Automatic Recognition of the Lighting Whistler waves from the Wave Data of SCM Boarded on ZH-1 satellite

2021 ◽  
Author(s):  
Jing Yuan ◽  
Zijie Wang ◽  
Dehe Yang ◽  
Qiao Wang ◽  
Zeren Zima ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Satellite Observation ◽  
Original Data ◽  
Electromagnetic Environment ◽  
Mel Frequency Cepstral Coefficients ◽  
Frequency Profile ◽  
Time Frequency ◽  
Wave Data ◽  
Audio Clip ◽  
Long Short Term Memory

<p>Lightning whistlers, found frequently in electromagnetic satellite observation, are the important tool to study electromagnetic environment of the earth space. With the increasing data from electromagnetic satellites, a considerable amount of time and human efforts are needed to detect lightning whistlers from these tremendous data. In recent years, algorithms for lightning whistlers automatic detection have been conducted. However, these methods can only work in the time-frequency profile (image) of the electromagnetic satellites data with two major limitations: vast storage memory for the time-frequency profile (image) and expensive computation for employing the methods to detect automatically the whistler from the time-frequency profile. These limitations hinder the methods work efficiently on ZH-1 satellite. To overcome the limitations and realize the real-time whistler detection automatically on board satellite, we propose a novel algorithm for detecting lightning whistler from the original observed data without transforming it to the time-frequency profile (image).</p><p>The motivation is that the frequency of lightning whistler is in the audio frequency range. It encourages us to utilize the speech recognition techniques to recognize the whistler in the original data \of SCM VLF Boarded on ZH-1. Firstly, we averagely move a 0.16 seconds window on the original data to obtain the patch data as the audio clip. Secondly, we extract the Mel-frequency cepstral coefficients (MFCCs) of the patch data as a type of cepstral representation of the audio clip. Thirdly, the MFCCs are input to the Long Short-Term Memory (LSTM) recurrent neutral networks to classification. To evaluate the proposed method, we construct the dataset composed of 10000 segments of SCM wave data observed from ZH-1 satellite(5000 segments which involving whistler and 5000 segments without any whistler). The proposed method can achieve 84% accuracy, 87% in recall, 85.6% in F1score.Furthermore, it can save more than 126.7MB and 0.82 seconds compared to the method employing the YOLOv3 neutral network for detecting whistler on each time-frequency profile.</p><p> </p><p>Key words: ZH-1 satellite, SCM,lightning whistler, MFCC, LSTM</p>

LSTM-Based Mosquito Genus Classification Using Their Wingbeat Sound

2021 ◽  
Author(s):  
Edmundo Toledo ◽  
Jose Gonzalez ◽  
Mariko Nakano ◽  
Daniel Robles ◽  
Adrian Hernandez ◽  
...  
Keyword(s):  
Area Under Curve ◽  
Short Term Memory ◽  
Activation Function ◽  
Operating Characteristics ◽  
Mel Frequency Cepstral Coefficients ◽  
Time Frequency ◽  
Long Short Term Memory ◽  
Fully Connected ◽  
Frequency Features ◽  
Mosquito Genus

In this paper, we propose Long-Short Term Memory (LSTM)-based mosquito’s genus classification, in which the time-frequency features are extracted from the wingbeat sound of mosquitos of three genera, Aedes, Anopheles and Culex. The extracted features are fed into the proposed LSTM-based classifier. We evaluated three time-frequency features, which are: Mel Spectrogram, Log-Mel spectrogram, and Mel-frequency Cepstral Coefficients (MFCC). The proposed scheme is composed by two LSTM layers and one Fully Connected layer connected to a SoftMax activation function. The classification accuracies using the three features are 92.97(±0.2)%, 96.71(±0.2)% and 96.65(±0.2)%, respectively. The Area Under Curve (AUC) of the Receiver Operating Characteristics (ROC) for each feature are also obtained, which are 0.9944, 0.9986 and 0.9987, respectively. The proposed classifier requires approximately 62,000 trainable parameters. This number is much smaller than that required for the state-of-arts CNNs, such as AlexNet and Vgg16. This compact configuration of the proposed scheme takes advantage of the mobile and IoT implementation, because the number of trainable parameters is directly proportional to the amount of memory and CPU required.

scholarly journals Time-frequency time-space long short-term memory networks for image classification of histopathological tissue

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Image Analysis ◽  
Short Term Memory ◽  
Disease Diagnosis ◽  
Short Term ◽  
Term Memory ◽  
Time Frequency ◽  
Time Space ◽  
Histopathological Images ◽  
Long Short Term Memory

AbstractImage analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultimate objective of the image analysis. Here, the time-frequency time-space long short-term memory network (TF-TS LSTM) developed for classification of time series is applied for classifying histopathological images. The deep learning is empowered by the use of sequential time-frequency and time-space features extracted from the images. Furthermore, unlike conventional classification practice, a strategy for class modeling is designed to leverage the learning power of the TF-TS LSTM. Tests on several datasets of histopathological images of haematoxylin-and-eosin and immunohistochemistry stains demonstrate the strong capability of the artificial intelligence (AI)-based approach for producing very accurate classification results. The proposed approach has the potential to be an AI tool for robust classification of histopathological images.

scholarly journals Audio Event Identification and Classification for Cricket Sports using LSTM

2019 ◽  
Vol 8 (4) ◽  
pp. 9924-9927
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Research Work ◽  
Short Term ◽  
Mel Frequency Cepstral Coefficients ◽  
Event Classification ◽  
Event Identification ◽  
Audio Event ◽  
Long Short Term Memory ◽  
Lstm Network

