Real-time GPU color-based segmentation of football players

In this paper, we propose a multi-camera application capable of processing high resolution images and extracting features based on colors patterns over graphic processing units (GPU). The goal is to work in real time under the uncontrolled environment of a sport event like a football match. Since football players are composed for diverse and complex color patterns, a Gaussian Mixture Models (GMM) is applied as segmentation paradigm, in order to analyze sport live images and video. Optimization techniques have also been applied over the C++ implementation using profiling tools focused on high performance. Time consuming tasks were implemented over NVIDIA's CUDA platform, and later restructured and enhanced, speeding up the whole process significantly. Our resulting code is around 4-11 times faster on a low cost GPU than a highly optimized C++ version on a central processing unit (CPU) over the same data. Real time has been obtained processing until 64 frames per second. An important conclusion derived from our study is the scalability of the application to the number of cores on the GPU. © 2011 Springer-Verlag.

Low-power DWT-based quasi-averaging algorithm and architecture for epileptic seizure detection

In this paper, we have developed a low-complexity algorithm for epileptic seizure detection with a high degree of accuracy. The algorithm has been designed to be feasibly implementable as battery-powered low-power implantable epileptic seizure detection system or epilepsy prosthesis. This is achieved by utilizing design optimization techniques at different levels of abstraction. Particularly, user-specific critical parameters are identified at the algorithmic level and are explicitly used along with multiplier-less implementations at the architecture level. The system has been tested on neural data obtained from in-vivo animal recordings and has been implemented in 90nm bulk-Si technology. The results show up to 90 % savings in power as compared to prevalent wavelet based seizure detection technique while achieving 97% average detection rate. Copyright 2010 ACM.

Multimedia Resource Allocation in mmWave 5G Networks

The 5G network infrastructure is driven by the evolution of today's most demanding applications. Already, multimedia applications such as on-demand HD video and IPTV require gigabit- per-second throughput and low delay, while future technologies include ultra HDTV and machine-to-machine communication. Mm-Wave technologies such as IEEE 802.15.3c and IEEE 802.11ad are ideal candidates to deliver high throughput to multiple users demanding differentiated QoS. Optimization is often used as a methodology to meet throughput and delay constraints. However, traditional optimization techniques are not suited to a mixed set of multimedia applications. Particle swarm optimization (PSO) is shown as a promising technique in this context. Channel-time allocation PSO (CTA-PSO) is successfully shown here to allocate resource even in scenarios where blockage of the 60 GHz signal poses significant challenges.

Implementation Experiences from Smart Grid Security Applications and Outlook on Future Research

Experiences from smart grid cyber-security incidents in the past decade have raised questions on the applicability and effectiveness of security measures and protection mechanisms applied to the grid. In this chapter we focus on the security measures applied under real circumstances in today’s smart grid systems. Beginning from real world example implementations, we first review cyber-security facts that affected the electrical grid, from US blackout incidents, to the Dragonfly cyber-espionage campaign currently focusing on US and European energy firms. Provided a real world setting, we give information related to energy management of a smart grid looking also in the optimization techniques that power control engineers perform into the grid components. We examine the application of various security tools in smart grid systems, such as intrusion detection systems, smart meter authentication and key management using Physical Unclonable Functions, security analytics and resilient control algorithms. Furthermore we present evaluation use cases of security tools applied on smart grid infrastructure test-beds that could be proved important prior to their application in the real grid, describing a smart grid intrusion detection system application and security analytics results. Anticipated experimental results from the use-cases and conclusions about the successful transitions of security measures to real world smart grid operations will be presented at the end of this chapter.

Resource Efficient Configuration of an Aircraft Assembly Line

The design of efficient assembly systems can significantly contribute to the profitability of products and the competitiveness of manufacturing industries. The configuration of a an efficient assembly line can be supported by suitable methodologies and techniques, such as design for manufacture and assembly, assembly sequence planning, assembly line balancing, lean manufacturing and optimization techniques. In this paper, these methods are applied with reference to the industrial case study of the assembly line of a Skycar light aircraft. The assembly process sequence is identified taking into account the analysis of the assembly structure and the required precedence constraints, and diverse techniques are applied to optimize the assembly line performance. Different line configurations are verified through discrete event simulation to assess the potential increase of efficiency and throughput in a digital environment and propose the most suitable configuration of the assembly line.

