Tombés entre les mailles du filet : expériences de vulnérabilisation chez les jeunes en situation de rue avec antécédents de placement

Les jeunes avec antécédents de placement sont surreprésentés parmi les jeunes adultes qui ont vécu un passage à la rue. Ce qui pourrait être interprété par certains comme un naufrage est vécu par d’autres comme une opportunité : l’expérience de la rue que font les jeunes est façonnée par leurs expériences antérieures. L’objectif de cette recherche était de combler un trou dans les connaissances concernant l’articulation entre l’expérience de placement et l’expérience de rue chez les jeunes. À partir de la méthodologie des récits de vie, j’ai rencontré six jeunes adultes en situation de rue qui, durant l’enfance ou l’adolescence, avaient fait l’objet d’un retrait du milieu familial en vertu de la Loi sur la protection de la jeunesse au Québec. Leur trajectoire a été étudiée sous l’angle de la « vulnérabilisation », un processus double d’appauvrissement matériel et de refoulement vers une position sociale dévalorisée. Les jeunes de mon étude ont vécu trois formes de vulnérabilisation dans le contexte du placement : la déliaison familiale, la disqualification professionnelle et sociale, et la stigmatisation. Les jeunes ont répondu à ces dynamiques en acceptant et en intériorisant la vulnérabilité, en la niant ou en la refusant, ou encore en la rationalisant et en la négociant. Cette étude permet de mieux comprendre l’articulation entre l’expérience du placement et celle de la rue chez les jeunes. Les résultats sont utiles pour informer d’autres études, ainsi que pour éclairer les pratiques auprès de cette population spécifique.

Production and Characterization of Lignin Peroxidases from Mangrove Ascomycetes

In this thesis, the production and characterization of ligninolytic enzymes using the fungi isolated from mangrove area are studied. The objective of the present work are isolation and screening of dye decolorizing micro-organisms from mangrove area, screening of the selected microorganisms for the production of lignin degrading enzymes, identification of the potent micro-organisms, characterization of the crude enzyme, lignin peroxidase, of the selected fungi—Aspergillus sp. SIP 11 and Penicillium sp. SIP 10 etc. This included the determination of the optimum pH, temperature, veratryl alcohol and H2O2 concentration. Besides the stability of crude LiP at different pHs and temperatures were studied. The immense applications, particularly in bioremediation, to which the lignin degrading micro-organisms could be used make this study important, the ascomycetes and deuteromycetes fungi, especially form the marine environment were studied with respect to their ligninolytic enzyme system making this study an initial step in unraveling the vast hidden potential of these microbes in bioremediation, the marine microbes are halophilic in nature which make them better suited to cope with the high salinity of industrial effluents thereby giving them added advantage in the filed of bioremediation. The thesis deals with the isolation and screening of lignin degrading enzyme-producing microbes from mangrove area. The identification of the most potent fungal isolates and characterization of LiP from these are also done.

Buried-object detection using free-space time-domain near-field measurements

The detection of buried objects using time-domain freespace measurements was carried out in the near field. The location of a hidden object was determined from an analysis of the reflected signal. This method can be extended to detect any number of objects. Measurements were carried out in the X- and Ku-bands using ordinary rectangular pyramidal horn antennas of gain 15 dB. The same antenna was used as the transmitter and recei er. The experimental results were compared with simulated results by applying the two-dimensional finite-difference time-domain(FDTD)method, and agree well with each other. The dispersi e nature of the dielectric medium was considered for the simulation.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.

Design and Development of data mining models for the predictions of manpower placement in the technical Domain

Data mining is one of the hottest research areas nowadays as it has got wide variety of applications in common man’s life to make the world a better place to live. It is all about finding interesting hidden patterns in a huge history data base. As an example, from a sales data base, one can find an interesting pattern like “people who buy magazines tend to buy news papers also” using data mining. Now in the sales point of view the advantage is that one can place these things together in the shop to increase sales. In this research work, data mining is effectively applied to a domain called placement chance prediction, since taking wise career decision is so crucial for anybody for sure. In India technical manpower analysis is carried out by an organization named National Technical Manpower Information System (NTMIS), established in 1983-84 by India's Ministry of Education & Culture. The NTMIS comprises of a lead centre in the IAMR, New Delhi, and 21 nodal centres located at different parts of the country. The Kerala State Nodal Centre is located at Cochin University of Science and Technology. In Nodal Centre, they collect placement information by sending postal questionnaire to passed out students on a regular basis. From this raw data available in the nodal centre, a history data base was prepared. Each record in this data base includes entrance rank ranges, reservation, Sector, Sex, and a particular engineering. From each such combination of attributes from the history data base of student records, corresponding placement chances is computed and stored in the history data base. From this data, various popular data mining models are built and tested. These models can be used to predict the most suitable branch for a particular new student with one of the above combination of criteria. Also a detailed performance comparison of the various data mining models is done.This research work proposes to use a combination of data mining models namely a hybrid stacking ensemble for better predictions. A strategy to predict the overall absorption rate for various branches as well as the time it takes for all the students of a particular branch to get placed etc are also proposed. Finally, this research work puts forward a new data mining algorithm namely C 4.5 * stat for numeric data sets which has been proved to have competent accuracy over standard benchmarking data sets called UCI data sets. It also proposes an optimization strategy called parameter tuning to improve the standard C 4.5 algorithm. As a summary this research work passes through all four dimensions for a typical data mining research work, namely application to a domain, development of classifier models, optimization and ensemble methods.

