Binorm—a fortran subroutine to calculate the percentiles of a standardized binormal distribution

BINORM is a FORTRAN subroutine for calculating the percentiles of a standardized binormal distribution. By using a linear transformation, the percentiles of a binormal distribution can be obtained. The percentiles of a binormal distribution are useful for plotting purposes, for establishing confidence intervals, and for sampling from a mixed population that consists of two normal distributions.     

Classification of electroencephalogram signal for the detection of epilepsy using Innovative Genetic Programming

Epilepsy, sometimes called seizure disorder, is a neurological condition that justifies itself as a susceptibility to seizures. A seizure is a sudden burst of rhythmic discharges of electrical activity in the brain that causes an alteration in behaviour, sensation, or consciousness. It is essential to have a method for automatic detection of seizures, as these seizures are arbitrary and unpredictable. A profound study of the electroencephalogram (EEG) recordings is required for the accurate detection of these epileptic seizures. In this study, an Innovative Genetic Programming framework is proposed for classification of EEG signals into seizure and nonseizure. An empirical mode decomposition technique is used for the feature extraction followed by genetic programming for the classification. Moreover, a method for intron deletion, hybrid crossover, and mutation operation is proposed, which are responsible for the increase in classification accuracy and a decrease in time complexity. This suggests that the Innovative Genetic Programming classifier has a potential for accurately predicting the seizures in an EEG signal and hints on the possibility of building a real‐time seizure detection system.     

Nonparametric estimation of pair-copula constructions with the empirical pair-copula

A pair-copula construction is a decomposition of a multivariate copula into a structured system, called regular vine, of bivariate copulae or pair-copulae. The standard practice is to model these pair-copulae parametrically, inducing a model risk, with errors potentially propagating throughout the vine structure. The empirical pair-copula provides a nonparametric alternative, which is conjectured to still achieve the parametric convergence rate. Its main advantage for the user is that it does not require the choice of parametric models for each of the pair-copulae constituting the construction. It can be used as a basis for inference on dependence measures, for selecting an appropriate vine structure, and for testing for conditional independence.     

Robust image registration by increment sign correlation

A novel and robust statistic as a similarity measure for robust image registration is proposed. The statistic is named as increment sign correlation because it is based on the average evaluation of incremental tendency of brightness in adjacent pixels. It is formalized to be a binary distribution or a Gaussian distribution for a large image size through statistical analysis and modeling. By utilizing the proposed statistical model, for example, we can theoretically determine a reasonable value of threshold for verification of matching. This sign correlation can also be proved to expectedly have the constant value 0.5 for any uncorrelated images to a template image, and then the property of the constancy can be utilized to analyze the high robustness for occlusion. The good performance for the case of saturation or highlight can also be proved through theoretical analysis and fundamental experiments. A basic algorithm for image scanning, search and registration over a large scene is represented with a technique for a fast version by the branch-and-bound approach. Many experimental evidences with real images are provided and discussed.     

Scalable distributed on-the-fly symbolic model checking

This paper presents a scalable method for parallel symbolic on-the-fly model checking in a distributed memory environment. Our method combines a scheme for on-the-fly model checking for safety properties with a scheme for scalable reachability analysis. We suggest an efficient, BDD-based algorithm for a distributed construction of a counterexample. The extra memory requirement for counterexample generation is evenly distributed among the processes by a memory balancing procedure. At no point during computation does the memory of a single process contain all the data. This enhances scalability. Collaboration between the parallel processes during counterexample generation reduces memory utilization for the backward step. We implemented our method on a standard, loosely- connected environment of workstations, using a high-performance model checker. Our initial performance evaluation, carried out on several large circuits, shows that our method can check models that are too large to fit in the memory of a single node. Our on-the-fly approach may find counterexamples even when the model is too large to fit in the memory of the parallel system.     

Local path planning using a new artificial potential function composition and its analytical design guidelines

This paper presents a new framework for path planning based on artificial potential functions (APFs). In this scheme, the APFs for path planning have a multiplicative and additive composition between APFs for goal destination and APFs for obstacle avoidance, unlike conventional composition where the APF for obstacle avoidance is added to the APF for goal destination. In particular, this paper presents a set of analytical guidelines for designing potential functions to avoid local minima for a number of representative scenarios based on the proposed framework for path planning. Specifically the following cases are addressed: (i) a non-reachable goal problem (a case in which the potential of the goal is overwhelmed by the potential of an obstacle), (ii) an obstacle collision problem (a case in which the potential of the obstacle is overwhelmed by the potential of the goal) and (iii) a narrow passage problem (a case in which the potential of the goal is overwhelmed by the potential of two obstacles). The example results for each case show that the proposed scheme can effectively construct a path-planning system with the capability of reaching a goal and avoiding obstacles despite possible local minima.     

Statically safe program generation with SafeGen

SafeGen is a meta-programming language for writing statically safe generators of Java programs. If a program generator written in SafeGen passes the checks of the SafeGen compiler, then the generator will only generate well-formed Java programs, for any generator input. In other words, statically checking the generator guarantees the correctness of any generated program, with respect to static checks commonly performed by a conventional compiler (including type safety, existence of a superclass, etc.). To achieve this guarantee, SafeGen supports only language primitives for reflection over an existing well-formed Java program, primitives for creating program fragments, and a restricted set of constructs for iteration, conditional actions, and name generation. SafeGen’s static checking algorithm is a combination of traditional type checking for Java, and a series of calls to a theorem prover to check the validity of first-order logical sentences, constructed to represent well-formedness properties of the generated program under all inputs. The approach has worked quite well in our tests, providing proofs for correct generators or pointing out interesting bugs.     

Robotic interception of moving objects using ideal proportional navigation guidance technique

This paper presents a novel approach to on-line, robot-motion planning for moving-object interception. The proposed approach utilizes a navigation-based technique, which is robust and computationally efficient for the interception of fast-maneuvering objects. Navigation-based techniques were originally developed for the control of missiles tracking free-flying targets. Unlike a missile, however, the end-effector of a robotic arm is connected to the ground, via a number of links and joints, subject to kinematic and dynamic constraints. Also, unlike a missile, the velocity of the robot and the moving object must be matched for a smooth grasp, thus, a hybrid interception scheme, which combines a navigation-based interception technique with a conventional trajectory tracking method is proposed herein for intercepting fast-maneuvering objects. The implementation of the proposed technique is discussed via numerous simulation examples.     

Learning Generative Models of Scene Features

We present a method for learning a set of generative models which are suitable for representing selected image-domain features of a scene as a function of changes in the camera viewpoint. Such models are important for robotic tasks, such as probabilistic position estimation (i.e. localization), as well as visualization. Our approach entails the automatic selection of the features, as well as the synthesis of models of their visual behavior. The model we propose is capable of generating maximum-likelihood views, as well as a measure of the likelihood of a particular view from a particular camera position. Training the models involves regularizing observations of the features from known camera locations. The uncertainty of the model is evaluated using cross validation, which allows for a priori evaluation of features and their attributes. The features themselves are initially selected as salient points by a measure of visual attention, and are tracked across multiple views. While the motivation for this work is for robot localization, the results have implications for image interpolation, image-based scene reconstruction and object recognition. This paper presents a formulation of the problem and illustrative experimental results.