Proposal of the CAD System for Melanoma Detection Using Reconfigurable Computing

This work proposes dedicated hardware to real-time cancer detection using Field-Programmable Gate Arrays (FPGA). The presented hardware combines a Multilayer Perceptron (MLP) Artificial Neural Networks (ANN) with Digital Image Processing (DIP) techniques. The DIP techniques are used to extract the features from the analyzed skin, and the MLP classifies the lesion into melanoma or non-melanoma. The classification results are validated with an open-access database. Finally, analysis regarding execution time, hardware resources usage, and power consumption are performed. The results obtained through this analysis are then compared to an equivalent software implementation embedded in an ARM A9 microprocessor

Parallel Implementation of Particle Swarm Optimization on FPGA

This brief proposes a parallel implementation, with fixed point, of the particle swarm optimization (PSO) algorithm on field-programmable gate array (FPGA). Results associated with the processing time and area occupancy on FPGA for several numbers of particles and dimensions were analyzed. Studies concerning the accuracy of the PSO response for the optimization problem using the Rastrigin function were also analyzed for the hardware implementation. The project was developed on the Virtex-6 xc6vcx240t 1ff1156 FPGA

Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil

COVID-19 is affecting healthcare resources worldwide, with lower and middle-income countries being particularly disadvantaged to mitigate the challenges imposed by the disease, including the availability of a sufficient number of infirmary/ICU hospital beds, ventilators, and medical supplies. Here, we use mathematical modelling to study the dynamics of COVID-19 in Bahia, a state in northeastern Brazil, considering the influences of asymptomatic/non-detected cases, hospitalizations, and mortality. The impacts of policies on the transmission rate were also examined. Our results underscore the difficulties in maintaining a fully operational health infrastructure amidst the pandemic. Lowering the transmission rate is paramount to this objective, but current local efforts, leading to a 36% decrease, remain insufficient to prevent systemic collapse at peak demand, which could be accomplished using periodic interventions. Non-detected cases contribute to a ∽55% increase in R0. Finally, we discuss our results in light of epidemiological data that became available after the initial analyses

Deep Neural Network Hardware Implementation Based on Stacked Sparse Autoencoder

Deep learning techniques have been gaining prominence in the research world in the past years, however, the deep learning algorithms have high computational cost, making them hard to be used to several commercial applications. On the other hand, new alternatives have been studied and some methodologies focusing on accelerating complex algorithms including those based on reconfigurable hardware has been showing significant results. Therefore, the objective of this work is to propose a neural network hardware implementation to be used in deep learning applications. The implementation was developed on a Field Programmable Gate Array (FPGA) and supports Deep Neural Network (DNN) trained with the Stacked Sparse Autoencoder (SSAE) technique. In order to allow DNNs with several inputs and layers on the FPGA, the systolic array technique was used in the entire architecture. Details regarding the designed implementation were evidenced, as well as the hardware area occupation in and the processing time for two different implementations. The results showed that both implementations achieved high throughput enabling Deep Learning techniques to be applied for problems with large data amounts

Proposal of the Tactile Glove Device

This project aims to develop a tactile glove device and a virtual environment inserted in the context of tactile internet. The tactile glove allows a human operator to interact remotely with objects from a 3D environment through tactile feedback or tactile sensation. In other words, the human operator is able to feel the contour and texture from virtual objects. Applications such as remote diagnostics, games, remote analysis of materials, and others in which objects could be virtualized can be significantly improved using this kind of device. These gloves have been an essential device in all research on the internet next generation called “Tactile Internet”, in which this project is inserted. Unlike the works presented in the literature, the novelty of this work is related to architecture, and tactile devices developed. They are within the 10 ms round trip latency limits required in a tactile internet environment. Details of hardware and software designs of a tactile glove, as well as the virtual environment, are described. Results and comparative analysis about round trip latency time in the tactile internet environment is developed

Parallel Implementation of Reinforcement Learning Q-Learning Technique for FPGA

Q-learning is an off-policy reinforcement learning technique, which has the main advantage of obtaining an optimal policy interacting with an unknown model environment. This paper proposes a parallel fixed-point Q-learning algorithm architecture implemented on field programmable gate arrays (FPGA) focusing on optimizing the system processing time. The convergence results are presented, and the processing time and occupied area were analyzed for different states and actions sizes scenarios and various fixed-point formats. The studies concerning the accuracy of the Q-learning technique response and resolution error associated with a decrease in the number of bits were also carried out for hardware implementation. The architecture implementation details were featured. The entire project was developed using the system generator platform (Xilinx), with a Virtex-6 xc6vcx240t-1ff1156 as the target FPGA

Reação do mercado acionário ao anúncio do goodwill apurado em combinação de negócios

