Recent results in the application of... this paper. The review takes the form of an analysis of the problems presented by different application requirements and characteristics. Issues covered include uniprocessor and multiprocessor systems, periodic and aperiodic processes, static and dynamic algorithms, transient overloads and resource usage. Protocols that limit and reduce blocking are discussed. Considerations are also given to scheduling Ada tasks.

A distributed hard real time system can be composed from a number of communicating tasks. One of the difficulties with building such systems is the problem of where to place the tasks. In general there are P T ways of allocating T tasks to P processors, and the problem of finding an optimal feasible allocation (where all tasks meet physical and timing constraints) is known to be NP-Hard. This paper describes an approach to solving the task allocation problem using a technique known as simulated annealing. It also defines a distributed hard realtime architecture and presents new analysis which enables timing requirements to be guaranteed. 1. INTRODUCTION Building real-time systems on distributed architectures presents engineers with a number of challenging problems. One issue is that of scheduling the communication media, another concerns the allocation of software components to the available processing resources. Distributed systems typically consist of a mixture of periodic and sp...

Unitary space-time modulation using multiple antennas promises reliable communication at high transmission rates. The basic principles are well understood and certain criteria for designing good unitary constellations have been presented. There exist two important design criteria for unitary space time codes. In the situation where the signal to noise ratio is large it is well known that the diversity product (DP) of a constellation should be as large as possible. It is less known that the diversity sum (DS) is a very important design criterion for codes working in a low SNR environment. For some special situations, it will be more practical and reasonable to consider a constellation optimized at a certain SNR interval. For this reason we introduce the diversity function as a general design criterion. So far, no general method to design good-performing constellations with large diversity for any number of transmit antennas and any transmission rate exists. In this paper we propose constellations with suitable structure which allow one to construct codes with excellent diversity using geometrical symmetry and numerical methods. We also demonstrate how these structured constellations out-perform currently existing constellations and explain why the proposed constellation structure admit simple decoding algorithm: sphere decoding. The presented design methods work for any dimensional constellation and for any transmission rate. Moreover codes based on the proposed structure are very flexible and can be optimized for any signal to noise ratio.

This paper presents a learning methodology based on a substructural classification model to solve decomposable classification problems. The proposed method consists of three important components: (1) a structural model that represents salient interactions between attributes for a given data, (2) a surrogate model which provides a functional approximation of the output as a function of attributes, and (3) a classification model which predicts the class for new inputs. The structural model is used to infer the functional form of the surrogate and its coefficients are estimated using linear regression methods. The classification model uses a maximally-accurate, least-complex surrogate to predict the output for given inputs. The structural model that yields an optimal classification model is searched using an iterative greedy search heuristic. Results show that the proposed method successfully detects key variable interactions in hierarchical problems, group them in linkages groups, and build maximally accurate classification models. The initial results on non-trivial hierarchical test problems indicate that the proposed method holds promise and have also shed light on several improvements to enhance the capabilities of the proposed method. 1

this paper, we aim at:

This paper presents a learning methodology based on a substructural classification model to solve decomposable classification problems. The proposed method consists of three important components: (1) a structural model that represents salient interactions between attributes for a given data, (2) a surrogate model which provides a functional approximation of the output as a function of attributes, and (3) a classification model which predicts the class for new inputs. The structural model is used to infer the functional form of the surrogate and its coefficients are estimated using linear regression methods. The classification model uses a maximally-accurate, least-complex surrogate to predict the output for given inputs. The structural model that yields an optimal classification model is searched using an iterative greedy search heuristic. Results show that the proposed method successfully detects the interacting variables in hierarchical problems, group them in linkages groups, and build maximally accurate classification models. The initial results on non-trivial hierarchical test problems indicate that the proposed method holds promise and have also shed light on several improvements to enhance the capabilities of the proposed method.

this report we focus on off-line schedulability analysis for priority-based system. First pre-emptive systems are covered, as much of the theory has been developed for these systems. We then consider how cooperative system can be analysed. In the remainder of this introduction we briefly consider how worst case resource usage can be obtained. For completeness, an appendix briefly reviews approaches for generating cyclic schedules. 1.1 Worst-case resource

Among the main algorithmic problems in the verification of sequential circuits are the computation of good orders of state variables and transition relation partitions. Existing model checking packages like SMV from CMU, VIS from Berkeley or Rulebase from IBM Haifa provide variants of Rudell's sifting algorithm for the variable ordering problem and greedy-type algorithms for the partition ordering problem. For both problems, we give new simulated annealing based algorithms. The impact of our approach is demonstrated on industrial arbiter circuits and ISCAS '89 benchmarks. In particular on large arbiter circuits from IBM our algorithms show a better space/time performance than previously given heuristics. Berlin, January 5, 1998 Efficient ordering of state variables and transition relation partitions in symbolic model checking Mathias Block Clemens Gropl y Harry Preu z Hans Jurgen Promel x Anand Srivastav -- Extended Abstract from January 5, 1998 Abstract Among the ...

There exist two important design criterions for unitary space time codes. In the situation where the signal to noise ratio is large it is well known that the diversity product of a constellation should be as large as possible. It is less known that the diversity sum is a very important design criterion for codes working in a low SNR environment.