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

共 3477 条记录，以下是331-340 订阅

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Two-Step High Precision Burst Pulse Detection in Low SNR Based on FPGA

Two-Step High Precision Burst Pulse Detection in Low SNR Bas...

引用

IEEE advanced Information Management,Communicates,Electronic and Automation Control conference (IMCEC)

作者： Jing Chen Jun Hu Zengping Chen Shujian Zheng School of Electronics and Communication Engineering Sun Yat-Sen University Shenzhen China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

To address the issue of unsatisfactory burst signal detection and insufficient time-of-arrival estimation accuracy in low SNR environments in modern digital signal receivers, this paper investigates a two-step high precision real-time signal detection method. The first step involves using the Short-Time Fourier Transform and Constant False Alarm Rate detection in the frequency domain to improve the detection of weak signals, and obtain the existence and rough position of signals. By using time-domain autocorrelation detection based on the detection results, the TOA of the signal is accurately estimated. The method is then implemented on the FPGA platform and verified in an actual environment, demonstrating practical value for engineering.

关键词： Image edge detection Frequency-domain analysis Time of arrival estimation signal processing algorithms Real-time systems Autocorrelation Time-domain analysis

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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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Advancing EEG Brain-Computer Interfaces with Efficient Machine Learning Classifications

Advancing EEG Brain-Computer Interfaces with Efficient Machi...

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Intelligent Technologies (ACOIT), Asian conference on

作者： Ramakrishnan Raman Vikram Kumar C. P. Sanjay Apurv Verma Shailesh Rastogi Dharmendra Pandey Symbiosis International (Deemed University) SRV Media Pvt. Ltd. IT & Business Analytics Sri Balaji University Pune (SBUP) Computer science and Engineering Faculty of Engineering and Technology Parul institute of Technology Parul University Symbiosis Institute of Business Management Nagpur Symbiosis International (Deemed University) Pune

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

In recent years, brain-computer interfaces (BCIs) have advanced significantly through the integration of electroencephalogram (EEG) data, though the complexity of EEG signals remains challenging for traditional classification methods. This paper introduces a novel machine learning framework to enhance classification efficiency in EEG-based BCIs. We employ advanced algorithms, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to capture temporal and spatial dependencies in EEG data. Our approach leverages a combination of feature extraction techniques and dimensionality reduction to optimize classification, improving accuracy and speed. We performed a comparative analysis using a standardized EEG dataset, revealing our model’s significant improvement in classification accuracy and computational efficiency over traditional classifiers. These results highlight our method’s potential in realtime BCI applications. This study contributes to the theoretical understanding of EEG signal processing and paves the way for more responsive and user-friendly BCI systems.

关键词： Accuracy Recurrent neural networks signal processing algorithms Brain modeling Feature extraction Electroencephalography Brain-computer interfaces Classification algorithms Convolutional neural networks Reliability

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Innovative Wearable EEG Solution for Stress Assessment and Control

Innovative Wearable EEG Solution for Stress Assessment and C...

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Intelligent Systems and advanced Applications (ICISAA), International conference on

作者： Aviral Patel Prerna Zagade Shravan Ambudkar Aboli Jadhav Bhavna Ambudkar Department of EnTC Symbiosis Institute of Technology Pune India Department of EnTC Symbiosis Professionals and Skills University Pune India Department of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Pune India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

In this work, wearable electroencephalogram (EEG) technology is investigated as a cutting-edge stress assessment and treatment method. An increasing number of people require simple, practical methods for monitoring and managing their stress levels because of the increasing prevalence of illnesses associated with stress. Wearable electroencephalography (EEG) devices offer a feasible path for the high temporal resolution monitoring of stress circumstances by permitting real-time, noninvasive brain activity measurements. This abstract examines the current status of wearable EEG technology, emphasizing how advancements in sensor technology, hardware downsizing, and signal processing techniques have made it simpler to integrate EEG monitoring into everyday life. The project also investigates the utility of EEG-based Brain-Computer Interfaces (BCIs) for stress detection and control, as well as the potential of EEG signals as biomarkers for stress-related cognitive processes. The paper also covers the methodological aspects of implementing EEG-based stress assessment protocols, including feature extraction, signal processing algorithms, and machine learning techniques for stress prediction. The end of the abstract discusses the therapeutic applications of wearable EEG devices in stress management therapy and points to potential directions for future study in this rapidly emerging field. Because wearable electroencephalography (EEG) technology offers new insights into the neurophysiological foundation of stress and makes individualized therapies based on individual stress profiles possible, it has the potential to completely transform paradigms for stress measurement and regulation.

