Bayesian Action–Perception Computational Model: Interaction of Production and Recognition of Cursive Letters

In this paper, we study the collaboration of perception and action representations involved in cursive letter recognition and production. We propose a mathematical formulation for the whole perception–action loop, based on probabilistic modeling and Bayesian inference, which we call the Bayesian Action–Perception (BAP) model. Being a model of both perception and action processes, the purpose of this model is to study the interaction of these processes. More precisely, the model includes a feedback loop from motor production, which implements an internal simulation of movement. Motor knowledge can therefore be involved during perception tasks. In this paper, we formally define the BAP model and show how it solves the following six varied cognitive tasks using Bayesian inference: i) letter recognition (purely sensory), ii) writer recognition, iii) letter production (with different effectors), iv) copying of trajectories, v) copying of letters, and vi) letter recognition (with internal simulation of movements). We present computer simulations of each of these cognitive tasks, and discuss experimental predictions and theoretical developments

Towards High-Level Specification, Synthesis, and Virtualization of Programmable Logic Designs

Current FPGA design flows do not readily support high-level, behavioural design or the use of run-time reconfiguration. Designers are thus discouraged from taking a high-level view of their systems and cannot fully exploit the benefits of programmable hardware. This paper reports on our advances towards the development of design technology that supports behavioural specification and compilation of FPGA designs and automatically manages FPGA chip virtualization

Determination of moisture content in lyophilized mannitol through intact glass vials using NIR micro-spectrometers

Determination of moisture content in lyophilized solids is fundamental to predict quality and stability of freeze-dried products, but conventional methods are time-consuming, invasive and destructive. The aim of this study was to develop and optimize a fast, inexpensive, noninvasive and nondestructive method for determination of moisture content in lyophilized mannitol, based on an NIR micro-spectrometer instead of a conventional NIR spectrometer. Measurements of lyophilized mannitol were performed through the bottom of rotating glass vials by means of a reflectance probe. The root mean standard error of prediction (RMSEP) and the correlation coefficient (R²pred), yielded by the pre-treatments and calibration method proposed, was 0.233% (w/w) and 0.994, respectively.<br>A determinação do conteúdo de umidade em sólidos liofilizados é fundamental para se prever a qualidade e a estabilidade de produtos liofilizados, mas os métodos convencionais consomem muito tempo, são invasivos e destrutivos. O objetivo desse estudo foi desenvolver e otimizar um método rápido, econômico, não invasivo e não destrutivo para a determinação do conteúdo de umidade em manitol liofilizado, com base em microespectrômetro de infravermelho próximo ao invés de um espectrômetro de infravermelho próximo convencional. As medidas de manitol liofilizado foram realizadas através do fundo de recipiente de vidro em rotação por meio de sonda de reflectância. A raíz do erro médio padrão de predição (RMSEP) e o coeficiente de correlação (R²pred) obtidos pelo prétratamento e pelo método de calibração proposto foram, respectivamente, 0,233% (p/p) e 0,994