The Experts below are selected from a list of 228 Experts worldwide ranked by ideXlab platform
Costas Xydeas - One of the best experts on this subject based on the ideXlab platform.
-
ICASSP - On improving Vector Excitation coders through the use of spherical lattice codebooks (SLCs)
International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: M. A. Ireton, Costas XydeasAbstract:Two novel techniques for use in VXC (Vector Excitation coding) speech coders are presented. The first enables massive Excitation codebooks (>or=20 b) to be used at realizable complexities by using a novel spherical lattice codebook for the Excitation codebook. The second technique is a generalization of the gain-optimized error measure which allows any number of gains to be calculated for each Excitation Vector. This multiple-gain VXC can be thought of as a hybrid between multipulse and VXC. >
E N Sokolov - One of the best experts on this subject based on the ideXlab platform.
-
Mechanisms of achromatic vision in invertebrates and vertebrates: a comparative study.
The Spanish journal of psychology, 2010Co-Authors: Alexander M. Chernorizov, E N SokolovAbstract:Intracellular recording in the retina of the snail, Helix pomatia L., reveals the existence of two types of cell responsive to diffuse flashes of achromatic or monochromatic light: B-type cells, which respond with sustained depolarization that is sometimes accompanied by spikes, and D-type cells, which respond with sustained hyperpolarization. The peak of spectral sensitivity for both B- and D-cells falls in the 450-500 nm range and coincides with range of maximal sensitivity for the rhodopsin family of photopigments. Within a proposed two-channel model of snail achromatic vision, responses of the B- and D-cells are represented by a two-dimensional 'Excitation Vector'. The length of the 'Excitation Vector' is approximately constant, and its direction correlates with light intensity. The Vector model of light encoding in the snail is discussed in relation to models of achromatic vision in vertebrates (fish, frog, monkey, and humans) based on psychophysical, behavioral and neurophysiological data. Intracellular data in the snail taken together with data from vertebrate animals support the hypothesis that a 2-dimensional model of brightness and darkness encoding utilizes a universal mechanism of 'Vector encoding' for light intensity in neuronal vision networks.
-
Segmentation, grouping and accentuation during stimuli perception
Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova, 2009Co-Authors: E N Sokolov, N I NezlinaAbstract:The paper is concerned with grouping, segmentation and accentuation occurring in the processes of stimuli perception. An universal model of these events is based on Vector coding in neuronal networks. Grouping is unification of objects or events into collections according to their similarity. Segmentation is separation of such groups up to small ensembles of units. In neuroscience grouping and segmentation are regarded as referred to neural mechanisms underlying perceptual and semantic processes resulting in a phenomenal attachment or separation. It is assumed that stimuli in neuronal nets are encoded by combinations of Excitations of cardinal neurons constituting Excitation Vectors. Differences among stimuli are formed as absolute values of their Excitation Vector differences. The more different are stimuli the separate are their perceptual and semantic representations. The more similar are respective stimuli, the less is their separation. It suggests that stimuli having similar Excitation Vectors would be grouped together. On the contrary stimuli with opposed Excitation Vectors would be segmented and pushed to different ensembles. The Vector encoding is expressed also for location in space. Thus spatial separation of objects is increasing with the increasing of their spatial Excitation Vector differences. The universal principle of Vector encoding of differences can be illustrated by color contrast: differences of contrast colors rise with increase of their Excitation Vector differences. Objects having similar Excitation Vectors constitute a group accentuated due to summation of their Excitation Vectors. Groups of objects characterized by different Excitation Vectors are mutually accentuated by a contrast mechanism. A plastic accentuation depends on novelty of stimulation being habituated during repeated stimulus presentations.
-
Perception and the conditioning reflex: Vector encoding.
International journal of psychophysiology : official journal of the International Organization of Psychophysiology, 2000Co-Authors: E N SokolovAbstract:Color perception is dependent on the generation of an Excitation Vector which, acting on a pool of color detectors (color detector map), produces a corresponding sensation. The generation of the color Excitation Vector starts at the retinal level, proceeds in the lateral geniculate body, and reaches color detectors at the cortical level. Following processing at the level of declarative memory and semantic maps, results in a verbal categorization of colors. Parallel to the Excitation Vector pathway, a network computing color differences is operating. The computation of color differences at the retinal level possibly takes place in phasic bipolar cells and progresses in the lateral geniculate body and at the cortical level. Detectors of color differences are assumed to be a basis of respective numerical estimations in humans. Data from frogs, fish, monkeys and humans are compared.
-
Vector representation of associative learning.
Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova, 2000Co-Authors: E N SokolovAbstract:I. P. Pavlov [12] has shown that conditioned reflexes are selective both with respect to conditioned stimuli and to conditioned reflexes elicited by those conditioned stimuli. At the neuronal level selective aspects of conditioned stimuli are based on detectors selectively tuned to respective stimuli. The selective aspects of conditioned reflexes are due to command neurons representing specific unconditioned reflexes. It can be assumed that conditioned reflexes result from association between selective detectors and specific command neurons. The detectors activated by a conditioned stimulus constitute a combination of Excitations--a detector Excitation Vector. The detector Excitation Vector acts on a command neuron via a set of plastic synapses--a synaptic weight Vector. Plastic synapses are modified in the process of learning making command neuron selectively tuned to a specific conditioned stimulus. The selective tuning of a particular command neuron to a specific Excitation Vector referred to a conditioned stimulus is a basis of associative learning. The probabilities of conditioned reflexes elicited by conditioned and differential stimuli implicitly contain information concerning Excitation Vectors that encode respective stimuli. Contribution of the Vector code to associative learning was explored combining differential color conditioning with intracellular recording from color-coding neurons. It was shown that colors in carps and monkeys are represented on a hypersphere in the four-dimensional space similar to human color space. The basis of the color space is constituted by red-green, blue-yellow, brightness and darkness neurons.
