This paper proposes a new bound on the number of processors and finish time for the problem of scheduling precedence graphs with communication costs. An algorithm (ETF) has been proposed by Hwang [1] for scheduling precedence graphs in systems with inter-processor communication times. In this paper the notion of the earliest starting time of a task is formulated for the context of lower bounds. A lower bound on the completion time of a schedule is defined. Each task can then be scheduled within a time interval without affecting the lower bound performance on the finish time. This leads to definition of a new lower bound performance on the finish time. This leads to definition of a new lower bound on the number of processors required to process the task graph. A derivation of the minimum time increase over the earliest completion time is also proposed for the case of smaller number of processors. Finally the paper proposes a lower bound on the minimum number if inter-processor communication links required to achieve optimum performance. Evaluation has been carried out by using a set of 360 small graphs. The bound on the finish time deviates at most by 5 % from the optimum solution in 96 % of the cases and performs well with respect to the minimum number of processors and communication links.

This paper presents a robust telerobotic system that consists of a real-time vision-based operator hand tracking system (client) and a slave robot (server) which are interconnected through a LAN. The tracking system (1) monitors the operator hand motion and (2) determines its position and orientation which are used to control the slave robot. Two digital cameras are used to monitor a four-ball based feature frame that is held by the operator hand. To determine the 3D position a tracking algorithm based on uncalibrated cameras with weak perspective projection model is used. This allows finding 3D differential position and orientation of operator hand. The features of proposed system are (1) a metric for color matching to discriminate the balls from their background, (2) a uniform and spiral search approach to speedup the detection, (3) tracking in the presence of partial occlusion, (4) consolidate detection by using shape and geometric matching, and (5) dynamic update of the reference colors. The operator can see the effects of the previous motion which enables making the necessary corrections through repetitive operator hand-eye interactions. Evaluation shows that the static and dynamic errors of tracking algorithm are 0.1% and 0.6% for a centered workspace of 20 3 inches 3 that is 40 to 60 inches away from cameras. Running the tracking algorithm on two PCs in parallel allowed (1) a parallel image grabbing delay of 60 ms, (2) a stereo matching delay of 50 ms, and (3) a global refresh rate of 9Hz.

Graphs are discrete structures composed of vertices and edges connecting these vertices. Graphs are used in almost all disciplines as abstract models for the representation and study of a wide range of relations and processes in physical, biological, social and information systems. Many practical problems in a variety of areas like computer and communication networks,-imply the existence of edges between corresponding vertices. Thus, focusing on the abstract graph model instead of studying each particular instance as a different real-world problem reveals common underlying properties, deficiencies and principles. In this way, efficient approaches to real-world problems emerge from the theoretical study of their abstractions. In this work, we use graph coloring to propose efficient solutions to scheduling problems arising in higher education. The objective of the graph coloring problem is to assign colors to graph vertices so that adjacent vertices, i.e., vertices connected by an edge, receive different colors. We consider as the objective of scheduling problems in higher education, like lecture and exam scheduling, to assign time/day slots to teaching or examination activities so that the maximum number of students can attend them with the fewest possible conflicts. Our main motivation has been the crucial issue of efficient course and exam schedules often arising in departments of the University of Patras, Greece. Students usually have to attend lectures or exams scheduled in overlapping or simultaneous time slots. However, course and exam schedules are created based on heuristic approaches which may work well on average but certainly leave several room for improvement. What if a graph-theoretic approach were used? Courses correspond to vertices of a graph and there is an edge between two vertices if and only if an appropriately selected minimum population of students attends corresponding courses (lectures/exams). Then, a coloring of such an underlying graph suggests an appropriate schedule for teaching/examination activities. Using a simple coloring algorithm and the MATLAB programming environment, we have designed and developed a scheduling application which receives as input courses and constraints and outputs an efficient lecture/examination schedule. Experimental evaluation suggests that our application works well in practice. Ongoing work focuses on the use of a more involved coloring algorithm for addressing more complex course scheduling instances while minimizing required time resources.

Finding general XOR-schemes to minimize memory and network contention for accessing arrays with arbitrary sets of data templates is presented. A combined XOR matrix is proposed together with a necessary and sufficient condition for conflict-free access. We present a new characterization of the baseline network. Finding an XOR matrix for combined templates is shown to be an NP-complete problem. A heuristic is proposed for finding XORmatrices by determining the constraints of each template matrix and solving a set of simultaneous equations for each row. Evaluation shows significant reduction of memory and network contention compared to interleaving and to static row-column-diagonals storage.

Scheduling precedence graphs with communication times is the theoretical basis for achieving efficient parallelism in message-passing machines. The lack of global information on the tasks, due to communication, has lead to develop local scheduling heuristics such as the Earliest-Task-First. Using knowledge on computation, communication, and system topology, a class of global priority-based scheduling heuristics called Generalized List Scheduling is proposed. The task-level is evaluated by backward scheduling the computation over the multiprocessor by using the best local heuristic. This leads to realistic measurement of the task priority for use in forward GLS scheduling.Experimental evaluation of local and GLS heuristics is carried out using extensive random graph generation and altering over the communication, inherent parallelism, and system topology. Analysis shows that local heuristics rely on locally maximizing the processor efficiency and gives acceptable deviations only when the inherent parallelism is large enough to cover the effective communication. This leads the local heuristics to achieve bounded speedup.GLS scheduling is based on combining two strategies: 1) differentiate critical computation and communications from others by scheduling critical paths first, and 2) implement effective management of processor utilization in order to increase the speedup. GLS scheduling maintains acceptable relative deviation versus change in parallelism, communication, and multiprocessor topology. The time complexity of GLS heuristics is O(pn 2 ) , where p and n are the number of processors and that of the tasks, respectively.