Biaxial seismic behaviour of reinforced concrete columns

A análise dos efeitos dos sismos mostra que a investigação em engenharia sísmica deve dar especial atenção à avaliação da vulnerabilidade das construções existentes, frequentemente desprovidas de adequada resistência sísmica tal como acontece em edifícios de betão armado (BA) de muitas cidades em países do sul da Europa, entre os quais Portugal. Sendo os pilares elementos estruturais fundamentais na resistência sísmica dos edifícios, deve ser dada especial atenção à sua resposta sob ações cíclicas. Acresce que o sismo é um tipo de ação cujos efeitos nos edifícios exige a consideração de duas componentes horizontais, o que tem exigências mais severas nos pilares comparativamente à ação unidirecional. Assim, esta tese centra-se na avaliação da resposta estrutural de pilares de betão armado sujeitos a ações cíclicas horizontais biaxiais, em três linhas principais. Em primeiro lugar desenvolveu-se uma campanha de ensaios para o estudo do comportamento cíclico uniaxial e biaxial de pilares de betão armado com esforço axial constante. Para tal foram construídas quatro séries de pilares retangulares de betão armado (24 no total) com diferentes características geométricas e quantidades de armadura longitudinal, tendo os pilares sido ensaiados para diferentes histórias de carga. Os resultados experimentais obtidos são analisados e discutidos dando particular atenção à evolução do dano, à degradação de rigidez e resistência com o aumento das exigências de deformação, à energia dissipada, ao amortecimento viscoso equivalente; por fim é proposto um índice de dano para pilares solicitados biaxialmente. De seguida foram aplicadas diferentes estratégias de modelação não-linear para a representação do comportamento biaxial dos pilares ensaiados, considerando não-linearidade distribuída ao longo dos elementos ou concentrada nas extremidades dos mesmos. Os resultados obtidos com as várias estratégias de modelação demonstraram representar adequadamente a resposta em termos das curvas envolventes força-deslocamento, mas foram encontradas algumas dificuldades na representação da degradação de resistência e na evolução da energia dissipada. Por fim, é proposto um modelo global para a representação do comportamento não-linear em flexão de elementos de betão armado sujeitos a ações biaxiais cíclicas. Este modelo tem por base um modelo uniaxial conhecido, combinado com uma função de interação desenvolvida com base no modelo de Bouc- Wen. Esta função de interação foi calibrada com recurso a técnicas de otimização e usando resultados de uma série de análises numéricas com um modelo refinado. É ainda demonstrada a capacidade do modelo simplificado em reproduzir os resultados experimentais de ensaios biaxiais de pilares.

Energy-aware Workflow Scheduling and Fuzzy Logic Based Capacity Provisioning in Cloud Environment

Thesis (Master's)--University of Washington, 2015

Natural Gas location design and operation

A major determinant of the level of effective natural gas supply is the ease to feed customers, minimizing system total costs. The aim of this work is the study of the right number of Gas Supply Units – GSUs - and their optimal location in a gas network. This paper suggests a GSU location heuristic, based on Lagrangean relaxation techniques. The heuristic is tested on the Iberian natural gas network, a system modelized with 65 demand nodes, linked by physical and virtual pipelines. Lagrangean heuristic results along with the allocation of loads to gas sources are presented, using a 2015 forecast gas demand scenario.

Intelligent Bio-Inspired system for manufacturing scheduling under uncertainties

A novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS), Case-based Reasoning (CBR), and Bio-Inspired Optimization Techniques (BIT) will be described. AC has emerged as a paradigm aiming at incorporating applications with a management structure similar to the central nervous system. The main intentions are to improve resource utilization and service quality. In this paper we envisage the use of MAS paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with AC properties, in order to reduce the complexity of managing manufacturing systems and human interference. The proposed CBR based Intelligent Scheduling System was evaluated under different dynamic manufacturing scenarios.

MASCEM - an electricity market simulator providing coalition support for virtual power players

This paper presents MASCEM - a multi-agent based electricity market simulator. MASCEM uses game theory, machine learning techniques, scenario analysis and optimization techniques to model market agents and to provide them with decision-support. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Players (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. The paper details some of the most important aspects considered in VPP formation and in the aggregation of new producers and includes a case study based on real data.

Optimal methodology for renewable energy dispatching in islanded operation

The management of energy resources for islanded operation is of crucial importance for the successful use of renewable energy sources. A Virtual Power Producer (VPP) can optimally operate the resources taking into account the maintenance, operation and load control considering all the involved cost. This paper presents the methodology approach to formulate and solve the problem of determining the optimal resource allocation applied to a real case study in Budapest Tech’s. The problem is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The problem has also been solved by Evolutionary Particle Swarm Optimization (EPSO). The obtained results are presented and compared.

Optimal dispatch with reactive power compensation by Genetic Algorithm

This paper presents a Unit Commitment model with reactive power compensation that has been solved by Genetic Algorithm (GA) optimization techniques. The GA has been developed a computational tools programmed/coded in MATLAB. The main objective is to find the best generations scheduling whose active power losses are minimal and the reactive power to be compensated, subjected to the power system technical constraints. Those are: full AC power flow equations, active and reactive power generation constraints. All constraints that have been represented in the objective function are weighted with a penalty factors. The IEEE 14-bus system has been used as test case to demonstrate the effectiveness of the proposed algorithm. Results and conclusions are dully drawn.

Meta-heuristics tunning using CBR for dynamic scheduling

This paper addresses the problem of Biological Inspired Optimization Techniques (BIT) parameterization, considering the importance of this issue in the design of BIT especially when considering real world situations, subject to external perturbations. A learning module with the objective to permit a Multi-Agent Scheduling System to automatically select a Meta-heuristic and its parameterization to use in the optimization process is proposed. For the learning process, Casebased Reasoning was used, allowing the system to learn from experience, in the resolution of similar problems. Analyzing the obtained results we conclude about the advantages of its use.

A framework for programming sensor networks with scheduling and resource-sharing optimizations

Several projects in the recent past have aimed at promoting Wireless Sensor Networks as an infrastructure technology, where several independent users can submit applications that execute concurrently across the network. Concurrent multiple applications cause significant energy-usage overhead on sensor nodes, that cannot be eliminated by traditional schemes optimized for single-application scenarios. In this paper, we outline two main optimization techniques for reducing power consumption across applications. First, we describe a compiler based approach that identifies redundant sensing requests across applications and eliminates those. Second, we cluster the radio transmissions together by concatenating packets from independent applications based on Rate-Harmonized Scheduling.

Multilayer Perceptron Neural Networks Training Through Charged System Search and its Application for Non-Technical Losses Detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others natureinspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids.