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检索条件"任意字段=Conference on Advanced Signal Processing Algorithms"

共 3499 条记录，以下是481-490 订阅

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Classifying EEG-based Upper Limb Motor Imagery Tasks for Brain-Robot Interface-based System Development

Classifying EEG-based Upper Limb Motor Imagery Tasks for Bra...

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International conference on advanced Robotics and Mechatronics (ICARM)

作者： Kuldeep Gurjar Sanskar Agrawal Kazi Newaj Faisal Rishi Raj Sharma Department of Electronics Engineering Defence Institute of Advanced Technology Pune India

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Cognitive robotics and engineering strive to develop brain-robot interfaces (BRI) that enable effective collaboration between robots and humans. The continued advancements in neuroscience, robotics, and machine learning are projected to broaden the scope of BRI applications, ultimately improving human-robot cooperation. A major area of focus for BRI-based system development involves mapping brain activities to control actions through motor imagery (MI) based technologies such as electroencephalogram (EEG). In this paper, a novel technique for classifying different tasks involving MI of upper limb movements from EEG signals is presented. To emphasize the inter-related information within various bands of EEG signals, ratio of band power (RBP)-based features are proposed for categorizing different upper limb-based MI tasks. The combination of these proposed features and an optimized KNN classifier produced remarkable accuracy in classifying diverse MI tasks from EEG signals. This approach was compared with recent methods applied to the same dataset, illustrating its superiority in performance. Furthermore, a comparison between the proposed RBP-based features and conventional band power-based features underscored the effectiveness of RBP-based attributes for classifying MI-related tasks. The improved effectiveness of the proposed approach could lead to progress in BRI-based systems development across a range of applications.

关键词： Accuracy Sensitivity signal processing algorithms Machine learning Nearest neighbor methods signal processing Motors Electroencephalography Reliability signal to noise ratio

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Using active asymmetry control and blind source separation to improve the accuracy of after develop overlay measurements 37

Using active asymmetry control and blind source separation t...

引用

conference on Metrology, Inspection, and Process Control XXXVII

作者： Lee, Jungmin Lee, Doogyu Lee, Eunji Yim, Inbeom Lee, Jeongjin Lee, Seung Yoon Hwang, Chan Noot, Marc van Leest, Arno Mathijssen, Simon Gao, Yao Yang, Seung-Bin Baek, Mi-Yeon Lee, Do-Haeng Park, Han-Gyeol Yim, Jong-Hyuk Kim, Thomas Lee, Ho-Hyuk Dahha, Kemal Smith-Meerman, Stefan Van Witteveen, Koen McNamara, Elliott McLaren, Matthew Samsung Seoul South Korea ASML Netherlands BV Veldhoven Netherlands

ISBN: (纸本)9781510660991;9781510661004

During metrology overlay recipe setup typically a wide range of different target designs are present to select from. The main goal of recipe setup is to select the most accurate target type-recipe combination at ADI (after develop inspection) without additional external information that can be used for production on a large set of wafers. We will introduce a method based on blind source separation to disentangle the contributions of target asymmetry and the overlay of the targets. Based on this separation, the most accurate target-recipe combination can be selected. On top of selecting the most accurate target-recipe combination, it is important to stabilize the difference between the overlay on device and the overlay as measured on the target. In order to increase that stability we will introduce advanced algorithms in the ADI measurements that use measured asymmetry parameters to correct for inline target asymmetry variation. We will show a metrology to device matching improvement of up to 40% on product wafers.

关键词： Overlay metrology Semiconducting wafers Metrology Matrices Polarization signal processing Wafer testing Diffraction Education and training Independent component analysis

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Research on Intelligent System of Key Technology of Long Distance Guided Wave Radar in Level Measurement of Nuclear Power Unit

Research on Intelligent System of Key Technology of Long Dis...

引用

IEEE International conference on Power, Intelligent Computing and Systems (ICPICS)

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

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

In this paper, a key technology intelligent system based on long distance guided wave radar is proposed to meet the special requirements of liquid level measurement in nuclear power units. By integrating advanced signal processing algorithm model, the system effectively overcomes the problem of insufficient accuracy and poor stability of traditional liquid level measurement method in complex nuclear power environment. An efficient signal processing algorithm is developed, which can accurately identify and analyze the guided wave radar echo signal, so as to achieve accurate measurement of liquid level. In addition, in order to verify the effectiveness of the algorithm and the reliability of the system, simulation modeling is also carried out in this paper. The liquid level variation under various practical conditions is simulated, and the response speed and measurement error of the system are evaluated strictly. The experimental results show that the proposed intelligent system can run stably in the complex nuclear power environment, its measurement accuracy is significantly higher than the state of the art, and it has good anti-interference ability and fast response characteristics.

