Automated traffic route identification through the shared nearest neighbour algorithm

Many organisations need to extract useful information from huge amounts of movement data. One example is found in maritime transportation, where the automated identification of a diverse range of traffic routes is a key management issue for improving the maintenance of ports and ocean routes, and accelerating ship traffic. This paper addresses, in a first stage, the research challenge of developing an approach for the automated identification of traffic routes based on clustering motion vectors rather than reconstructed trajectories. The immediate benefit of the proposed approach is to avoid the reconstruction of trajectories in terms of their geometric shape of the path, their position in space, their life span, and changes of speed, direction and other attributes over time. For clustering the moving objects, an adapted version of the Shared Nearest Neighbour algorithm is used. The motion vectors, with a position and a direction, are analysed in order to identify clusters of vectors that are moving towards the same direction. These clusters represent traffic routes and the preliminary results have shown to be promising for the automated identification of traffic routes with different shapes and densities, as well as for handling noise data.

Multi-centre validation of an automatic algorithm for fast 4D myocardial segmentation in cine CMR datasets

Quantitative analysis of cine cardiac magnetic resonance (CMR) images for the assessment of global left ventricular morphology and function remains a routine task in clinical cardiology practice. To date, this process requires user interaction and therefore prolongs the examination (i.e. cost) and introduces observer variability. In this study, we sought to validate the feasibility, accuracy, and time efficiency of a novel framework for automatic quantification of left ventricular global function in a clinical setting.

Effects of a randomized trial of exercise on body composition of liver transplanted patients

Liver transplantation is used as a only therapy so far, that stop the progression of some aspects of familial amyloidotic polyneuropathy disease (FAP) an autossomic neurodegenerative disease. FAP often results in severe functional limitations. Transplantation requires aggressive medication which impairs bone and muscle metabolism. Malnutrition plus weight loss is already one feature of FAP patients. All this may produce negative consequences on body composition. The effect of exercise training in FAP patients after a liver transplant (FAPTX) is currently unknown. The purpose of this study is to evaluate the effects of a six months exercise training program on body composition in FAPTX patients.

Lanczos algorithm on the grassmann manifold

5th. European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2008) 8th. World Congress on Computational Mechanics (WCCM8)

IOPT Petri Net State Space Generation Algorithm with Maximal-Step Execution Semantics

This paper presents an algorithm to efficiently generate the state-space of systems specified using the IOPT Petri-net modeling formalism. IOPT nets are a non-autonomous Petri-net class, based on Place-Transition nets with an extended set of features designed to allow the rapid prototyping and synthesis of system controllers through an existing hardware-software co-design framework. To obtain coherent and deterministic operation, IOPT nets use a maximal-step execution semantics where, in a single execution step, all enabled transitions will fire simultaneously. This fact increases the resulting state-space complexity and can cause an arc "explosion" effect. Real-world applications, with several million states, will reach a higher order of magnitude number of arcs, leading to the need for high performance state-space generator algorithms. The proposed algorithm applies a compilation approach to read a PNML file containing one IOPT model and automatically generate an optimized C program to calculate the corresponding state-space.

Genetic algorithm methodology applied to intelligent house control

In recent years the use of several new resources in power systems, such as distributed generation, demand response and more recently electric vehicles, has significantly increased. Power systems aim at lowering operational costs, requiring an adequate energy resources management. In this context, load consumption management plays an important role, being necessary to use optimization strategies to adjust the consumption to the supply profile. These optimization strategies can be integrated in demand response programs. The control of the energy consumption of an intelligent house has the objective of optimizing the load consumption. This paper presents a genetic algorithm approach to manage the consumption of a residential house making use of a SCADA system developed by the authors. Consumption management is done reducing or curtailing loads to keep the power consumption in, or below, a specified energy consumption limit. This limit is determined according to the consumer strategy and taking into account the renewable based micro generation, energy price, supplier solicitations, and consumers’ preferences. The proposed approach is compared with a mixed integer non-linear approach.

Ant colony search algorithm for the optimal power flow problem

To maintain a power system within operation limits, a level ahead planning it is necessary to apply competitive techniques to solve the optimal power flow (OPF). OPF is a non-linear and a large combinatorial problem. The Ant Colony Search (ACS) optimization algorithm is inspired by the organized natural movement of real ants and has been successfully applied to different large combinatorial optimization problems. This paper presents an implementation of Ant Colony optimization to solve the OPF in an economic dispatch context. The proposed methodology has been developed to be used for maintenance and repairing planning with 48 to 24 hours antecipation. The main advantage of this method is its low execution time that allows the use of OPF when a large set of scenarios has to be analyzed. The paper includes a case study using the IEEE 30 bus network. The results are compared with other well-known methodologies presented in the literature.

