Wavelet-based algorithm to the evaluation of contrasted hepatocellular carcinoma in CT-images after transarterial chemoembolization

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A transmission power self-optimization technique for wireless sensor networks

Wireless sensor networks (WSNs) are generally used to monitor hazardous events in inaccessible areas. Thus, on one hand, it is preferable to assure the adoption of the minimum transmission power in order to extend as much as possible the WSNs lifetime. On the other hand, it is crucial to guarantee that the transmitted data is correctly received by the other nodes. Thus, trading off power optimization and reliability insurance has become one of the most important concerns when dealing with modern systems based on WSN. In this context, we present a transmission power self-optimization (TPSO) technique for WSNs. The TPSO technique consists of an algorithm able to guarantee the connectivity as well as an equally high quality of service (QoS), concentrating on the WSNs efficiency (Ef), while optimizing the transmission power necessary for data communication. Thus, the main idea behind the proposed approach is to trade off WSNs Ef against energy consumption in an environment with inherent noise. Experimental results with different types of noise and electromagnetic interference (EMI) have been explored in order to demonstrate the effectiveness of the TPSO technique.

Optimizing urban traffic flow using genetic algorithm with petri net analysis as fitness function

This paper describes a new methodology adopted for urban traffic stream optimization. By using Petri net analysis as fitness function of a Genetic Algorithm, an entire urban road network is controlled in real time. With the advent of new technologies that have been published, particularly focusing on communications among vehicles and roads infrastructures, we consider that vehicles can provide their positions and their destinations to a central server so that it is able to calculate the best route for one of them. Our tests concentrate on comparisons between the proposed approach and other algorithms that are currently used for the same purpose, being possible to conclude that our algorithm optimizes traffic in a relevant manner.

Construction of pediatric homogeneous phantoms for optimization of chest and skull radiographs

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Multiobjective Genetic Algorithm applied to dengue control

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A self-learning simulated annealing algorithm for global optimizations of electromagnetic devices

A self-learning simulated annealing algorithm is developed by combining the characteristics of simulated annealing and domain elimination methods. The algorithm is validated by using a standard mathematical function and by optimizing the end region of a practical power transformer. The numerical results show that the CPU time required by the proposed method is about one third of that using conventional simulated annealing algorithm.