Neurons in Cat Primary Visual Cortex cluster by degree of tuning but not by absolute spatial phase or temporal response phase

Neighboring neurons in cat primary visual cortex (V1) have similar preferred orientation, direction, and spatial frequency. How diverse is their degree of tuning for these properties? Are they also clustered in their tuning for the spatial phase of a flashed grating ("absolute spatial phase") or the temporal phase of a drifting grating ("temporal response phase")? To address these questions, we used tetrode recordings to simultaneously isolate multiple cells at single recording sites and record their responses to flashed and drifting gratings of multiple orientations, spatial frequencies, and spatial/temporal phases. We recorded the responses of 761 cells presented with drifting gratings and 409 cells presented with flashed gratings. We found that orientation tuning width, spatial frequency tuning width and direction selectivity index all showed significant clustering. Absolute spatial phase and temporal response phase, however, showed no clustering. We also present an algorithm that improves the performance of spike-sorting algorithms, for use in analyzing cells recorded using tetrodes. A cluster of spikes corresponding to a putative cell obtained through automatic or manual spike sorting algorithms may contain spikes from other cells with similarly-shaped waveforms. Our algorithm preferentially removes contaminating spikes from other cells, thereby decreasing the level of contamination of each unit. We call this procedure "pruning", as it entails removing portions of the cluster that are determined to be more likely to contain contaminating spikes than the cluster as a whole. Testing of the algorithm on data in which "ground truth" is known shows excellent performance, for example on average giving a percentage reduction in false positive spikes 8.2 times the percentage reduction in true positive spikes, and reducing the degree of contamination by an average of about 13%

Automatic age estimation system for face images

Humans are the most important tracking objects in surveillance systems. However, human tracking is not enough to provide the required information for personalized recognition. In this paper, we present a novel and reliable framework for automatic age estimation based on computer vision. It exploits global face features based on the combination of Gabor wavelets and orthogonal locality preserving projections. In addition, the proposed system can extract face aging features automatically in real-time. This means that the proposed system has more potential in applications compared to other semi-automatic systems. The results obtained from this novel approach could provide clearer insight for operators in the field of age estimation to develop real-world applications. © 2012 Lin et al

CLASSIFYING HEARTRATE BY CHANGE DETECTION AND WAVELET METHODS FOR EMERGENCY PHYSICIANS

10 pages.Heart Rate Variability (HRV) carries a wealth of information about the physiological state and the behaviour of a living subject. Indeed, the heart rate variation is intrinsically linked to the autonomic nervous system: the Parasympathetic and Sympathetic systems. Thus, any imbalance in these two opposite systems results in a variation of the cardiac frequency modulation. It is also recognized that this alternation between equilibrium and disequilibrium (frequency variability) is an indicator of well being and good health. In other words, decreased heart rate variability is always linked to stress, fatigue and decreased physical performances. The aim of this work is to exploit the heart rate signals to detect situations of stress in different populations: emergency physicians, sportsmen, animal behaviours, etc...This paper introduces a methodological framework for the detection of stress and eventually well being. Our contribution is based on first extracting high and low frequencies energies which are linked to the Parasympathetic and Sympathetic systems. We then detect change points on these energies using the Filtered Derivative with p-value (FDpV) method. Finally, we develop a typology of cardiac activity by distinguishing homogeneous groups or state profiles having a characteristic similarity. We apply our methodology on a real dataset corresponding to an emergency doctor

Feature Extraction Methods by Various Concepts using SOM

Image retrieval systems gained traction with the increased use of visual and media data. It is critical to understand and manage big data, lot of analysis done in image retrieval applications. Given the considerable difficulty involved in handling big data using a traditional approach, there is a demand for its efficient management, particularly regarding accuracy and robustness. To solve these issues, we employ content-based image retrieval (CBIR) methods within both supervised , unsupervised pictures. Self-Organizing Maps (SOM), a competitive unsupervised learning aggregation technique, are applied in our innovative multilevel fusion methodology to extract features that are categorised. The proposed methodology beat state-of-the-art algorithms with 90.3% precision, approximate retrieval precision (ARP) of 0.91, and approximate retrieval recall (ARR) of 0.82 when tested on several benchmark datasets

