The object of the paper is the development of a new human computer communication (HCC) interface for configuration and animation of a signing semantroid. The work presented in this paper builds on two recent research results: (1) the concepts of semantroid (=semantic android - an avatar limited to head and hands), and semantic intensity (=semantic content); and (2) the KUI (keyboard user interface) method, a keyboard-based method of configuration and animation of 3D hand gestures and facial expressions. In particular, the paper focuses on: (1) development of a new set of keyboard-controlled facial parameters of the semantroid for animation of American Sign Language (ASL) non-manuals, and (2) design of an efficient HCC interface for input and encoding recording of ASL handshapes and facial expressions.

The paper focuses on the development of a human-computer communication method which utilizes the user's typing skills to control the facial expression of a computer-generated 3D face. The method is based on the realization that the human face is a movable and deformable system with 26 degrees of freedom (the same number as the letters of the English alphabet). Therefore it is possible to create a parameterized graphical facial model confined to a set of 26 parameters each one controlled by a letter key. The method is an extension of the KUI technique (Adamo-Villani and Beni, 2004; Adamo-Villani and Beni, 2003) recently developed to encode hand gestures.

Swarms are physical realizations of self-organizing distributed robotic systems (DRS). This paper provides a rigorous analysis of swarm behavior and introduces a new methodology for using swarms to solve DRS pattern reconfiguration problems. The authors introduce the linear swarm model and show that it is an iterative method for asynchronously solving linear systems of equations under physically relevant constraints. The main result of the paper is a proof of a sufficient condition for the asynchronous convergence of a linear swarm to a synchronously achievable configuration. This is important since a large class of DRS self-organizing tasks can be mapped into reconfigurations of patterns in swarms.

A new, biologically motivated approach to self-organizing pattern formation and function determination in a distributed array of many identical cells is presented. Methods for asynchronous pattern formation using linear swarm models, pattern formation by boundary pattern generation and propagation, and differentiation by message coding are proposed. A gradient descent based algorithm for linear swarms that guarantees convergence is introduced. 2D pattern formation in an array of identical cells is demonstrated by simulation.

We have studied the rate of reconfiguration of Swarms with external disturbances, such as occurs in problems of fire fighting with constant resources against changing fire lines. We have carried out the investigation by using actual computer experiments on Swarms composed of 10 to 18 CPU's. The goal was to establish the dependence of the reconfiguration rate on the actual physical parameters of the CPU's used, the number of units, the initial configuration errors and the total resources of the Swarm. The results confirm qualitatively earlier finding by computer simulation. They are also confirming the theoretical result that, under rather general conditions, the Swarm can be made to converge in a time which is independent on its size. The type of disturbances under which this is possible are the most likely to occur in physical, practical problems. New results on the dependence of the rate of convergence on the shape of the external signal, confirm the intuitive notion that the Swarm will reconfigure faster if the disturbance has high spatial frequency since the parallelism of the Swarm algorithm is being utilized to a larger extent than for low frequency disturbances.>

Investigates the stability of synchronized distributed control of 1D and 2D swarm structures. A new distributed control method for swarm structures is proposed. The dynamic range of swarm structures under synchronized distributed control is discussed. The authors prove that both 1D and 2D swarm structures are stable with appropriate weights in the sum of adjacent errors if the vertical disturbances vary sufficiently more slowly than the response time of the servo systems of the agents. It is also proven that the Lyapunov functions of swarm structures are monotonically decreasing, and tend to zero as the time goes to infinity; hence there is no local minimum.>

A new type of a swarm, named multi-agent supporting system (MASS), is introduced for supporting structures where the configuration formed by many agents must be maintained even though there exist unknown disturbances. A control system for a MASS is also proposed on the basis of a new estimator of disturbance. The proposed disturbance estimator is described by inverting the stable digital inverse system. With this disturbance estimator, the unknown disturbance acting on each agent in the MASS is cancelled by the input, and influences less the output of the system. A computer simulation has been executed to demonstrate the effectiveness of the proposed method in comparison with the control method without the disturbance estimator.>

