Beliefs and Conflicts in a Real World Multi-Agent System

In a real world multiagent system, where the agents are faced with partial, incomplete and intrinsically dynamic knowledge, conflicts are inevitable. Frequently, different agents have goals or beliefs that cannot hold simultaneously. Conflict resolution methodologies have to be adopted to overcome such undesirable occurrences. In this paper we investigate the application of distributed belief revision techniques as the support for conflict resolution in the analysis of the validity of the candidate beams to be produced in the CERN particle accelerators. This CERN multiagent system contains a higher hierarchy agent, the Specialist agent, which makes use of meta-knowledge (on how the con- flicting beliefs have been produced by the other agents) in order to detect which beliefs should be abandoned. Upon solving a conflict, the Specialist instructs the involved agents to revise their beliefs accordingly. Conflicts in the problem domain are mapped into conflicting beliefs of the distributed belief revision system, where they can be handled by proven formal methods. This technique builds on well established concepts and combines them in a new way to solve important problems. We find this approach generally applicable in several domains.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

SCADA House Intelligent Management for Energy Efficiency Analysis in Domestic Consumers

The implementation of smart homes allows the domestic consumer to be an active player in the context of the Smart Grid (SG). This paper presents an intelligent house management system that is being developed by the authors to manage, in real time, the power consumption, the micro generation system, the charge and discharge of the electric or plug-in hybrid vehicles, and the participation in Demand Response (DR) programs. The paper proposes a method for the energy efficiency analysis of a domestic consumer using the SCADA House Intelligent Management (SHIM) system. The main goal of the present paper is to demonstrate the economic benefits of the implemented method. The case study considers the consumption data of some real cases of Portuguese house consumption over 30 days of June of 2012, the Portuguese real energy price, the implementation of the power limits at different times of the day and the economic benefits analysis.

A Learning Algorithm and System Approach to Address Exceptional Events in Domestic Consumption Management

The integration of the Smart Grid concept into the electric grid brings to the need for an active participation of small and medium players. This active participation can be achieved using decentralized decisions, in which the end consumer can manage loads regarding the Smart Grid needs. The management of loads must handle the users’ preferences, wills and needs. However, the users’ preferences, wills and needs can suffer changes when faced with exceptional events. This paper proposes the integration of exceptional events into the SCADA House Intelligent Management (SHIM) system developed by the authors, to handle machine learning issues in the domestic consumption context. An illustrative application and learning case study is provided in this paper.

Adaptive modeling and high quality spectral estimation for speech enhancement

In this work an adaptive modeling and spectral estimation scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for speech enhancement. Both speech and noise signals are modeled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. The model parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The speech enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. This approach is particularly useful as a pre-processing module for parametric based speech recognition systems that rely on spectral time dependent models. The system performance has been evaluated by a set of human listeners and by spectral distances. In both cases the use of this pre-processing module has led to improved results.

Adaptive filtering for high quality HMM based speech synthesis

In this work an adaptive filtering scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for Hidden Markov Model (HMM) based speech synthesis quality enhancement. The objective is to improve signal smoothness across HMMs and their related states and to reduce artifacts due to acoustic model's limitations. Both speech and artifacts are modelled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. Themodel parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The quality enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. The system's performance has been evaluated using mean opinion score tests and the proposed technique has led to improved results.

Adaptation as a part of system resilience. insights from fish producers organizations in Portugal

Complex problems of globalized society challenge its adaptive capacity. However, it is precisely the nature of these human induced problems that provide enough evidence to show that adaptability may not be on a resilient path. This thesis explores the ambiguity of the idea of adaptation (and its practice) and illustrates the ways in which adaptability contributes to resilience of social ecological systems. The thesis combines a case study and grounded theory approach and develops an analytical framework to study adaptability in resource users’ organizations: from what it depends on and what the key challenges are for resource management and system resilience. It does so for the specific case of fish producers’ organizations (POs) in Portugal. The findings suggest that while ecological and market context, including the type of crisis, may influence the character of fishers’ adaptation within POs (i.e. anticipatory, maladaptive and reactively adaptive), it does not determine it. Instead, it makes agency even more crucial (i.e. leadership, trust and agent’s perceptions in terms of their impact on fishers’ motivation to learn from each other). In sum, it was found that internal adaptation can improve POs’ contribution to fishery management and resilience, but it is not a panacea and may, in some cases, increase system vulnerability to change. Continuous maladaptation of some Portuguese POs points at a basic institutional problem (fish market regime), which clearly reduces fisheries resilience as it promotes overfishing. However, structural change may not be sufficient to address other barriers to Portuguese fishers’ (PO members) adaptability, such as history (collective memory) and associated problematic self-perceptions. The agency (people involved in structures and practices) also needs to change. What and how institutional change and agency change build on one another (e.g. comparison of fisheries governance in Portugal and other EU countries) is a topic to be explored in further research.

