scholarly journals TEXTURE CLASSIFICATION USING WEIGHTED PROBABILISTIC NEURAL NETWORKS

2012 ◽  
pp. 126-128
Author(s):  
M. KIRANMAYEE ◽  
M. SUBBARAO
Keyword(s):  
Texture Classification ◽  
Probabilistic Neural Network ◽  
Signal To Noise Ratio ◽  
Contextual Information ◽  
The Novel ◽  
Probabilistic Neural Networks ◽  
Classification Rate ◽  
Previous Frame ◽  
Feature Based ◽  
Initial Classification

Texture classification is basically the problem of classifying pixels in an image according to their textural cues. This is different from conventional image segmentation as the texture is characterized using both the gray value for a given pixel and gray-level pattern in the neighborhood surrounding the pixel. In this project, the novel temporal updating approach is developed for weighted probabilistic neural network (WPNN) classifiers that can be used to classify the textures. This is done by utilizing the temporal contextual information and adjusting the WPNN to adapt to such changes. Whenever a new set of images arrives, an initial classification is first performed using the WPNN updated to the last frame while at the same time, a prediction using PNN is also based on the classification results of previous frame. The result of both the PNN and WPNN are then compared. Compared to the PNN, WPNN includes weighting factors between pattern layers and summation layer of the PNN. Performance of this approach is compared with model based and feature based methods in terms of signal to noise ratio and classification rate.

Application of multidimensional data analysis to chromatography

2013 ◽  
Vol 18 (2-3) ◽  
pp. 101-108 ◽  
Author(s):  
Tadeusz Satława ◽  
Joanna Grabska-Chrząstowska ◽  
Przemysław Korohoda
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Supervised Machine Learning ◽  
Multidimensional Data ◽  
Support Vector ◽  
Correct Classification Rate ◽  
Probabilistic Neural Networks ◽  
Classification Rate ◽  
Vector Machines ◽  
Methods Of Learning

Abstract This work presents analysis of chromatographic signal used to identify substances in samples. First part consists of chromatography overview and description of three classification methods (neural network with backpropagation, probabilistic neural network with Parzen window and support vector machines). Designed algorithm consists of several stages: signal filtering, peak detection and its approximation with sum of two Gaussian functions. The parameters of that two curves are the features vectors describing the peak of the substance. The last step is classification, for which two types of supervised machine learning were compared, based on the whole signal and on features vectors. Both types were tested for different classificators and their parameters. Verification was based on 55 chromatography signals. The best results for both methods of learning were achieved for probabilistic neural networks. The correct classification rate was 82% for the whole signal and 93% for feature vectors.

Application of Novel Low Current OBIRCH Amplifier and Nanoprobing to Identify Subtle Leakages in Advanced Node Technologies

2018 ◽  
Author(s):  
Satish Kodali ◽  
Liangshan Chen ◽  
Yuting Wei ◽  
Tanya Schaeffer ◽  
Chong Khiam Oh
Keyword(s):  
Signal To Noise Ratio ◽  
Semiconductor Industry ◽  
Fault Isolation ◽  
Ring Oscillator ◽  
High Signal ◽  
The Novel ◽  
Resistance Change ◽  
Three Samples ◽  
Relative Threshold ◽  
First Time

Abstract Optical beam induced resistance change (OBIRCH) is a very well-adapted technique for static fault isolation in the semiconductor industry. Novel low current OBIRCH amplifier is used to facilitate safe test condition requirements for advanced nodes. This paper shows the differences between the earlier and novel generation OBIRCH amplifiers. Ring oscillator high standby leakage samples are analyzed using the novel generation amplifier. High signal to noise ratio at applied low bias and current levels on device under test are shown on various samples. Further, a metric to demonstrate the SNR to device performance is also discussed. OBIRCH analysis is performed on all the three samples for nanoprobing of, and physical characterization on, the leakage. The resulting spots were calibrated and classified. It is noted that the calibration metric can be successfully used for the first time to estimate the relative threshold voltage of individual transistors in advanced process nodes.

scholarly journals Decision-related feedback in visual cortex lacks spatial selectivity