A new operational definition of cluster is proposed, and a fuzzy clustering algorithm with minimal biases is formulated by making use of the maximum entropy principle to maximize the entropy of the centroids with respect to the data points (clustering entropy). The authors make no assumptions on the number of clusters or their initial positions. For each value of an adimensional scale parameter /spl beta/', the clustering algorithm makes each data point iterate towards one of the cluster's centroids, so that both hard and fuzzy partitions are obtained. Since the clustering algorithm can make a multiscale analysis of the given data set one can obtain both hierarchy and partitioning type clustering. The relative stability with respect to /spl beta/' of each cluster structure is defined as the measurement of cluster validity. The authors determine the specific value of /spl beta/' which corresponds to the optimal positions of cluster centroids by minimizing the entropy of the data points with respect to the centroids (clustered entropy). Examples are given to show how this least biased method succeeds in getting perceptually correct clustering results.>

The authors extend the problem of stationary waves in cyclic swarms to two dimensions. It is shown that for the systems of linear equations describing a difference equation with periodic boundary conditions and specific conservation conditions. One can find an algorithm to solve asynchronously the systems of linear equations. Conversely, if the authors have an algorithm to solve asynchronous systems of linear equations describing a difference equation with periodic boundary conditions, they can show that the result is the solution of the same systems of linear equations under specific conservation conditions.

The realization of sensing swarms composed of value-specific sensing elements requires distributed cooperation among the members of the swarm. The number of value-specific sensing elements must change under changing external conditions. The recalculations of the optimal distribution of sensing elements can be carried out from the knowledge of the (changed) average value of the sensed feature. Mathematically this can be done by using the maximum-entropy principle. However, distribution operation requires solving two additional problems: the new average value must be known to each sensing element, and a new configuration must be achieved autonomously (i.e. under distributed control). For the solution to these problems the authors introduce a circulating swarm model in which the elements of the swarm circulate in a ring while performing the sensing operations and the self-organization. They show which elementary operations can be used in an algorithm for self-organization of the swarm in a new configuration optimized for sensing.>

Any mobile robotic task requires information about the environment so that an appropriate physical action may be taken. A sensory action allows the robot to collect this necessary information. This paper is concerned with developing a timeoptimal sensing strategy. We propose to relate the time of a particular action to its usefulness in the robotic task as the basis of the strategy. The specific approach requires dynamic models of sensors, kinematic models of robots, error models of the sensory and kinematic processes, and a metric to compute the distance between physical states of the robot. The sensing strategy is described as the extremization of a payoff function defined in terms of the sensory action time and the localization accuracy of the current physical state of the robot with respect to its goal state using sensory information.

The authors consider the specific problems most likely to be critical to progress in distributed robotic systems. This perspective suggests that the most promising approach to the design of practical algorithms for complex tasks might be to develop hybrid solutions comprising three stages: (1) use of interacting many-body techniques to form one dimensional substructures in continuous space; (2) application of cellular robot systems techniques to reconfigure the substructure with the desired qualities; and (3) application of standard control theory techniques for maintaining the configuration during subsequent operation.>

Proposes a new method of robot self-organization for a class of robotic tasks which cannot be carried out by a single robot but must be carried out by a group of robots. The authors make use of a deterministic 'reverse' annealing clustering method based on a maximum-minimum entropy solution. They show that this reverse annealing method, which results in a better way of escaping local minima than other fuzzy clustering methods, is well suited to applications in distributed robotics since it provides a natural way of following the self-organizing evolution of a system of robots. In particular, two opposite classes of self-organizing behavior are studied: the self-organization 'from few groups to many units' and the self-organization 'from many units to a few groups'.>

The authors present a fuzzy validity criterion based on a validity function which identifies compact and separate fuzzy c-partitions without assumptions as to the number of substructures inherent in the data. This function depends on the data set, geometric distance measure, distance between cluster centroids and more importantly on the fuzzy partition generated by any fuzzy algorithm used. The function is mathematically justified via its relationship to a well-defined hard clustering validity function, the separation index for which the condition of uniqueness has already been established. The performance of this validity function compares favorably to that of several others. The application of this validity function to color image segmentation in a computer color vision system for recognition of IC wafer defects which are otherwise impossible to detect using gray-scale image processing is discussed.>