关键词： Electric potential Medical treatment signal processing algorithms Transforms Electroencephalography Biomedical monitoring Stress Monitoring signal resolution Stress measurement

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Enhancing Wind Speed Forecasting with Multi-Meteorological Factors LSTM Models and Novel Loss Function Design

Enhancing Wind Speed Forecasting with Multi-Meteorological F...

引用

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

作者： Xinyi Li Jing Hu Dufu Liu Dongling Wang Feihu Huang Cheng Ji School of Computer Chengdu University of Information Technology Chengdu China Huafeng Meteorological Media Group Co. Ltd. Beijing China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Wind power generation has grown in importance as renewable energy technology has advanced; nonetheless, wind speed forecasting remains a concern. Traditional approaches are inadequate in dealing with the nonlinearity and long-term dependency of wind speed data. The suggested method aims to present a deep learning architecture that incorporates several meteorological elements, with a hybrid loss employed to increase the wind speed prediction effect. The experiment used meteorological data from the Hangzhou Asian Games. Comparative results show that our suggested strategy outperformed ARIMA and Random Forest models in measures by up to 10.86%. Ablation studies demonstrated that including wind speed along with other meteorological data can improve performance, highlighting the importance of wind speed in predicting.

关键词： Adaptation models Visualization Wind speed Deep architecture signal processing algorithms Predictive models Wind power generation signal processing Forecasting Long short term memory

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A High-Density Electromyography Sensor Based on MEMS E-Skin for Detecting Activity of Motor Units

A High-Density Electromyography Sensor Based on MEMS E-Skin ...

引用

IEEE International conference on Micro Electro Mechanical Systems

作者： Yirong Wang Shuihan Shao Chunpeng Jiang Bin Yang Jingquan Liu National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong University Shanghai China Department of Micro/Nano Electronics School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

This paper introduces a compact, ultrathin, and stretchable high-density surface electromyography (sEMG) electrode designed for targeted signal acquisition from 16 or more channels in localized muscle regions around the mouth and neck during swallowing. These signals are processed into motor unit potentials through advanced decomposition algorithms. The electrode features a serpentine structure approximately 6 micrometers thick, enabling conformal contact with the skin and accommodating cyclic stretching up to 160%, thereby minimizing motion artifacts and ensuring high-quality signal capture. Surface modifications with iridium oxide and platinum black are applied to reduce contact impedance. The high channel density and small form factor facilitate detailed analysis of deep motor unit activity in targeted muscles using high-density sEMG decomposition techniques.

关键词： Electrodes Micromechanical devices Surface impedance Electronic skin signal processing algorithms Muscles Motors Electromyography Impedance Surface treatment

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Automatic Attendance Management System Using CNN

Automatic Attendance Management System Using CNN

引用

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

作者： S.V.N Sreenivasu Ch. Rajani Birlangi Usha Rani Arangi Dasaradha Shaik Rafi Dept of CSE Narasaraopeta Engineering College Narasaraopet Ap India Dept of Information Technology Aditiya Institute of Technology and Management (Autonomous) Tekkali AP India Dept of CSM Aditiya Institute of Technology and Management (Autonomous) Tekkali AP India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

Facial recognition technology plays a crucial role in various applications, from enhancing security at banks and organizations to streamlining attendance tracking in public gatherings and educational institutions. Traditional methods of attendance marking, such as signatures, names, and biometrics, can be time-consuming and error-prone. To address these challenges, a smart attendance system is proposed, leveraging Deep Learning, Convolutional Neural Networks (CNN), and the OpenCV library in Python for efficient face detection and recognition. The system utilizes advanced algorithms, including Eigen faces and fisher faces, to recognize faces accurately. While deep learning models excel with large datasets, they may not perform optimally with few samples. By comparing input faces with images in the dataset, the system automatically updates recognized names and timestamps into a CSV file, which is then sent to the respective organization's head. Additionally, the system allows users to upload a single photo or a group photo, and it returns matched photos as output using a CNN. This feature enhances the system's flexibility and usability, providing users with a convenient way.

关键词： Deep learning Face recognition Impedance matching signal processing algorithms Organizations signal processing Libraries Convolutional neural networks Security Usability

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Research on the Resource Allocation Optimization Model of Automobile Based on Cloud Computing Resource Scheduling Algorithm

Research on the Resource Allocation Optimization Model of Au...