HARNESSING iNKT CELLS WITH RECOMBINANT CD1d FUSION PROTEINS TO REDIRECT INNATE AND ADAPTIVE IMMUNITY TO THE TUMOR

Les thérapies du cancer, comme la radiothérapie et la chimiothérapie, sont couramment utilisées mais ont de nombreux effets secondaires. Ces thérapies invasives pour le patient nécessitent d'être améliorées et de nombreuses avancées ont été faites afin d'adapter et de personnaliser le traitement du cancer. L'immunothérapie a pour but de renforcer le système immunitaire du patient et de le rediriger de manière spécifique contre la tumeur. Dans notre projet, nous activons les lymphocytes Invariant Natural Killer T (iNKT) afin de mettre en place une immunothérapie innovatrice contre le cancer. Les cellules iNKT sont une unique sous-population de lymphocytes T qui ont la particularité de réunir les propriétés de l'immunité innée ainsi qu'adaptative. En effet, les cellules iNKT expriment à leur surface des molécules présentes aussi sur les cellules tueuses NK, caractéristique de l'immunité innée, ainsi qu'un récepteur de cellules T (TCR) qui représente l'immunité adaptative. Les cellules iNKT reconnaissent avec leur TCR des antigènes présentés par la molécule CD1d. Les antigènes sont des protéines, des polysaccharides ou des lipides reconnus par les cellules du système immunitaire ou les anticorps pour engendrer une réponse immunitaire. Dans le cas des cellules iNKT, l'alpha-galactosylceramide (αGC) est un antigène lipidique fréquemment utilisé dans les études cliniques comme puissant activateur. Après l'activation des cellules iNKT avec l'αGC, celles-ci produisent abondamment et rapidement des cytokines. Ces cytokines sont des molécules agissant comme des signaux activateurs d'autres cellules du système immunitaire telles que les cellules NK et les lymphocytes T. Cependant, les cellules iNKT deviennent anergiques après un seul traitement avec l'αGC c'est à dire qu'elles ne peuvent plus être réactivées, ce qui limite leur utilisation dans l'immunothérapie du cancer. Dans notre groupe, Stirnemann et al ont publié une molécule recombinante innovante, composée de la molécule CD1d soluble et chargée avec le ligand αGC (αGC/sCD1d). Cette protéine est capable d'activer les cellules iNKT tout en évitant l'anergie. Dans le système immunitaire, les anticorps sont indispensables pour combattre une infection bactérienne ou virale. En effet, les anticorps ont la capacité de reconnaître et lier spécifiquement un antigène et permettent l'élimination de la cellule qui exprime cet antigène. Dans le domaine de l'immunothérapie, les anticorps sont utilisés afin de cibler des antigènes présentés seulement par la tumeur. Ce procédé permet de réduire efficacement les effets secondaires lors du traitement du cancer. Nous avons donc fusionné la protéine recombinante αGC/CD1d à un fragment d'anticorps qui reconnaît un antigène spécifique des cellules tumorales. Dans une étude préclinique, nous avons démontré que la protéine αGC/sCD1d avec un fragment d'anticorps dirigé contre la tumeur engendre une meilleure activation des cellules iNKT et entraîne un effet anti-tumeur prolongé. Cet effet anti-tumeur est augmenté comparé à une protéine αGC/CD1d qui ne cible pas la tumeur. Nous avons aussi montré que l'activation des cellules iNKT avec la protéine αGC/sCD1d-anti-tumeur améliore l'effet anti- tumoral d'un vaccin pour le cancer. Lors d'expériences in vitro, la protéine αGC/sCD1d-anti- tumeur permet aussi d'activer les cellules humaines iNKT et ainsi tuer spécifiquement les cellules tumorales humaines. La protéine αGC/sCD1d-anti-tumeur représente une alternative thérapeutique prometteuse dans l'immunothérapie du cancer. - Les cellules Invariant Natural Killer T (iNKT), dont les effets anti-tumoraux ont été démontrés, sont de puissants activateurs des cellules Natural Killer (NK), des cellules dendritiques (DC) et des lymphocytes T. Cependant, une seule injection du ligand de haute affinité alpha-galactosylceramide (αGC) n'induit une forte activation des cellules iNKT que durant une courte période. Celle-ci est alors suivie d'une longue phase d'anergie, limitant ainsi leur utilisation pour la thérapie. Comme alternative prometteuse, nous avons montré que des injections répétées d'αGC chargé sur une protéine recombinante de CD1d soluble (αGC/sCD1d) chez la souris entraînent une activation prolongée des cellules iNKT, associée à une production continue de cytokine. De plus, le maintien de la réactivité des cellules iNKT permet de prolonger l'activité anti-tumorale lorsque la protéine αGC/sCD1d est fusionnée à un fragment d'anticorps (scFv) dirigé contre la tumeur. L'inhibition de la croissance tumorale n'est optimale que lorsque les souris sont traitées avec la protéine αGC/sCD1d-scFv ciblant la tumeur, la protéine αGC/sCD1d-scFv non-appropriée étant moins efficace. Dans le système humain, les protéines recombinantes αGC/sCD1d-anti-HER2 et anti-CEA sont capables d'activer et de faire proliférer des cellules iNKT à partir de PBMCs issues de donneurs sains. De