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Multiple Attention-Guided Neural Architecture Search for Hyperspectral image Classification 22

Multiple Attention-Guided Neural Architecture Search for Hyp...

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Proceedings of the 2022 4th International conference on Video, signal and image processing

作者： Yingluo Song Aili Wang Shiyu Dai Haibin Wu Yuji Iwahori Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin Universiy of Science and Technology China Department of Computer Science Chubu University Japan

ISBN: (纸本)9781450397810

Hyperspectral images(HSIs) have rich spatial and spectral information, which provide a strong basis for distinguishing different landcover objects. the HSI classification methods based on deep learning have achieved good performance. Recently, several Neural Architecture Search (NAS) algorithms have been proposed for HSI to further improve the accuracy to a new level. In this paper, the neural structure search method based onmultiple attention mechanism search space is proposed for hyperspectral images. In the search process, the network selects attention mechanism operation at an appropriate layer, which can help the network focus on useful features and discard useless features. In our experiments, we have verified the superiority of this method on two hyperspectral datasets.

关键词： image classification Hyperspectral images Neural Architecture Search attention mechanism

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Multimodal Fusion Intelligent Driving Fatigue Monitoring and Warning in Police Traffic Scenarios

Multimodal Fusion Intelligent Driving Fatigue Monitoring and...

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signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Guancheng Chen Long Chen Kaiwen Tan Pinxian Xie School of Criminal Investigation People’s Public Security University of China Beijing China School of Computer Science and Engineering Wuhan University of Engineering Wuhan China School of Traffic Management People’s Public Security University of China Beijing China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

the study presents a multimodal fusion intelligent driving fatigue monitoring and warning system tailored for police traffic scenarios. this system integrates diverse data modalities, including facial expressions, body posture, heart rate, and speech, to achieve comprehensive and precise fatigue detection. Leveraging edge-cloud collaborative computing and infrared image recognition technology, the system operates efficiently across various environmental conditions, enhancing its reliability and stability. the system's adaptability is further demonstrated through its compatibility with different levels of autonomous driving (L1 to L5), offering customized solutions that range from assisted to fully autonomous driving. Specially designed for police operations, the system provides features such as fleet management, equipment integration, and real-time connectivity to command centers, which collectively improve the safety and effectiveness of law enforcement tasks. Compared to traditional single-modality systems, this multimodal approach significantly enhances detection accuracy, stability, and functional diversity, positioning it as a competitive solution for widespread application in both general and specialized driving contexts.

关键词： Heart rate Accuracy Law enforcement image edge detection Collaboration Fatigue Real-time systems Safety Monitoring Autonomous vehicles

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Slag Detection System Based on Vibration Using Chaos and Nerve Cell theory

Slag Detection System Based on Vibration Using Chaos and Ner...

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the 4th International conference on Continuous Casting of Steel in Developing Countries

作者： TIAN Lu~(1,2) HUANG Yu-jun~(1,2) HUANG Qi-cai~(1,2) 1.Ramon Science and Technology Co., Ltd, Hengyang 421001, Hunan 2. Beijing Research Institute of Ramon, Beijing 100080

At the process of molten steel casting, outflow slag must be detected to improve the quality of steel product and continuous casting efficiency. After lots of investigation of home and aboard slag detection technology, data analysis and data summarization, the dissertation brings forward the slag detection system of the casting state of steel by analyzing the vibration signal of shroud operation arm using chaos and nerve cell method. the collection and process of molten steel vibration signal and the method of slag characteristic parameter identification has been researched deeply, and basic principles of the application of neural network in casting state recognition are expounded. And the frame and function design of slag detection system are all briefly introduced. the results testing in Xiang Steelworks proved the effectiveness of the slag detection system, which can satisfy the demand for real-time, improve pure measure of molten steel and continuous casting efficiency and create benefit for the steelworks evidently.

关键词： chaos theory neural network slag detection wavelet analysis vibration signal processing

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An Improved Method for ECG Morphological Features Extraction from Scanned ECG Records

An Improved Method for ECG Morphological Features Extraction...

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2012 4th International conference on Bioinformatics and Biomedical Technology（ICBBT 2012）

作者： Srinivasan Jayaraman Vani Damodaran TCS Innovation Labs Tata Consultancy Services Bangalore India Engineering Design Indian Institute of Technology Madras (IITM) Chennai India

Electrocardiogram (ECG) has been the preliminary diagnostic tool to detect cardiovascular diseases. ECG is generally recorded on a thermal paper which cannot be stored for a long time for analysis, as the thermal trace gets erased gradually. To store the trace, the records are scanned and saved as images to maintain medical record. the memory occupied by this method is high and the regeneration of signal accuracy is less. this paper aims to extract the ECG morphological feature extraction, in addition to extraction of ECG trace from the paper and converting it to a digital time series signal. thus an improved method of morphological feature extraction technique has been adopted in this paper. Digital time series signal and morphological feature extraction were tested on a database of 25 paper records and the accuracy is 95% and 97.5% respectively. Further work is extended to perform an automatic arrhythmias classification using DTW from the obtained morphological parameters.

