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共 147 条记录，以下是71-80 订阅

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Intelligent information processing of Fiber Bragg Grating biosensor for stress monitoring in microfluidics system 3

Intelligent information processing of Fiber Bragg Grating bi...

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3rd international Computing Imaging conference, CITA 2023

作者： Zhang, Zijun Lu, Zhenghao Wang, Jing Pan, Han Li, Peiyun Li, Xiaoyao Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China Departmen of Physics and Astronomy University College London London United Kingdom Key Laboratory of More Electric Aircraft Technology of Zhejiang Province University of Nottingham Ningbo China Ningbo China Departmen of Applied Computational Science and Engineering Imperial College London London United Kingdom

ISBN: (纸本)9781510671409

A Fiber Bragg Grating (FBG) biosensor with good biocompatibility promises label-free, anti-electromagnetic interference, and non-destructive detection of stress monitoring, it holds the potential to rapidly identify the stress changes in microfluidics systems. Based on signal processing, the correlation between wavelength shift in FBG and flowrate is discussed, an alternative to manual processing of wavelength offset calculation is analyzed, and the automatic selection of FBG wavelength change under high noise conditions is realized. In addition, we propose some technologies of intelligent processing algorithms such as machine learning (BP—back propagation neural network) and moving average. Finally, the feasibility and stability of the signal processing method are verified by experimental validation and multiple data sets testing. This study provides a promising intelligent information processing strategy for rapid and accurate stress sensing of microfluidics systems. © 2023 SPIE.

关键词： Fiber Bragg gratings

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Multiple Feature Extraction Based on PPG signal to Realize Blood Pressure Measurement of Composite Multi-channel Convolutional Neural Network Model 3

Multiple Feature Extraction Based on PPG Signal to Realize B...

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3rd IEEE international conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023

作者： Lu, Cheng-Gang Wang, Lei Department of Electrical Engineering Feng Chia University Taichung Taiwan

ISBN: (纸本)9798350333862

Photoplethysmography (PPG) changes according to arterial blood pressure (ABP). The change in the physiological signal reflects the change in the functional state of the heart and vascular system. To prevent cardiovascular disease due to hypertension, the PPG signal has been widely used for non-invasive continuous estimation of blood pressure. In recent years, artificial neural network-based algorithms have found the relationship between ABP and related physiological signals. Compared with traditional cuff measurement methods, it is more intuitive to understand the overall blood pressure trend and avoid invasive pressure Measured bleeding risk. Most artificial neural networks use an electrocardiogram (ECG) and PPG at the same time, and the ECG acquisition device needs electrodes and wires to the chest cavity, which indirectly affects the convenience of physiological signal acquisition. Using three feature extraction techniques with different detailed attributes for a single PPG measurement and substituting short-time Fourier transform (STFT), continuous wavelet transform (CWT), and Hilbert-Huang transform (HHT) into the compound multi-channel convolutional neural network(CMCNN) model, we evaluate the overall blood pressure value. Considering that the distorted data caused by motion artifacts causes the training results to fail to converge, we select Physionet's MIMIC-III database and use data purification-based processing steps. We screen out the artifact labels for PPG and ABP at first and then use the artifact labels to perform seven-level data segmentation. Finally, using STFT, CWT, and HHT, we obtain spectrogram, scalogram, and Hilber spectrum. The original waveform of PPG and the spectrum, the scalogram, and the Hilbert spectrum are used as input data for learning and analysis. Compare with the traditional machine learning method, the blood pressure estimation results is closer to the actual situation. © 2023 IEEE.