plus, la protéine αGC/sCD1d-scFv a la capacité d'activer directement des clones iNKT humains en l'absence de cellules présentatrices d'antigènes (CPA), contrairement au ligand αGC libre. Mais surtout, la lyse des cellules tumorales par les iNKT humaines n'est obtenue que lorsqu'elles sont incubées avec la protéine αGC/sCD1d-scFv anti- tumeur. En outre, la redirection de la cytotoxicité des cellules iNKT vers la tumeur est supérieure à celle obtenue avec une stimulation par des CPA chargées avec l'αGC. Afin d'augmenter les effets anti-tumoraux, nous avons exploité la capacité des cellules iNKT à activer l'immunité adaptive. Pour ce faire, nous avons combiné l'immunothérapie NKT/CD1d avec un vaccin anti-tumoral composé d'un peptide OVA. Des effets synergiques ont été obtenus lorsque les traitements avec la protéine αGC/sCD1d-anti-HER2 étaient associés avec le CpG ODN comme adjuvant pour la vaccination avec le peptide OVA. Ces effets ont été observés à travers l'activation de nombreux lymphocytes T CD8+ spécifique de la tumeur, ainsi que par la forte expansion des cellules NK. Les réponses, innée et adaptive, élevées après le traitement avec la protéine αGC/sCD1d-anti-HER2 combinée au vaccin OVA/CpG ODN étaient associées à un fort ralentissement de la croissance des tumeurs B16- OVA-HER2. Cet effet anti-tumoral corrèle avec l'enrichissement des lymphocytes T CD8+ spécifiques observé à la tumeur. Afin d'étendre l'application des protéines αGC/sCD1d et d'améliorer leur efficacité, nous avons développé des fusions CD1d alternatives. Premièrement, une protéine αGC/sCD1d dimérique, qui permet d'augmenter l'avidité de la molécule CD1d pour les cellules iNKT. Dans un deuxième temps, nous avons fusionné la protéine αGC/sCD1d avec un scFv dirigé contre le récepteur 3 du facteur de croissance pour l'endothélium vasculaire (VEGFR-3), afin de cibler l'environnement de la tumeur. Dans l'ensemble, ces résultats démontrent que la thérapie médiée par la protéine recombinante αGC/sCD1d-scFv est une approche prometteuse pour rediriger l'immunité innée et adaptive vers le site tumoral. - Invariant Natural Killer T cells (iNKT) are potent activators of Natural Killer (NK), dendritic cells (DC) and T lymphocytes, and their anti-tumor activities have been well demonstrated. However, a single injection of the high affinity CD1d ligand alpha-galactosylceramide (αGC) leads to a strong but short-lived iNKT cell activation followed by a phase of long-term anergy, limiting the therapeutic use of this ligand. As a promising alternative, we have demonstrated that when αGC is loaded on recombinant soluble CD1d molecules (αGC/sCD1d), repeated injections in mice led to the sustained iNKT cell activation associated with continued cytokine secretion. Importantly, the retained reactivity of iNKT cell led to prolonged antitumor activity when the αGC/sCD1d was fused to an anti-tumor scFv fragments. Optimal inhibition of tumor growth was obtained only when mice were treated with the tumor-targeted αGC/CD1d-scFv fusion, whereas the irrelevant αGC/CD1d-scFv fusion was less efficient. When tested in a human system, the recombinant αGC/sCD1d-anti-HER2 and -anti-CEA fusion proteins were able to expand iNKT cells from PBMCs of healthy donors. Furthermore, the αGC/sCD1d-scFv fusion had the capacity to directly activate human iNKT cells clones without the presence of antigen-presenting cells (APCs), in contrast to the free αGC ligand. Most importantly, tumor cell killing by human iNKT cells was obtained only when co- incubated with the tumor targeted sCD1d-antitumor scFv, and their direct tumor cytotoxicity was superior to the bystander killing obtained with αGC-loaded APCs stimulation. To further enhance the anti-tumor effects, we exploited the ability of iNKT cells to transactivate the adaptive immunity, by combining the NKT/CD1d immunotherapy with a peptide cancer vaccine. Interestingly, synergistic effects were obtained when the αGC/sCD1d- anti-HER2 fusion treatment was combined with CpG ODN as adjuvant for the OVA peptide vaccine, as seen by higher numbers of activated antigen-specific CD8 T cells and NK cells, as compared to each regimen alone. The increased innate and adaptive immune responses upon combined tumor targeted sCD1d-scFv treatment and OVA/CpG vaccine were associated with a strong delay in B16-OVA-HER2 melanoma tumor growth, which correlated with an enrichment of antigen-specific CD8 cells at the tumor site. In order to extend the application of the CD1d fusion, we designed alternative CD1d fusion proteins. First, a dimeric αGC/sCD1d-Fc fusion, which permits to augment the avidity of the CD1d for iNKT cells and second, an αGC/sCD1d fused to an anti vascular endothelial growth factor receptor-3 (VEGFR-3) scFv, in order to target tumor stroma environment. Altogether, these results demonstrate that the iNKT-mediated immunotherapy via recombinant αGC/sCD1d-scFv fusion is a promising approach to redirect the innate and adaptive antitumor immune response to the tumor site.

