Energy Distribution Evaluation Using Renyi Entropy Measures With Application in EEG Data Analysis

The ”corrected” EEG recordings, after artifact removing, may be the subject of further investigations, for example segmentation and energy distribution, resulting new informa- tion to be used for feature extraction, of great help for medical diagnosis. The paper presents a generally method for energy distribution evalua- tion using measures of R´enyi entropy. The pre- sented approach ensures the possibility of quan- titative analysis of the information contained in time-frequency distribution of EEG signals. The proposed procedure is applied with good results in the analysis of a sample lowpass event-related potentials (ERP) data, collected from 13 scalp and 1 EOG electrodes.

Robust Real Time Multiple Human Detection and Tracking for Automatic Visual Surveillance System

Detection of human for visual surveillance system provides most important rule for advancement in the design of future automation systems. Human detection and tracking are important for future automatic visual surveillance system (AVSS). In this paper we have proposed a flexible technique for proper human detection and tracking for the design of AVSS. We used graph cut for segment human as a foreground image by eliminating background, extract some feature points by using HOG, SVM classifier for proper classification and finally we used particle filter for tracking those of detected human. Our system can easily detect and track humans in poor lightening conditions, color, size, shape, and clothing due to the use of HOG feature descriptor and particle filter. We use graph cut based segmentation technique, therefore our system can handle occlusion at about 88%. Due to the use of HOG to extract features our system can properly work in indoor as well as outdoor environments with 97.61% automatic human detection and 92% automatic human detection and tracking accuracy of multiple human

One-Step Predictive H2 FIR Tracking under Persistent Disturbances and Data Errors

Information loss often occurs in industrial processes under unspecified impacts and data errors. Therefore robust predictors are required to assure the performance. We design a one-step H2 optimal finite impulse response (H2-OFIR) predictor under persistent disturbances, measurement errors, and initial errors by minimizing the squared weighted Frobenius norms for each error. The H2-OFIR predictive tracker is tested by simulations assuming Gauss-Markov disturbances and data errors. It is shown that the H2-OFIR predictor has a better robustness than the Kalman and unbiased FIR predictor. An experimental verification is provided based on the moving robot tracking problem

UFIR State Estimator for Network Systems with Two-Step Delayed and Lost Data

Wireless communication over networks often produces issues associated with delayed and missing data. In this paper, we consider one-step and two-step delays. The state space model is transformed to have no delay with new system and observation matrices. To mitigate the effect, we develop the unbiased finite impulse response (UFIR) filter, Kalman filter (KF), and game theory H∞ filter for Bernoulli-distributed delays with possible packet dropouts. A comparative study of the filters developed is provided under the uncertain noise and transmission probability. Numerical simulation is conducted employing a GPSbased tracking network system. A better performance of the UFIR filter is demonstrated experimentally

GNSS Signal Processing with EKF and UKF for Stationary User Position Estimation

Precise and accurate estimation of state vectors is an important process during position determination. In this study, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) of stationary user, state vectors defined in Earth Centered Inertial (ECI) coordinate system, accompanied by GNSS measurement data. It is aimed to make estimations with methods. EKF and UKF methods were compared with each other. In this study, the effects of nonlinear motion analysis and linearization methods on state vector estimations were investigated. Thanks to this study, estimations of the positioning information required during the specific tasks of many moving platforms have been made.

Automatic Segmentation of Skin Regions in Thermographic Images: an Experimental Study

Infrared thermography can be applied in medical applications, such as monitoring skin temperature in inflammatory processes. The possibility for health care professionals and patients to be able to easily, quickly and economically, at anytime and anywhere, monitor the skin temperature distribution through the acquisition of images to control skin infections is extremely important nowadays. This work aims to develop an automatic methodology for the segmentation, identification, analysis and diagnosis of skin inflammation based on thermographic images. The study compares thermographic images from subregions of the hand skin and presents an experimental investigation to segment and identify features in the images automatically. Left and righthand images from two volunteers’ obtained in different conditions, such as cold action, activity action (opening and closing the hand), and friction action (rub both hands), were considered and analyzed. The obtained results demonstrate the feasibility of the implemented procedures and encourage developing and implementing an operating system to monitor skin infections in thermographic images.

Dimension Reduction Analysis of Signal Manifold for Vowels in Time and Frequency Domain

In this paper, the LLE and ISOMAP algorithms in manifold learning are applied them to the analysis of vowel signals in time and frequency domain. Time domain simulation results show that the two dimensionality reduction methods can implement two-dimensional visualization of signals while preserving the high-dimensional manifold structure of original signals as much as possible. The time-frequency domain dimension reduction analysis of vowel signal manifold effectively solves the problem that high-dimensional speech signals can’t be intuitively felt, and provides a new potential way for signal classification. The frequency domain analysis is further optimized on the basis of time domain simulation. Because half of the amplitude values in DFT is used in the simulation, the two-dimensional manifold of the signal is roughly linearly distributed, which can effectively reduce redundancy and make the signal more compactly expressed in the frequency domain

On Stochastic Differential Equations Generating Non-gaussian Continuous Markov Process

Continuous Markov processes widely used as a tool for modeling random phenomena in numerous applications, can be defined as solutions of generally nonlinear stochastic differential equations (SDEs) with certain drift and diffusion coefficients which together governs the process’ probability density and correlation functions. Usually it is assumed that the diffusion coefficient does not depend on the process' current value. For presentation of non-Gaussian real processes this assumption becomes undesirable, leads generally to complexity of the correlation function estimation. We consider its analysis for the process with particular pairs of the drift and diffusion coefficients providing the given stationary probability distribution of the considered process

Real Time Implementation of Face Recognition based Smart Attendance System

This research work addresses the issue of incorporating an automatic attendance system to the frame of an institution using face detection and recognition techniques. The proposed system aims at reducing computational time with available hardware to yield more efficient results. The proposed model utilizes Histogram Oriented Gradients and facial encodings derived from facial landmarks. It also addresses the problems related to accuracy of facial recognition and the resource requirement for quick, real-time facial recognition by applying multi-processing. The improvement in performance in terms of accuracy across two different methods, and the improvement in terms of time requirement for the same method using different strategies have also been documented for demonstration. The designed system demonstrates the effectiveness of task parallelization with a minimum amount of hardware desiderata. The system has been designed to an optimum self-sustaining ecosystem which can efficiently operate on its own accord and compute comprehensible feedback without the requirement of any third-party human interference. A Graphical User Interface has been incorporated into the system for maximum user comprehensibility

QoS Scheduling Algorithm for Videoclips Denoising

This article presents a general frame-work for scheduling videoclips denoising processes ensuring the quality of service (QoS). In general, a denoising algorithm has two phases which are run sequentially: the first one determines the noisy pixels in the videoclip frames and the second applies a median filtering over the each frame considering the only good pixels. In all such denoising algorithms, the first phase is run for multiple times depend on the noise power. The second phase also may be executed more than one time but this depends on the specific algorithm. The issue in such applications is the denoising process may not terminate within its deadline. The proposed solution adapts the execution time in such way so the deadline to be respected by determining the remaining time to the deadline before running each phase and reducing the number of runs in each phase in order to not exceed the deadline. The goals of the article are the following: presents the QoS scheduling algorithm and proposes an implementation solution of based on Blackfin microcomputer with support of Visual DSP kernel (VDK). The article is organized in 5 sections: a briefly introduction to set up the general context of quality of services in videoclips denoising applications and to present the original video processing algorithm, two sections that present the proposed solution and its VDK implementation, the performance evaluation and the conclusions.