SeaMo plus : a virtual real-time multimedia service framework on handhelds to enable remote real-time patient monitoring for mobile doctors

The key requirements for enabling real-time remote healthcare service on a mobile platform, in the present day heterogeneous wireless access network environment, are uninterrupted and continuous access to the online patient vital medical data, monitor the physical condition of the patient through video streaming, and so on. For an application, this continuity has to be sufficiently transparent both from a performance perspective as well as a Quality of Experience (QoE) perspective. While mobility protocols (MIPv6, HIP, SCTP, DSMIP, PMIP, and SIP) strive to provide both and do so, limited or non-availability (deployment) of these protocols on provider networks and server side infrastructure has impeded adoption of mobility on end user platforms. Add to this, the cumbersome OS configuration procedures required to enable mobility protocol support on end user devices and the user's enthusiasm to add this support is lost. Considering the lack of proper mobility implementations that meet the remote healthcare requirements above, we propose SeaMo+ that comprises a light-weight application layer framework, termed as the Virtual Real-time Multimedia Service (VRMS) for mobile devices to provide an uninterrupted real-time multimedia information access to the mobile user. VRMS is easy to configure, platform independent, and does not require additional network infrastructure unlike other existing schemes. We illustrate the working of SeaMo+ in two realistic remote patient monitoring application scenarios.

Providing QoS in a cognitive radio network

We consider the problem of wireless channel allocation (whenever the channels are free) to multiple cognitive radio users in a Cognitive Radio Network (CRN) so as to satisfy their Quality of Service (QoS) requirements efficiently. The CRN base station may not know the channel states of all the users. The multiple channels are available at random times. In this setup Opportunistic Splitting can be an attractive solution. A disadvantage of this algorithm is that it requires the metrics of all users to be an independent, identically distributed sequence. However we use a recently generalized version of this algorithm in which the optimal parameters are learnt on-line through stochastic approximation and metrics can be Markov. We provide scheduling algorithms which maximize weighted-sum system throughput or are throughput or delay optimal. We also consider the scenario when some traffic streams are delay sensitive.

A Novel and Scalable LED Communication System

We describe our novel LED communication infrastructure and demonstrate its scalability across platforms. Our system achieves 50 kilo bits per second on very simple SoCs and scales to megabits bits per second rates on dual processor based mobile phone platforms.

On Whitespace Identification Using Randomly Deployed Sensors

This work considers the identification of the available whitespace, i.e., the regions that do not contain any existing transmitter within a given geographical area. To this end, n sensors are deployed at random locations within the area. These sensors detect for the presence of a transmitter within their radio range r(s) using a binary sensing model, and their individual decisions are combined to estimate the available whitespace. The limiting behavior of the recovered whitespace as a function of n and r(s) is analyzed. It is shown that both the fraction of the available whitespace that the nodes fail to recover as well as their radio range optimally scale as log(n)/n as n gets large. The problem of minimizing the sum absolute error in transmitter localization is also analyzed, and the corresponding optimal scaling of the radio range and the necessary minimum transmitter separation is determined.

Human Action Recognition in Compressed Domain using PBL-McRBFN Approach

Large variations in human actions lead to major challenges in computer vision research. Several algorithms are designed to solve the challenges. Algorithms that stand apart, help in solving the challenge in addition to performing faster and efficient manner. In this paper, we propose a human cognition inspired projection based learning for person-independent human action recognition in the H.264/AVC compressed domain and demonstrate a PBL-McRBEN based approach to help take the machine learning algorithms to the next level. Here, we use gradient image based feature extraction process where the motion vectors and quantization parameters are extracted and these are studied temporally to form several Group of Pictures (GoP). The GoP is then considered individually for two different bench mark data sets and the results are classified using person independent human action recognition. The functional relationship is studied using Projection Based Learning algorithm of the Meta-cognitive Radial Basis Function Network (PBL-McRBFN) which has a cognitive and meta-cognitive component. The cognitive component is a radial basis function network while the Meta-Cognitive Component(MCC) employs self regulation. The McC emulates human cognition like learning to achieve better performance. Performance of the proposed approach can handle sparse information in compressed video domain and provides more accuracy than other pixel domain counterparts. Performance of the feature extraction process achieved more than 90% accuracy using the PTIL-McRBFN which catalyzes the speed of the proposed high speed action recognition algorithm. We have conducted twenty random trials to find the performance in GoP. The results are also compared with other well known classifiers in machine learning literature.

Action Recognition from Motion Capture Data using Meta-cognitive RBF Network Classifier

Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).

A novel index for identification of weak nodes for reactive compensation to improve voltage stability

In this study, a new reactive power loss index (RPLI) is proposed for identification of weak buses in the system. This index is further used for determining the optimal locations for placement of reactive compensation devices in the power system for additional voltage support. The new index is computed from the reactive power support and loss allocation algorithm using Y-bus method for the system under intact condition and as well as critical/severe network contingencies cases. Fuzzy logic approach is used to select the important and critical/severe line contingencies from the contingency list. The inherent characteristics of the reactive power in system operation is properly addressed while determining the reactive power loss allocation to load buses. The proposed index is tested on sample 10-bus equivalent system and 72-bus practical equivalent system of Indian southern region power grid. The validation of the weak buses identification from the proposed index with that from other existing methods in the literature is carried out to demonstrate the effectiveness of the proposed index. Simulation results show that the identification of weak buses in the system from the new RPLI is completely non-iterative, thus requires minimal computational efforts as compared with other existing methods in the literature.