Immune system's role in viral encephalitis.

Viral infections can be a major thread for the central nervous system (CNS), therefore, the immune system must be able to mount a highly proportionate immune response, not too weak, which would allow the virus to proliferate, but not too strong either, to avoid collateral damages. Here, we aim at reviewing the immunological mechanisms involved in the host defense in viral CNS infections. First, we review the specificities of the innate as well as the adaptive immune responses in the CNS, using several examples of various viral encephalitis. Then, we focus on three different modes of interactions between viruses and immune responses, namely human Herpes virus-1 encephalitis with the defect in innate immune response which favors this disease; JC virus-caused progressive multifocal leukoencephalopathy and the crucial role of adaptive immune response in this example; and finally, HIV infection with the accompanying low grade chronic inflammation in the CNS in some patients, which may be an explanation for the presence of cognitive disorders, even in some well-treated HIV-infected patients. We also emphasize that, although the immune response is generally associated with viral replication control and limited cellular death, an exaggerated inflammatory reaction can lead to tissue damage and can be detrimental for the host, a feature of the immune reconstitution inflammatory syndrome (IRIS). We will briefly address the indication of steroids in this situation.

Genetic programming for the RoboCup Rescue Simulation System

The Robocup Rescue Simulation System (RCRSS) is a dynamic system of multi-agent interaction, simulating a large-scale urban disaster scenario. Teams of rescue agents are charged with the tasks of minimizing civilian casualties and infrastructure damage while competing against limitations on time, communication, and awareness. This thesis provides the first known attempt of applying Genetic Programming (GP) to the development of behaviours necessary to perform well in the RCRSS. Specifically, this thesis studies the suitability of GP to evolve the operational behaviours required of each type of rescue agent in the RCRSS. The system developed is evaluated in terms of the consistency with which expected solutions are the target of convergence as well as by comparison to previous competition results. The results indicate that GP is capable of converging to some forms of expected behaviour, but that additional evolution in strategizing behaviours must be performed in order to become competitive. An enhancement to the standard GP algorithm is proposed which is shown to simplify the initial search space allowing evolution to occur much quicker. In addition, two forms of population are employed and compared in terms of their apparent effects on the evolution of control structures for intelligent rescue agents. The first is a single population in which each individual is comprised of three distinct trees for the respective control of three types of agents, the second is a set of three co-evolving subpopulations one for each type of agent. Multiple populations of cooperating individuals appear to achieve higher proficiencies in training, but testing on unseen instances raises the issue of overfitting.

