A study of the cost structure of the cashew industry in kerala, karnataka and tamilnadu

Since the world demand for cashew kernels has been rising steadily for several years in the past, conferring significant price increase the processing of cashew remains a highly profitable lndustry. India being the earliest and largest supplier of cashew kernels in the world market it is our prestigious obligations to reestablish her pristine monopoly. Further the added importance ot the indutry in the Socio economic context of the State of Kerala makes various measures impervative in order to bring back to the industry its pristine glory at the late sixties to give a face lift and to stabilize the industry. This present study adopts a comprehensive frame work of analysis compassing the major issues involved in the cultivation, distribution, import, processing and marketing of cashew undcr the private and public sector, migration of the industry and the financial requirements of the industry.

PERFORMANCE OF DIFFERENT CLASSIFIERS IN SPEECH RECOGNITION

Speech is the most natural means of communication among human beings and speech processing and recognition are intensive areas of research for the last five decades. Since speech recognition is a pattern recognition problem, classification is an important part of any speech recognition system. In this work, a speech recognition system is developed for recognizing speaker independent spoken digits in Malayalam. Voice signals are sampled directly from the microphone. The proposed method is implemented for 1000 speakers uttering 10 digits each. Since the speech signals are affected by background noise, the signals are tuned by removing the noise from it using wavelet denoising method based on Soft Thresholding. Here, the features from the signals are extracted using Discrete Wavelet Transforms (DWT) because they are well suitable for processing non-stationary signals like speech. This is due to their multi- resolutional, multi-scale analysis characteristics. Speech recognition is a multiclass classification problem. So, the feature vector set obtained are classified using three classifiers namely, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Naive Bayes classifiers which are capable of handling multiclasses. During classification stage, the input feature vector data is trained using information relating to known patterns and then they are tested using the test data set. The performances of all these classifiers are evaluated based on recognition accuracy. All the three methods produced good recognition accuracy. DWT and ANN produced a recognition accuracy of 89%, SVM and DWT combination produced an accuracy of 86.6% and Naive Bayes and DWT combination produced an accuracy of 83.5%. ANN is found to be better among the three methods.

Changing Role of Academic Libraries in the E-learning Environment: Issues and Challenges

The development of computer and network technology is changing the education scenario and transforming the teaching and learning process from the traditional physical environment to the digital environment. It is now possible to access vast amount of information online and enable one to one communication without the confines of place or time. While E-learning and teaching is unlikely to replace face-to-face training and education it is becoming an additional delivery method, providing new learning opportunities to many users. It is also causing an impact on library services as the increased use of ICT and web based learning technologies have paved the way for providing new ICT based services and resources to the users. Online learning has a crucial role in user education, information literacy programmes and in training the library professionals. It can help students become active learners, and libraries will have to play a greater role in this process of transformation. The significance of libraries within an institution has improved due to the fact that academic libraries and information services are now responsible for e-learning within their organization.

A Distributed Model for Mobile Robot Environment-Learning and Navigation

A distributed method for mobile robot navigation, spatial learning, and path planning is presented. It is implemented on a sonar-based physical robot, Toto, consisting of three competence layers: 1) Low-level navigation: a collection of reflex-like rules resulting in emergent boundary-tracing. 2) Landmark detection: dynamically extracts landmarks from the robot's motion. 3) Map learning: constructs a distributed map of landmarks. The parallel implementation allows for localization in constant time. Spreading of activation computes both topological and physical shortest paths in linear time. The main issues addressed are: distributed, procedural, and qualitative representation and computation, emergent behaviors, dynamic landmarks, minimized communication.

Stereo-Based Head Pose Tracking Using Iterative Closest Point and Normal Flow Constraint

In this text, we present two stereo-based head tracking techniques along with a fast 3D model acquisition system. The first tracking technique is a robust implementation of stereo-based head tracking designed for interactive environments with uncontrolled lighting. We integrate fast face detection and drift reduction algorithms with a gradient-based stereo rigid motion tracking technique. Our system can automatically segment and track a user's head under large rotation and illumination variations. Precision and usability of this approach are compared with previous tracking methods for cursor control and target selection in both desktop and interactive room environments. The second tracking technique is designed to improve the robustness of head pose tracking for fast movements. Our iterative hybrid tracker combines constraints from the ICP (Iterative Closest Point) algorithm and normal flow constraint. This new technique is more precise for small movements and noisy depth than ICP alone, and more robust for large movements than the normal flow constraint alone. We present experiments which test the accuracy of our approach on sequences of real and synthetic stereo images. The 3D model acquisition system we present quickly aligns intensity and depth images, and reconstructs a textured 3D mesh. 3D views are registered with shape alignment based on our iterative hybrid tracker. We reconstruct the 3D model using a new Cubic Ray Projection merging algorithm which takes advantage of a novel data structure: the linked voxel space. We present experiments to test the accuracy of our approach on 3D face modelling using real-time stereo images.