Orientador: Professor Doutor Luciano Márcio SchererDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Sociais Aplicadas, Programa de Pós-Graduação em Contabilidade. Defesa : Curitiba, 27/08/2018Inclui referências: p.84-90Resumo: Existe uma necessidade crescente de pesquisas que consideram a influência do goodwill no mercado de ações, não de forma periférica, mas de forma central nas fusões e aquisições de empresa, pois é um fator de incerteza que pode ser traduzido em um maior risco pelos investidores e/ou uma sinalização de uma compra estratégica. Dessa forma, a presente dissertação, à luz da Hipótese do Mercados Eficientes (HME) na sua forma Semiforte, tem como objetivo investigar à intensidade do goodwill na combinação de negócios e a respectiva relevância para o mercado. Para operacionalização do estudo, foi levantada uma amostra com 269 combinações de negócios, entre os anos de 2010 e 2017, dentre as quais verificou-se em somente 97 eventos a informação quanto à intensidade de goodwill de maneira isolada. O estudo utilizou, como variável dependente, o retorno anormal das ações das empresas adquirentes e, como variável independente, a intensidade do goodwill na aquisição de uma empresa, ou seja, o quanto foi alocado em ágio por expectativa de rentabilidade futura do preço pago na adquirida. Os dados foram analisados por meio da estatística descritiva, comparação de médias, matriz de correlação e regressões multivariadas em mínimos quadrados ordinários. Como resultados, os testes paramétricos mostraram que houve retornos anormais estatisticamente significativos durante a Janela de Eventos. De modo adicional, foi possível verificar que existe uma tendência de quanto mais alta a intensidade do goodwill, maior o retorno anormal das ações. Como ponto de destaque, averiguou-se que o retorno anormal tende a ter uma correlação oposta à reação do mercado na data do anúncio da aquisição. Por fim, conclui-se que quando o mercado reage de forma negativa após o anúncio da aquisição, a divulgação de goodwill tende a refletir em retornos anormais positivos. Palavras-chave: Ativo intangível; Ágio; Intensidade do goodwill; Combinação de negócios.Abstract: There is a growing need for research that considers the influence of goodwill on the stock market, not peripherally way, but centrally in corporate mergers and acquisitions, as it is a factor of uncertainty that can be translated into increased risk by investors and/or a sign of a strategic purchase. The present dissertation, in the light of the Efficient Market Hypothesis (HME), aims to investigate the intensity of goodwill in the business combination and its relevance to the market. To conclude the study, a sample of 269 business combinations was collected between 2010 and 2017, which in only 97 events it was possible to verify the information regarding the intensity of goodwill. The study used as a dependent variable the abnormal return of the shares of the acquiring companies and as an independent variable the intensity of goodwill in the acquisition of a company, meaning how much was allocated in goodwill by an expectation of future profitability of the price paid in the acquired company. The respective data analysis was performed through descriptive statistics, mean comparison, correlation matrix and multivariate regressions in ordinary minimum squares. As indicated results, parametric tests showed that there was statistically significant abnormal returns during the Event. In this work, it was also possible to verify that there is a tendency of the higher the intensity of goodwill, the greater the abnormal return of the shares. As an important point, it was found that the abnormal return tends to have a correlation opposite to the market reaction on the date of the acquisition announcement. Finally, it is concluded that when the market reacts negatively after the announcement of the acquisition, the disclosure of goodwill tends to reflect in abnormal positive returns. Keywords: Intangible assets; Goodwill; Intensity of goodwill; Business combination

High-Performance Parallel Implementation of Genetic Algorithm on FPGA

Genetic algorithms (GAs) are used to solve search and optimization problems in which an optimal solution can be found using an iterative process with probabilistic and non-deterministic transitions. However, depending on the problem’s nature, the time required to find a solution can be high in sequential machines due to the computational complexity of genetic algorithms. This work proposes a full-parallel implementation of a genetic algorithm on field-programmable gate array (FPGA). Optimization of the system’s processing time is the main goal of this project. Results associated with the processing time and area occupancy (on FPGA) for various population sizes are analyzed. Studies concerning the accuracy of the GA response for the optimization of two variables functions were also evaluated for the hardware implementation. However, the high-performance implementation proposed in this paper is able to work with more variable from some adjustments on hardware architecture. The results showed that the GA full-parallel implementation achieved throughput about 16 millions of generations per second and speedups between 17 and 170,000 associated with several works proposed in the literature

Multi-Objective Optimization of Electric Arc Furnace Using the Non-Dominated Sorting Genetic Algorithm II

Combining classical technologies with modern intelligent algorithms, this paper introduces a new approach for the optimisation and modelling of the EAF-based steel-making process based on a multi-objective optimisation using evolutionary computing and machine learning. Using a large amount of real-world historical data containing 6423 consecutive EAF heats collected from a melt shop in an established steel plant this work not only creates machine learning models for both EAF and ladle furnaces but also simultaneously minimises the total scrap cost and EAF energy consumption per ton of scrap. In the modelling process, several algorithms are tested, tuned, evaluated and compared before selecting Gradient Boosting as the best option to model the data analysed. A similar approach is followed for the selection of the multi-objective optimisation algorithm. For this task, six techniques are tested and compared based on the hypervolume performance indicator to just then select the Non-dominated Sorting Genetic Algorithm II ( NSGA-II ) as the best option. Given this applied research focus on a real manufacturing process, real-world constraints and variables such as individual scrap price, scrap availability, tap additives and ambient temperature are used in the models developed here. A comparison with an equivalent EAF model from the literature showed a 13% improvement using the mean absolute error in the EAF energy usage prediction as a comparative metric. The multi-objective optimisation resulted in reductions in the energy consumption costs that ranged from 1.87% up to 8.20% among different steel grades and scrap cost reductions ranging from 1.15% up to 5.2%. The machine learning models and the optimiser were ultimately deployed with a graphical user interface allowing the melt-shop staff members to make informed decisions while controlling the EAF operation