Priming the Governance System for Climate Change Adaptation: The Application of a Social Ecological Inventory (SEI) to Engage Actors in Niagara, Canada

Adaptive systems of governance are increasingly gaining attention in respect to complex and uncertain social-ecological systems. Adaptive co-management is one strategy to make adaptive governance operational and holds promise with respect to community climate change adaptation as it facilitates participation and learning across scales and fosters adaptive capacity and resilience. Developing tools which hasten the realization of such approaches are growing in importance. This paper describes explores the Social Ecological Inventory (SEI) as a tool to 'prime' a regional climate change adaptation network. The SEI tool draws upon the social-ecological systems approach in which social and ecological systems are considered linked. SEIs bridge the gap between conventional stakeholder analysis and biological inventories and take place through a six phase process. A case study describes the results of applying an SEI to prime an adaptive governance network for climate change adaptation in the Niagara Region of Canada. Lessons learned from the case study are discussed and highlight how the SEI catalyzed the adaptive co-management process in the case. Future avenues for SEIs in relation to climate change adaptation emerge from this exploratory work and offer opportunities to inform research and adaptation planning.

Amorçage cognitif pour amélioration de l’acquisition de la connaissance dans un système tutoriel intelligent

Cette thèse vise à définir une nouvelle méthode d’enseignement pour les systèmes tutoriels intelligents dans le but d’améliorer l’acquisition des connaissances. L’apprentissage est un phénomène complexe faisant intervenir des mécanismes émotionnels et cognitifs de nature consciente et inconsciente. Nous nous intéressons à mieux comprendre les mécanismes inconscients du raisonnement lors de l’acquisition des connaissances. L’importance de ces processus inconscients pour le raisonnement est bien documentée en neurosciences, mais demeure encore largement inexplorée dans notre domaine de recherche. Dans cette thèse, nous proposons la mise en place d’une nouvelle approche pédagogique dans le domaine de l’éducation implémentant une taxonomie neuroscientifique de la perception humaine. Nous montrons que cette nouvelle approche agit sur le raisonnement et, à tour de rôle, améliore l’apprentissage général et l’induction de la connaissance dans un environnement de résolution de problème. Dans une première partie, nous présentons l’implémentation de notre nouvelle méthode dans un système tutoriel visant à améliorer le raisonnement pour un meilleur apprentissage. De plus, compte tenu de l’importance des mécanismes émotionnels dans l’apprentissage, nous avons également procédé dans cette partie à la mesure des émotions par des capteurs physiologiques. L’efficacité de notre méthode pour l’apprentissage et son impact positif observé sur les émotions a été validée sur trente et un participants. Dans une seconde partie, nous allons plus loin dans notre recherche en adaptant notre méthode visant à améliorer le raisonnement pour une meilleure induction de la connaissance. L’induction est un type de raisonnement qui permet de construire des règles générales à partir d’exemples spécifiques ou de faits particuliers. Afin de mieux comprendre l’impact de notre méthode sur les processus cognitifs impliqués dans ce type de raisonnement, nous avons eu recours à des capteurs cérébraux pour mesurer l’activité du cerveau des utilisateurs. La validation de notre approche réalisée sur quarante-trois volontaires montre l’efficacité de notre méthode pour l’induction de la connaissance et la viabilité de mesurer le raisonnement par des mesures cérébrales suite à l’application appropriée d’algorithmes de traitement de signal. Suite à ces deux parties, nous clorons la thèse par une discussion applicative en décrivant la mise en place d’un nouveau système tutoriel intelligent intégrant les résultats de nos travaux.

