Block-processing adaptive filter for human eye movements

Adaptive processing techniques can be divided into two categories: block processing and recursive methods. With block processing methods, incoming data are divided into blocks, and each block is processed as a whole to estimate predictor coefficients. With recursive methods, predictor parameters are updated as each new data point becomes available and computed thorugh a set of recursive algorithms. In this paper, five block processing adaptive filters are used in the prediction of the human eye movements. They are two-point-linear predictor (TPLP), five-point-quadratic predictor (FPQP), nine-point-cubic predictor (NPCP), polynomial-filter predictor 1 (PFP1), which is a linear convex combination of a TPLP and an FPQP, and polynomial-filter predictor 2 (PFP2), which is a linear convex combination of a TPLP, as FPQP, and an NPCP. These predictors were tested with various signals such as saccadic eye movements, sinusoidal, cubic, triangular, and parabolic signals. The results show that the TPLP is the best predictor for triangular signal and the NPCP is the best predictor for sinusoidal signal. Conversely, the FPQP is the best predictor for parabolic and cubic signals. The results also suggest that the PFP1 and PFP2 show significant improvement over that of the TPLP, FPQP, and NPCP in long-range prediction.     

Re-evaluating systems engineering concepts using systems thinking

Humans (individually, on teams, and in organizations) can follow simple processes to increase their probability of success. Many authors, both technical and nontechnical, have described processes for doing various things like designing a system, attaining business excellence, and solving personal and professional problems. The amazing similarities in these diverse processes suggest that there is a general process that might be closely related to human thinking. This general process was abstracted into the SIMILAR Process. This paper shows how the SIMILAR Process was used to help redescribe the requirements discovery process and system design process     

Frequency limitations and optimal step size for the two-point central difference derivative algorithm with applications to human eye movement data

There are many algorithms for calculating derivatives. The two-point central difference algorithm is the simplest. Besides simplicity, the two most important characteristics of this algorithm are accuracy and frequency response. The frequency content of the data prescribes a lower limit on the sampling rate. The smoothness and accuracy of the data determine the optimal step size. We discuss the low-pass filter characteristics of this algorithm and derive the optimal step size for two types of human eye movement data. To calculate the velocity of fast (saccadic) eye movements, the algorithm should have a cutoff frequency of 74 Hz. For typical slow (smooth pursuit) eye movements, a step size of 25 or 50 ms is optimal.     

The perceptual illusion of baseball's rising fastball and breaking curveball.

The rising fastball and the breaking curveball are impossible according to principles of physics and physiology, yet many baseball players claim they exist. The simulation and model presented suggest that the rising fastball and breaking curveball are perceptual illusions caused by the batter misestimating the speed of the pitch. This model uses signals from known primary visual processes only. This model was enhanced by adding an acceleration term. The enhanced model more accurately predicts the position of the ball when it crosses the plate. These models are compared and contrasted to models by M. McBeath (1990) and R. J. Bootsma (in press). Sensitivity analyses have shown that the model and simulation are robust with respect to their parameter values. The model is most sensitive to the estimated distance to the ball, and the simulation is most sensitive to the estimated speed of the pitch. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Tutorial on Quality Function Deployment

ABSTRACT

Quality function deployment (QFD) helps to introduce the idea of quality in early phases of the design cycle and to reevaluate quality considerations throughout the system's entire life cycle. This article presents a tutorial example of using QFD to design a product. It shows which quality controls in the manufacturing process are most important to ensure customer satisfaction.     

Predicting final eye position halfway through a saccade

When the visual environment is to be changed during a saccadic eye movement, it is useful to predict the final eye position before the eye comes to rest. We have built a microcomputer-based instrument to make such predictions. Two techniques were used: one based on the saccadic peak-velocity versus magnitude relationship, and the second based on peak-velocity occurring in the middle of the saccade. The second technique has been tailored to take advantage of the differences between temporal and nasal saccades. Depending on saccade duration, final eye position was predicted 4 to 60 ms before the end of the saccade.     

Improving Product Design Using Quality Function Deployment: The School Furniture Case in Developing Countries

This paper presents a quality function deployment (QFD) analysis of the design of school furniture in developing countries, using Costa Rica as the baseline. The dynamic hierarchy process model for QFD was used to help the product development team make effective decisions in satisfying the requirements of the customer constrained by limited resources. A number of total quality management (TQM) tools were employed during the development of the school furniture solution. A dynamic, cross-functional team organization was used. A simple form of quality function deployment was used to identify the desirable product design, safety, and service features.     

The ideal moment of inertia for a baseball or softball bat

In selecting a baseball or a softball bat, both weight and weight distribution should be considered. However, these considerations must be individualized, because there is large variability in how different batters swing a bat and in how each batter swings different bats. Previous research has defined the ideal bat weight as that weight that maximizes the batted-ball speed based on measurements of individual swings, the concept of the coefficient of restitution, and the laws of conservation of momentum. In this paper, a method is given that extends this approach to recent bat designs where the moment of inertia can be specified. The data presented in this paper show that all of the players in our study would probably profit from using end-loaded bats.