Search results for " Detection"
showing 10 items of 1676 documents
Speech Activity Detection under Adverse Noisy Conditions at Low SNRs
2021
Speech originating from the noisy environments degrades the speech quality and intelligibility, thus reducing the human perceived Quality of Experience (QoE). For example, surveillance using drone during natural catastrophe needs an efficient speech recognition device to recognise the speech of the frozen human in presence of drone noise to save their life. Therefore, it often requires to pre-process the noisy speech in order to reduce the noise artifacts and enhance the speech. This paper detects the speech activity using Voice Activity Detection (VAD). The VAD distinguishes speech activity (speech presence) and speech inactivity (silence/noise) by extracting the speech features and compar…
Mismatch brain response to speech sound changes in rats
2011
Understanding speech is based on neural representations of individual speech sounds. In humans, such representations are capable of supporting an automatic and memory-based mechanism for auditory change detection, as reflected by the mismatch negativity of event-related potentials. There are also findings of neural representations of speech sounds in animals, but it is not known whether these representations can support the change detection mechanism analogous to that underlying the mismatch negativity in humans. To this end, we presented synthesized spoken syllables to urethane-anesthetized rats while local field potentials were epidurally recorded above their primary auditory cortex. In a…
Hardware-accelerated spike train generation for neuromorphic image and video processing
2014
Recent studies concerning Spiking Neural Networks show that they are a powerful tool for multiple applications as pattern recognition, image tracking, and detection tasks. The basic functional properties of SNN reside in the use of spike information encoding as the neurons are specifically designed and trained using spike trains. We present a novel and efficient frequency encoding algorithm with Gabor-like receptive fields using probabilistic methods and targeted to FPGA for online pro-cessing. The proposed encoding is versatile, modular and, when applied to images, it is able to perform simple image transforms as edge detection, spot detection or removal, and Gabor-like filtering without a…
Frequency spike encoding using Gabor-like receptive fields
2014
Abstract Spiking Neural Networks (SNN) are a popular field of study. For a proper development of SNN algorithms and applications, special encoding methods are required. Signal encoding is the first step since signals need to be converted into spike trains as the primary input to an SNN. We present an efficient frequency encoding system using receptive fields. The proposed encoding is versatile and it can provide simple image transforms like edge detection, spot detection or removal, or Gabor-like filtering. The proposed encoding can be used in many application areas as image processing and signal processing for detection and classification.
Chronic obstructive pulmonary disease in severe mental illness: A timely diagnosis to advance the process of quitting smoking
2021
This study receives founding by the Spanish Ministry of Economy, Industry, and Competitiveness, Instituto Carlos III (FIS PI16/00802).
Rotor bar pre-fault detection in the squirrel cage induction motors
2015
The paper deals with a diagnosis technique to detect and monitor incipient faults in the rotor bars of squirrel gage induction motors. The failure mode analysis is performed monitoring the motor axial vibrations. To accomplish the task, the authors present a mathematical model that allows relating the occurrence and the severity of the faults to the presence and the magnitude of some frequency components of the axial vibration spectrum. To validate the proposed approach, the results obtained by applying the mathematical model are compared with the ones obtained by experimental tests done on both healthy and faulty motors.
Design of an image processing integrated circuit for real time edge detection
2003
Presents the design of a real time image processing micro-system to detect defects on manufacturing products. The analysis method is based on an edge detection algorithm (differential operators) to select the information related to the structure of the objects present in the image. The edge calculation function has been integrated in a standard cell circuit using a CMOS 1.5 mu m process. The ASIC has been implemented and tested in an image processing microsystem with a CCD camera. Results show an improvement of performances (speed, noise, size reduction system characterization, etc. . .) in comparison with the first prototypes (software implementation and printed board with standard compone…
Distance-based functions for image comparison
1999
The interest in digital image comparison is steadily growing in the computer vision community. The definition of a suitable comparison measure for non-binary images is relevant in many image processing applications. Visual tasks like segmentation and classification require the evaluation of equivalence classes. Measures of similarity are also used to evaluate lossy compression algorithms and to define pictorial indices in image content based retrieval methods. In this paper we develop a distance-based approach to image similarity evaluation and we present several image distances which are based on low level features. The sensitivity and eAectiveness are tested on real data. ” 1999 Published…
Fast Estimation of the Median Covariation Matrix with Application to Online Robust Principal Components Analysis
2017
International audience; The geometric median covariation matrix is a robust multivariate indicator of dispersion which can be extended without any difficulty to functional data. We define estimators, based on recursive algorithms, that can be simply updated at each new observation and are able to deal rapidly with large samples of high dimensional data without being obliged to store all the data in memory. Asymptotic convergence properties of the recursive algorithms are studied under weak conditions. The computation of the principal components can also be performed online and this approach can be useful for online outlier detection. A simulation study clearly shows that this robust indicat…
Algorithms and tools for protein-protein interaction networks clustering, with a special focus on population-based stochastic methods
2014
Abstract Motivation: Protein–protein interaction (PPI) networks are powerful models to represent the pairwise protein interactions of the organisms. Clustering PPI networks can be useful for isolating groups of interacting proteins that participate in the same biological processes or that perform together specific biological functions. Evolutionary orthologies can be inferred this way, as well as functions and properties of yet uncharacterized proteins. Results: We present an overview of the main state-of-the-art clustering methods that have been applied to PPI networks over the past decade. We distinguish five specific categories of approaches, describe and compare their main features and …