Target Classification Based On Kinematic Data From AIS/ADS-B, Using Statistical Features Extraction and Boosting

2019 ◽  
Author(s):  
Raphael Ginoulhac ◽  
Frederic Barbaresco ◽  
Jean-Yves Schneider ◽  
Jean-Marie Pannier ◽  
Sebastien Savary
Keyword(s):  
Features Extraction ◽  
Statistical Features ◽  
Target Classification ◽  
Kinematic Data

FUZZY LOGIC-BASED TARGET CLASSIFICATION USING KINEMATIC DATA

2011 ◽  
Vol 42 (6) ◽  
pp. 430-446
Author(s):  
Mateus de Araujo Fernandes ◽  
Hallysson Oliveira ◽  
Karl Heinz Kienitz
Keyword(s):  
Fuzzy Logic ◽  
Target Classification ◽  
Kinematic Data

Wireless Heart Abnormality Monitoring Kit Based on Raspberry Pi

2018 ◽  
Vol 35 ◽  
pp. 96-108
Author(s):  
Khudhur A. Alfarhan ◽  
Mohd Yusoff Mashor ◽  
Abdul Rahman Mohd Saad ◽  
Mohammad Iqbal Omar
Keyword(s):  
Heart Diseases ◽  
Normal Sinus Rhythm ◽  
Features Extraction ◽  
Raspberry Pi ◽  
Support Vector ◽  
Ecg Signal ◽  
Statistical Features ◽  
Classification Approach ◽  
Ecg Signals ◽  
Heart Monitoring

Heart monitoring kits are only available for bedridden patients and the traditional heart monitoring kits have many wires that are obstacle patients’ mobility. Most of the existing heart monitoring kits can not detect heart diseases. Thus, the current study proposed a wireless heart monitoring kit to monitor patients with a heart abnormality. The proposed kit can detect and classify four arrhythmia types as well as normal ECG with high accuracy. The design and development of the wireless heart abnormality monitoring kit (WHAMK) in this research were divided into three stages. These stages are the development of an arrhythmias detection and classification method using artificial intelligence approach, design and implementation of the kit hardware, and design and coding of the kit software. Arrhythmias classification approach is divided into four stages, namely obtaining the electrocardiograph (ECG) signals, preprocessing, features extraction and classification. The features extraction method are based on statistical features. The library support vector machine (LIBSVM) was used to classify the ECG signals. The hardware of the kit is divided into two parts, namely ECG body sensor (EBS), and processing and displaying unit (PDU). EBS working on acquiring the ECG signal from patient's body. PDU working on processing the collected ECG signal, plotting it and detecting the arrhythmias. Arrhythmias classification approach was developed by using statistical features and LIBSVM. They were implemented in the software of the kit to enable it to detect the arrhythmias in the real-time and fully automatically. The kit can detect and classify four arrhythmia types as well as normal sinus rhythm (NSR). These types of arrhythmia are premature atrial contraction (PAC), premature ventricles contraction (PVC), Bradycardia and Tachycardia. The proposed kit gave a good accuracy for detecting and classifying Arrhythmia with the overall accuracy of 96.2%.

Artifact cleaning of motor imagery EEG by statistical features extraction using wavelet families

2020 ◽  
Vol 48 (12) ◽  
pp. 2219-2241
Author(s):  
Perattur Nagabushanam ◽  
Selvaraj Thomas George ◽  
Devaraj Raveena Judie Dolly ◽  
Subramanyam Radha
Keyword(s):  
Motor Imagery ◽  
Features Extraction ◽  
Statistical Features

Schistosomal Hepatic Fibrosis Classification

2018 ◽  
Vol 7 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Dalia S. Ashour ◽  
Dina M. Abou Rayia ◽  
Nilanjan Dey ◽  
Amira S. Ashour ◽  
Ahmed Refaat Hawas ◽  
...  
Keyword(s):  
Neural Network ◽  
Liver Fibrosis ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Features Extraction ◽  
The Other ◽  
Parasitic Disease ◽  
Statistical Features ◽  
The Neural Network ◽  
Current Article

Schistosomiasis is serious liver tissues' parasitic disease that leads to liver fibrosis. Microscopic liver tissue images at different stages can be used for assessment of the fibrosis level. In the current article, the different stages of granuloma were classified after features extraction. Statistical features extraction was used to extract the significant features that characterized each stage. Afterward, different classifiers, namely the Decision Tree, Nearest Neighbor and the Neural Network are employed to carry out the classification process. The results established that the cubic k-NN, cosine k-NN and medium k-NN classifiers achieved superior classification accuracy compared to the other classifiers with 88.3% accuracy value.

On target classification using kinematic data

2004 ◽  
Vol 5 (1) ◽  
pp. 15-21 ◽  
Author(s):  
Branko Ristic ◽  
Neil Gordon ◽  
Amanda Bessell
Keyword(s):  
Target Classification ◽  
Kinematic Data
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