“Reconstruction of Gene Regulatory Network from Expression Profile of Plasma RNA Data of Colorectal Cancer Patients using Soft Computing Techniques”

Microarray data analysis is one of data mining tool which is used to extract meaningful information hidden in biological data. One of the major focuses on microarray data analysis is the reconstruction of gene regulatory network that may be used to provide a broader understanding on the functioning of complex cellular systems. Since cancer is a genetic disease arising from the abnormal gene function, the identification of cancerous genes and the regulatory pathways they control will provide a better platform for understanding the tumor formation and development. The major focus of this thesis is to understand the regulation of genes responsible for the development of cancer, particularly colorectal cancer by analyzing the microarray expression data. In this thesis, four computational algorithms namely fuzzy logic algorithm, modified genetic algorithm, dynamic neural fuzzy network and Takagi Sugeno Kang-type recurrent neural fuzzy network are used to extract cancer specific gene regulatory network from plasma RNA dataset of colorectal cancer patients. Plasma RNA is highly attractive for cancer analysis since it requires a collection of small amount of blood and it can be obtained at any time in repetitive fashion allowing the analysis of disease progression and treatment response.

" Nonlinear Signal Processing of Electroencephalogram .' Application in the Study of Neurodynamics. "

Interfacings of various subjects generate new field ofstudy and research that help in advancing human knowledge. One of the latest of such fields is Neurotechnology, which is an effective amalgamation of neuroscience, physics, biomedical engineering and computational methods. Neurotechnology provides a platform to interact physicist; neurologist and engineers to break methodology and terminology related barriers. Advancements in Computational capability, wider scope of applications in nonlinear dynamics and chaos in complex systems enhanced study of neurodynamics. However there is a need for an effective dialogue among physicists, neurologists and engineers. Application of computer based technology in the field of medicine through signal and image processing, creation of clinical databases for helping clinicians etc are widely acknowledged. Such synergic effects between widely separated disciplines may help in enhancing the effectiveness of existing diagnostic methods. One of the recent methods in this direction is analysis of electroencephalogram with the help of methods in nonlinear dynamics. This thesis is an effort to understand the functional aspects of human brain by studying electroencephalogram. The algorithms and other related methods developed in the present work can be interfaced with a digital EEG machine to unfold the information hidden in the signal. Ultimately this can be used as a diagnostic tool.

Speech Recognition of Malayalam Numbers

Digit speech recognition is important in many applications such as automatic data entry, PIN entry, voice dialing telephone, automated banking system, etc. This paper presents speaker independent speech recognition system for Malayalam digits. The system employs Mel frequency cepstrum coefficient (MFCC) as feature for signal processing and Hidden Markov model (HMM) for recognition. The system is trained with 21 male and female voices in the age group of 20 to 40 years and there was 98.5% word recognition accuracy (94.8% sentence recognition accuracy) on a test set of continuous digit recognition task.

Automated Transcription System for Malayalam Language

Malayalam is one of the 22 scheduled languages in India with more than 130 million speakers. This paper presents a report on the development of a speaker independent, continuous transcription system for Malayalam. The system employs Hidden Markov Model (HMM) for acoustic modeling and Mel Frequency Cepstral Coefficient (MFCC) for feature extraction. It is trained with 21 male and female speakers in the age group ranging from 20 to 40 years. The system obtained a word recognition accuracy of 87.4% and a sentence recognition accuracy of 84%, when tested with a set of continuous speech data.

