A flexible side information generation framework for distributed video coding

One of the most efficient approaches to generate the side information (SI) in distributed video codecs is through motion compensated frame interpolation where the current frame is estimated based on past and future reference frames. However, this approach leads to significant spatial and temporal variations in the correlation noise between the source at the encoder and the SI at the decoder. In such scenario, it would be useful to design an architecture where the SI can be more robustly generated at the block level, avoiding the creation of SI frame regions with lower correlation, largely responsible for some coding efficiency losses. In this paper, a flexible framework to generate SI at the block level in two modes is presented: while the first mode corresponds to a motion compensated interpolation (MCI) technique, the second mode corresponds to a motion compensated quality enhancement (MCQE) technique where a low quality Intra block sent by the encoder is used to generate the SI by doing motion estimation with the help of the reference frames. The novel MCQE mode can be overall advantageous from the rate-distortion point of view, even if some rate has to be invested in the low quality Intra coding blocks, for blocks where the MCI produces SI with lower correlation. The overall solution is evaluated in terms of RD performance with improvements up to 2 dB, especially for high motion video sequences and long Group of Pictures (GOP) sizes.

Low Complexity Intra Mode Selection for Efficient Distributed Video Coding

Motion compensated frame interpolation (MCFI) is one of the most efficient solutions to generate side information (SI) in the context of distributed video coding. However, it creates SI with rather significant motion compensated errors for some frame regions while rather small for some other regions depending on the video content. In this paper, a low complexity Infra mode selection algorithm is proposed to select the most 'critical' blocks in the WZ frame and help the decoder with some reliable data for those blocks. For each block, the novel coding mode selection algorithm estimates the encoding rate for the Intra based and WZ coding modes and determines the best coding mode while maintaining a low encoder complexity. The proposed solution is evaluated in terms of rate-distortion performance with improvements up to 1.2 dB regarding a WZ coding mode only solution.

A efetividade do dentifrício fluoretado no controle da cárie dental: uma meta-análise

OBJETIVO: Realizar uma meta-análise sobre a efetividade da escovação com dentifrício fluoretado na redução da cárie dental para calcular o efeito das diferentes hipóteses testadas. MÉTODOS: Foram revisados artigos publicados sobre a efetividade da educação em saúde oral no período compreendido entre 1980 e 1998, indexados nas bases de dados Medline e Lilacs. Para avaliação da qualidade dos artigos, foram utilizados os critérios de rigor metodológico de Kay & Locker após serem submetidos a um comitê de experts formado por pesquisadores I-A do CNPq. Dos 43 artigos identificados, 22 (51,2%) atingiram os critérios de rigor metodológico propostos. O cálculo do tamanho do efeito da intervenção de cada estudo foi feito a partir das diferenças de resultados entre os grupos de teste e controle. O efeito global foi calculado para cada agrupamento de estudos segundo as hipóteses testadas. RESULTADOS: O aumento da concentração do flúor parece estar associado com o aumento do efeito (redução global = -0,17 IC 95% -0,22/-0,12). O maior percentual de redução de cárie foi verificado na comparação entre dentifrícios fluoretados e aqueles sem flúor (redução global = -0,29 IC 95% -0,34/-0,24). A adição de anti-microbianos (redução global = -0,03 IC 95% -0,07/+0,02), diferenças nos sistemas abrasivos (redução global = -0,02 IC 95% -0,09/+0,04) e diferenças nos princípios ativos não aumentam a efetividade dos mesmos (redução global = -0,04 IC 95% -0,10/+0,01). CONCLUSÕES: As maiores reduções de cárie foram encontradas nos estudos com escovação supervisionada. A síntese confirmou a importância da escovação com dentifrício fluoretado no controle da cárie dental. Contudo, aponta para uma ênfase nos aspectos medicamentosos relacionados ao controle da doença, em detrimento de ações educativas específicas. A heterogeneidade de resultados indica que sejam considerados os contextos de implantação das práticas preventivas no processo de sua avaliação.