-
Vector code differences and similarities
Behavioral and Brain Sciences, 1998Co-Authors: E N SokolovAbstract:Edelman suggests that any shape is encoded by an Excitation Vector with components corresponding to Excitations of corresponding neuronal modules. This results in discrimination of stimuli in a shape space of low dimensionality. Similar Vector encoding is present in color vision. Red-green, blue-yellow, bright and dark neurons are modules that represent a number of different color stimuli in color space of low dimensionality. Vector encoding allows effective computation of color differences and color similarities. Such a neuronal Vector-encoding approach has also been applied to the perception of visual movement, line orientation, and stereopsis.
Ovidio Mario Bucci - One of the best experts on this subject based on the ideXlab platform.
-
Blind Focusing of Electromagnetic Fields in Hyperthermia Exploiting Target Contrast Variations
IEEE Transactions on Biomedical Engineering, 2015Co-Authors: Gennaro Bellizzi, Ovidio Mario BucciAbstract:This paper suggests a novel approach to the blind focusing of the electromagnetic field for microwave hyperthermia. The idea is to induce a contrast variation in the target and to exploit this variation for the synthesis of the Excitations of the antenna array employed for the focusing, by performing a differential scattering measurement. In particular, the Excitation Vector is set as the right singular Vector associated with the largest singular value of the differential scattering matrix, obtained as difference of two scattering matrixes measured by the antenna array itself before and after the contrast change. As a result, the approach is computationally effective and totally blind, not requiring any a priori knowledge of the electric and geometric features of the region hosting the target, as well as of its spatial position with respect to the antenna array.
-
Blind focusing in microwave hyperthermia by target contrast variation
The 8th European Conference on Antennas and Propagation (EuCAP 2014), 2014Co-Authors: Gennaro Bellizzi, Ovidio Mario BucciAbstract:The paper suggests a novel blind focusing approach for microwave hyperthermia. The idea is to exploit a contrast variation induced in the diseased tissue for the synthesis of the Excitations of the antenna array employed for the field focusing, by performing a differential scattering measurement. In particular, the Excitation Vector is set as the right singular Vector associated to the largest singular value of the differential scattering data matrix, obtained as the difference of the scattering matrixes measured, by the antenna array itself, before and after the contrast change. The approach is computationally effective and totally blind, not requiring any a priori knowledge on the electric and geometric features of the region hosting the target and on its spatial location with respect to the antenna array.
M. A. Ireton - One of the best experts on this subject based on the ideXlab platform.
-
ICASSP - On improving Vector Excitation coders through the use of spherical lattice codebooks (SLCs)
International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: M. A. Ireton, Costas XydeasAbstract:Two novel techniques for use in VXC (Vector Excitation coding) speech coders are presented. The first enables massive Excitation codebooks (>or=20 b) to be used at realizable complexities by using a novel spherical lattice codebook for the Excitation codebook. The second technique is a generalization of the gain-optimized error measure which allows any number of gains to be calculated for each Excitation Vector. This multiple-gain VXC can be thought of as a hybrid between multipulse and VXC. >
Huapeng Zhao - One of the best experts on this subject based on the ideXlab platform.
-
Diagnosis of thin wire antenna arrays using hybrid method of moments-sparse source reconstruction
2016 URSI Asia-Pacific Radio Science Conference (URSI AP-RASC), 2016Co-Authors: Ying Zhang, Huapeng ZhaoAbstract:This paper proposes to diagnose failure of thin wire antenna arrays by combining method of moments with sparse source reconstruction. The method of moments is first applied to link the array Excitation Vector with the near field observations, which results in a matrix equation with array Excitation Vector as unknowns. The sparse source reconstruction is then used to solve the matrix equation, which gives the array Excitation Vector. Compared to existing methods, the proposed method takes the mutual coupling effect into consideration, and it is more accurate.
-
Diagnosis of Array Failure in Impulsive Noise Environment Using Unsupervised Support Vector Regression Method
IEEE Transactions on Antennas and Propagation, 2013Co-Authors: Huapeng Zhao, Ying Zhang, Aniello Buonanno, Michele D'ursoAbstract:Fast and accurate diagnosis of array failure is important for the maintenance of array antennas. Locations of failing elements are usually detected using near field data, which may be polluted by noises. Most existing diagnosis methods assume non-impulsive noise in the measurement data. However, the practical measurement environment may contain impulsive noises, which has not been considered in existing methods. This work proposes to impose a penalty function in the residual between the measured and recovered near field. The impulsive noise in the near field data can then be suppressed by using an appropriate function as the penalty function. Furthermore, minimum lp-norm is imposed on the Excitation Vector. The condition imposed on the noise and the minimum lp-norm constraint on the Excitation Vector constitutes an optimization problem, which is solved using the unsupervised support Vector regression. The proposed method is more accurate than existing methods when impulsive noise presents in the near field data, and it is able to deal with a wide range of number of failing elements by adjusting the value of p. Numerical results are presented to show the advantages of the proposed method and to study the choice of p.