From First Contact to Close Encounters: A Developmentally Deep Perceptual System for a Humanoid Robot

This thesis presents a perceptual system for a humanoid robot that integrates abilities such as object localization and recognition with the deeper developmental machinery required to forge those competences out of raw physical experiences. It shows that a robotic platform can build up and maintain a system for object localization, segmentation, and recognition, starting from very little. What the robot starts with is a direct solution to achieving figure/ground separation: it simply 'pokes around' in a region of visual ambiguity and watches what happens. If the arm passes through an area, that area is recognized as free space. If the arm collides with an object, causing it to move, the robot can use that motion to segment the object from the background. Once the robot can acquire reliable segmented views of objects, it learns from them, and from then on recognizes and segments those objects without further contact. Both low-level and high-level visual features can also be learned in this way, and examples are presented for both: orientation detection and affordance recognition, respectively. The motivation for this work is simple. Training on large corpora of annotated real-world data has proven crucial for creating robust solutions to perceptual problems such as speech recognition and face detection. But the powerful tools used during training of such systems are typically stripped away at deployment. Ideally they should remain, particularly for unstable tasks such as object detection, where the set of objects needed in a task tomorrow might be different from the set of objects needed today. The key limiting factor is access to training data, but as this thesis shows, that need not be a problem on a robotic platform that can actively probe its environment, and carry out experiments to resolve ambiguity. This work is an instance of a general approach to learning a new perceptual judgment: find special situations in which the perceptual judgment is easy and study these situations to find correlated features that can be observed more generally.

Teaching an Old Robot New Tricks: Learning Novel Tasks via Interaction with People and Things

As AI has begun to reach out beyond its symbolic, objectivist roots into the embodied, experientialist realm, many projects are exploring different aspects of creating machines which interact with and respond to the world as humans do. Techniques for visual processing, object recognition, emotional response, gesture production and recognition, etc., are necessary components of a complete humanoid robot. However, most projects invariably concentrate on developing a few of these individual components, neglecting the issue of how all of these pieces would eventually fit together. The focus of the work in this dissertation is on creating a framework into which such specific competencies can be embedded, in a way that they can interact with each other and build layers of new functionality. To be of any practical value, such a framework must satisfy the real-world constraints of functioning in real-time with noisy sensors and actuators. The humanoid robot Cog provides an unapologetically adequate platform from which to take on such a challenge. This work makes three contributions to embodied AI. First, it offers a general-purpose architecture for developing behavior-based systems distributed over networks of PC's. Second, it provides a motor-control system that simulates several biological features which impact the development of motor behavior. Third, it develops a framework for a system which enables a robot to learn new behaviors via interacting with itself and the outside world. A few basic functional modules are built into this framework, enough to demonstrate the robot learning some very simple behaviors taught by a human trainer. A primary motivation for this project is the notion that it is practically impossible to build an "intelligent" machine unless it is designed partly to build itself. This work is a proof-of-concept of such an approach to integrating multiple perceptual and motor systems into a complete learning agent.

Neural Networks

We present an overview of current research on artificial neural networks, emphasizing a statistical perspective. We view neural networks as parameterized graphs that make probabilistic assumptions about data, and view learning algorithms as methods for finding parameter values that look probable in the light of the data. We discuss basic issues in representation and learning, and treat some of the practical issues that arise in fitting networks to data. We also discuss links between neural networks and the general formalism of graphical models.

On the difficulty of feature-based attentional modulations in visual object recognition: A modeling study.

Numerous psychophysical experiments have shown an important role for attentional modulations in vision. Behaviorally, allocation of attention can improve performance in object detection and recognition tasks. At the neural level, attention increases firing rates of neurons in visual cortex whose preferred stimulus is currently attended to. However, it is not yet known how these two phenomena are linked, i.e., how the visual system could be "tuned" in a task-dependent fashion to improve task performance. To answer this question, we performed simulations with the HMAX model of object recognition in cortex [45]. We modulated firing rates of model neurons in accordance with experimental results about effects of feature-based attention on single neurons and measured changes in the model's performance in a variety of object recognition tasks. It turned out that recognition performance could only be improved under very limited circumstances and that attentional influences on the process of object recognition per se tend to display a lack of specificity or raise false alarm rates. These observations lead us to postulate a new role for the observed attention-related neural response modulations.