Papel do polimorfismo Pro12 Ala no gene PPAR Gama 2 na obesidade e diabetes mellitus tipo 2 - uma meta análise

A obesidade e a diabetes mellitus tipo 2 (DM2) são considerados dois grandes problemas de saúde pública. A má alimentação e a falta de atividade física encontram-se entre os principais desencadeadores de um crescente número de indivíduos obesos, diabéticos e com sensibilidade à insulina diminuída. Este aumento tem motivado a comunidade científica a investigar cada vez mais para o elevado contributo da herança genética associada aos fatores sociais e nutricionais. O gene dos recetores ativados por proliferadores do peroxissoma gama 2 (PPARγ2) desempenha um papel importante no metabolismo lipídico. Uma vez que o PPARγ2 é maioritariamente expresso no tecido adiposo, uma redução moderada da sua atividade tem influência na sensibilidade à insulina, diabetes, e outros parâmetros metabólicos. Vários estudos sugerem que tanto fatores genéticos como fatores ambientais (tais como a dieta), poderão estar envolvidos na formação de padrões associados ao polimorfismo Pro12Ala com a composição corporal em diferentes populações humanas. Os diversos estudos genéticos envolvendo o estudo do polimorfismo Pro12Ala do PPARγ2 na suscetibilidade de possuir risco de diabetes e obesidade em várias populações têm proposto conclusões diversas. Em alguns parece haver mais associações do que outros e, às vezes, não demonstram sequer associação. Desta forma, o presente trabalho teve como objectivo contribuir para a elucidação do impacto do polimorfismo Pro12Ala do PPARγ2 na resistência à insulina associada à DM2 e na obesidade, mediante estudo sistematizado da literatura existente até à data, através de meta análise. Do total de uma pesquisa de 63 publicações, foram incluídos 32 artigos no presente estudo, sendo que destes 25 foram incluídos na síntese qualitativa e 11 incluídos na sintese quantitativa. No presente trabalho pode-se concluir que existe evidência estatística que suporta a hipótese de que o polimorfismo Pro12Ala do PPARγ2 pode ser considerado um fator protetor para a DM2 [p <0,05 e OR (odds ratio) 0,702, com IC (intervalos de confiança) com valores que nunca incluem o 1]. No entanto, e mediante os mesmos pressupostos, o mesmo polimorfismo pode ser considerado um fator de risco ao desenvolvimento de obesidade, pela evidência estatística [p <0,05 e OR de 1,196, com IC com valores que nunca incluem o 1].

Augmented LDPC Graph for Distributed Video Coding with Multiple Side Information

The advances made in channel-capacity codes, such as turbo codes and low-density parity-check (LDPC) codes, have played a major role in the emerging distributed source coding paradigm. LDPC codes can be easily adapted to new source coding strategies due to their natural representation as bipartite graphs and the use of quasi-optimal decoding algorithms, such as belief propagation. This paper tackles a relevant scenario in distributedvideo coding: lossy source coding when multiple side information (SI) hypotheses are available at the decoder, each one correlated with the source according to different correlation noise channels. Thus, it is proposed to exploit multiple SI hypotheses through an efficient joint decoding technique withmultiple LDPC syndrome decoders that exchange information to obtain coding efficiency improvements. At the decoder side, the multiple SI hypotheses are created with motion compensated frame interpolation and fused together in a novel iterative LDPC based Slepian-Wolf decoding algorithm. With the creation of multiple SI hypotheses and the proposed decoding algorithm, bitrate savings up to 8.0% are obtained for similar decoded quality.

Energy and reserve provision dispatch considering distributed generation and demand response

In competitive electricity markets with deep concerns at the efficiency level, demand response programs gain considerable significance. In the same way, distributed generation has gained increasing importance in the operation and planning of power systems. Grid operators and utilities are taking new initiatives, recognizing the value of demand response and of distributed generation for grid reliability and for the enhancement of organized spot market´s efficiency. Grid operators and utilities become able to act in both energy and reserve components of electricity markets. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The proposed method has been computationally implemented and its application is illustrated in this paper using a 32 bus distribution network with 32 medium voltage consumers.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

An integrated approach for distributed energy resource short term scheduling in smart grids considering realistic power system simulation

The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .

MASDScheGATS: a prototype system for dynamic scheduling

A manufacturing system has a natural dynamic nature observed through several kinds of random occurrences and perturbations on working conditions and requirements over time. For this kind of environment it is important the ability to efficient and effectively adapt, on a continuous basis, existing schedules according to the referred disturbances, keeping performance levels. The application of Meta-Heuristics and Multi-Agent Systems to the resolution of this class of real world scheduling problems seems really promising. This paper presents a prototype for MASDScheGATS (Multi-Agent System for Distributed Manufacturing Scheduling with Genetic Algorithms and Tabu Search).

Distributed generation producers' reserve management

Sustainable development concerns are being addressed with increasing attention, in general, and in the scope of power industry, in particular. The use of distributed generation (DG), mainly based on renewable sources, has been seen as an interesting approach to this problem. However, the increasing of DG in power systems raises some complex technical and economic issues. This paper presents ViProd, a simulation tool that allows modeling and simulating DG operation and participation in electricity markets. This paper mainly focuses on the operation of Virtual Power Producers (VPP) which are producers’ aggregations, being these producers mainly of DG type. The paper presents several reserve management strategies implemented in the scope of ViProd and the results of a case study, based on real data.

ANN based support for distributed energy resources scheduling in smart grids

The future scenarios for operation of smart grids are likely to include a large diversity of players, of different types and sizes. With control and decision making being decentralized over the network, intelligence should also be decentralized so that every player is able to play in the market environment. In the new context, aggregator players, enabling medium, small, and even micro size players to act in a competitive environment, will be very relevant. Virtual Power Players (VPP) and single players must optimize their energy resource management in order to accomplish their goals. This is relatively easy to larger players, with financial means to have access to adequate decision support tools, to support decision making concerning their optimal resource schedule. However, the smaller players have difficulties in accessing this kind of tools. So, it is required that these smaller players can be offered alternative methods to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), intended to support smaller players’ resource scheduling. The used methodology uses a training set that is built using the energy resource scheduling solutions obtained with a reference optimization methodology, a mixed-integer non-linear programming (MINLP) in this case. The trained network is able to achieve good schedule results requiring modest computational means.

Multilevel negotiation in smart grids for VPP management of distributed resources

A multilevel negotiation mechanism for operating smart grids and negotiating in electricity markets considers the advantages of virtual power player management.

Virtual power producers simulation - negotiating renewable distributed generation in competitive electricity markets

Sustainable development concerns made renewable energy sources to be increasingly used for electricity distributed generation. However, this is mainly due to incentives or mandatory targets determined by energy policies as in European Union. Assuring a sustainable future requires distributed generation to be able to participate in competitive electricity markets. To get more negotiation power in the market and to get advantages of scale economy, distributed generators can be aggregated giving place to a new concept: the Virtual Power Producer (VPP). VPPs are multi-technology and multisite heterogeneous entities that should adopt organization and management methodologies so that they can make distributed generation a really profitable activity, able to participate in the market. This paper presents ViProd, a simulation tool that allows simulating VPPs operation, in the context of MASCEM, a multi-agent based eletricity market simulator.

Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

Intelligent multi-player smart grid management considering distributed energy resources and demand response

The smart grid concept is rapidly evolving in the direction of practical implementations able to bring smart grid advantages into practice. Evolution in legacy equipment and infrastructures is not sufficient to accomplish the smart grid goals as it does not consider the needs of the players operating in a complex environment which is dynamic and competitive in nature. Artificial intelligence based applications can provide solutions to these problems, supporting decentralized intelligence and decision-making. A case study illustrates the importance of Virtual Power Players (VPP) and multi-player negotiation in the context of smart grids. This case study is based on real data and aims at optimizing energy resource management, considering generation, storage and demand response.