Audio event identification is an emerging research topic to augment the automation of audio tagging, context-based audio event retrieval, audio surveillance and much more. In this research work, audio event classification for cricket commentary is done by using long short term memory (LSTM) neural network. Mel-frequency cepstral coefficients (MFCC) features are extracted from the audio commentary and trained with LSTM neural network. The trained LSTM network is validated and attained an accuracy of 95%.

scholarly journals Research on a Cognitive Distraction Recognition Model for Intelligent Driving Systems Based on Real Vehicle Experiments

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4426
Author(s):  
Qinyu Sun ◽  
Chang Wang ◽  
Yingshi Guo ◽  
Wei Yuan ◽  
Rui Fu
Keyword(s):  
Short Term Memory ◽  
Distracted Driving ◽  
Characteristic Parameters ◽  
Short Term ◽  
Term Memory ◽  
Recognition Model ◽  
Time Frequency ◽  
Cognitive Distraction ◽  
Memory Network ◽  
Long Short Term Memory

The accurate and prompt recognition of a driver’s cognitive distraction state is of great significance to intelligent driving systems (IDSs) and human-autonomous collaboration systems (HACSs). Once the driver’s distraction status has been accurately identified, the IDS or HACS can actively intervene or take control of the vehicle, thereby avoiding the safety hazards caused by distracted driving. However, few studies have considered the time–frequency characteristics of the driving behavior and vehicle status during distracted driving for the establishment of a recognition model. This study seeks to exploit a recognition model of cognitive distraction driving according to the time–frequency analysis of the characteristic parameters. Therefore, an on-road experiment was implemented to measure the relative parameters under both normal and distracted driving via a test vehicle equipped with multiple sensors. Wavelet packet analysis was used to extract the time–frequency characteristics, and 21 pivotal features were determined as the input of the training model. Finally, a bidirectional long short-term memory network (Bi-LSTM) combined with an attention mechanism (Atten-BiLSTM) was proposed and trained. The results indicate that, compared with the support vector machine (SVM) model and the long short-term memory network (LSTM) model, the proposed model achieved the highest recognition accuracy (90.64%) for cognitive distraction under the time window setting of 5 s. The determination of time–frequency characteristic parameters and the more accurate recognition of cognitive distraction driving achieved in this work provide a foundation for human-centered intelligent vehicles.

scholarly journals Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 802
Author(s):  
Xue-Bo Jin ◽  
Wei-Zhen Zheng ◽  
Jian-Lei Kong ◽  
Xiao-Yi Wang ◽  
Min Zuo ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Short Term Memory ◽  
Co2 Concentration ◽  
Original Data ◽  
Experimental Investigations ◽  
Temporal Data ◽  
Short Term ◽  
Long Time ◽  
Threshold Filter ◽  
Long Short Term Memory

Smart agricultural greenhouses provide well-controlled conditions for crop cultivation but require accurate prediction of environmental factors to ensure ideal crop growth and management efficiency. Due to the limitations of existing predictors in dealing with massive, nonlinear, and dynamic temporal data, this study proposes a bidirectional self-attentive encoder–decoder framework (BEDA) to construct the long-time predictor for multiple environmental factors with high nonlinearity and noise in a smart greenhouse. Firstly, the original data are denoised by wavelet threshold filter and pretreatment operations. Secondly, the bidirectional long short-term-memory is selected as the fundamental unit to extract time-serial features. Then, the multi-head self-attention mechanism is incorporated into the encoder–decoder framework to improve the prediction performance. Experimental investigations are conducted in a practical greenhouse to accurately predict indoor environmental factors (temperature, humidity, and CO2) from noisy IoT-based sensors. The best model for all datasets was the proposed BEDA method, with the root mean square error of three factors’ prediction reduced to 2.726, 3.621, and 49.817, and with an R of 0.749 for temperature, 0.848 for humidity, and 0.8711 for CO2 concentration, respectively. The experimental results show that the favorable prediction accuracy, robustness, and generalization of the proposed method make it suitable to more precisely manage greenhouses.

scholarly journals Efficient Spectrum Occupancy Prediction Exploiting Multidimensional Correlations through Composite 2D-LSTM Models

2020 ◽  
Author(s):  
Mehmet Ali Aygül ◽  
Mahmoud Nazzal ◽  
Mehmet İzzet Sağlam ◽  
Daniel Benevides da Costa ◽  
Hasan Fehmi Ateş ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Mobile Network ◽  
Base Station ◽  
Time Frequency ◽  
Spectrum Occupancy ◽  
Cognitive Radio Systems ◽  
Radio Systems ◽  
Long Short Term Memory ◽  
Occupancy Prediction ◽  
Mobile Network Operators

In cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently use the spectrum. Spectrum occupancy prediction is a convenient way of revealing opportunities based on previous occupancies. Studies have demonstrated that usage of the spectrum has a high correlation over multidimensions which includes time, frequency, and space. Accordingly, recent literature uses tensor-based methods to exploit the multidimensional spectrum correlation. However, these methods share two main drawbacks. First, they are computationally complex. Second, they need to re-train the overall model when no information is received from any base station for any reason. Different than the existing works, this paper proposes a method for dividing the multidimensional correlation exploitation problem into a set of smaller sub-problems. This division is achieved through composite two-dimensional (2D)-long short-term memory (LSTM) models. Extensive experimental results reveal a high detection performance with more robustness and less complexity attained by the proposed method. The real-world measurements provided by one of the leading mobile network operators in Turkey validate these results.

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