Learning Three-Dimensional Shape Models for Sketch Recognition

Artifacts made by humans, such as items of furniture and houses, exhibit an enormous amount of variability in shape. In this paper, we concentrate on models of the shapes of objects that are made up of fixed collections of sub-parts whose dimensions and spatial arrangement exhibit variation. Our goals are: to learn these models from data and to use them for recognition. Our emphasis is on learning and recognition from three-dimensional data, to test the basic shape-modeling methodology. In this paper we also demonstrate how to use models learned in three dimensions for recognition of two-dimensional sketches of objects.

Visual research and visual analysis

Understand representation and basic semiotic theory i.e. signs, meaning and myth Use visual analysis to decode an image

En busca de una nueva sociedad. Los aportes de la teoría feminista a la reformulación del mundo moderno

Este artículo analiza las aportaciones de las teorías feministas a la reformulación de los paradigmas de la teoría política actual. Se retoman dos ejes temáticos: el problema del reconocimiento de la diferencia y la redefinición de la dicotomía público-privado, ambos introducidos en el debate político moderno por el feminismo. En el caso de la diferencia, se analiza específicamente el problema de la representación y su legitimidad. La dicotomía privado-público se aborda desde el enfoque de la ética del cuidado y sus paralelismos con los principios de la economía social. La conclusión afirma que el feminismo es, en esencia, una reflexión sobre la condición del ser humano, concreto en su sexualidad, pero universal en su derecho al reconocimiento.-----This article analyzes the contributions of the feminist theories to the reformulation of the current political theory paradigms. Two topical axes are reexamined: the issue of the recognition of differences and the redefinition of the public-private dichotomy, both introduced into the modern political discussion by the feminism. In the case of the difference, representation and its legitimacy issue are analyzed in particular. The private-public dichotomy is approached from the ethics of care viewpoint and its parallelisms with the principles of social economy. It concludes that feminism is, in essence, a meditation on the human being condition, concrete on his or her sexuality, but universal on his or her right to recognition.

Occupational therapy and its relevance in social projects. relationships, learning and opportunities: collection of experiences of University of Alberta Mscot students in Bogotá, Colombia

Occupational therapists are equipped to promote wellbeing through occupation and to enable participation and meaningful engagement of people in their social and physical environments (WFOT, 2012). As such, the role of the occupational therapists is profoundly linked to the social, cultural and environmental characteristics of the contexts in which occupations take place. The central role that context plays in occupational performance creates an interesting dichotomy for the occupational therapist: on one hand, a profound understanding of cultural and social factors is required from the Occupational Therapy (OT) in order to develop a meaningful and successful collaboration with the person; on the other hand, the ability of the occupational therapists to recognize and explore the contextual factor of an occupation-person dyad transcends cultural and spatial barriers. As a result, occupational therapists are equipped to engage in international collaboration and practice, and as such face unique and enriching challenges. International fieldwork experiences have become a tool through which occupational therapists in training can develop the critical skills for understanding the impact of cultural and social factors on occupation. An OT student in an international fieldwork experience faces numerous challenges in leading a process that is both relevant and respectful to the characteristics of the local context: language, cultural perceptions of occupation and personhood, religious backgrounds, health care access, etc. These challenges stand out as ethical considerations that must be considered when navigating an international fieldwork experience (AOTA, 2009). For more than five years now, the Faculty of Rehabilitation Medicine (FRM) of the University of Alberta (UoFA) and the School of Medicine and Health Sciences at the Universidad del Rosario (UR), Bogota, Colombia, have sustained a productive and meaningful international collaboration. This collaboration includes a visit by Dr. Albert Cook, professor of the FRM and former dean, to the UR as the main guest speaker in the International Congress of Technologies for Disability Support (IBERDISCAP) in 2008. Furthermore, Dr. Cook was a speaker in the research seminar of the Assistive Technology Research Group of the Universidad del Rosario. Following Dr. Cook’s visit, Professors Liliana Álvarez and Adriana Ríos travelled to Edmonton and initiated collaboration with the FRM, resulting in the signing of an agreement between the FRM and the UR in 2009, agreement that has been maintained to this day. The main goal of this agreement is to increase academic and cultural cooperation between the UR and the UofA. Other activities have included the cooperation between Dr. Kim Adams (who has largely maintained interest and effort in supporting the capacity building of the UR rehabilitation programs in coordinating the provision of research placement opportunities for UR students at the UofA), an Assistive Technology course for clinicians and students led by Dr. Adams, and a research project that researched the use of basic cell phones to provide social interaction and health information access for people with disabilities in a low-income community in Colombia (led by Tim Barlott, OT, MSc, under the supervision of Dr. Adams). Since the beginning, the occupational therapy programs of the Universidad del Rosario and the University of Alberta have promoted this collaboration and have strived to engage in interactions that provide further development opportunities for students and staff. As part of this process, the international placement experience of UofA OT students was born under the leadership of: Claudia Rozo, OT program director at UR, placement and academic leadership of Elvis Castro and Angélica Monsalve, professors of the occupational therapy program at UR; and Dr. Lili Liu, OT department director at UofA, Cori Schmitz, Academic coordinator of clinical education at the UofA; and Tim Barlott and Liliana Álvarez leading the international and cross-cultural aspect of this collaboration.This publication summarizes and illustrates the process of international placement in community settings in Colombia, undertaken by occupational therapy students of the University of Alberta. It is our hope that this document can provide and document the ethical considerations of international fieldwork experience, the special characteristics of communities and the ways in which cultural and social competences are developed and help international students navigate the international setting. We also hope that this document will stimulate discussion among professional and academic communities about the importance and richness of international placement experiences and encourage staff and students to articulate their daily efforts with the global occupational therapy agenda.

