Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social cooperation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the very recent state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

Barbieri & Peters (B&P) question Gartzke & Li’s (G&L’s) conclusion that the contradictory findings between Barbieri andOneal & Russett on the trade–conflict question can be explained by their use of alternative measures. There are problems with G&L’s analysis. First, G&L’s findings are based on analyses with measures incompatible with Barbieri’s. Second, G&L adopt measures that are not truly dyadic. Third, G&L draw erroneous conclusions from their mathematics. B&P explain these problems and present empirical analyses that show that even when controlling for economic openness, as G&L propose, dyadic interdependence is still positively associated with conflict.B&P find support for G&L’s conclusion that openness promotes peace.

A method for the automatic measurement of femur length in fetal ultrasound images is presented. Fetal femur length measurements are used to estimate gestational age by comparing the measurement to a typical growth chart. Using a real-time ultrasound system, sonographers currently indicate the femur endpoints on the ultrasound display station with a mouse-like device. The measurements are subjective, and have been proven to be inconsistent. The automatic approach described exploits prior knowledge of the general range of femoral size and shape by using morphological operators, which process images based on shape characteristics. Morphological operators are used first to remove the background (noise) from the image, next to refine the shape of the femur and remove spurious artifacts, and finally to produce a single pixel-wide skeleton of the femur. The skeleton endpoints are assumed to be the femur endpoints. The length of the femur is calculated as the distance between those endpoints. A comparison of the measurements obtained with the manual and with the automated techniques is included.

This paper describes the definition and testing of a new type of median filter for images. The topological median filter implements some existing ideas and some new ideas on fuzzy connectedness to improve, over a conventional median filter, the extraction of edges in noise. The concept of alpha-connectivity is defined and used to create an algorithm for computing the degree of connectedness of a pixel to all the other pixels in an arbitrary neighborhood. The resulting connectivity map of the neighborhood effectively disconnects peaks in the neighborhood that are separated from the center pixel by a valley in the brightness topology. The median of the connectivity map is an estimate of the median of the peak or plateau to which the center pixel belongs. Unlike the conventional median filter, the topological median is relatively unaffected by disconnected features in the neighborhood of the center pixel. Four topological median filters are defined. Qualitative and statistical analyses of the four filters are presented. It is demonstrated that edge detection can be more accurate on topologically median filtered images than on conventionally median filtered images.

This paper addresses the problem of automotie akill acquisition by a mbot. It reports that six triols of a reach-grasp-release-rYt~ct skill are suflicient for learning a canonical description of the task under the follouting circumstances: The mbot is Robonaut, NASA's space-capable, desterous humanoid. Robonaut was teleoperated by a person using full immersion Virtual Reality technology that tmnsforms the openator's a n and hand motions into those of the robot. The operator's sole S O U I C~ of real-time feedback was visual. DUTing the s*: trials all of the Robot's senaory inputs and motor control pammeters were recorded as time-series. Later the time-series from each trial was paditioned into the same number of episodes as a function of changes in the motor parameter sequence. The episodes were time normalized and avemged D C M S I trials The resultant motor parameter sequence and sensor signals w e r~ used to eontml the mbot without the teleopemtor. The mbot wuas able to perform the task autonomously with mbot starting positions and object locations both similar to, and different from the original trials.Fig. 1. Robonaut, NASA's space capable humanoid robot