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Corner detection algorithm is an algorithm which helps in identifying the corners in an image. Corners are mainly formed by the combination of two or more edges. These corners may or may not define the boundary of an image. Here the method used is Harris corner detection algorithm. It helps in pointing out the corners in a color image, for each component. This improves the detection efficiency and...
The hand-held devices revolutionized the way users interact and demands support for regional languages. Handwritten interfaces simplify the communication in regional languages without the need for multiple key presses. The handwriting interfaces need to recognize characters while writing. Thus the proposed work attempted to design an efficient SVM-based recognizer for online Recognition of isolated...
Computerized automatic recognition of brain tumors in magnetic resonance images (MRI) is a challenging task. Tumors are available at different location, size, shape, and texture of these lesions. Due to intensity similarities between brain lesions and normal tissues, the challenges for the researcher remain for developing progressive more algorithms in the tumor detection. Selection of single spectral...
Evidence Based Medicine (EBM) is the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. Extracting Information from considerable bodies of text is useful & challenging. A generative probabilistic aspect mining model is used here to study the collection. Frequency based approach in aspect based opinion mining extracts high...
In these day, as the e-commerce industry is growing and becoming complex, everyone uses online websites for getting reviews and giving the reviews on the website in the form of comments. This comment varies from worst level to best level. So in order to categorize these comments or to predict the best outcome among the posted comments recommendation is needed there is a need for recommendation system...
With the increase of multi media technology and internet there is a rapid growth in storing and retrieving of documents. Government has taken several methods for documents to scan and stored digitally for future use. Even though the documents are available in the digital format, but it is very difficult to search for a single word or phrase. Traditional optical character recognition techniques (OCR)...
Photoplethysmograph is an optical procedure used to determine the physiological parameters identified with cardiovascular system. This paper focuses on the design of PPG acquisition system with reflectance type of sensor circuit to extract the features such as systolic peak, diastolic peak and dicrotic notch. The designed system is tested on 50 subjects of different age groups and the amplitude of...
This Empirical mode decomposition (EMD) is a kind of multi-scale transformation theory which is suitable for nonlinear and non-stationary signal processing. It is not necessary to select the basis function in advance, and can adaptively adjust according to the characteristics of the signal itself. Extracting the intrinsic mode function (IMF) is an important process for the applications for empirical...
In this research, a prototype of home appliances control system based on steady-state visually evoked potential (SSVEP) is designed. The system is designed using two SSVEP datasets with different characteristics: the first dataset consists eight frequencies within 6-12 Hz, while the second consists frequencies of 8, 14, and 28 Hz. The EEG signal from the datasets is processed using three components:...
Electromyography (EMG) is used to measure and keep information of the electrical activity that produced by muscles during contract and relax. The electrical activity is detected with the help of EMG electrodes. This review paper will focus on usage of common EMG signal recording techniques which is surface electromyography (sEMG). During sEMG recording, there are some recognized noises and motion...
EMG pattern recognition has been studied for control of prostheses and rehabilitation systems for decades. Existing research platforms for developing EMG pattern recognition algorithms are typically based on MATLAB and the collection of EMG signals is often done by expensive, non-portable data acquisition systems. The requirement of these resources usually limits the use of these platforms in the...
Our study aimed to determine hemispheric differences using Galvanic Skin Response (GSR) which is measure to emotional sweating with signal processing and feature extraction methods. Active sportsmans (n=17) and non-sportsmans (n=21) who are student at university have been used in this study. The average of ages is 20±0.4. We worked on GSR records which have been denoise with signal processing method...
This paper investigates detection of patterns in brain waves while induced with mental stress. Electroencephalogram (EEG) is the most commonly used brain signal acquisition method as it is simple, economical and portable. An automatic EEG based stress recognition system is designed and implemented in this study with two effective stressors to induce different levels of mental stress. The Stroop colour-word...
Our focus in this research is on the use of deep learning approaches for human activity recognition (HAR) scenario, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are predefined human activities. Here, we present a feature learning method that deploys convolutional neural networks (CNN) to automate feature learning from the raw inputs...
Snore sound (SnS) data has been demonstrated to carry very important information for diagnosis and evaluation of sleep related breathing disorders with high prevalence, such as Primary Snoring and Obstructive Sleep Apnea (OSA) — a serious chronic sleep disorder with a big community. With the increasing number of collected SnS data from subjects, how to handle such large amount of data is a big challenge,...
A rough set-based approach to classification of EEG signals registered while subjects were performing real and imagery motions is presented in the paper. The appropriate subset of EEG channels is selected, the recordings are segmented, and features are extracted, based on time-frequency decomposition of the signal. Rough set classifier is trained in several scenarios, comparing accuracy of classification...
Analysis and recognition of motion patterns from data acquired by body-worn inertial sensors is an emerging technology in sports. In this paper we propose an effective method for recognition of fencing footwork using a single body-worn accelerometer. We present a challenging dataset consisting of six actions, which were performed by ten persons and repeated ten times by each of them. We propose a...
Computational Auditory Scene Analysis (CASA) is typically achieved by statistical models trained offline on available data. Their performance relies heavily on the assumption that the process generating the data along with the recording conditions are stationary over time. Nowadays, there is a high demand for methodologies and tools dealing with a series of problems tightly coupled with non-stationary...
Language is the ability to know any complex era in a real world application. Approximate number of languages are 6700. Different regions in a world have different languages spoken. When a human meets another human, speaking different language, it is difficult to identify the one what next person is talking about or in which language. Hence, the main focus is recognition of language which is spoken...
Classification of activities of daily living is of paramount importance in modern healthcare applications. However, hardware monitoring constraints lead frequently to missing raw values, dramatically affecting the performance of machine learning algorithms. In this work, we study the problem of efficient estimation of missing linear acceleration and angular velocity measurements, experimenting on...
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