Conditions are presented for the global asymptotic stability of the hybrid position/force control of robot manipulators, using Lyapunov's direct method. Although hybrid control schemes have been studied in depth, the stability problem has not been clarified completely. The authors deal with the stability of the hybrid control scheme in Cartesian coordinates of the work space, especially from the viewpoint of coupling between position and force control. Results show not only the global asymptotic stability conditions but also the physical interpretation or meaning of the stability conditions by citing an example of a two-degree-of-freedom manipulator.>

The use of color vision as a tool for machine vision provides a powerful means of performing rapid, accurate inspection of microelectronic structures. Since microelectronics fabrication is in large part a thin film technology, and since thin films have characteristic colors, this approach extends the range of optical analysis possible. We have constructed a color vision system used to measure thin film dielectric materials. Color matching is performed rapidly (<100 msecs) and with resolution better than 20 Å. The resolution limit has been so far set only by the samples available for measurement. We have further extended the capability of the system beyond simple color matching to identify true unknown samples whose thickness fall within the range of the original system database. Feed-back control of the illumination has been incorporated into the system; we present data on the effect of shifts in lighting or magnification. Microscopic, as well as broad area measurements (for uniformity) can be made.

Connectivity analysis (maximally connected component labeling plus optional geometric feature collection) has previously been applied to only 2-dimensional (2-D) binary valued images. By carefully examining the property of 6-connectivity, it is found that it may also be applied to 2-D multi-valued (m-ary) images, which, together with thresholders, recognizers or classifiers of multiple valued output, promises more efficient low level processing. The idea is further generalized to multi-dimensional spaces so that the connectivity analysis may be performed on n-D (with n ≥ 1) m-ary (with m ≥ 2) images. Formal definition of 6-connectivity in n-D space and a labeling algorithm is presented followed by a brief discussion of its potential applications.

We present a new method of automated inspection of microelectronic structures. The method is based on color rather than black and white vision, and is a first such application of color vision to inspection for microelectronics fabrication. We describe the general method and demonstrate its practical implementation in the measurement of oxide thicknesses. A key result of this work is to have established sensitivity criteria for color detection in microelectronic structures. The resolution achieved allows us to measure differences in oxide thickness to approximately 30 Angstroms or better. By using the Ohta coordinates, our system can discrimimate between cyclically repeating hues. This determination can be done very rapidly (approximately 100 milliseconds) and does not require a complex (and thus expensive) computer system. An additional advantage of our method is the possibility of more easily and flexibly performing oxide thickness measurements in situ, than can be accomplished with standard techniques, such as ellipsometry.

We present a new method of inference applicable to robots and other intelligent machines. Inferences drawn by intelligent machines are based on measurements gathered through sensory perception. We demonstrate that the methods for managing uncertainty of meaning, which recently have been extended to a wide variety of non-human systems, generally yield qualitatively incorrect results when applied to the uncertainty of evidence available to an intelligent machine. We show that even in very simple machines, no amount of sophistication in the mathematical algorithms can compensate for incorrect assumptions about the physical model. Conversely, we also demonstrate that once the essential structure of the physical model is correctly described, classical probability theory yields simple algorithms for the evaluation of the degree of evidence as it propagates through complex inference networks, including diagnostic trees and multicausal nets. As a first application, we have derived the probability algorithms relevant to diagnosing the malfunctioning of a thermal evaporator. For this system, an inference network has been constructed and compared to an implementation based on a MYCIN-type expert system. The laboratory implementation of the system is also described.

We give a rigorous interpretation of the ways of combining evidence in Mycin-type expert systems. We demonstrate the probabilistic inexactness of the Mycin model and its incompleteness for combining evidence from quantitative data extracted in well-defined physical systems. Finally, we present a new method of combining evidence including both logical and physical combinations of evidence. the method is applicable to intelligent machines capable of gathering evidence via sensors.

We describe a feasibility demonstration of an 'intelligent' robot system for inspection and testing of electronic properties of devices. The system consists of a Seiko RT-3000 robot, an Automatix Autovision II vision system, two CCD Panasonic cameras plus peripherals. The latter include two testing probe-stations, a passive-hand with magnetic pick-up fingertips, racks and pallets. The system is interconnected through serial RS-232 ports and I/O switches. The system is capable of relocating devices with a precision of 0.1 mm. The initial part-presentation requires only a ≈ 10 cm precision. The system is presently set up to demonstrate the measurement of capacitance and dark- current of InGaAs PIN photodetectors.