Hand gesture based digit recognition

Recognition of static hand gestures in our daily plays an important role in human-computer interaction. Hand gesture recognition has been a challenging task now a days so a lot of research topic has been going on due to its increased demands in human computer interaction. Since Hand gestures have been the most natural communication medium among human being, so this facilitate efficient human computer interaction in many electronics gazettes . This has led us to take up this task of hand gesture recognition. In this project different hand gestures are recognized and no of fingers are counted. Recognition process involve steps like feature extraction, features reduction and classification. To make the recognition process robust against varying illumination we used lighting compensation method along with YCbCr model. Gabor filter has been used for feature extraction because of its special mathematical properties. Gabor based feature vectors have high dimension so in our project 15 local gabor filters are used instead of 40 Gabor filters. The objective in using fifteen Gabor filters is used to mitigate the complexity with improved accuracy. In this project the problem of high dimensionality of feature vector is being solved by using PCA. Using local Gabor filter helps in reduction of data redundancy as compared to that of 40 filters. Classification of the 5 different gestures is done with the use of one against all multiclass SVM which is also compared with Euclidean distance and cosine similarity while the former giving an accuracy of 90.86%

ABC Method and Fractional Momentum Layer for the FDTD Method to Solve the Schrödinger Equation on Unbounded Domains

The finite­difference time­domain (FDTD) method and its generalized variant (G­FDTD) are efficient numerical tools for solving the linear and nonlinear Schrödinger equations because not only are they explicit, allowing parallelization, but they also provide high­order accuracy with relatively inexpensive computational costs. In addition, the G­FDTD method has a relaxed stability condition when compared to the original FDTD method. It is important to note that the existing simulations of the G­FDTD scheme employed analytical solutions to obtain function values at the points along the boundary; however, in simulations for which the analytical solution is unknown, theoretical approximations for values at points along the boundary are desperately needed. Hence, the objective of this dissertation research is to develop absorbing boundary conditions (ABCs) so that the G­FDTD method can be used to solve the nonlinear Schrödinger equation when the analytical solution is unknown. To create the ABCs for the nonlinear Schrödinger equation, we initially determine the associated Engquist­Majda one­way wave equations and then proceed to develop a finite difference scheme for them. These ABCs are made to be adaptive using a windowed Fourier transform to estimate a value of the wavenumber of the carrier wave. These ABCs were tested using the nonlinear Schrödinger equation for 1D and 2D soliton propagation as well as Gaussian packet collision and dipole radiation. Results show that these ABCs perform well, but they have three key limitations. First, there are inherent reflections at the interface of the interior and boundary domains due to the different schemes used the two regions; second, to use the ABCs, one needs to estimate a value for the carrier wavenumber and poor estimates can cause even more reflection at the interface; and finally, the ABCs require different schemes in different regions of the boundary, and this domain decomposition makes the ABCs tedious both to develop and to implement. To address these limitations for the FDTD method, we employ the fractional­order derivative concept to unify the Schrödinger equation with its one­way wave equation over an interval where the fractional order is allowed to vary. Through careful construction of a variable­order fractional momentum operator, outgoing waves may enter the fractionalorder region with little to no reflection and, inside this region, any reflected portions of the wave will decay exponentially with time. The fractional momentum operator is then used to create a fractional­order FDTD scheme. Importantly, this single scheme can be used for the entire computational domain, and the scheme smooths the abrupt transition between the FDTD method and the ABCs. Furthermore, the fractional FDTD scheme relaxes the precision needed for the estimated carrier wavenumber. This fractional FDTD scheme is tested for both the linear and nonlinear Schrödinger equations. Example cases include a 1D Gaussian packet scattering off of a potential, a 1D soliton propagating to the right, as well as 2D soliton propagation, and the collision of Gaussian packets. Results show that the fractional FDTD method outperforms the FDTD method with ABCs

An information-theoretic approach to the gravitational-wave burst detection problem

The observational era of gravitational-wave astronomy began in the Fall of 2015 with the detection of GW150914. One potential type of detectable gravitational wave is short-duration gravitational-wave bursts, whose waveforms can be difficult to predict. We present the framework for a new detection algorithm for such burst events -- \textit{oLIB} -- that can be used in low-latency to identify gravitational-wave transients independently of other search algorithms. This algorithm consists of 1) an excess-power event generator based on the Q-transform -- \textit{Omicron} --, 2) coincidence of these events across a detector network, and 3) an analysis of the coincident events using a Markov chain Monte Carlo Bayesian evidence calculator -- \textit{LALInferenceBurst}. These steps compress the full data streams into a set of Bayes factors for each event; through this process, we use elements from information theory to minimize the amount of information regarding the signal-versus-noise hypothesis that is lost. We optimally extract this information using a likelihood-ratio test to estimate a detection significance for each event. Using representative archival LIGO data, we show that the algorithm can detect gravitational-wave burst events of astrophysical strength in realistic instrumental noise across different burst waveform morphologies. We also demonstrate that the combination of Bayes factors by means of a likelihood-ratio test can improve the detection efficiency of a gravitational-wave burst search. Finally, we show that oLIB's performance is robust against the choice of gravitational-wave populations used to model the likelihood-ratio test likelihoods