关键词： ECG Morphology, ECG to Digital, EMR, Adaptive Binarization, Iterative morphological image processing, DTW

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DAASPP: Depth-Wise Asymmetric Atrous Spatial Pyramid Pooling for COVID-19 Lesion Segmentation

DAASPP: Depth-Wise Asymmetric Atrous Spatial Pyramid Pooling...

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IEEE International conference on signal and image processing applications (ICSIPA)

作者： Siti Raihanah Abdani Syed Mohd Zahid Syed Zainal Ariffin Nursuriati Jamil Shafaf Ibrahim School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Selangor Malaysia

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

Medical image segmentation plays a vital role in automated disease screening and diagnosing systems. One of the diseases that depend on image segmentation output is COVID-19, in which the diseased lesion is informative in order to determine the disease severity. the task of segmenting COVID-19 lesions is a challenging process, not only due to computational costs and hardware limitations but also because of the varying sizes and shapes of the lesions. In this study, we proposed to integrate a multiscale unit, specifically the ASPP, into a base DABNet, resulting in a lightweight module called DAASPP. the addition of a multiscale unit is meant to further improve the network capability in extracting lesion features of various scales so that small-and large-scale lesions can be segmented accurately. there are two variations of DAASPP have been proposed: one with a single-unit ASPP and another network with a dual-unit ASPP. Both variations are placed at the last and second last DABNet blocks due to the availability of complex features at these two layers. Four different scales are explored for each network variation, specifically the 2, 3, 4, and 5 dilated convolution layers. the results showed that DAASPP with a single-unit ASPP and four parallel scale branches demonstrated the best segmentation performance, achieving mean IoU, mean Dice coefficient, and mean accuracy scores of 0.6629, 0.7461, and 0.9991, respectively. this improvement in extracting the lesion regions will help in producing a better diagnosis system of the COVID-19 severity level.

关键词： COVID-19 image segmentation Shape Convolution Feature extraction Hardware Computational efficiency

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Harnessing AI and Imaging Innovations for Enhanced Detection and Diagnosis of Age-Related Macular Degeneration: An In-Depth Review

Harnessing AI and Imaging Innovations for Enhanced Detection...

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Artificial Intelligence and signal processing (AISP), INDIA, International conference on

作者： Sana Swaroop Manda Rama Narasinga Rao Department of Computer Science & Engineering GITAM University Visakhapatnam Andhra Pradesh India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

Age-related macular degeneration (AMD) is a leading cause of irreversible vision impairment among the elderly population worldwide, affecting over 196 million people globally. this study delves into innovative approaches for improving the detection and management of AMD through advanced technological solutions. To investigate the impact of ultraviolet (UV) and blue light exposure on AMD, the research analyzes their contribution to oxidative stress and retinal damage and evaluates potential protective measures or interventions to mitigate these effects. By leveraging state-of-the-art machine learning algorithms and advanced image processing techniques, the research aims to enhance the precision and efficiency of AMD diagnosis. the statistical burden of AMD underscores its significant impact on global health, with projections indicating a rising prevalence due to aging populations, lifestyle factors, and increasing digital screen use among younger generations. Effective management hinges on early detection and accurate monitoring of AMD biomarkers, which these methodologies seek to facilitate. Experimental evaluations demonstrate promising outcomes in diagnostic accuracy and scalability, highlighting the potential for widespread adoption in clinical practice. Furthermore, these advancements contribute to broader efforts in global eye health by offering scalable, AI-driven solutions that can improve patient outcomes and streamline healthcare workflows. By addressing the complexities of AMD diagnosis, this research supports healthcare providers in delivering timely interventions and personalized care, ultimately reducing the burden of AMD-related vision loss on individuals and healthcare systems.

关键词： Macular degeneration Training Technological innovation Accuracy Medical services Streaming media signal processing Ophthalmology Stress Stress measurement

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Industrial Video Surveillance image Denoising based on Non-subsampled Contourlet Transform

Industrial Video Surveillance Image Denoising based on Non-s...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International conference on IEEE International conference on

作者： Qingyun Zhang Zhuo Chen Pengfei Xie Yingxu Wang Ruibo Shi Qian Zhou Yuduo Pu Dept. of Electronic and Information Eng. Shandong University of Science and Technology Qingdao China Rizhao Guangming Electric Power Service Co. Ltd. Rizhao China State Grid Information & Telecommunication Branch Beijing China Wuhan Fiberhome Technical Services Co. Ltd Wuhan China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

With the continuous expansion of industrial video scale, visualization technology has become an indispensable technical means. However, during the collection and transmission of images, they may be affected by factors such as the device itself and environmental factors, which can lead to noise pollution and a decrease in image quality, which is not conducive to image post-processing. therefore, industrial video surveillance image denoising has become an important research topic, and using various methods to improve image quality has become an important research direction. In order to address the ringing effect caused by hard threshold denoising, this paper proposes an adaptive smooth non-subsampled contourlet transform (NSCT) denoising algorithm. the proposed method utilizes the correlation of each sub-band to improve the denoising function, making it adaptive. the simulation results show that the algorithm proposed in this paper improves subjective perception such as contrast and detail clarity of remote sensing images, as well as objective evaluation indicators such as peak signal-to-noise ratio, information entropy, and correlation coefficient

关键词： image quality Correlation Wiener filters Heuristic algorithms Simulation Noise reduction Transforms Video surveillance Remote sensing image denoising

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Depression Detection Based on Eeg signals Using Machine Learning Algorithms

Depression Detection Based on Eeg Signals Using Machine Lear...