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Katrina R. Quinn ◽  
Lenka Seillier ◽  
Daniel A. Butts ◽  
Hendrikje Nienborg
Keyword(s):  
Contextual Information ◽  
Relevant Information ◽  
Common Mechanism ◽  
Spatial Selectivity ◽  
Population Responses ◽  
Stimulus Choice ◽  
Visual Areas ◽  
Computational Work ◽  
Feature Based ◽  
The Brain

AbstractFeedback in the brain is thought to convey contextual information that underlies our flexibility to perform different tasks. Empirical and computational work on the visual system suggests this is achieved by targeting task-relevant neuronal subpopulations. We combine two tasks, each resulting in selective modulation by feedback, to test whether the feedback reflected the combination of both selectivities. We used visual feature-discrimination specified at one of two possible locations and uncoupled the decision formation from motor plans to report it, while recording in macaque mid-level visual areas. Here we show that although the behavior is spatially selective, using only task-relevant information, modulation by decision-related feedback is spatially unselective. Population responses reveal similar stimulus-choice alignments irrespective of stimulus relevance. The results suggest a common mechanism across tasks, independent of the spatial selectivity these tasks demand. This may reflect biological constraints and facilitate generalization across tasks. Our findings also support a previously hypothesized link between feature-based attention and decision-related activity.

Event-Locked Time-Resolved Fourier Spectroscopy

1997 ◽  
Vol 51 (8) ◽  
pp. 1106-1112 ◽  
Author(s):  
H. Weidner ◽  
R. E. Peale
Keyword(s):  
Signal To Noise Ratio ◽  
Low Cost ◽  
Michelson Interferometer ◽  
Far Infrared ◽  
Time Base ◽  
The Novel ◽  
Time Resolved ◽  
Mathematical Algorithm ◽  
Scan Time ◽  
Recorded Data

A low-cost method of adding time-resolving capability to commercial Fourier transform spectrometers with a continuously scanning Michelson interferometer has been developed. This method is specifically designed to eliminate noise and artifacts caused by mirror-speed variations in the interferometer. The method exists of two parts: (1) a novel timing scheme for synchronizing the transient events under study and the digitizing of the interferogram and (2) a mathematical algorithm for extracting the spectral information from the recorded data. The novel timing scheme is a modification of the well-known interleaved, or stroboscopic, method. It achieves the same timing accuracy, signal-to-noise ratio, and freedom from artifacts as step-scan time-resolving Fourier spectrometers by locking the sampling of the interferogram to a stable time base rather than to the occurrences of the HeNe fringes. The necessary pathlength-difference information at which samples are taken is obtained from a record of the mirror speed. The resulting interferograms with uneven pathlength-difference spacings are transformed into wavenumber space by least-squares fits of periodic functions. Spectra from the far-infrared to the upper visible at resolutions up to 0.2 cm−1 are used to demonstrate the utility of this method.

Optimization of the Kernel Functions in a Probabilistic Neural Network Analyzing the Local Pattern Distribution

2002 ◽  
Vol 14 (5) ◽  
pp. 1183-1194 ◽  
Author(s):  
I. Galleske ◽  
J. Castellanos
Keyword(s):  
Neural Network ◽  
Covariance Matrix ◽  
Probabilistic Neural Network ◽  
Local Environment ◽  
Kernel Functions ◽  
Gaussian Kernel ◽  
Classification Rate ◽  
Training Pattern ◽  
The Neural Network ◽  
Pattern Distribution

This article proposes a procedure for the automatic determination of the elements of the covariance matrix of the gaussian kernel function of probabilistic neural networks. Two matrices, a rotation matrix and a matrix of variances, can be calculated by analyzing the local environment of each training pattern. The combination of them will form the covariance matrix of each training pattern. This automation has two advantages: First, it will free the neural network designer from indicating the complete covariance matrix, and second, it will result in a network with better generalization ability than the original model. A variation of the famous two-spiral problem and real-world examples from the UCI Machine Learning Repository will show a classification rate not only better than the original probabilistic neural network but also that this model can outperform other well-known classification techniques.

