Digital Signal and Image Processing using MATLAB®

Blanchet, Gérard; Charbit, Maurice

doi:10.1002/9780470612385

Cited by 67 publications

(27 citation statements)

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“…According to this equation, a system with maximum Shannon entropy (with fully random behavior) has non-repetitive values in the array, while Shannon entropy is equal to zero in a deterministic system (e.g., matrix of ones). The number of possible values of array cells (i.e., histogram count) is directly related to the Shannon entropy 84 .…”

Section: -2 the Shannon Entropy Of Brain Networkmentioning

confidence: 99%

Synchrony and complexity in state-related EEG networks: an application of spectral graph theory

Ghaderi

Baltaretu

Andevari

et al. 2019

Preprint

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To characterize differences between different state-related brain networks, statistical graph theory approaches have been employed to identify informative, topological properties. However, dynamical properties have been studied little in this regard. Our goal here was to introduce spectral graph theory as a reliable approach to determine dynamic properties of functional brain networks and to find how topological versus dynamical features differentiate between such networks. To this goal, 45 participants performed no task with eyes open (EO) or 2 closed (EC) while electroencephalography data were recorded. These data were used to create weighted adjacency matrices for each condition (rest state EO and rest state EC). Then, using the spectral graph theory approach and Shannon entropy, we identified dynamical properties for weighted graphs, and we compared these features with topological aspects of graphs. The results showed that spectral graph theory can distinguish different state-dependent neural networks with different synchronies. On the other hand, correlation analysis indicated that although dynamical and topological properties of random networks are completely independent, these network features can be related in the case of brain generated graphs. In conclusion, the spectral graph theory approach can be used to make inferences about various state-related brain networks, for healthy and clinical populations. Author SummeryBy considering functional communications across different brain regions, a complex network is achieved that is known as functional brain network.Topologically, this network is constructed by different nodes (activity of brain regions or signals over recording electrodes) and different edges (similarity, correlation or phase difference between nodes). Paths, clusters, hubs, and centrality of nodes are examples of topological properties of these networks.However, synchrony and stability of functional brain networks can not be revealed by consideration of topological properties. Alternatively, spectral graph theory (SGT) can demonstrate the dynamic, synchrony and stability of graphs. But this approach has been studied little in brain network analysis. Here, we employed SGT, as well as topological methods, to investigate which approaches 3 are more reliable to find differences between distinct state-related brain networks. On the other hand, we investigated correlations between topology and dynamic in different type of networks (brain generated and random networks).We found that SGT measures can clearly distinguish between distinct staterelated brain networks and it can reveal synchrony and complexity of these networks. Also, results show that although dynamic and topology of randomgenerated graph are completely independent, these properties exhibit several correlations in the case of functional brain networks.

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Section: -2 the Shannon Entropy Of Brain Networkmentioning

confidence: 99%

Synchrony and complexity in state-related EEG networks: an application of spectral graph theory

Ghaderi

Baltaretu

Andevari

et al. 2019

Preprint

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“…This will lead to non-zero points in the resulting pattern that seem not to belong to the main beam of the pattern and that may be caused by the mechanical system drift [2] and incomplete cancellation of nontarget fields related with the large time difference between the two measurements. In order to remove the blur effect on the obtained pattern and get a purer pattern, a simple and efficient filter [23] can be applied to the problem, like median filter. If the definition in Ref.…”

Section: Filteringmentioning

confidence: 99%

“…If the definition in Ref. 23 is customized to the problem in this article, the new values at each (p,q) points in the pattern are found by calculating the median of neighbors of each (p,q); that is, if S TM ðp; qÞ is the magnitude value at a single point (p,q) in data grid, the filter associates the mean value STM(p,q) with the point with coordinates (p,q) in rl by r2 rectangular window centered on (p,q). If r 1 and r 2 are taken odd and if z(n) represents the sorted sequence Z t ð Þ !…”