Malayalam Isolated Digit Recognition using HMM and PLP cepstral coefficient

Development of Malayalam speech recognition system is in its infancy stage; although many works have been done in other Indian languages. In this paper we present the first work on speaker independent Malayalam isolated speech recognizer based on PLP (Perceptual Linear Predictive) Cepstral Coefficient and Hidden Markov Model (HMM). The performance of the developed system has been evaluated with different number of states of HMM (Hidden Markov Model). The system is trained with 21 male and female speakers in the age group ranging from 19 to 41 years. The system obtained an accuracy of 99.5% with the unseen data

Connected digit speech recognition system for Malayalam language

A connected digit speech recognition is important in many applications such as automated banking system, catalogue-dialing, automatic data entry, automated banking system, etc. This paper presents an optimum speaker-independent connected digit recognizer forMalayalam language. The system employs Perceptual Linear Predictive (PLP) cepstral coefficient for speech parameterization and continuous density Hidden Markov Model (HMM) in the recognition process. Viterbi algorithm is used for decoding. The training data base has the utterance of 21 speakers from the age group of 20 to 40 years and the sound is recorded in the normal office environment where each speaker is asked to read 20 set of continuous digits. The system obtained an accuracy of 99.5 % with the unseen data.

A Comparative study of HMM and SVM in Malayalam Digit Recognition

A primary medium for the human beings to communicate through language is Speech. Automatic Speech Recognition is wide spread today. Recognizing single digits is vital to a number of applications such as voice dialling of telephone numbers, automatic data entry, credit card entry, PIN (personal identification number) entry, entry of access codes for transactions, etc. In this paper we present a comparative study of SVM (Support Vector Machine) and HMM (Hidden Markov Model) to recognize and identify the digits used in Malayalam speech.

Orgodynamik als Konzept und Methode einer transpersonal orientierten Forschungs- und Lehrpraxis pädagogisch-psychologischer Bewusstseinsschulung in der Erwachsenenbildung

In der vorliegenden Dissertation geht es um die Dokumentation, theoretische Begründung und Auswertung des in 25 Jahren Praxis entwickelten Curriculums der Bewusstseinsschulung und -weitung der Orgodynamik. Dabei geht es insbesondere um den Vergleich und die forschungsorientierte Verknüpfung verschiedener Traditionen der Bewusstseinsbildung, der ihnen zugrunde liegenden Konzepte und anthropologischen Dimensionen im Schnittfeld pädagogischer, psychologischer und spiritueller Perspektiven. In Anlehnung an das von Fuhr/Dauber (2002) entwickelte Modell, der Praxisentwicklungsforschung, welche die Verflechtung von Theorie und Praxis ansteuert, wird der orgodynamische Ansatz wissenschaftlich dokumentiert und theoretisch begründet. Über eine induktive Vorgehensweise werden die historischen Wurzeln konzeptionell dargelegt, die verborgenen Paradigmen herausgearbeitet, sowie das Curriculum erläutert und ausgewertet. In einem ersten theorieorientierten Kapitel wird das orgodynamische Methodenspektrum in seinem Grundmodell und den vier zentralen Dimensionen (mentale, körperliche, emotionale, energetische Dimension) aufgezeigt und mit theoretischen Hintergrundkonzepten verglichen und verknüpft. Die vier sich überlappenden Methodengruppen der mental, körperlich, emotional und energetisch orientierten Bewusstseinsarbeit werden differenziert dargestellt und in ihrer Beziehung zueinander diskutiert. Anhand eines Modells (Methodenrad) wird die multi-dimensionale Perspektive des Methodenspektrums, in einer nichthierarchischen Zuordnung sichtbar. Im zweiten theorieorientierten Hauptteil werden zunächst die zentralen vier Paradigmen der Orgodynamik (Präsenz, Multidimensionalität, Flow/Fließendes Gewahrsein, Bezogenheit) vorgestellt, theoretisch und praxisbezogen entfaltet und in einer Paradigmen-Landkarte zueinander in Beziehung gesetzt. Dabei werden die kategorialen Ausführungen durchgehend an Praxisbeispielen veranschaulicht und im Blick auf drei vorgestellte Zugänge zur Bewusstseinsweitung (Immersion, Integration und Dekonstruktion) exemplarisch didaktisch kommentiert. Im dritten Hauptteil wird das Curriculum im Zusammenhang mit einer Auswertungsmatrix erläutert. Diese dient als Überprüfungsinstrument. Mit ihrer Hilfe werden die verschiedenen methodischen Zugangsweisen und Arbeitsformen dieses Ansatzes, exemplarisch anhand von 2 Ausbildungswochen, im Blick der Multidimensionalität dokumentiert. Damit wird diese multidimensional angelegte Praxis exemplarisch bis in methodische Details nachvollziehbar und in dialogisch-selbstreflexiver Form überprüfbar. Exemplarisch werden in einem Exkurs erste Itemvorschläge gemacht, welche die wissenschaftliche Anschlussfähigkeit an neuere Forschung im transpersonalen Bereich aufzeigen. Das innere Anliegen der vorliegenden Arbeit zeigt in der Verschränkung von Theorie und Praxis, dass die Paradigmen der Orgodynamik, Präsenz, Multidimensionalität, fließendes Gewahrsein und bewusste Bezogenheit vier pädagogisch umgesetzte Paradigmen für eine Bewusstseinserforschung in der Erwachsenenbildung sind. Stichworte: Multidimensional, Bewusstseinserforschung, Bewusstseinsweite, Präsenz, bewusste Bezogenheit, Flow/Fließendes Gewahrsein, das „Größere“, Immersion, Integration, Dekonstruktion, pädagogische Paradigmen, Erwachsenenbildung, Multidimensionales Methodenspektrum, Orgodynamik, Körpertherapie. ---------------------------

