Kurtosis Approach to Solution of a Nonlinear ICA Problem

An algorithm for solving a particular nonlinear independent-component-analysis (ICA) problem, that differs from prior algorithms for solving the same problem, has been devised. The problem in question of a type known in the art as a post nonlinear mixing problem is a useful approximation of the problem posed by the mixing and subsequent nonlinear distortion of sensory signals that occur in diverse scientific and engineering instrumentation systems

Object Recognition using Feature- and Color-Based Methods

An improved adaptive method of processing image data in an artificial neural network has been developed to enable automated, real-time recognition of possibly moving objects under changing (including suddenly changing) conditions of illumination and perspective. The method involves a combination of two prior object-recognition methods one based on adaptive detection of shape features and one based on adaptive color segmentation to enable recognition in situations in which either prior method by itself may be inadequate. The chosen prior feature-based method is known as adaptive principal-component analysis (APCA); the chosen prior color-based method is known as adaptive color segmentation (ACOSE). These methods are made to interact with each other in a closed-loop system to obtain an optimal solution of the object-recognition problem in a dynamic environment. One of the results of the interaction is to increase, beyond what would otherwise be possible, the accuracy of the determination of a region of interest (containing an object that one seeks to recognize) within an image. Another result is to provide a minimized adaptive step that can be used to update the results obtained by the two component methods when changes of color and apparent shape occur. The net effect is to enable the neural network to update its recognition output and improve its recognition capability via an adaptive learning sequence. In principle, the improved method could readily be implemented in integrated circuitry to make a compact, low-power, real-time object-recognition system. It has been proposed to demonstrate the feasibility of such a system by integrating a 256-by-256 active-pixel sensor with APCA, ACOSE, and neural processing circuitry on a single chip. It has been estimated that such a system on a chip would have a volume no larger than a few cubic centimeters, could operate at a rate as high as 1,000 frames per second, and would consume in the order of milliwatts of power

Method and System for Object Recognition Search

A method for object recognition using shape and color features of the object to be recognized. An adaptive architecture is used to recognize and adapt the shape and color features for moving objects to enable object recognition

Shape and Color Features for Object Recognition Search

A bio-inspired shape feature of an object of interest emulates the integration of the saccadic eye movement and horizontal layer in vertebrate retina for object recognition search where a single object can be used one at a time. The optimal computational model for shape-extraction-based principal component analysis (PCA) was also developed to reduce processing time and enable the real-time adaptive system capability. A color feature of the object is employed as color segmentation to empower the shape feature recognition to solve the object recognition in the heterogeneous environment where a single technique - shape or color - may expose its difficulties. To enable the effective system, an adaptive architecture and autonomous mechanism were developed to recognize and adapt the shape and color feature of the moving object. The bio-inspired object recognition based on bio-inspired shape and color can be effective to recognize a person of interest in the heterogeneous environment where the single technique exposed its difficulties to perform effective recognition. Moreover, this work also demonstrates the mechanism and architecture of the autonomous adaptive system to enable the realistic system for the practical use in the future

Sequential State Estimates Subject to Equality Constraints

Typical probability-based sequential state estimators generate point estimates which, while mathematically optimal, may be physically impossible estimates of the system state. For example, if a state variable of a dynamic system can attain only a discrete set of values, it is probable that a probability-based estimate of that state variable will not attain one of the elements of the discrete set of values. While, in many problems, this may not greatly affect the overall design of a system in which the estimator is a component, there are many situations in which this result might produce unreliable results, including system instability. In this paper, a sequential estimator is discussed which generates state estimates for linear, time-invariant discrete-time dynamic systems in which the state is subject to an instantaneous equality constraint. That is, at each sample time the state is constrained to lie in a given region of the state space. For the example above, the point estimate of the state is constrained to attain one of the set of discrete values which the state variable must attain. It is shown that the solution of this problem, at each time instant, requires only the unconstrained linear sequential estimate at that instant and the instantaneous constraints which define the constraint region. If the linear estimate satisfies the constraints, then it is also the constrained estimate. If the unconstrained estimate does not satisfy the constraints, then the solution is generated from the solution of a set of static nonlinear equations

A Signal Processing Technique for Improving the Accuracy of MEMS Inertial Sensors

Navigation, guidance and control for small space vehicles require inertial measurement sensors which are small, inexpensive, low power, reliable and accurate. Micro inertial sensors, such as MEMS gyroscopes, can provide small, inexpensive, low power devices; however, the accuracy of these devices is insufficient for many space applications. Signal processing methods can be used to provide the necessary accuracy. The individual outputs of many nominally identical micro sensors can be combined to generate a single accurate measurement. An extended Kalman filter (EKF) which includes the dynamics of every sensor can be used for such a combination; however, the \u27curse of dimensionality\u27 limits the number of sensors which can be used. In this paper, a new EKF technique for combining many sensors is proposed which, using a common nominal model for the micro sensors and a single EKF with the state dimension of a single sensor, has accuracy comparable to the high dimensional EKF and is significantly more accurate than a single sensor

Sequential State Estimates Subject to Multiple Equality Constraints

Typical probability-based sequential state estimators generate point estimates which, while mathematically optimal, may be physically impossible estimates of the system state. For example, if a state variable of a dynamic system can attain only a discrete set of values, it is probable that a probability-based estimate of that state variable will not attain one of the elements of the discrete set of values. While, in many problems, this may not greatly affect the overall design of a system in which the estimator is a component, there are many situations in which this result might produce unreliable results, including system instability. In this paper, a sequential estimator is discussed which generates state estimates for linear, time-invariant discrete-time dynamic systems in which the state is subject to an instantaneous equality constraint. That is, at each sample time the state is constrained to lie in a given region of the state space. For the example above, the point estimate of the state is constrained to attain one of the set of discrete values which the state variable must attain. It is shown that the solution of this problem, at each time instant, requires only the unconstrained linear sequential estimate at that instant and the instantaneous constraints which define the constraint region. If the linear estimate satisfies the constraints, then it is also the constrained estimate. If the unconstrained estimate does not satisfy the constraints, then the solution is generated from the solution of a set of static nonlinear equations

A Dynamic System Matching Technique for Improving the Accuracy of Subsystems

Many complex systems can be described as a combination of subsystems, that is, the elements of the complex system are themselves systems. A subsystem design is often modelled by a differential equation with several coefficients rather than as a constant, as a resistor is modelled, or as a single parameter equation, as a capacitor is modelled. Manufacturing errors produce subsystems whose coefficients vary from the desired coefficients of subsystem design. The coefficient variations contribute to modelling errors in the subsystem, and thus to modelling errors in the complex system. As is the case of elements in an electronic circuit, one way of controlling the variability of a manufactured subsystem is to impose tight control on the manufacturing process so that the component values are within some specified, acceptable bounds. This can be expensive and, in some applications, it may be impossible to achieve acceptable bounds. In a recent paper, [2], the authors presented a method for combining the measurements from many MEMS gyroscopes using a technique based on the concept of dynamic element matching. This was shown to effectively control the modelling errors when the outputs of the many micro-gyroscopes are corrupted by manufacturing errors. This technique essentially transforms the effects of the many manufacturing errors into an additive white noise in the subsystem output. The effect of the additive white noise can be effectively decreased by applying a filter, e.g., a Kalman filter, to the output signal. In this paper, this technique is generalized to applying the concept of dynamic element matching to complex subsystems which may be \u27elements\u27 in more complex systems. Because the method deals with systems rather than elements, it will be called dynamic system matching