关键词： Blood pressure

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A Technique Based on Ensemble machine learning for The Analysis of Electronic Nose signals 3

A Technique Based on Ensemble Machine Learning for The Analy...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Taneja, Amit Rajamani, Santhosh Kumar Shekhawat, Deepika Chanti, Yerrolla Joshi, Kapil Garg, Ashish Chandigarh University AIT-CSE India MAEER MIT Pune's MIMER Medical College and Dr BSTR Hospital TalegaonDabhade Department of E.N.T Maharashtra Pune India Manipal University Department of Computer Science & Engineering Rajasthan Jaipur India SR University School of Computer Science and Artificial Intelligent Telangana Warangal India Uttaranchal University Uttaranchal Institute of Technology India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350399264

Numerous applications have been documented for electronic nose (e-nose) devices, which are valued for their speed, affordability, and lack of invasiveness. The e-nose scheme is a useful instrument for analyzing volatile chemicals as a distinct 'fingerprint,' especially in the meat industry. The pattern identification algorithm's capacity to decode e-nose signals is crucial to the e-nose system's effectiveness in a wide variety of uses. However, positive results using ensemble approaches have been reported in a number of data types. In this study, an ensemble learning strategy is proposed for e-nose signal analysis, particularly for evaluating beef quality. Not only are learning algorithms and sensor array optimization using ensemble approaches. Ensemble FSA is created by combining three filter-based feature selection algorithms (FSAs) - reliefF, chi-square, and Gini index - for sensor array optimization. When using a single FSA on a collection of homogenous e-nose data for beef quality monitoring, it is common for the results to be inconsistent or unexpected. In addition, multi-class regression and categorization problems are handled by using ensemble learning techniques. We use bagging and boosting algorithms, shown here by Random Forest (RF) and Adaboost. Both a support vector machine (SVM) and a decision tree are used as independent learners, and their results are compared to those of the ensemble. Experiments show that our ensemble method is effective and generalizable for e-nose signal processing. Optimized sensor combination using filter-based FSA consistently outperforms previous methods in both classification and regression. Additionally, despite using a reduced sensor complement, Adaboost as a boosting algorithm yields the highest quality prediction. © 2023 IEEE.

关键词： Support vector machines

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Hand Gesture Recognition Using machine learning for Bionic Applications: Forearm Case Study 2nd

Hand Gesture Recognition Using Machine Learning for Bionic A...

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3rd international conference on Digital Technologies and Applications

作者： Lamsellak, Oussama Benlghazi, Ahmad Chetouani, Abdelaziz Benali, Abdelhamid ORAS Team LaMAO Lab Oujda 60000 Morocco Mohammed First Univ Oujda Elect & Syst Lab ENSA Oujda Oujda 60000 Morocco

ISBN: (纸本)9783031298561;9783031298578

The ubiquitous use of accelerometers and gyroscopes can provide meaningful knowledge about the recognition of human limb movements and offers significant potential for the design of human-machine interaction and the control of bionic systems. Our research puts an emphasis on linking acceleration data produced by hand movements to surface electromyograms (sEMG) collected in the arm in order to actively participate in robotic prosthesis control and the development of digital healthcare systems. The goal of this work is to demonstrate how to model a flexible system of hand gesture recognition using a dual-sensor accelerometer and gyroscope as a first stage, with the ability to evolve and improve its efficiency by incorporating other signals in subsequent stages of feature extraction and processing. Our research is primarily focused on identifying the triaxial signal generated via the accelerometer and gyroscope that appears when the hand is performing six gestures. Based on amassive gesture library collected over a long period of time by nine individuals belonging to three age categories and different genders, a forearm gesture classification model has been successfully developed, implementing a novel set of measurement criteria and statistical characteristics by virtue of five reputed machine-learning classifiers with an overall accuracy of 97.9% for training and 88.0% for testing, which are employed to differentiate and learn the forearm direction classes and interpret the performances.

关键词： Gesture Recognition machine learning Accelerometer EMG signals

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Aircraft Fuel Flow Modeling and Performance Optimization Using machine learning 3rd

Aircraft Fuel Flow Modeling and Performance Optimization Usi...

