A feed-forward spiking model of shape-coding by IT cells

The ability to recognize a shape is linked to figure-ground (FG) organization. Cell preferences appear to be correlated across contrast-polarity reversals and mirror reversals of polygon displays, but not so much across FG reversals. Here we present a network structure which explains both shape-coding by simulated IT cells and suppression of responses to FG reversed stimuli. In our model FG segregation is achieved before shape discrimination, which is itself evidenced by the difference in spiking onsets of a pair of output cells. The studied example also includes feature extraction and illustrates a classification of binary images depending on the dominance of vertical or horizontal borders.

Analysis of Satellite Data for Forest Segmentation

In this work we study the classification of forest types using mathematics based image analysis on satellite data. We are interested in improving classification of forest segments when a combination of information from two or more different satellites is used. The experimental part is based on real satellite data originating from Canada. This thesis gives summary of the mathematics basics of the image analysis and supervised learning , methods that are used in the classification algorithm. Three data sets and four feature sets were investigated in this thesis. The considered feature sets were 1) histograms (quantiles) 2) variance 3) skewness and 4) kurtosis. Good overall performances were achieved when a combination of ASTERBAND and RADARSAT2 data sets was used.

Reconeixement de veu mitjançant xarxes neuronals

La interacció home-màquina per mitjà de la veu cobreix moltes àrees d’investigació. Es destaquen entre altres, el reconeixement de la parla, la síntesis i identificació de discurs, la verificació i identificació de locutor i l’activació per veu (ordres) de sistemes robòtics. Reconèixer la parla és natural i simple per a les persones, però és un treball complex per a les màquines, pel qual existeixen diverses metodologies i tècniques, entre elles les Xarxes Neuronals. L’objectiu d’aquest treball és desenvolupar una eina en Matlab per al reconeixement i identificació de paraules pronunciades per un locutor, entre un conjunt de paraules possibles, i amb una bona fiabilitat dins d’uns marges preestablerts. El sistema és independent del locutor que pronuncia la paraula, és a dir, aquest locutor no haurà intervingut en el procés d’entrenament del sistema. S’ha dissenyat una interfície que permet l’adquisició del senyal de veu i el seu processament mitjançant xarxes neuronals i altres tècniques. Adaptant una part de control al sistema, es podria utilitzar per donar ordres a un robot com l’Alfa6Uvic o qualsevol altre dispositiu.

Spectrophotometric determination of famotidine using sulphonphthalein dyes

Four new extraction-free spectrophotometric methods have been established for the quantitation of famotidine (FMT). The methods are based on the formation of yellow ion-pair complexes between FMT and four sulphonphthalein dyes viz., bromothymol blue (method A), bromophenol blue (method B), bromocresol purple (method C) and bromocresol green (method D) in dioxane or acetone medium. The experimental variables such as reagent concentration, solvent medium and reaction time have been carefully optimized to achieve the highest sensitivity. The proposed methods were applied successfully to the determination of famotidine in tablets with good accuracy and precision and without interferences from common excipients. The results obtained by the proposed methods were compared favorably with those of the reference method.

Simple and sensitive spectrophotometric methods for the determination of acebutolol hydrochloride in bulk sample and pharmaceutical preparations

A direct, extraction-free spectrophotometric method has been developed for the determination of acebutolol hydrochloride (ABH) in pharmaceutical preparations. The method is based on ion-pair complex formation between the drug and two acidic dyes (sulphonaphthalein) namely bromocresol green (BCG) and bromothymol blue (BTB). Conformity to Beer's law enabled the assay of the drug in the range of 0.5-13.8 µg mL-1 with BCG and 1.8-15.9 µg mL-1 with BTB. Compared with a reference method, the results obtained were of equal accuracy and precision. In addition, these methods were also found to be specific for the analysis of acebutolol hydrochloride in the presence of excipients, which are co-formulated in the drug.

Implications fonctionnelles du collicule inférieur chez l'humain

Le rôle du collicule inférieur dans les divers processus auditif demeure à ce jour méconnu chez l’humain. À l’aide d’évaluations comportementales et électrophysiologiques, le but des études consiste à examiner l’intégrité fonctionnelle du système nerveux auditif chez une personne ayant une lésion unilatérale du collicule inférieur. Les résultats de ces études suggèrent que le collicule inférieur n’est pas impliqué dans la détection de sons purs, la reconnaissance de la parole dans le silence et l’interaction binaurale. Cependant, ces données suggèrent que le collicule inférieur est impliqué dans la reconnaissance de mots dans le bruit présentés monauralement, la discrimination de la fréquence, la reconnaissance de la durée, la séparation binaurale, l’intégration binaurale, la localisation de sources sonores et, finalement, l’intégration multisensorielle de la parole.