A Beacon based Localization System for Autonomous Mobile Robots and its Applications in Traffic and Transport Control

ACCURATE sensing of vehicle position and attitude is still a very challenging problem in many mobile robot applications. The mobile robot vehicle applications must have some means of estimating where they are and in which direction they are heading. Many existing indoor positioning systems are limited in workspace and robustness because they require clear lines-of-sight or do not provide absolute, driftfree measurements.The research work presented in this dissertation provides a new approach to position and attitude sensing system designed specifically to meet the challenges of operation in a realistic, cluttered indoor environment, such as that of an office building, hospital, industrial or warehouse. This is accomplished by an innovative assembly of infrared LED source that restricts the spreading of the light intensity distribution confined to a sheet of light and is encoded with localization and traffic information. This Digital Infrared Sheet of Light Beacon (DISLiB) developed for mobile robot is a high resolution absolute localization system which is simple, fast, accurate and robust, without much of computational burden or significant processing. Most of the available beacon's performance in corridors and narrow passages are not satisfactory, whereas the performance of DISLiB is very encouraging in such situations. This research overcomes most of the inherent limitations of existing systems.The work further examines the odometric localization errors caused by over count readings of an optical encoder based odometric system in a mobile robot due to wheel-slippage and terrain irregularities. A simple and efficient method is investigated and realized using an FPGA for reducing the errors. The detection and correction is based on redundant encoder measurements. The method suggested relies on the fact that the wheel slippage or terrain irregularities cause more count readings from the encoder than what corresponds to the actual distance travelled by the vehicle.The application of encoded Digital Infrared Sheet of Light Beacon (DISLiB) system can be extended to intelligent control of the public transportation system. The system is capable of receiving traffic status input through a GSM (Global System Mobile) modem. The vehicles have infrared receivers and processors capable of decoding the information, and generating the audio and video messages to assist the driver. The thesis further examines the usefulness of the technique to assist the movement of differently-able (blind) persons in indoor or outdoor premises of his residence.The work addressed in this thesis suggests a new way forward in the development of autonomous robotics and guidance systems. However, this work can be easily extended to many other challenging domains, as well.

Energy Management in Wireless Sensor Networks: A Crosslayer,Channel Adaptive Approach Towards Performance Optimization

The proliferation of wireless sensor networks in a large spectrum of applications had been spurered by the rapid advances in MEMS(micro-electro mechanical systems )based sensor technology coupled with low power,Low cost digital signal processors and radio frequency circuits.A sensor network is composed of thousands of low cost and portable devices bearing large sensing computing and wireless communication capabilities. This large collection of tiny sensors can form a robust data computing and communication distributed system for automated information gathering and distributed sensing.The main attractive feature is that such a sensor network can be deployed in remote areas.Since the sensor node is battery powered,all the sensor nodes should collaborate together to form a fault tolerant network so as toprovide an efficient utilization of precious network resources like wireless channel,memory and battery capacity.The most crucial constraint is the energy consumption which has become the prime challenge for the design of long lived sensor nodes.

Modified Block Adaptive Predictive Coder For Speech Processing

This thesis investigated the potential use of Linear Predictive Coding in speech communication applications. A Modified Block Adaptive Predictive Coder is developed, which reduces the computational burden and complexity without sacrificing the speech quality, as compared to the conventional adaptive predictive coding (APC) system. For this, changes in the evaluation methods have been evolved. This method is as different from the usual APC system in that the difference between the true and the predicted value is not transmitted. This allows the replacement of the high order predictor in the transmitter section of a predictive coding system, by a simple delay unit, which makes the transmitter quite simple. Also, the block length used in the processing of the speech signal is adjusted relative to the pitch period of the signal being processed rather than choosing a constant length as hitherto done by other researchers. The efficiency of the newly proposed coder has been supported with results of computer simulation using real speech data. Three methods for voiced/unvoiced/silent/transition classification have been presented. The first one is based on energy, zerocrossing rate and the periodicity of the waveform. The second method uses normalised correlation coefficient as the main parameter, while the third method utilizes a pitch-dependent correlation factor. The third algorithm which gives the minimum error probability has been chosen in a later chapter to design the modified coder The thesis also presents a comparazive study beh-cm the autocorrelation and the covariance methods used in the evaluaiicn of the predictor parameters. It has been proved that the azztocorrelation method is superior to the covariance method with respect to the filter stabf-it)‘ and also in an SNR sense, though the increase in gain is only small. The Modified Block Adaptive Coder applies a switching from pitch precitzion to spectrum prediction when the speech segment changes from a voiced or transition region to an unvoiced region. The experiments cont;-:ted in coding, transmission and simulation, used speech samples from .\£=_‘ajr2_1a:r1 and English phrases. Proposal for a speaker reecgnifion syste: and a phoneme identification system has also been outlized towards the end of the thesis.