This study uses a discrete wavelet transform based feature extraction method to examine the effect of Om meditation on the brain. With twenty-three healthy engineering college students between the age group of twenty to twenty-two chosen as subjects for the study, EEG signals were obtained before performing Om meditation as well as after performing Om meditation. EEG signals were classified into Gamma, Beta, Alpha, Theta and Delta bands by using detailed coefficients and approximate coefficient obtained by five level wavelet transform. Feature such as relative power using the Welch method was extracted from each band and was analyzed using two way repeated analysis of variance. Findings reveal increased theta power and higher theta amplitude in after condition at all regions in comparison to the before the condition of meditation but results were not significant. No significant results were found in any other band. As described in other studies, increased theta power is a sign of relaxation. Results revealed through the study were promising results for single day testing and immediate effect of Om meditation. The study emphasizes the importance of Om meditation which could work wonders for people under stress if adopted as a daily routine. As Om meditation is simple and easy to practice, it could open a new horizon for naïve meditators. Key-Words: Mantra meditation, EEG, Om.
Singular Value Decomposition (SVD) and unscented Kalman filter (UKF) are interlaced using the Euler angles as the attitude parameter in order to estimate the satellite s angular motion parameters about center of mass. Magnetometer and sun sensor are used as the vector measurements for SVD in addition to the angular ate measurements from rate gyro for UKF; therefore, the output of the SVD shaped the nontraditional approach as SVD aided UKF algorithm using the linear measurements.
Cytopathology is a branch which deals with study diseases at the microscopic cell level. Now a day, deadly disease like cancer has become a major challenge in the field of medical research. If these diseases are identified in the early stages, then they are curable. There are many methods to ease, faster and produce accurate results of this analysis. One such approach is using image processing techniques. This paper applies segmentation techniques to find important feature like cell count. This feature is further used to differentiate between normal and abnormal cells. Abnormal cells are the one which may turn into cancerous cells. Hence the early detection of such abnormal cells before they turn into cancerous cells helps to diagnose patient early. The proposed method was evaluated using cervical cells.
Adaptive algorithms of spline-wavelet decomposition in a linear space over metrized fields are proposed. The algorithms provide a priori given estimate of the deviation of the main flow from the initial one. Comparative estimates of data of the main flow under different characteristics of the irregularity of the initial flow are done. The limiting characteristics of data, when the initial flow is generated by abstract differentiable functions, are discussed. The constructions of adaptive grid and pseudo-equidistant grid and relative quantity of their knots are considered, flows of elements of linear normed spaces and formulas of decomposition and reconstruction are discussed. Wavelet decomposition of the flows is obtained with using of spline-wavelet decomposition. Sufficient condition of the construction is obtained. Applications to different spaces of matrix of fixed order and to spaces of infinite-dimension vectors with numerical elements (rational, real, complex and p-adic elements) are considered
This paper proposes a new method for optimal design of minimum-length, minimum-phase, low-group-delay FIR filter by employing convex optimization, discrete signal processing (DSP), and polynomial stabilization techniques. The design of a length-N FIR filter is formulated as a convex second-order cone programming (SOCP). In order to design a minimum-phase FIR filter as the necessary condition for having low group delay, the algorithm guarantees that all the filter’s zeros are inside the unit circle (minimum-phase). In addition, the quasiconvex optimization problem is developed to minimize the length of minimumphase, low-group-delay FIR filter. To this end, for a typical low-pass FIR filter, the length of the filter is minimized such that the optimum magnitude response is satisfied, the minimum-phase characteristic is maintained, and the low-group-delay is achieved. The proposed design algorithm only relies on one parameter (cut-off frequency) and the rest of filter parameters are automatically optimized as the trade-off between having minimum-length, minimum-phase, maximum stopband attenuation and low group delay. The effectiveness and performance of proposed approach is demonstrated and compared with other approaches over a set of examples. It is illustrated that this approach converges to the optimal solution in a few iterations.