Problèmes de perception de la parole dans le bruit chez les enfants présentant un trouble de traitement auditif

Il est bien connu que les enfants qui présentent un trouble de traitement auditif (TTA) ont de la difficulté à percevoir la parole en présence de bruit de fond. Cependant, il n’existe aucun consensus quant à l’origine de ces difficultés d’écoute. Ce programme de recherche est consacré à l’étude des incapacités sous-jacentes aux problèmes de perception de la parole dans le bruit chez les enfants présentant un TTA. Le Test de Phrases dans le Bruit (TPB) a été développé afin d’examiner si les difficultés de perception de la parole dans le bruit d’enfants ayant un TTA relèvent d’incapacités auditives, d’incapacités cognitivo-linguistiques ou des deux à la fois. Il comprend cinq listes de 40 phrases, composées de 20 phrases hautement prévisibles (HP) et de 20 phrases faiblement prévisibles (FP), de même qu’un bruit de verbiage. Le niveau de connaissance du mot clé (mot final) de chaque phrase a été vérifié auprès d’un groupe d’enfants âgés entre 5 et 7 ans. De plus, le degré d’intelligibilité des phrases dans le bruit et le niveau de prévisibilité ont été mesurées auprès d’adultes pour assurer l’équivalence entre les listes. Enfin, le TPB a été testé auprès d’un groupe de 15 adultes et d’un groupe de 69 enfants sans trouble auditif avant de l’administrer à des enfants ayant un TTA. Pour répondre à l’objectif général du programme de recherche, dix enfants présentant un TTA (groupe TTA) et dix enfants jumelés selon le genre et l’âge sans difficulté auditive (groupe témoin) ont été soumis aux listes de phrases du TPB selon différentes conditions sonores. Le groupe TTA a obtenu des performances significativement plus faibles comparativement au groupe témoin à la tâche de reconnaissance du mot final des phrases présentées en même temps qu’un bruit de verbiage compétitif, aux rapports signal-sur-bruit de 0, +3 et +4 dB. La moyenne de la différence des scores obtenue entre les phrases HP et FP à chaque condition expérimentale de bruit était similaire entre les deux groupes. Ces résultats suggèrent que les enfants ayant un TTA ne se distinguent pas des enfants du groupe témoin au plan de la compétence cognitivo-linguistique. L’origine des difficultés d’écoute de la parole dans le bruit dans le cas de TTA serait de nature auditive. Toutefois, les résultats des analyses de groupe diffèrent de ceux des analyses individuelles. Les divers profils de difficultés d’écoute identifiés auprès de cette cohorte appuient l’importance de continuer les investigations afin de mieux comprendre l’origine des problèmes de perception de la parole dans le bruit dans le cas de TTA. En connaissant mieux la nature de ces difficultés, il sera possible d’identifier les stratégies d’intervention de réadaptation spécifiques et efficaces.

Analysis of scoliosis trunk deformities using ICA

This paper describes a method for analyzing scoliosis trunk deformities using Independent Component Analysis (ICA). Our hypothesis is that ICA can capture the scoliosis deformities visible on the trunk. Unlike Principal Component Analysis (PCA), ICA gives local shape variation and assumes that the data distribution is not normal. 3D torso images of 56 subjects including 28 patients with adolescent idiopathic scoliosis and 28 healthy subjects are analyzed using ICA. First, we remark that the independent components capture the local scoliosis deformities as the shoulder variation, the scapula asymmetry and the waist deformation. Second, we note that the different scoliosis curve types are characterized by different combinations of specific independent components.

Recurrence Quantification Analysis of System Signals for Detecting Tool and Chatter in Turning

In this thesis, the applications of the recurrence quantification analysis in metal cutting operation in a lathe, with specific objective to detect tool wear and chatter, are presented.This study is based on the discovery that process dynamics in a lathe is low dimensional chaotic. It implies that the machine dynamics is controllable using principles of chaos theory. This understanding is to revolutionize the feature extraction methodologies used in condition monitoring systems as conventional linear methods or models are incapable of capturing the critical and strange behaviors associated with the metal cutting process.As sensor based approaches provide an automated and cost effective way to monitor and control, an efficient feature extraction methodology based on nonlinear time series analysis is much more demanding. The task here is more complex when the information has to be deduced solely from sensor signals since traditional methods do not address the issue of how to treat noise present in real-world processes and its non-stationarity. In an effort to get over these two issues to the maximum possible, this thesis adopts the recurrence quantification analysis methodology in the study since this feature extraction technique is found to be robust against noise and stationarity in the signals.The work consists of two different sets of experiments in a lathe; set-I and set-2. The experiment, set-I, study the influence of tool wear on the RQA variables whereas the set-2 is carried out to identify the sensitive RQA variables to machine tool chatter followed by its validation in actual cutting. To obtain the bounds of the spectrum of the significant RQA variable values, in set-i, a fresh tool and a worn tool are used for cutting. The first part of the set-2 experiments uses a stepped shaft in order to create chatter at a known location. And the second part uses a conical section having a uniform taper along the axis for creating chatter to onset at some distance from the smaller end by gradually increasing the depth of cut while keeping the spindle speed and feed rate constant.The study concludes by revealing the dependence of certain RQA variables; percent determinism, percent recurrence and entropy, to tool wear and chatter unambiguously. The performances of the results establish this methodology to be viable for detection of tool wear and chatter in metal cutting operation in a lathe. The key reason is that the dynamics of the system under study have been nonlinear and the recurrence quantification analysis can characterize them adequately.This work establishes that principles and practice of machining can be considerably benefited and advanced from using nonlinear dynamics and chaos theory.