scholarly journals Computing Discrete Adriatic Indices of Probabilistic Neural Network

2020 ◽  
Vol 13 (5) ◽  
pp. 1149-1161
Author(s):  
T Deepika ◽  
V. Lokesha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Topological Index ◽  
Probabilistic Neural Network ◽  
Topological Indices ◽  
Probabilistic Neural Networks ◽  
Mathematical Chemistry ◽  
International Academy

A Topological index is a numeric quantity which characterizes the whole structure of a graph. Adriatic indices are also part of topological indices, mainly it is classified into two namely extended variables and discrete adriatic indices, especially, discrete adriatic indices are analyzed on the testing sets provided by the International Academy of Mathematical Chemistry (IAMC) and it has been shown that they have good presaging substances in many compacts. This contrived attention to compute some discrete adriatic indices of probabilistic neural networks.

scholarly journals Design of a multimodal hearing system

2013 ◽  
Vol 10 (1) ◽  
pp. 483-501 ◽  
Author(s):  
Bernd Tessendorf ◽  
Matjaz Debevc ◽  
Peter Derleth ◽  
Manuela Feilner ◽  
Franz Gravenhorst ◽  
...  
Keyword(s):  
Contextual Information ◽  
Body Movement ◽  
Audio Signal ◽  
The Novel ◽  
Missing Information ◽  
Sound Processing ◽  
Acoustic Environment ◽  
Novel Approach ◽  
Hearing Instruments ◽  
Hearing System

Hearing instruments (HIs) have become context-aware devices that analyze the acoustic environment in order to automatically adapt sound processing to the user?s current hearing wish. However, in the same acoustic environment an HI user can have different hearing wishes requiring different behaviors from the hearing instrument. In these cases, the audio signal alone contains too little contextual information to determine the user?s hearing wish. Additional modalities to sound can provide the missing information to improve the adaption. In this work, we review additional modalities to sound in HIs and present a prototype of a newly developed wireless multimodal hearing system. The platform takes into account additional sensor modalities such as the user?s body movement and location. We characterize the system regarding runtime, latency and reliability of the wireless connection, and point out possibilities arising from the novel approach.

scholarly journals Development of a Smart Helmet for Strategical BCI Applications

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1867 ◽  
Author(s):  
Li-Wei Ko ◽  
Yang Chang ◽  
Pei-Lun Wu ◽  
Heng-An Tzou ◽  
Sheng-Fu Chen ◽  
...  
Keyword(s):  
Data Collection ◽  
Signal To Noise Ratio ◽  
The Novel ◽  
Contact Impedance ◽  
Practical Applications ◽  
Dry Electrodes ◽  
Average Accuracy ◽  
Eeg Data ◽  
Electrophysiological Measurements ◽  
User Acceptability

Conducting electrophysiological measurements from human brain function provides a medium for sending commands and messages to the external world, as known as a brain–computer interface (BCI). In this study, we proposed a smart helmet which integrated the novel hygroscopic sponge electrodes and a combat helmet for BCI applications; with the smart helmet, soldiers can carry out extra tasks according to their intentions, i.e., through BCI techniques. There are several existing BCI methods which are distinct from each other; however, mutual issues exist regarding comfort and user acceptability when utilizing such BCI techniques in practical applications; one of the main challenges is the trade-off between using wet and dry electroencephalographic (EEG) electrodes. Recently, several dry EEG electrodes without the necessity of conductive gel have been developed for EEG data collection. Although the gel was claimed to be unnecessary, high contact impedance and low signal-to-noise ratio of dry EEG electrodes have turned out to be the main limitations. In this study, a smart helmet with novel hygroscopic sponge electrodes is developed and investigated for long-term usage of EEG data collection. The existing electrodes and EEG equipment regarding BCI applications were adopted to examine the proposed electrode. In the impedance test of a variety of electrodes, the sponge electrode showed performance averaging 118 kΩ, which was comparable with the best one among existing dry electrodes, which averaged 123 kΩ. The signals acquired from the sponge electrodes and the classic wet electrodes were analyzed with correlation analysis to study the effectiveness. The results indicated that the signals were similar to each other with an average correlation of 90.03% and 82.56% in two-second and ten-second temporal resolutions, respectively, and 97.18% in frequency responses. Furthermore, by applying the proposed differentiable power algorithm to the system, the average accuracy of 21 subjects can reach 91.11% in the steady-state visually evoked potential (SSVEP)-based BCI application regarding a simulated military mission. To sum up, the smart helmet is capable of assisting the soldiers to execute instructions with SSVEP-based BCI when their hands are not available and is a reliable piece of equipment for strategical applications.

