George K. Papakonstantinou

The autonomous mobile robot SENARIO: a sensor aided intelligent navigation system for powered wheelchairs

The SENARIO project is develoing a sensor-aided intelligent navigation system that provides high-level navigational aid to users of powered wheelchairs. The authors discuss new and improved technologies developed within SENARIO concerning task/path planning, sensing and positioning for indoor mobile robots as well as user interface issues. The autonomous mobile robot SENARIO, supports semi- or fully autonomous navigation. In semi-autonomous mode the system accepts typical motion commands through a voice-activated or standard joystick interface and supports robot motion with obstacle/collision avoidance features. Fully autonomous mode is a superset of semi-autonomous mode with the additional ability to execute autonomously high-level go-to-goal commands. At its current stage, the project has succeeded in fully supporting semi-autonomous navigation, while experiments on the fully autonomous mode are very encouraging.

An attribute grammar for QRS detection

Abstract In automatic electrocardiogram (ECG) processing the detection of the QRS complexes is of fundamental importance. Many algorithms have been developed for this purpose. These algorithms are divided into three categories: (1) non-syntactic (2) syntactic and (3) hybrid. A syntactic algorithm, described by an attribute grammar, is presented in this paper.

Behavioral synthesis with systemC

Having to cope with the continuously increasing complexity of modern digital systems, hardware designers are considering more and more seriously language based methodologies for parts of their designs. Last year the introduction of a new language for hardware descriptions, the SystemC C++ class library, initiated a closer relationship between software and hardware descriptions and development tools. This paper presents a synthesis environment and the corresponding synthesis methodology, based on traditional compiler generation techniques, which incorporate SystemC, VHDL and Verilog to transform existing algorithmic software models into hardware system implementations. Following this approach, reusability of software components is introduced in the hardware world and time-to-market is decreased, as shown by experimental results.

Dynamic multi phase scheduling for heterogeneous clusters

Distributed computing systems are a viable and less expensive alternative to parallel computers. However, concurrent programming methods in distributed systems have not been studied as extensively as for parallel computers. Some of the main research issues are how to deal with scheduling and load balancing of such a system, which may consist of heterogeneous computers. In the past, a variety of dynamic scheduling schemes suitable for parallel loops (with independent iterations) on heterogeneous computer clusters have been obtained and studied. However, no study of dynamic schemes for loops with iteration dependencies has been reported so far. In this work we study the problem of scheduling loops with iteration dependencies for heterogeneous (dedicated and non-dedicated) clusters. The presence of iteration dependencies incurs an extra degree of difficulty and makes the development of such schemes quite a challenge. We extend three well known dynamic schemes (CSS, TSS and DTSS) by introducing synchronization points at certain intervals so that processors compute in pipelined fashion. Our scheme is called dynamic multi-phase scheduling (DMPS) and we apply it to loops with iteration dependencies. We implemented our new scheme on a network of heterogeneous computers and studied its performance. Through extensive testing on two real-life applications (the heat equation and the Floyd-Steinberg algorithm), we show that the proposed method is efficient for parallelizing nested loops with dependencies on heterogeneous systems.

Optimal Scheduling for UET/UET-UCT Generalizedn-Dimensional Grid Task Graphs

Then-dimensional grid is one of the most representative patterns of data flow in parallel computation. Many scientific algorithms, which require nearest neighbor communication in a lattice space, are modeled by a task graph with the properties of a simple or enhanced grid. The two most frequently used scheduling models for grids are the unit execution time-zero communication delay (UET) and the unit execution time?unit communication time (UET-UCT). In this paper we introduce an enhanced model of then-dimensional grid by adding extra diagonal edges and allowing unequal boundaries for each dimension. For this generalized grid topology we establish the optimal makespan for both cases of UET/UET-UCT grids. Then we give a closed formula that calculates the minimum number of processors required to achieve the optimal makespan. Finally, we propose a low-complexity optimal time and processor scheduling strategy for both cases.

Dynamic dramatization of multimedia story presentations

We describe a novel dynamic dramatization method for narrative presentations. This method accepts as input the original story material, along with a description of its plot written in a special-purpose language. It then analyzes the plot to iden~ interesting dramatic situations in the story. Based on this content analysis, a presentation manager organizes the presentation and enriches it with appropriate multimedia effects. These effects are associated with interesting dramatic situations, and serve to increase suspense and emphasize plot developments in the narrative. Our method can be used for the development of intelligent front-ends to story databases, for directing assistants in computer-based renditions of narrative works, or for real-time direction of interactive entertainment systems. We are integrating this system in an interactive storytelling environment for Greek mythology.

Mixture Density Estimation Based on Maximum Likelihood and Sequential Test Statistics

We address the problem of estimating an unknown probability density function from a sequence of input samples. We approximate the input density with a weighted mixture of a finite number of Gaussian kernels whose parameters and weights we estimate iteratively from the input samples using the Maximum Likelihood (ML) procedure. In order to decide on the correct total number of kernels we employ simple statistical tests involving the mean, variance, and the kurtosis, or fourth moment, of a particular kernel. We demonstrate the validity of our method in handling both pattern classification (stationary) and time series (nonstationary) problems.

EXACT MINIMIZATION OF ESOP EXPRESSIONS WITH LESS THAN EIGHT PRODUCT TERMS

An algorithm is proposed in this paper that finds exact exclusive-or sum-of-products of an arbitrary function, provided the number of product terms is less than eight. If the number of product terms in the minimal expression is more than seven, then the algorithm detects it and heuristically returns near-optimal expressions. The algorithm is time and space efficient even for functions with many input variables.

Global path planning for autonomous qualitative navigation

We describe a novel global path planning method for autonomous qualitative navigation in indoor environments. Global path planning operates on top of a qualitative map of the environment that describes variations in sensor behavior between adjacent regions in space. The method takes into consideration the global topology of the environment and applies a set of criteria that can minimize the errors in the navigational accuracy of a robotic wheelchair. Our approach uses a modified version of the Dijkstras shortest path algorithm that takes into consideration the curvature of the trajectory and the off-wall distance of the map points. The algorithm computes in real-time a set of optimal paths for reaching the destination. We have tested our global path planning method in simulation in representative indoor environments with above average complexity. Based on these experiments we have determined empirically a set of values for the parameters of the algorithm that almost always lead to the selection of optimal paths in these environments.

A fast piecewise linear approximation algorithm

Abstract A piecewise linear approximation algorithm is suggested in this paper. The algorithm is fast since it works in one pass through the given data and it does not require much memory space. Moreover the peak points are preserved and become corners of the resulting piecewise linear curve. Hence the algorithm is suitable for real time applications in which the peak points are carrying important information and therefore should be calculated accurately.