Synthesis of Maitra cascades by means of spectral coefficients

1983 ◽  
Vol 130 (4) ◽  
pp. 101 ◽  
Author(s):  
M. Stanković ◽  
ž. Tošić ◽  
S. Nikolić
Keyword(s):  
Spectral Coefficients

scholarly journals Approximation of Ensemble Boundary Using Spectral Coefficients

2019 ◽  
Vol 30 (4) ◽  
pp. 1272-1277
Author(s):  
T. Windeatt ◽  
C. Zor ◽  
N. C. Camgoz
Keyword(s):  
Spectral Coefficients

scholarly journals Analysis of stimulus-related activity in rat auditory cortex using complex spectral coefficients

2013 ◽  
Vol 110 (3) ◽  
pp. 621-639 ◽  
Author(s):  
Bryan M. Krause ◽  
Matthew I. Banks
Keyword(s):  
Low Frequency ◽  
Phase Changes ◽  
Induced Response ◽  
Top Down ◽  
Time Frequency ◽  
State Dependent ◽  
Spectral Coefficients ◽  
The Mean ◽  
Sensory Responses ◽  
Mean Variance

The neural mechanisms of sensory responses recorded from the scalp or cortical surface remain controversial. Evoked vs. induced response components (i.e., changes in mean vs. variance) are associated with bottom-up vs. top-down processing, but trial-by-trial response variability can confound this interpretation. Phase reset of ongoing oscillations has also been postulated to contribute to sensory responses. In this article, we present evidence that responses under passive listening conditions are dominated by variable evoked response components. We measured the mean, variance, and phase of complex time-frequency coefficients of epidurally recorded responses to acoustic stimuli in rats. During the stimulus, changes in mean, variance, and phase tended to co-occur. After the stimulus, there was a small, low-frequency offset response in the mean and modest, prolonged desynchronization in the alpha band. Simulations showed that trial-by-trial variability in the mean can account for most of the variance and phase changes observed during the stimulus. This variability was state dependent, with smallest variability during periods of greatest arousal. Our data suggest that cortical responses to auditory stimuli reflect variable inputs to the cortical network. These analyses suggest that caution should be exercised when interpreting variance and phase changes in terms of top-down cortical processing.

scholarly journals Optimal Spectral Decomposition (OSD) for Ocean Data Assimilation

2015 ◽  
Vol 32 (4) ◽  
pp. 828-841 ◽  
Author(s):  
Peter C. Chu ◽  
Robin T. Tokmakian ◽  
Chenwu Fan ◽  
L. Charles Sun
Keyword(s):  
Data Assimilation ◽  
Spectral Decomposition ◽  
Basis Functions ◽  
Laplacian Operator ◽  
Generalized Fourier Series ◽  
Ocean Data Assimilation ◽  
Linear Algebraic Equations ◽  
Spectral Coefficients ◽  
Optimal Mode ◽  
Mode Truncation

AbstractOptimal spectral decomposition (OSD) is applied to ocean data assimilation with variable (temperature, salinity, or velocity) anomalies (relative to background or modeled values) decomposed into generalized Fourier series, such that any anomaly is represented by a linear combination of products of basis functions and corresponding spectral coefficients. It has three steps: 1) determination of the basis functions, 2) optimal mode truncation, and 3) update of the spectral coefficients from innovation (observational increment). The basis functions, depending only on the topography of the ocean basin, are the eigenvectors of the Laplacian operator with the same lateral boundary conditions as the assimilated variable anomalies. The Vapnik–Chervonkis dimension is used to determine the optimal mode truncation. After that, the model field updates due to innovation through solving a set of a linear algebraic equations of the spectral coefficients. The strength and weakness of the OSD method are demonstrated through a twin experiment using the Parallel Ocean Program (POP) model.

scholarly journals An Efficient Isolated Speech Recognition Based on the Adaptive Rate Processing and Analysis

2018 ◽  
Author(s):  
Saeed MIAN QAISAR
Keyword(s):  
Speech Recognition ◽  
Selection Process ◽  
Signal Acquisition ◽  
Potential Candidate ◽  
Rehabilitation Centers ◽  
Novel Approach ◽  
Adaptive Rate ◽  
Event Driven ◽  
Spectral Coefficients ◽  
Dynamic Time

This paper proposes a novel approach, based on the adaptive rate processing and analysis, for the isolated speech recognition. The idea is to smartly combine the event-driven signal acquisition and windowing along with adaptive rate processing, analysis and classification for realizing an effective isolated speech recognition. The incoming speech signal is digitized with an event-driven A/D converter (EDADC). The output of EDADC is windowed with an activity selection process. These windows are later on resampled uniformly with an adaptive rate interpolator. The resampled windows are de-noised with an adaptive rate filter and their spectrum are computed with an adaptive resolution short time Fourier transform (ARSTFT). Later on, the magnitude, Delta and Delta-Delta spectral coefficients are extracted. The Dynamic Time Warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The system functionality is tested for a case study and results are presented. An 8.2 times reduction in acquired number of samples is achieved by the devised approach as compared to the classical one. It aptitudes a significant computational gain and power consumption reduction of the proposed system over the counter classical ones. An average subject dependent isolated speech recognition accuracy of 96.8% is achieved. It shows that the proposed approach is a potential candidate for the automatic speech recognition applications like rehabilitation centers, smart call centers, smart homes, etc.

Dynamics of the change in spectral coefficients of plant leaves under illumination

1968 ◽  
Vol 9 (1) ◽  
pp. 743-745 ◽  
Author(s):  
V. E. Palamaryuk ◽  
S. G. Guminetskii
Keyword(s):  
Plant Leaves ◽  
Spectral Coefficients
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