Video magnification is a computational procedure to reveal subtle variations during video frames that are invisible to the naked eye. A new spatio-temporal method which makes use of CONNECTIVITY BASED MAPPING of the wavelet sub-bands is introduced here for exaggerating of small motions during video frames. In this method, firstly the wavelet transformed frames are mapped to CONNECTIVITY space and then decomposed into different spatial frequency bands by applying Laplacian Pyramid to determine the pixels having more chance to be a part of a movement. Finally each candidate is partially magnified BASED on its time history. The performance of the proposed method is evaluated on real videos which contain several subtle motions. Parameters for performance evaluation are presented and obtained results are compared with one of the state-of-the-art video magnification methods. Increased true positive rate parallel with simultaneous decrease in false positive rate confirms the effectiveness of the proposed method in amplifying subtle motions.

‎We investigate the‎~ ‎index $I_{f} (G) = \sum_{vw \in E(G)} f(d_G (v),d_G (w))$ of a graph $G$‎, ‎where $f$ is a symmetric function of two variables satisfying certain conditions‎, ‎$E(G)$ is the edge set of $G$‎, ‎and $d_G (v)$ and $d_G (w)$ are the degrees of vertices $v$ and $w$ in $G$‎, ‎respectively‎. ‎Those conditions are satisfied by functions that can be used to define the general sum-CONNECTIVITY index $\chi_{a}$‎, ‎general Randi\'{c} index $R_{a}$‎, ‎general reduced second Zagreb index $GRM_a$ for some $a \in \mathbb{R}$‎, ‎general Sombor index $SO_{a,b}$‎, ‎general augmented Zagreb index $AZI_{a,b}$ and by one other generalization $M_{a,b}$ for some $a‎, ‎b \in \mathbb{R}$‎. ‎The general augmented Zagreb index is a new index defined in this paper‎. ‎We obtain a sharp upper bound on $I_f$ for graphs with given order and CONNECTIVITY‎, ‎and a sharp lower bound on $I_f$ for $2$-connected graphs with given order‎. ‎Our upper bound holds for $M_{a,b}$ and $SO_{a,b}$ where $a‎, ‎b \ge 1$; $\chi_a$ and $R_a$ where $a \ge 1$; and $GRM_{a}$ where $a >‎ -1$. ‎Our lower bound holds for $M_{a,b}$ where $a \ge 0$ and $b \ge‎ -‎a$; $SO_{a,b}$ where $a‎, ‎b \ge 0$ or $a‎, ‎b \le 0$; $AZI_{a,b}$ where $a \ge‎ -‎2$ and $b \ge 0$; $\chi_a$ and $R_a$ where $a \ge 0$; and $GRM_{a}$ where $a >‎ -‎2$.

‎Let G be a connected graph and S be a k element subset of the vertex set V(G)‎. ‎The Steiner-k distance d_{G}(S) between vertices of S is the minimum size among all connected subgraphs whose vertex set contains S‎. ‎In this paper‎, ‎we have defined the Steiner k-eccentric CONNECTIVITY index and derived a closed formula for the same in case of some standard graphs‎. ‎Also‎, ‎we have used Steiner 3-eccentric CONNECTIVITY index to predict values of boiling point of some primary and secondary amines‎, ‎cross sectional area and molar refraction of alcohols‎. ‎For each‎, ‎regression model is developed and statistical analysis is conducted and these have ensured at least 97\% accuracy‎.

The Sensitivity of Quantitative electroencephalogram (Brain MAPPING) was Compared with Transient ischemic Attack (TIA) and computerized tomography (C.T. Scan) Finding. Twenty Patients were tested with Quantitative EEG (Brain MAPPING) within 24h from the onset of neurological symptoms where as CT Scan was performed within 24h from the onset of attack. EEG was recorded from 16 electrodes placed upon the Scale to the international 10-20 system. Spectral analysis was carried out for each channel. Absolute and Relative power were calculated for Delta - Theta -Alpha and Beta frequency bands and such data were successively represented in color coded maps. The results indicated that EEG MAPPING showed focal abnormality in delta band in 17 of 20 cases.      

Inertial Navigation System (INS) is one of the navigation systems widely used in various land-BASED, aerial, and marine applications. Among all types of INS, Microelectromechanical System (MEMS)-BASED INS can be widely utilized, owing to their low cost, lightweight, and small size. However, due to the manufacturing technology, MEMS-BASED INS suffers from deterministic and stochastic errors, which increase positioning errors over time. In this paper, a new effective noise reduction method is proposed that can provide more accurate outputs of MEMS-BASED inertial sensors. The intelligent method in this paper is a combined denoising method that combines Wavelet Transform (WT), Permutation Entropy (PE), Support Vector Regression (SVR), and Genetic Algorithm (GA). Firstly, WT is employed to obtain a time-frequency representation of raw data. Secondly, a four-element feature vector is formed. These four features are (1) amplitude of frequency, (2) its ratio to mean of amplitudes of all frequencies, (3) location of frequency in time-frequency representation, and (4) judgment on behaviors of frequency that is obtained by utilizing PE. Thirdly, BASED on the feature vector, the GA-SVR algorithm predicts amplitudes of all frequencies in the time-frequency representation of the denoised signal. Finally, by employing inverse WT the denoised signal is obtained. In this work, the outputs of the Inertial Measurement Unit (IMU) in ADIS16407 sensor, as a low-cost and MEMS-BASED INS, have been utilized for data collection. The proposed method has been compared with other noise reduction methods and the achieved results verify superior improvement than other methods.