Information Exchange and Fusion in Dynamic and Heterogeneous Distributed Environments

Context awareness, dynamic reconfiguration at runtime and heterogeneity are key characteristics of future distributed systems, particularly in ubiquitous and mobile computing scenarios. The main contributions of this dissertation are theoretical as well as architectural concepts facilitating information exchange and fusion in heterogeneous and dynamic distributed environments. Our main focus is on bridging the heterogeneity issues and, at the same time, considering uncertain, imprecise and unreliable sensor information in information fusion and reasoning approaches. A domain ontology is used to establish a common vocabulary for the exchanged information. We thereby explicitly support different representations for the same kind of information and provide Inter-Representation Operations that convert between them. Special account is taken of the conversion of associated meta-data that express uncertainty and impreciseness. The Unscented Transformation, for example, is applied to propagate Gaussian normal distributions across highly non-linear Inter-Representation Operations. Uncertain sensor information is fused using the Dempster-Shafer Theory of Evidence as it allows explicit modelling of partial and complete ignorance. We also show how to incorporate the Dempster-Shafer Theory of Evidence into probabilistic reasoning schemes such as Hidden Markov Models in order to be able to consider the uncertainty of sensor information when deriving high-level information from low-level data. For all these concepts we provide architectural support as a guideline for developers of innovative information exchange and fusion infrastructures that are particularly targeted at heterogeneous dynamic environments. Two case studies serve as proof of concept. The first case study focuses on heterogeneous autonomous robots that have to spontaneously form a cooperative team in order to achieve a common goal. The second case study is concerned with an approach for user activity recognition which serves as baseline for a context-aware adaptive application. Both case studies demonstrate the viability and strengths of the proposed solution and emphasize that the Dempster-Shafer Theory of Evidence should be preferred to pure probability theory in applications involving non-linear Inter-Representation Operations.

Development of thermo-analytical prediction and classification models for food in thermal devices using a multi sensor system and artificial neural networks

Die thermische Verarbeitung von Lebensmitteln beeinflusst deren Qualität und ernährungsphysiologischen Eigenschaften. Im Haushalt ist die Überwachung der Temperatur innerhalb des Lebensmittels sehr schwierig. Zudem ist das Wissen über optimale Temperatur- und Zeitparameter für die verschiedenen Speisen oft unzureichend. Die optimale Steuerung der thermischen Zubereitung ist maßgeblich abhängig von der Art des Lebensmittels und der äußeren und inneren Temperatureinwirkung während des Garvorgangs. Das Ziel der Arbeiten war die Entwicklung eines automatischen Backofens, der in der Lage ist, die Art des Lebensmittels zu erkennen und die Temperatur im Inneren des Lebensmittels während des Backens zu errechnen. Die für die Temperaturberechnung benötigten Daten wurden mit mehreren Sensoren erfasst. Hierzu kam ein Infrarotthermometer, ein Infrarotabstandssensor, eine Kamera, ein Temperatursensor und ein Lambdasonde innerhalb des Ofens zum Einsatz. Ferner wurden eine Wägezelle, ein Strom- sowie Spannungs-Sensor und ein Temperatursensor außerhalb des Ofens genutzt. Die während der Aufheizphase aufgenommen Datensätze ermöglichten das Training mehrerer künstlicher neuronaler Netze, die die verschiedenen Lebensmittel in die entsprechenden Kategorien einordnen konnten, um so das optimale Backprogram auszuwählen. Zur Abschätzung der thermische Diffusivität der Nahrung, die von der Zusammensetzung (Kohlenhydrate, Fett, Protein, Wasser) abhängt, wurden mehrere künstliche neuronale Netze trainiert. Mit Ausnahme des Fettanteils der Lebensmittel konnten alle Komponenten durch verschiedene KNNs mit einem Maximum von 8 versteckten Neuronen ausreichend genau abgeschätzt werden um auf deren Grundlage die Temperatur im inneren des Lebensmittels zu berechnen. Die durchgeführte Arbeit zeigt, dass mit Hilfe verschiedenster Sensoren zur direkten beziehungsweise indirekten Messung der äußeren Eigenschaften der Lebensmittel sowie KNNs für die Kategorisierung und Abschätzung der Lebensmittelzusammensetzung die automatische Erkennung und Berechnung der inneren Temperatur von verschiedensten Lebensmitteln möglich ist.