关键词： Accuracy Power measurement Liquids Radar measurements signal processing algorithms Radar Stability analysis Level measurement Intelligent systems Nuclear measurements

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An Improved RT-DETR Model for Detection of Tobacco Impurities

An Improved RT-DETR Model for Detection of Tobacco Impuritie...

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International conference on Vision, Image and signal processing (ICVISP)

作者： Chengjin Tao Zhongxin Ma Lijun Yun Zaiqing Chen Bin Li Cheng Shen Yunnan Provincial Tobacco Company Kunming China Yunnan Normal University Engineering Research Center of Computer Vision and Intelligent Control Technology Kunming China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Tobacco, as a crop of significant economic importance, plays a pivotal role in global agriculture and industry. However, during the processing of tobacco products, the presence of tobacco impurities (TIs) poses a major challenge, as it substantially diminishes product quality. Traditional methods for detecting TIs are often inefficient and limited in scope, underscoring the need for precise and reliable detection techniques. This study addresses these challenges by introducing an improved Real-Time Detection Transformer (RT-DETR) model. By integrating the ShuffleNet V2 architecture and a novel Multi-Branch-Edge-Information-Select (MBEIS) block, the model enhances feature extraction and fusion capabilities, enabling efficient detection even in complex scenarios. Furthermore, a standardized imaging environment, consisting of controlled lighting and a black box setup, was employed to ensure consistent data collection, and the dataset was augmented using advanced data enhancement techniques. Experimental evaluations demonstrate that the proposed model achieves a precision of 92.7%, a recall of 93.5 %, and a mean average precision (mAP) of 87.9%, outperforming mainstream detection algorithms. In addition, the reduction in model parameters and computational complexity ensures superior real-time performance, making it both lightweight and effective for industrial applications. The results highlight the model's strong potential as a robust solution for detection of TIs, providing a significant technological advancement for the tobacco industry. This research not only addresses critical quality assurance challenges but also offers a valuable reference for applying advanced detection technologies in other industrial domains.

关键词： Accuracy Impurities Computational modeling Computer architecture signal processing Transformers Real-time systems Product design Quality assessment Sorting

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Alzheimer's disease MRI image recognition method based on deep learning

Alzheimer's disease MRI image recognition method based on de...

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

作者： Huang Jiahao College of Mechanical and Electronic Engineering Nanjing Forestry University Nanjing China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Globally, the incidence of Alzheimer's disease is increasing. Although there are many diagnostic methods, traditional methods often rely on professional knowledge and consume much time. Therefore, there is an urgent need to introduce automated technology to improve diagnostic efficiency and accuracy. This study used three deep learning models, namely convolutional neural network (CNN), ResNet, and VGGNet. It optimized them according to data characteristics for Alzheimer's MRI image recognition, providing a reference for diagnosing Alzheimer's disease. This study used the dataset from Dataset Alzheimer, based on the data preprocessing of Alzheimer's Disease Neuroimaging Initiative (ADNI). The model was trained after the dataset was processed by data augmentation, pixel value scaling and MOTE oversampling. The experimental results indicated that the VGGNet model attained the highest overall accuracy at 76%, and the CNN and ResNet models reached accuracies of 75% and 74%, respectively. Nonetheless, the model's proficiency in reliably identifying categories with limited samples necessitates enhancement. Furthermore, further analysis is necessary to assess the model's generalisation potential and its relevance in clinical environments. Future research may explore more advanced model architectures, incorporating attention processes and generative adversarial networks (GANs), to enhance the model's recognition capabilities and its suitability for therapeutic applications.

关键词： Deep learning Accuracy Image recognition Magnetic resonance imaging Transfer learning signal processing algorithms signal processing Convolutional neural networks Reliability Alzheimer's disease

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Diabetic Retinopathy Detection Using CNN With Res-LSTMDN

Diabetic Retinopathy Detection Using CNN With Res-LSTMDN

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International conference on Communications and signal processing

作者： R. Gayathri S Karthikeyan T KausheekRaj S Keerthana Department of Electronics and Communication Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

For diabetic patients, early diagnosis and treatment of diabetic retinopathy (DR) are essential since they can avert irreversible visual loss. The ways we currently find DR aren't very accurate or fast, so we need better ways to do it automatically. CNNs, a type of machine learning algorithm, have been shown to work well for analyzing medical images and could be a good option for detecting DR. Among CNN architectures, ResNet-152 has gained reputation for its depth and effectiveness in image classification tasks. However, to further enhance its performance in DR detection, this study does a modification to the final layer of ResNet-152. Specifically, the final layer is replaced with Long-Short Term Memory (LSTM) and Deep Neural Network (DNN) components, resulting in an ensemble approach termed Res-LSTMDN. This novel architecture aims to improve the hierarchical feature learning of ResNet-152 while integrating the sequential memory capabilities of LSTM and the flexibility of DNN. Res-LSTMDN uses advanced technology to study eye images, specifically those from people with diabetes. By finding unique patterns in these images, this technique aims to improve the accuracy of detecting diabetes-related eye problems. This will allow for early intervention, potentially preventing vision loss. As part of the ongoing efforts to develop advanced computer tools for diabetes diagnosis, Res-LSTMDN has the potential to significantly impact global diabetes management strategies.