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3rd international conference on signal and Data processing, ICSDP 2023

作者： Dharrao, Deepak Misal, Mrinank Rai, Naivedya Joshi, Nirgoon Rajagopalan, Siddharth Pangavhane, Madhuri Bongale, Anupkumar Pune India Pune India Pune India Pune India

ISBN: (纸本)9789819795772

The modeling of fuel flow in aircraft is a critical element in evaluating their performance. However, many of the existing approaches rely on highly aggregated data, which may not provide the necessary accuracy required by the aviation industry. To address this challenge, a new data-driven approach is proposed in this study. This method leverages machine-learning techniques and uses full-flight data from aircraft sensors to develop fuel flow models. The approach focuses on identifying the features that impact fuel flow rates during different flight phases, using a unique deep-learning approach for modeling. The results demonstrate a significant improvement over traditional practices, providing a more comprehensive and accurate alternative for performance evaluations. As a result, airlines and maintenance providers can optimize their operations and reduce fuel costs while meeting environmental targets. The proposed method achieved a 99.25% adjusted R2 score, highlighting the potential of machine learning tools in developing accurate and detailed fuel flow models based on full-flight data, providing significant benefits for the aviation industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Jet fuel

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Data Urbanity: Smart City Evolution Through IoT and Data Science 3

Data Urbanity: Smart City Evolution Through IoT and Data Sci...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Singh, Ajay Kumar, Manni Chandigarh University Department.of Computer Science and Engineering Punjab Mohali India

ISBN: (纸本)9798350343632

The rapid increase in population density in urban cities has created a demand for suitable infrastructure and services to meet the needs of urban residents. To address this demand, urban planners and developers are turning to data science for assistance in creating smart city solutions. The Internet of Things (IoT) has played a significant role in the application of data science in urban planning, as IoT devices such as sensors and smart devices provide vast amounts of data that can be used to gain insights into the operations of the city and make data-driven decisions to enhance its productivity, efficiency, and overall quality of life. This paper provides a general overview of data science in urban planning for smart city development. It highlights the importance of data science in urban planning and the role it plays in creating smarter, more effective cities. By applying data analytics, machine learning, and other data science approaches, urban planners can gain insights into various aspects of urban life, including traffic patterns, energy usage, and public safety. These insights can then be used to develop strategies that improve traffic flow, reduce energy waste, and enhance overall quality of life in cities. The study aims to describe the function of data science in urban planning, examine case studies and best practices, discuss the challenges and limitations associated with applying data science in urban planning, and provide suggestions for further research and advancements in the field. By investigating how data science can be effectively used in urban planning for smart city development, this study contributes to the growing body of knowledge on creating more sustainable and livable cities. © 2023 IEEE.

关键词： Artificial intelligence Biomarkers cardiovascular disease Computed Tomography (CT) Data analysis Deep learning Diagnostic methods Early detection Electrocardiogram (ECG/EKG) Health informatics heart disease machine learning Magnetic Resonance Imaging (MRI) Medical imaging Predictive modeling Remote monitoring Risk assessment signal processing Ultrasound Wearable devices

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Time-Weighted Frequency Domain Audio Representation with GMM Estimator for Anomalous Sound Detection 48

Time-Weighted Frequency Domain Audio Representation with GMM...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Guan, Jian Liu, Youde Zhu, Qiaoxi Zheng, Tieran Han, Jiqing Wang, Wenwu Harbin Engineering University Group of Intelligent Signal Processing College of Computer Science and Technology China Harbin Institute of Technology School of Computer Science and Technology China University of Technology Sydney Centre for Audio Acoustics and Vibration Australia University of Surrey Centre for Vision Speech and Signal Processing United Kingdom

ISBN: (纸本)9781728163277

Although deep learning is the mainstream method in unsupervised anomalous sound detection, Gaussian Mixture Model (GMM) with statistical audio frequency representation as input can achieve comparable results with much lower model complexity and fewer parameters. Existing statistical frequency representations, e.g. the log-Mel spectrogram's average or maximum over time, do not always work well for different machines. This paper presents Time-Weighted Frequency Domain Representation (TWFR) with the GMM method (TWFR-GMM) for anomalous sound detection. The TWFR is a generalized statistical frequency domain representation that can adapt to different machine types, using the global weighted ranking pooling over time-domain. This allows GMM estimator to recognize anomalies, even under domain-shift conditions, as visualized with a Mahalanobis distance-based metric. Experiments on DCASE 2022 Challenge Task2 dataset show that our method has better detection performance than recent deep learning methods. TWFR-GMM is the core of our submission that achieved the 3rd place in DCASE 2022 Challenge Task2. © 2023 IEEE.