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

作者： Kang Kai Zhu Peipei Zhang Fan China Automotive Technology and Research Center Co. Ltd. Tianjin China China Auto Information Technology (Tianjin) Co. Ltd. Tianjin China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

This paper proposes a global resource allocation optimization model based on cloud computing, aiming to improve the global resource management efficiency of automobile enterprises through cloud computing resource scheduling algorithm. The system makes full use of the flexibility and scalability of cloud computing, and designs a set of advanced resource scheduling algorithms, which can dynamically adjust the allocation of computing resources according to the changes in demand in different markets and regions around the world, ensuring that resources can be optimally configured at different times and regions. The simulation results show that the model has significantly improved the resource allocation efficiency compared with traditional methods, which not only greatly improves the resource utilization rate, but also effectively reduces the operating costs of enterprises. The cloud computing resource scheduling algorithm shows higher computing efficiency and economic benefits when dealing with complex resource allocation problems. Compared with the traditional static scheduling method, the algorithm proposed in this paper shows its excellent adaptability when dealing with fluctuations in global resource demand, and provides a new technical path and practical basis for the resource allocation optimization of automobile enterprises in the context of globalization.

关键词： Cloud computing Scheduling algorithms Optimization models Simulation Globalization signal processing algorithms Scheduling Resource management Automobiles Optimization

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Research on the Model of Improving Engineering Safety Management Decision Support System Using Data Mining Technology

Research on the Model of Improving Engineering Safety Manage...

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

作者： Ke Xiaofei Zhanjiang University of Science and Technology Zhanjiang China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

With the increasing scale and complexity of modern engineering projects, the challenges faced by engineering safety management are becoming increasingly severe. To address this problem, this paper proposes a model of engineering safety management decision support system based on data mining technology. The model uses advanced algorithms such as gradient boosting decision tree (GBDT) to extract valuable information from a large amount of engineering safety data to classify and predict potential risks. The system architecture consists of modules such as data preprocessing, risk identification, risk assessment and decision support, which can effectively improve the accuracy of risk identification. Through simulation tests on actual engineering project data sets, the risk prediction accuracy of the system model reached 92.5%, which is about 15% higher than that of traditional methods. In addition, the system shows good generalization ability in different engineering scenarios, reducing the impact of human factors on safety management decisions. Experimental results show that the system cannot only help engineering managers identify high-risk areas, but also provide optimized safety management decision suggestions, significantly improving the safety of engineering projects. The research in this paper shows that the data-driven engineering safety management model provides a new decision support means for the field of engineering management.

关键词： Decision support systems Accuracy Safety management signal processing algorithms Predictive models Boosting Prediction algorithms Data models Data mining Decision trees

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Performance Evaluation of Deep Learning Models for Alzheimer’s Disease Detection

Performance Evaluation of Deep Learning Models for Alzheimer...

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International conference on Communications and signal processing

作者： Abdul Talha Ch. Dhanasree E. Divya Kandagatla Srikar Prabhas U Syed Abudhagir Department of ECE B. V. Raju Institute of Technology Narsapur Medak Telangana India Department of Data Science and Artificial Intelligence Faculty of Science and Technology (IcfaiTech) IFHE Hyderabad Telangana India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Alzheimer's disease (AD) presents as a neurodegenerative condition characterized by dementia, impacting cognitive function, memory, and behavior. Swift identification is crucial for effective intervention and better patient outcomes. This research aims to tackle this urgent need by harnessing deep learning techniques to advance early AD detection. By merging magnetic resonance imaging (MRI) with advanced deep learning algorithms, the objective is to create a precise, non-invasive approach for early AD diagnosis. The primary goal is to uncover new features and significantly boost the accuracy of AD detection. The method involves examining convolutional neural networks alongside MRI scans to reveal patterns indicative of AD progression. Notably, the proposed model achieves impressive evaluation metrics: 99.19% accuracy, 0.023% loss, 99.08% f1-score, and 99.11% precision. This endeavor seeks to demonstrate substantial advancements in AD detection accuracy compared to existing methods. Through this interdisciplinary approach, the aim is to drive progress in early AD diagnosis, ultimately leading to more effective interventions and enhanced quality of life for those impacted by this challenging condition accuracy compared to existing methods.

关键词： Deep learning Performance evaluation Magnetic resonance imaging Merging signal processing algorithms signal processing Feature extraction

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