关键词： Representation learning Diabetic retinopathy Visualization Machine learning algorithms Memory management signal processing Convolutional neural networks

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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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Electromyography Controlled Smart Prosthetic Arm

Electromyography Controlled Smart Prosthetic Arm

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IEEE International conference on Intelligent Techniques in Control, Optimization and signal processing (INCOS)

作者： Kalpana Murugan L.V.Naresh Reddy CH. Veerandra S.Sai Hussain N. Amarnadh Manoranjan Kumar Department of ECE Kalasalingam Academy of Research and Education Krishnankoil India Nanochip skills(Venkylabs) Bengaluru Karnataka India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Prosthetic technology has advanced to the point where intelligent prosthetic hands have been designed in an effort to restore functional capacities and improve quality of life for those who have undergone upper limb amputations. A novel electromyography (EMG)-based smart prosthetic hand is being built in this effort using deep learning techniques. Using non-invasive surface electromyography (EMG) data— the electrical impulses generated by muscles during contractions—the suggested smart prosthetic hand decodes the user's intended motions. To interpret and understand the complex EMG data associated with different hand motions, deep learning techniques called convolutional neural networks (CNNs) are employed. Deep learning made it possible through instruction allows for natural and real-time movement of the prosthetic hand. Smooth and instantaneous control over the prosthetic hand is made possible by the taught deep learning model, which accurately maps EMG signals to particular hand movements. Lastly, this demonstrates a deep learning algorithm-driven EMG-based smart prosthetic hand that may be able to enable people who have lost upper limb function regain dexterity when using prosthetic limbs.

关键词： Deep learning Prosthetic limbs signal processing algorithms Arms signal processing Electromyography Real-time systems

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Transmission Design for STAR-RIS Assisted Dual-User Uplink MIMO Based on Statistical CSI

Transmission Design for STAR-RIS Assisted Dual-User Uplink M...

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International conference on Wireless Communications and signal processing (WCSP)

作者： Mingyang Niu Jun Zhang Kaizhe Xu Yu Han Shi Jin Xiao Li Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Communications and Networking School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In this paper, a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is employed to assist uplink dual-user multiple-input multiple-output (MIMO) communications, which can create virtual links from users in both transmission and reflection region to the base station throughout the entire space, thereby enhancing coverage and reducing energy consumption. We aim to jointly design the transmit covariance matrix for the two users and the phase shift matrices for the STAR-RIS, based solely on statistical channel state information. The implemented alternating optimization algorithm for joint design is achieved by utilizing the derived closed-form expression of the ergodic sum rate. Specifically, the optimal transmit covariance matrices for the two users are determined using a water-filling algorithm, meanwhile the suboptimal solutions for the phase shift matrices of the STAR-RIS are obtained via the projection gradient ascent method. Simulation results demonstrate that the proposed algorithm achieves superior system performance.

关键词： Virtual links System performance Simulation signal processing algorithms signal processing Reflection Covariance matrices Uplink Optimization Space stations

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IoT Medical Device with Digital signal processing for Photoplethysmography Derived Respiratory Rate

IoT Medical Device with Digital Signal Processing for Photop...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International conference on

作者： Maria Therese B. Bonifacio Jae Malonie B. De Austria Ramon G. Garcia Electrical Electronics and Computer Engineering Mapua University Manila Philippines School of EECE Mapua University Manila Philippines

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Self-monitoring respiratory and cardiac health is challenging without proper equipment and medical knowledge. While Masimo offers ideal remote monitoring through a pulse oximeter, device accessibility is still a significant issue. Thus, we designed a novel device designed for remote monitoring of vital signs using the Internet of Things (IoT) technology and sensor technology in healthcare. Leveraging the capabilities of the modest MAX30102 sensor and ESP32 microcontroller, the device integrates seamlessly with IoT networks, enabling real-time transmission of heart and respiratory rate (RR) data with general assessment feedback to a convenient cellphone application. A sophisticated digital signal processing (DSP) algorithm was implemented in the device, which accurately derives RR from photoplethysmography (PPG) heart rate signals. This DSP-based device ensures precision in RR measurement and facilitates self-monitoring. Mean Squared Error (MSE) and Bland-Altman analysis results demonstrated the device's accuracy and reliability in capturing vital sign data. By bridging IoT capabilities with advanced DSP techniques, the device is a promising solution for remote patient monitoring to improve healthcare.

关键词： Pulse oximeter Patient monitoring signal processing algorithms Digital signal processing Medical services Photoplethysmography Real-time systems

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