The role of neuropsychology in the assessment and treatment of persons with epilepsy.

Several areas of current interest in the neuropsychology of epilepsy are briefly reviewed in this article. These include variables pertaining to seizures, seizure history, antiepileptic drugs, and methods of neuropsychological evaluation. It is apparent that epilepsy is a multifaceted area: Psychologists not only can be of great assistance to patients with this condition, but may also learn a great deal from this complex disorder. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Anthropometric data for describing the kinematics of the human hand.

The major goal of this investigation was to collect statistically-based anthropometry describing the kinematics of the human hand and to model this anthropometry as a function of external hand measurements, so that it may be predicted noninvasively. Joint centres were anatomically estimated as the centre of curvature of the head of the bone proximal to the given joint. Joint centres determined using Reuleaux's method for PIP and DIP were within 1.4 mm of this anatomical estimate. Models using bone length as the independent variable explain more than 97% of the variability in the anatomically estimated joint centre position along the mid-line of the bone. Models for estimating the lengths of the kinematic segments using external hand length as the independent variable account for between 49 and 99% of the variability in segment length. Models for estimating the axial location of the finger MCP and thumb CMC joints with respect to the distal wrist crease using external hand length as the independent variable account for between 82 and 96% of the variability in these locations. Models for estimating the radio-ulnar location of the finger MCP and thumb CMC joints with respect to the long axis of the third metacarpal using external hand breadth as the independent variable account for between 30 and 74% of the variability in these locations.     

Properties of titanium-containing sol-gel films modified with copper and vanadium oxides

Translated from Steklo i Keramika, No. 1, pp. 4–6, January, 1994.     

The effect of Gaussian error in object recognition

In model based recognition, the goal is to locate an instance of one or more known objects in an image. The problem is compounded in real images by the presence of clutter, occlusion, and sensor error, which can lead to “false negatives”, failures to recognize the presence of the object, and “false positives”, in which the algorithm incorrectly identifies an occurrence of the object. The probability of either event is affected by parameters within the recognition algorithm, which are almost always chosen in an ad-hoc fashion. The effect of the parameter values on the likelihood that the recognition algorithm will make a mistake are usually not understood explicitly. To address the problem, we explicitly model the noise that occurs in the image. In a typical recognition algorithm, hypotheses about the position of the object are tested against the evidence in the image, and an overall score is assigned to each hypothesis. We use a statistical model to determine what score a correct or incorrect hypothesis is likely to have, and use standard binary hypothesis testing techniques to distinguish correct from incorrect hypotheses. Using this approach, we can compare algorithms and noise models, and automatically choose values for internal system thresholds to minimize the probability of making a mistake