electrical Engineering University College London London UK

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Constructive interference (CI) precoding, which converts the harmful multi-user interference into beneficial signals, is a promising and efficient interference management scheme in multi-antenna communication systems. However, CI-based symbol-level precoding (SLP) experiences high computational complexity as the number of symbol slots increases within a transmission block, rendering it unaffordable in practical communication systems. In this paper, we propose a symbol-level extrapolation (SLE) strategy to extrapolate the precoding matrix by leveraging the relationship between different symbol slots within in a transmission block, during which the channel state information (CSI) remains constant, where we design a closed-form iterative algorithm based on SLE both for PSK and QAM modulation. In order to further reduce the computational complexity, a sub-optimal closed-form solution based on SLE is further developed. Extensive simulation results illustrate that the proposed algorithms can dramatically reduce the computational complexity and time complexity with only marginal performance loss, compared with the conventional SLP design methods.

关键词： Wireless communication Phase shift keying Extrapolation Quadrature amplitude modulation Precoding Simulation Symbols signal processing algorithms Modulation Interference

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