scholarly journals Improving Speech Quality for Hearing Aid Applications Based on Wiener Filter and Composite of Deep Denoising Autoencoders

Signals ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 138-156
Author(s):  
Raghad Yaseen Lazim ◽  
Zhu Yun ◽  
Xiaojun Wu
Keyword(s):  
Hearing Loss ◽  
Signal To Noise Ratio ◽  
Hearing Aid ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Wiener Filter ◽  
Speech Quality ◽  
Denoising Autoencoder ◽  
Noise Component ◽  
Perceptual Evaluation

In hearing aid devices, speech enhancement techniques are a critical component to enable users with hearing loss to attain improved speech quality under noisy conditions. Recently, the deep denoising autoencoder (DDAE) was adopted successfully for recovering the desired speech from noisy observations. However, a single DDAE cannot extract contextual information sufficiently due to the poor generalization in an unknown signal-to-noise ratio (SNR), the local minima, and the fact that the enhanced output shows some residual noise and some level of discontinuity. In this paper, we propose a hybrid approach for hearing aid applications based on two stages: (1) the Wiener filter, which attenuates the noise component and generates a clean speech signal; (2) a composite of three DDAEs with different window lengths, each of which is specialized for a specific enhancement task. Two typical high-frequency hearing loss audiograms were used to test the performance of the approach: Audiogram 1 = (0, 0, 0, 60, 80, 90) and Audiogram 2 = (0, 15, 30, 60, 80, 85). The hearing-aid speech perception index, the hearing-aid speech quality index, and the perceptual evaluation of speech quality were used to evaluate the performance. The experimental results show that the proposed method achieved significantly better results compared with the Wiener filter or a single deep denoising autoencoder alone.

scholarly journals A Novel Mutual Information Based Feature Set for Drivers’ Mental Workload Evaluation Using Machine Learning

2020 ◽  
Vol 10 (8) ◽  
pp. 551
Author(s):  
Mir Riyanul Islam ◽  
Shaibal Barua ◽  
Mobyen Uddin Ahmed ◽  
Shahina Begum ◽  
Pietro Aricò ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mutual Information ◽  
Mental Workload ◽  
Contextual Information ◽  
Absolute Error ◽  
The Novel ◽  
Promising Technique ◽  
Extensive Evaluation ◽  
Target Values ◽  
Classification Tasks

Analysis of physiological signals, electroencephalography more specifically, is considered a very promising technique to obtain objective measures for mental workload evaluation, however, it requires a complex apparatus to record, and thus, with poor usability in monitoring in-vehicle drivers’ mental workload. This study proposes a methodology of constructing a novel mutual information-based feature set from the fusion of electroencephalography and vehicular signals acquired through a real driving experiment and deployed in evaluating drivers’ mental workload. Mutual information of electroencephalography and vehicular signals were used as the prime factor for the fusion of features. In order to assess the reliability of the developed feature set mental workload score prediction, classification and event classification tasks were performed using different machine learning models. Moreover, features extracted from electroencephalography were used to compare the performance. In the prediction of mental workload score, expert-defined scores were used as the target values. For classification tasks, true labels were set from contextual information of the experiment. An extensive evaluation of every prediction tasks was carried out using different validation methods. In predicting the mental workload score from the proposed feature set lowest mean absolute error was 0.09 and for classifying mental workload highest accuracy was 94%. According to the outcome of the study, it can be stated that the novel mutual information based features developed through the proposed approach can be employed to classify and monitor in-vehicle drivers’ mental workload.

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