Analysis of System Signals Based on Cross Recurrence Method for System Dynamics Characterization

Natural systems are inherently non linear. Recurrent behaviours are typical of natural systems. Recurrence is a fundamental property of non linear dynamical systems which can be exploited to characterize the system behaviour effectively. Cross recurrence based analysis of sensor signals from non linear dynamical system is presented in this thesis. The mutual dependency among relatively independent components of a system is referred as coupling. The analysis is done for a mechanically coupled system specifically designed for conducting experiment. Further, cross recurrence method is extended to the actual machining process in a lathe to characterize the chatter during turning. The result is verified by permutation entropy method. Conventional linear methods or models are incapable of capturing the critical and strange behaviours associated with the dynamical process. Hence any effective feature extraction methodologies should invariably gather information thorough nonlinear time series analysis. The sensor signals from the dynamical system normally contain noise and non stationarity. In an effort to get over these two issues to the maximum possible extent, this work adopts the cross recurrence quantification analysis (CRQA) methodology since it is found to be robust against noise and stationarity in the signals. The study reveals that the CRQA is capable of characterizing even weak coupling among system signals. It also divulges the dependence of certain CRQA variables like percent determinism, percent recurrence and entropy to chatter unambiguously. The surrogate data test shows that the results obtained by CRQA are the true properties of the temporal evolution of the dynamics and contain a degree of deterministic structure. The results are verified using permutation entropy (PE) to detect the onset of chatter from the time series. The present study ascertains that this CRP based methodology is capable of recognizing the transition from regular cutting to the chatter cutting irrespective of the machining parameters or work piece material. The results establish this methodology to be feasible for detection of chatter in metal cutting operation in a lathe.

Document Image Segmentation and Compression using Artificial Neural Networks and Evolutionary Methods

The work is intended to study the following important aspects of document image processing and develop new methods. (1) Segmentation ofdocument images using adaptive interval valued neuro-fuzzy method. (2) Improving the segmentation procedure using Simulated Annealing technique. (3) Development of optimized compression algorithms using Genetic Algorithm and parallel Genetic Algorithm (4) Feature extraction of document images (5) Development of IV fuzzy rules. This work also helps for feature extraction and foreground and background identification. The proposed work incorporates Evolutionary and hybrid methods for segmentation and compression of document images. A study of different neural networks used in image processing, the study of developments in the area of fuzzy logic etc is carried out in this work

Author Identification in Malayalam using n-grams

Author identification is the problem of identifying the author of an anonymous text or text whose authorship is in doubt from a given set of authors. The works by different authors are strongly distinguished by quantifiable features of the text. This paper deals with the attempts made on identifying the most likely author of a text in Malayalam from a list of authors. Malayalam is a Dravidian language with agglutinative nature and not much successful tools have been developed to extract syntactic & semantic features of texts in this language. We have done a detailed study on the various stylometric features that can be used to form an authors profile and have found that the frequencies of word collocations can be used to clearly distinguish an author in a highly inflectious language such as Malayalam. In our work we try to extract the word level and character level features present in the text for characterizing the style of an author. Our first step was towards creating a profile for each of the candidate authors whose texts were available with us, first from word n-gram frequencies and then by using variable length character n-gram frequencies. Profiles of the set of authors under consideration thus formed, was then compared with the features extracted from anonymous text, to suggest the most likely author.

Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Content Based Video Retrieval using SURF Descriptor

This paper presents a Robust Content Based Video Retrieval (CBVR) system. This system retrieves similar videos based on a local feature descriptor called SURF (Speeded Up Robust Feature). The higher dimensionality of SURF like feature descriptors causes huge storage consumption during indexing of video information. To achieve a dimensionality reduction on the SURF feature descriptor, this system employs a stochastic dimensionality reduction method and thus provides a model data for the videos. On retrieval, the model data of the test clip is classified to its similar videos using a minimum distance classifier. The performance of this system is evaluated using two different minimum distance classifiers during the retrieval stage. The experimental analyses performed on the system shows that the system has a retrieval performance of 78%. This system also analyses the performance efficiency of the low dimensional SURF descriptor.

Automatic Image Annotation Using SURF Descriptors

In recent years there is an apparent shift in research from content based image retrieval (CBIR) to automatic image annotation in order to bridge the gap between low level features and high level semantics of images. Automatic Image Annotation (AIA) techniques facilitate extraction of high level semantic concepts from images by machine learning techniques. Many AIA techniques use feature analysis as the first step to identify the objects in the image. However, the high dimensional image features make the performance of the system worse. This paper describes and evaluates an automatic image annotation framework which uses SURF descriptors to select right number of features and right features for annotation. The proposed framework uses a hybrid approach in which k-means clustering is used in the training phase and fuzzy K-NN classification in the annotation phase. The performance of the system is evaluated using standard metrics.