关键词： Deep learning

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Real-Time Traffic Management System Using machine learning and Image processing 3rd

Real-Time Traffic Management System Using Machine Learning a...

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3rd international conference on Data Science, machine learning and Applications, ICDSMLA 2021

作者： Sheela, S. Nataraj, K.R. Rekha, K.R. Don Bosco Institute of Technology Bengaluru India SJB Institute of Technology Bengaluru India

ISBN: (纸本)9789811959356

In major and rapidly developing urban regions across the world, traffic congestion is unavoidable. Congestion during rush hour is an unavoidable consequence, especially in large metropolitan. In the current circumstance, the traditional technique works well only if the count is low, as the density of vehicles on one side of the lane road grows if the traffic is heavier on one side of the road than on the other;otherwise, the approach fails. As a result, our goal is to create a traffic system that can switch signals as well as track and handle signals in real time. signal switching will be done in this project based on real-time image detection and accuracy of vehicular traffic. Another goal is to put in place a traffic management system that gives priority to the lane where the ambulance arrives that offers ambulance detection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Traffic congestion

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Unlocking Personality Insights: Assessing Videos with Deep learning Techniques 3rd

Unlocking Personality Insights: Assessing Videos with Deep L...

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3rd international conference on signal and Data processing, ICSDP 2023

作者： Athilakshmi, R. Jansi, R. Dutta, Indira Amulya Pavikars, M.M. Department of Computational Intelligence Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9789819795772

In contemporary times, monitoring criminal behavior and providing mental health therapy requires various activities related to an individual’s character. The development of an automated process for personality assessment through behavior analysis is still under research. Our contribution to this field involves combining deep learning with a regression-based machine learning model through a data pipeline for both audio and video data. We propose a comprehensive classification pipeline that utilizes supervised methods such as LSTM, GRU, and RNN, coupled with Visual Geometry Group (VGG) models for analyzing audio and visual data. The aim is to draw a comparative analysis between these methods to effectively analyze personalities. Our approach yields quality results, achieving an accuracy rate of 88.9% by combining VGG and NN for processing audio-visual data to analyze one’s personality composition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Natural language processing systems

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Discriminative Infinite Fuzzy Restricted Boltzmann machine learning for HRRPs Classification 3

Discriminative Infinite Fuzzy Restricted Boltzmann Machine L...

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3rd international conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Wang, Zechao Wei, Xizhang College of Electronics and Communication Engineering Sun Yat-Sen University Shenzhen Campus Guangdong Shenzhen518107 China

ISBN: (纸本)9781510671485

Deep learning architectures have been widely fostered throughout the last years and used in various applications, such as object recognition, image reconstruction, and signal processing. Recognition based on radar high resolution range profiles sequences(HRRPs) is essential to robust radar target recognition. Generally, it is almost always better to recognize radar targets in low-clutter environments. Recognition methods commonly cannot extract features from HRRP sequences, especially when the environmental background is not "clean." The Restricted Boltzmann machine (RBM) is an excellent generative learning model. By extending its parameters from real numbers to fuzzy parameters and adding a hidden layer dynamic random permutation algorithm, we developed the discriminative infinite fuzzy restricted Boltzmann machine (Dis-iFRBM). The proposed model possesses better robust recognition capability than the discriminative infinitely restricted Boltzmann machine (Dis-iRBM). In order to verify the recognition stability and robustness of Dis-iFRBM, radar HRRP sequences recognition experiments are conducted in this paper. The radar HRRPs recognition experiments were conducted to validate the identity stability and robustness of Dis-iFRBM. The experimental results on several HRRPs datasets show that the proposed method is more effective in a "noisy" environment than other models. Moreover, the proposed recognition model have some significant advantages over the traditional structures. © 2023 SPIE.

关键词： Image reconstruction

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