About the Stabilization of a Nonlinear Perturbed Difference Equation

This paper investigates the local asymptotic stabilization of a very general class of instable autonomous nonlinear difference equations which are subject to perturbed dynamics which can have a different order than that of the nominal difference equation. In the general case, the controller consists of two combined parts, namely, the feedback nominal controller which stabilizes the nominal (i.e., perturbation-free) difference equation plus an incremental controller which completes the stabilization in the presence of perturbed or unmodeled dynamics in the uncontrolled difference equation. A stabilization variant consists of using a single controller to stabilize both the nominal difference equation and also the perturbed one under a small-type characterization of the perturbed dynamics. The study is based on Banach fixed point principle, and it is also valid with slight modification for the stabilization of unstable oscillatory solutions.

Sistema de classificação da gestão ambiental pública municipal baseado em lógica nebulosa

A partir da Lei n. 6.938 de 31 de agosto de 1981, que constituiu o Sistema Nacional do Meio Ambiente, criou-se o Conselho Nacional do Meio Ambiente e instituiu-se o Cadastro Técnico Federal de Atividades e Instrumentos de Defesa Ambiental, a gestão ambiental pública ganhou um espaço cada vez maior nas administrações municipais, com a implementação de instrumentos de gestão ambiental propiciando aos municípios a possibilidade de ações efetivas que contribuam para uma melhor qualidade de vida a população. Esse trabalho propõe a criação de um método de classificação municipal que indicará qual o nível da gestão ambiental do município. Verificando o número de instrumentos de gestão ambiental constituído e o número de problemas ambientais ocorridos em cada município na visão do gestor local nos anos de 2006/2008. E ainda qual a influência do IDH tanto na implementação de tais instrumentos de gestão ambiental, como nas ocorrências dos problemas ambientais. Tal classificação tem a intenção de verificar se o município encontra-se bem aparelhado no que se refere à gestão ambiental, auxiliando para futuras decisões nas ações da política ambiental local. O foco desse trabalho serão os municípios dos estados de Minas Gerais, Piauí e Rio de Janeiro. Os resultados serão processados via o software MATLAB utilizando lógica nebulosa (fuzzy) e apresentados em um website utilizando as linguagens de programação JSP, HTML, JavaScript e esse website armazenado em um servidor TomCat e tais resultados serão apresentados nas formas de valores alfanuméricos em tabelas e espaciais através de mapas temáticos em uma solução sig-web. Os dados estão armazenados em um Sistema Gerenciador de Banco de Dados PostgreSQL com sua extensão espacial PostGIS, e o acesso aos mapas será feito através do servidor de mapas MapServer.

Utilización de la lógica borrosa en la selección de personas e ideas para la participación en programas públicos de ayuda a la creación de empresas

[ES] Se propone en este trabajo un modelo de control borroso que ayude a filtrar y seleccionar las solicitudes de subvención que pueda recibir una institución pública en un programa de fomento para la creación y desarrollo de nuevas iniciativas empresariales. Creemos que la utilización de la lógica borrosa presenta ventajas sobre los procedimientos ordinarios ya que nos movemos en un escenario de actuación complejo y vago. El control borroso introduce el conocimiento de los expertos de un modo muy natural mediante variables lingüísticas y procesos de inferencia propios del lenguaje ordinario, lo que facilita la toma de decisiones en situaciones complejas. Nuestro modelo considera por un lado la idea empresarial y por otro la persona . Los indicadores y criterios que los expertos consideran relevantes para la evaluación de la subvención son modelados mediante variables lingüísticas y tratados como antecedentes y consecuentes de un motor de inferencia borroso, cuya salida nos proporciona la valoración final de la solicitud. Al final de nuestro trabajo resolvemos un caso práctico sencillo para aclarar el procedimiento.

Índice de saúde social do adolescente baseado em lógica nebulosa.

Este trabalho consiste de uma análise exploratória sobre municípios do Sudeste com população acima de 100mil habitantes abordando dois problemas: a violência e a educação. Na violência abordaremos índices de homicídios na adolescência trabalhando com a faixa de adolescentes de 12 a 18 anos. Na educação trabalharemos com o Índice de Desenvolvimento Educacionail Brasileiro referenciado ao último ano do ensino fundamental. Trabalhando com os indicadores citados, abordaremos esses problemas gerando um Índice de Saúde social do Adolescente utilizando a lógica Fuzzy, conjunto nebuloso. Classificando os municípios do Sudeste visando identificar municípios com qualidade de vida melhor para esses adolescentes, expectativa de vida e melhoria na educação. Baixos índices de homicídios e altos índices educacionais desenvolvendo uma ferramenta útil para auxiliar na tomada de decisões no tocante a políticas públicas nos Municípios e Estados gerando um indicador de municípios com qualidade de vida para os adolescentes! Trabalhamos com dados do ano de 2007 tanto para o homicídio quanto para a educação, os valores apresentados nos índices foram divididos em quintis, processados via o software MATLAB utilizando lógica nebulosa (fuzzy), classificados e apresentados nas formas de valores alfanuméricos em tabelas espaciais com o software Quantum Gis através de mapas temáticos das regiões estudadas.