scholarly journals Continuous Gesture Recognition Based on Time Sequence Fusion Using MIMO Radar Sensor and Deep Learning

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 869 ◽  
Author(s):  
Wentai Lei ◽  
Xinyue Jiang ◽  
Long Xu ◽  
Jiabin Luo ◽  
Mengdi Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Continuous Wave ◽  
Short Term Memory ◽  
Estimation Algorithm ◽  
Intermediate Frequency ◽  
Mimo Radar ◽  
Time Sequence ◽  
Band Pass Filter ◽  
Pass Filter

Gesture recognition that is based on high-resolution radar has progressively developed in human-computer interaction field. In a radar recognition-based system, it is challenging to recognize various gesture types because of the lacking of gesture transversal feature. In this paper, we propose an integrated gesture recognition system that is based on frequency modulated continuous wave MIMO radar combined with deep learning network for gesture recognition. First, a pre-processing algorithm, which consists of the windowed fast Fourier transform and the intermediate-frequency signal band-pass-filter (IF-BPF), is applied to obtain improved Range Doppler Map. A range FFT based MUSIC (RFBM) two-dimensional (2D) joint super-resolution estimation algorithm is proposed to obtain a Range Azimuth Map to obtain gesture transversal feature. Range Doppler Map and Range Azimuth Map then respectively form a Range Doppler Map Time Sequence (RDMTS) and a Range Azimuth Map Time Sequence (RAMTS) in gesture recording duration. Finally, a Dual stream three-dimensional (3D) Convolution Neural Network combined with Long Short Term Memory (DS-3DCNN-LSTM) network is designed to extract and fuse features from both RDMTS and RAMTS, and then classify gestures with radial and transversal change. The experimental results show that the proposed system could distinguish 10 types of gestures containing transversal and radial motions with an average accuracy of 97.66%.

scholarly journals SmartScope: An AI-Powered Digital Auscultation Device To Detect Cardiopulmonary Diseases

2021 ◽  
Author(s):  
Aadhav Prabu
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Critical Factor ◽  
Raspberry Pi ◽  
Band Pass Filter ◽  
Learning Models ◽  
Pass Filter ◽  
Delayed Treatment ◽  
Learning Agent ◽  
Machine Learning Models

<p>Cardiopulmonary diseases are leading causes of death worldwide, accounting for nearly 15 million deaths annually. Accurate diagnosis and routine monitoring of these diseases by auscultation are crucial for early intervention and treatment. However, auscultation using a conventional stethoscope is low in amplitude and subjective, leading to possible missed or delayed treatment. My research aimed to develop a stethoscope called SmartScope powered by machine-learning to aid physicians in rapid analysis, confirmation, and augmentation of cardiopulmonary auscultation. Additionally, SmartScope helps patients take personalized auscultation readings at home effectively as it performs an intelligent selection of auscultation points interactively and quickly using the reinforcement learning agent: Deep Q-Network. SmartScope consists of a Raspberry Pi-enabled device, machine-learning models, and an iOS app. Users initiate the auscultation process through the app. The app communicates with the device using MQTT messaging to record the auscultation, which is augmented by an active band-pass filter and an amplifier. Additionally, the auscultation readings are refined by a Gaussian-shaped frequency filter and segmented by a Long Short-Term Memory Network. The readings are then classified using two Convolutional Recurrent Neural Networks. The results are displayed within the app and LCD. After the machine-learning models were trained, 90% accuracy for cardiopulmonary diseases was achieved, and the number of auscultation points was reduced threefold. SmartScope is an affordable, comprehensive, and user-friendly device that patients and physicians can widely use to monitor and accurately diagnose diseases like COPD, COVID-19, Asthma, and Heart Murmur instantaneously, as time is a critical factor in saving lives.</p>

scholarly journals A simple faulted phase-based fault distance estimation algorithm for a loop distribution system

2022 ◽  
Vol 25 (1) ◽  
pp. 14
Author(s):  
Shwe Myint ◽  
Warit Wichakool
Keyword(s):  
Traveling Wave ◽  
Distribution System ◽  
Signal To Noise Ratio ◽  
Distance Estimation ◽  
Estimation Algorithm ◽  
Estimation Accuracy ◽  
Discrete Wavelet ◽  
Band Pass Filter ◽  
Localization Algorithm ◽  
Pass Filter

This paper presents a single ended faulted phase-based traveling wave fault localization algorithm for loop distribution grids which is that the sensor can get many reflected signals from the fault point to face the complexity of localization. This localization algorithm uses a band pass filter to remove noise from the corrupted signal. The arriving times of the faulted phase-based filtered signals can be obtained by using phase-modal and discrete wavelet transformations. The estimated fault distance can be calculated using the traveling wave method. The proposed algorithm presents detail level analysis using three detail levels coefficients. The proposed algorithm is tested with MATLAB simulation single line to ground fault in a 10 kV grounded loop distribution system. The simulation result shows that the faulted phase time delay can give better accuracy than using conventional time delays. The proposed algorithm can give fault distance estimation accuracy up to 99.7% with 30 dB contaminated signal-to-noise ratio (SNR) for the nearest lines from the measured terminal.