Integration of knowledge-based, qualitative and numeric tools for real time dynamic systems supervision

The proposal presented in this thesis is to provide designers of knowledge based supervisory systems of dynamic systems with a framework to facilitate their tasks avoiding interface problems among tools, data flow and management. The approach is thought to be useful to both control and process engineers in assisting their tasks. The use of AI technologies to diagnose and perform control loops and, of course, assist process supervisory tasks such as fault detection and diagnose, are in the scope of this work. Special effort has been put in integration of tools for assisting expert supervisory systems design. With this aim the experience of Computer Aided Control Systems Design (CACSD) frameworks have been analysed and used to design a Computer Aided Supervisory Systems (CASSD) framework. In this sense, some basic facilities are required to be available in this proposed framework: ·

On intentional and social agents with graded attitudes

La principal contribución de esta Tesis es la propuesta de un modelo de agente BDI graduado (g-BDI) que permita especificar una arquitetura de agente capaz de representar y razonar con actitudes mentales graduadas. Consideramos que una arquitectura BDI más exible permitirá desarrollar agentes que alcancen mejor performance en entornos inciertos y dinámicos, al servicio de otros agentes (humanos o no) que puedan tener un conjunto de motivaciones graduadas. En el modelo g-BDI, las actitudes graduadas del agente tienen una representación explícita y adecuada. Los grados en las creencias representan la medida en que el agente cree que una fórmula es verdadera, en los deseos positivos o negativos permiten al agente establecer respectivamente, diferentes niveles de preferencias o de rechazo. Las graduaciones en las intenciones también dan una medida de preferencia pero en este caso, modelan el costo/beneficio que le trae al agente alcanzar una meta. Luego, a partir de la representación e interacción de estas actitudes graduadas, pueden ser modelados agentes que muestren diferentes tipos de comportamiento. La formalización del modelo g-BDI está basada en los sistemas multi-contextos. Diferentes lógicas modales multivaluadas se han propuesto para representar y razonar sobre las creencias, deseos e intenciones, presentando en cada caso una axiomática completa y consistente. Para tratar con la semántica operacional del modelo de agente, primero se definió un calculus para la ejecución de sistemas multi-contextos, denominado Multi-context calculus. Luego, mediante este calculus se le ha dado al modelo g-BDI semántica computacional. Por otra parte, se ha presentado una metodología para la ingeniería de agentes g-BDI en un escenario multiagente. El objeto de esta propuesta es guiar el diseño de sistemas multiagentes, a partir de un problema del mundo real. Por medio del desarrollo de un sistema recomendador en turismo como caso de estudio, donde el agente recomendador tiene una arquitectura g-BDI, se ha mostrado que este modelo es valioso para diseñar e implementar agentes concretos. Finalmente, usando este caso de estudio se ha realizado una experimentación sobre la flexibilidad y performance del modelo de agente g-BDI, demostrando que es útil para desarrollar agentes que manifiesten conductas diversas. También se ha mostrado que los resultados obtenidos con estos agentes recomendadores modelizados con actitudes graduadas, son mejores que aquellos alcanzados por los agentes con actitudes no-graduadas.