Section: Filteringmentioning

confidence: 99%

Multimode Vivaldi antenna

et al. 2010

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worth mentioning that these discrepancies are difficult to predict in advance, due to imperfections in the manufacturing process, but can be overcome using external tuning circuits to adjust the operating frequency. Figure 8 shows the simulated and measured radiation patterns in the E plane (X-Y) and in H plane (Y-Z) of the antenna for all modes. The antenna exhibits directional endfire radiation at the angle u 5 908 in all modes and over all the operating frequencies. This is due to the V-shaped form, which privileges radiation in the direction of the V-shape. The simulated and measured gain of single-band mode is 3.3 dBi and 2.96 dBi, respectively. The simulated and measured gains obtained in the wideband, dual-band and triple-band modes are given in Tables (2-4). CONCLUSIONIn this article, a novel directional end-fire antenna switchable between a slot dipole and a tapered slot antenna is presented to achieve multiband and wideband operations have been presented. By using five PIN diode switches in the structure, four modes have been obtained corresponding to a wideband, singleband, dual-band, and triple-band operations. These operations were achieved by activating/deactivating parts of the antenna structure. The proposed design has the benefit of having all the above configurations in the same structure. The simulated and measured results have been demonstrated. The antenna is believed to be useful for multiband applications requiring a stable radiation patterns in the different operating modes. ABSTRACT: In this article, Electromagnetic(EM) measurements campaign in controlled environment (antenna measurement laboratory) carried out to test, theories on EM bistatic scattering from buried metal detection has been presented. Both background signal subtraction algorithms and image processing techniques were applied. For each measurement, the brilliant area (BA), the ratio of pattern area(containing only non-zero points) to the total scan area, has been calculated and formula relating BA with the width of the block, which is the distance from the front face of the block to the location where the metal is embedded, has been established. An average absolute error between calculated BA and measured BA is varying between 2.38% and 4.3%. We generated and proposed a mathematical model for estimating embedded depth, and measurement results are in good track with real one. ACKNOWLEDGMENT

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“…6 and 7 (n 1 [n] →Non-Linear Unknown System (NLUS ) → n 2 [n]). Unfamiliar environment properties apply here (signal distortion due to the environmentinterference [5], delay [7], etc).…”

Section: Layermentioning

confidence: 99%

“…However, these conventional filtration techniques cannot be used because of the spectrum time variability of the interfering and useful signals [5,7].…”

Section: Layermentioning

confidence: 99%

A Robust Approach For Acoustic Noise Suppression In Speech Using ANFIS

Martínek

Kelnar

Vaňuš

et al. 2015

Journal of Electrical Engineering

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The authors of this article deals with the implementation of a combination of techniques of the fuzzy system and artificial intelligence in the application area of non-linear noise and interference suppression. This structure used is called an Adaptive Neuro Fuzzy Inference System (ANFIS). This system finds practical use mainly in audio telephone (mobile) communication in a noisy environment (transport, production halls, sports matches, etc). Experimental methods based on the two-input adaptive noise cancellation concept was clearly outlined. Within the experiments carried out, the authors created, based on the ANFIS structure, a comprehensive system for adaptive suppression of unwanted background interference that occurs in audio communication and degrades the audio signal. The system designed has been tested on real voice signals. This article presents the investigation and comparison amongst three distinct approaches to noise cancellation in speech; they are LMS (least mean squares) and RLS (recursive least squares) adaptive filtering and ANFIS. A careful review of literatures indicated the importance of non-linear adaptive algorithms over linear ones in noise cancellation. It was concluded that the ANFIS approach had the overall best performance as it efficiently cancelled noise even in highly noise-degraded speech. Results were drawn from the successful experimentation, subjective-based tests were used to analyse their comparative performance while objective tests were used to validate them. Implementation of algorithms was experimentally carried out in Matlab to justify the claims and determine their relative performances.

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Digital Signal and Image Processing using MATLAB®

Cited by 67 publications

References 0 publications

Synchrony and complexity in state-related EEG networks: an application of spectral graph theory

Synchrony and complexity in state-related EEG networks: an application of spectral graph theory

Multimode Vivaldi antenna

A Robust Approach For Acoustic Noise Suppression In Speech Using ANFIS

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