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Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, IEEE International Maghreb Meeting of the conference on

作者： Fatima M. Alneqrat Ali Hussein Maamar Asmaa Nasr Mohammed Hajer A. Aghnaya College of Electronic Technology - Bani Walid

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

Depression, a psychological disorder with detrimental effects on an individual's thoughts, actions, and mood, afflicts millions of people. As it has evolved into a serious illness, there is a pressing need for accurate diagnostic methods. this research employs features extracted from EEG signals, utilizing the Discrete wavelet Transform (DWT) algorithm to diagnose depression. Two machine-learning algorithms, Support Vector Machines (SVM) and K-nearest Neighbors (KNN), were employed for classification. the study, conducted using a dataset from Hospital University Sains Malaysia (HUSM), demonstrates the efficacy of this approach. Results indicate that the KNN algorithm achieved a remarkable test accuracy of 99.51 %, while SVM reached 91.18%, both utilizing features derived from the DWT algorithm. these models successfully differentiate between depressed patients and healthy controls with high accuracy rates. In summary, this research highlights the potential of EEG signals as a biomarker for identifying depression, offering significant advancements in diagnosis and treatment approaches for this condition.

关键词： Support vector machines Accuracy Machine learning algorithms signal processing algorithms Nearest neighbor methods Depression Feature extraction

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Audio Classification through MFCC features using RNN algorithm

Audio Classification through MFCC features using RNN algorit...

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Innovation in Technology (ASIANCON), Asian conference on

作者： Kilaru Devi Bhargavi V. Deepa I. Harshitha Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Recurrent Neural Network (RNN) algorithm, is renowned for its effectiveness in object localization and classification tasks within the realm of computer vision. this study combines RNN capabilities with the analysis of Mel-Frequency Cepstral Coefficients(MFCC) features, which have demonstrated effectiveness in capturing unique audio signal characteristics. Mel-Frequency Cepstral Coefficients (MFCCs) serve as a compact representation of the spectral content of audio signals, enabling the identification of distinctive features crucial for detecting anomalies introduced during the generation of deepfake audio. the Recurrent Neural Network (RNN) algorithm is applied to these MFCC features. RNN excels in tasks such as image recognition, object detection, and semantic segmentation. Previous research in the field of audio forensics has explored various methodologies for detecting manipulated audio content. While some approaches focus on signal processing techniques, others employ machine learning algorithms. MFCCs have consistently demonstrated their efficacy in capturing audio features relevant to authenticity. the proposed approach enhances the detection of fake audio, a critical challenge in the era of advanced digital media manipulation. By combining the strengths of MFCC features and the RNN algorithm, the model aims to learn and identify intricate patterns associated with genuine audio, enabling accurate differentiation between authentic and manipulated audio sources. By adapting RNN to analyze MFCC features, the model gains the capability to discern intricate patterns associated with genuine audio, thereby distinguishing between authentic and manipulated audio sources.

关键词： Adaptation models Technological innovation Recurrent neural networks Machine learning algorithms Semantic segmentation signal processing algorithms signal processing Feature extraction Classification algorithms Mel frequency cepstral coefficient

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Research on a Hyperplane Decomposition NMF Algorithm Applied to Speech signal Separation 22

Research on a Hyperplane Decomposition NMF Algorithm Applied...

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Proceedings of the 2022 4th International conference on Video, signal and image processing

作者： Chaofeng Lan Han Ye Yan Liu Xiaoxia Guo Yulan Han School of Measurement and Communication Engineering Harbin University of Science and Technology China

ISBN: (纸本)9781450397810

Non-negative Matrix Factorization (NMF) solves the single-channel speech separation problem. However, problem solving mostly separates the pure speech signal from the mixed speech signal with noise. this paper proposes an NMF algorithm based on hyperplane decomposition to separate speech for this problem. Based on the NMF algorithm, the observed signal is decomposed into different hyperplanes, a nonlinear mapping between the amplitude of the observed signal and the amplitude of the signal to be separated is constructed, and the speech signal is reconstructed according to the amplitude spectrum of the pure speech signal to realize the separation of the two original speech signals. the correlation coefficients between the reconstructed speech signal and the original speech signal after decomposition into different numbers of hyperplanes are compared and analyzed. the effects of different numbers of hyperplanes on the separation effect of speech signals are discussed. the study shows that when the number of hyperplanes is 20, the similarity coefficient after speech separation is 0.8356, and the separation effect is better. Meanwhile, the algorithm proposed in this paper can obtain more extracted features, which can be used as the training data of deep learning algorithm and provide some technical reference for speech separation.

关键词： Hyperplane decomposition Speech separation NMF algorithm

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