Immunogenicity of WHO-17D and Brazilian 17DD yellow fever vaccines: a randomized trial

OBJECTIVE: To compare the immunogenicity of three yellow fever vaccines from WHO-17D and Brazilian 17DD substrains (different seed-lots). METHODS: An equivalence trial was carried out involving 1,087 adults in Rio de Janeiro. Vaccines produced by Bio-Manguinhos, Fiocruz (Rio de Janeiro, Brazil) were administered following standardized procedures adapted to allow blocked randomized allocation of participants to coded vaccine types (double-blind). Neutralizing yellow fever antibody titters were compared in pre- and post-immunization serum samples. Equivalence was defined as a difference of no more than five percentage points in seroconversion rates, and ratio between Geometric Mean Titters (GMT) higher than 0.67. RESULTS: Seroconversion rates were 98% or higher among subjects previously seronegative, and 90% or more of the total cohort of vaccinees, including those previously seropositive. Differences in seroconversion ranged from -0.05% to -3.02%. The intensity of the immune response was also very similar across vaccines: 14.5 to 18.6 IU/mL. GMT ratios ranged from 0.78 to 0.93. Taking the placebo group into account, the vaccines explained 93% of seroconversion. Viremia was detected in 2.7% of vaccinated subjects from Day 3 to Day 7. CONCLUSIONS: The equivalent immunogenicity of yellow fever vaccines from the 17D and 17DD substrains was demonstrated for the first time in placebo-controlled double-blind randomized trial. The study completed the clinical validation process of a new vaccine seed-lot, provided evidence for use of alternative attenuated virus substrains in vaccine production for a major manufacturer, and for the utilization of the 17DD vaccine in other countries.

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.

Ancillary services dispatch using linear programming and genetic algorithm approaches

Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tools must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.

Reactogenicity of yellow fever vaccines in a randomized, placebo-controlled trial

OBJECTIVE: To compare the reactogenicity of three yellow fever (YF) vaccines from WHO-17D and Brazilian 17DD substrains (different seed-lots) and placebo. METHODS: The study involved 1,087 adults eligible for YF vaccine in Rio de Janeiro, Brazil. Vaccines produced by Bio-Manguinhos, Fiocruz (Rio de Janeiro, Brazil) were administered ("day 0") following standardized procedures adapted to allow blinding and blocked randomization of participants to coded vaccine types. Adverse events after immunization were ascertained in an interview and in diary forms filled in by each participant. Liver enzymes were measured on days 0, 4-20 and 30 of the study. Viremia levels were measured on days 4 to 20 of follow-up. The immune response was verified through serologic tests. RESULTS: Participants were mostly young males. The seroconversion rate was above 98% among those seronegative before immunization. Compared to placebo, the excess risk of any local adverse events ranged from 0.9% to 2.5%, whereas for any systemic adverse events it ranged from 3.5% to 7.4% across vaccine groups. The excess risk of events leading to search for medical care or to interruption of work activities ranged from 2% to 4.5%. Viremia was detected in 3%-6% of vaccinees up to 10 days after vaccination. Variations in liver enzyme levels after vaccination were similar in placebo and vaccine recipients. CONCLUSIONS: The frequency of adverse events post-immunization against YF, accounting for the background occurrence of nonspecific signs and symptoms, was shown for the first time to be similar for vaccines from 17D and 17DD substrains. The data also provided evidence against viscerotropism of vaccine virus.

A study on the encryption algorithm for RFID tag (SEED : 8 Rounds × 64 bits block)

Although it is always weak between RFID Tag and Terminal in focus of the security, there are no security skills in RFID Tag. Recently there are a lot of studying in order to protect it, but because it has some physical limitation of RFID, that is it should be low electric power and high speed, it is impossible to protect with the skills. At present, the methods of RFID security are using a security server, a security policy and security. One of them the most famous skill is the security module, then they has an authentication skill and an encryption skill. In this paper, we designed and implemented after modification original SEED into 8 Round and 64 bits for Tag.

Comparação do cálculo de dose com os algoritmos Analytical Anisotropic Algorithm e Pencil Beam Convolution na patologia de pulmão

Mestrado em Radioterapia.

Comparação entre o pencil beam convolution algorithm e o analytical anisotropic algorithm em tumores de mama

Mestrado em Radioterapia

Dependent component analysis: a hyperspectral unmixing algorithm

Linear unmixing decomposes a hyperspectral image into a collection of reflectance spectra of the materials present in the scene, called endmember signatures, and the corresponding abundance fractions at each pixel in a spatial area of interest. This paper introduces a new unmixing method, called Dependent Component Analysis (DECA), which overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical properties of hyperspectral data. DECA models the abundance fractions as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. The performance of the method is illustrated using simulated and real data.