Research on Processing Techniques of Logging-While-Drilling Signal Based on BPSK Continuous Wave

2011 ◽  
Vol 88-89 ◽  
pp. 688-694 ◽  
Author(s):  
Fang Yuan ◽  
Xian Feng Gong
Keyword(s):  
Horizontal Well ◽  
Continuous Wave ◽  
Filter Method ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Difference Filter ◽  
Band Pass ◽  
Logging While Drilling ◽  
Processing Techniques ◽  
Theory And Experiment

The system of Logging-While-Drilling based on BPSK continuous wave is important for horizontal well prospecting. In this paper, the processing techniques of BPSK signal received on the ground have been studied. However, it is difficult to acquire the useful signals, since including most of reflective and mud pump noises. Based on the signal’s characteristic of sine, a new difference filter method, which is in the time field, is put forward. The method combines with the band-pass filter, which is in the frequency field, to reject the noises. Costas phase-locked loop technology and number control oscillator have been used in decoding technology, which can demodulate phase information and decode the BPSK signal. By the analyses in theory and experiment, the signal can be correctly decoded.

scholarly journals SmartScope: An AI-Powered Digital Auscultation Device To Detect Cardiopulmonary Diseases

2021 ◽  
Author(s):  
Aadhav Prabu
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Critical Factor ◽  
Raspberry Pi ◽  
Band Pass Filter ◽  
Learning Models ◽  
Pass Filter ◽  
Delayed Treatment ◽  
Learning Agent ◽  
Machine Learning Models

<p>Cardiopulmonary diseases are leading causes of death worldwide, accounting for nearly 15 million deaths annually. Accurate diagnosis and routine monitoring of these diseases by auscultation are crucial for early intervention and treatment. However, auscultation using a conventional stethoscope is low in amplitude and subjective, leading to possible missed or delayed treatment. My research aimed to develop a stethoscope called SmartScope powered by machine-learning to aid physicians in rapid analysis, confirmation, and augmentation of cardiopulmonary auscultation. Additionally, SmartScope helps patients take personalized auscultation readings at home effectively as it performs an intelligent selection of auscultation points interactively and quickly using the reinforcement learning agent: Deep Q-Network. SmartScope consists of a Raspberry Pi-enabled device, machine-learning models, and an iOS app. Users initiate the auscultation process through the app. The app communicates with the device using MQTT messaging to record the auscultation, which is augmented by an active band-pass filter and an amplifier. Additionally, the auscultation readings are refined by a Gaussian-shaped frequency filter and segmented by a Long Short-Term Memory Network. The readings are then classified using two Convolutional Recurrent Neural Networks. The results are displayed within the app and LCD. After the machine-learning models were trained, 90% accuracy for cardiopulmonary diseases was achieved, and the number of auscultation points was reduced threefold. SmartScope is an affordable, comprehensive, and user-friendly device that patients and physicians can widely use to monitor and accurately diagnose diseases like COPD, COVID-19, Asthma, and Heart Murmur instantaneously, as time is a critical factor in saving lives.</p>

scholarly journals Hand Gesture Recognition in Video Sequences Using Deep Convolutional and Recurrent Neural Networks

2020 ◽  
Vol 25 (1) ◽  
pp. 57-61
Author(s):  
Falah Obaid ◽  
Amin Babadi ◽  
Ahmad Yoosofan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Gesture Recognition ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Hand Gesture ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

AbstractDeep learning is a new branch of machine learning, which is widely used by researchers in a lot of artificial intelligence applications, including signal processing and computer vision. The present research investigates the use of deep learning to solve the hand gesture recognition (HGR) problem and proposes two models using deep learning architecture. The first model comprises a convolutional neural network (CNN) and a recurrent neural network with a long short-term memory (RNN-LSTM). The accuracy of model achieves up to 82 % when fed by colour channel, and 89 % when fed by depth channel. The second model comprises two parallel convolutional neural networks, which are merged by a merge layer, and a recurrent neural network with a long short-term memory fed by RGB-D. The accuracy of the latest model achieves up to 93 %.

scholarly journals Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7710
Author(s):  
Anis Malekzadeh ◽  
Assef Zare ◽  
Mahdi Yaghoobi ◽  
Hamid-Reza Kobravi ◽  
Roohallah Alizadehsani
Keyword(s):  
Deep Learning ◽  
Fractal Dimensions ◽  
Epileptic Seizures ◽  
Band Pass Filter ◽  
Eeg Signals ◽  
Brain Disorder ◽  
Pass Filter ◽  
Nonlinear Features ◽  
Statistical Frequency ◽  
Conventional Machine

Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.

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