Saif Abrar Syed

Bio: Saif Abrar Syed is an academic researcher. The author has contributed to research in topics: SystemC & VHDL. The author has an hindex of 1, co-authored 1 publications receiving 55 citations.

Comprehensive Abstraction of VHDL RTL Cores to ESL SystemC

Abstract: ..................................................................................................... 90 KOKKUVÕTE .................................................................................................. 92 ACKNOWLEDGEMENTS ............................................................................... 94 Appendix A ........................................................................................................ 95 Appendix B ...................................................................................................... 103 Appendix C ...................................................................................................... 109 Appendix D ...................................................................................................... 117 Appendix E ...................................................................................................... 125 Appendix F ...................................................................................................... 133

From Determinacy Analysis to Zero Factor Free Determinacy Analysis and Universal Generator of Hypotheses: Development of Algorithms

Abstract: ....................................................................................................... 8 ACKNOWLEDGEMENTS ............................................................................... 10 Abbreviations ..................................................................................................... 11 1 INTRODUCTION ..................................................................................... 12 1.1 Motivation .......................................................................................... 13 1.2 Methods and theories ......................................................................... 14 1.2.1 Determinacy Analysis ................................................................ 14 1.2.2 Generator of Hypotheses ............................................................ 16 1.2.3 Theory of Monotone Systems .................................................... 17 1.3 Research aims .................................................................................... 17 1.4 Overview of developments ................................................................ 18 1.5 Previously published work ................................................................. 21 1.6 Contribution of the thesis ................................................................... 22 1.7 Organisation of the thesis ................................................................... 22 2 THEORETICAL BACKGROUND ........................................................... 23 2.1 Data mining ........................................................................................ 23 2.2 Machine Learning .............................................................................. 24 2.2.1 Definitions of learning from examples ...................................... 25 2.3 Classification ..................................................................................... 26 2.3.1 Decision trees ............................................................................. 27 2.3.2 Classification rules ..................................................................... 28 2.4 Association rule mining ..................................................................... 30 2.4.1 Frequent itemsets ....................................................................... 31 2.4.2 Association rules ........................................................................ 34 2.5 Combining classification and association rules ................................. 38 2.6 Theory of Monotone Systems ............................................................ 41 2.6.1 Algorithm MONSA ................................................................... 42 2.7 Determinacy analysis ......................................................................... 50

At-Speed Testing and Test Quality Evaluation for High-Performance Pipelined Systems

Abstract: T thesis addresses a series of closely related problems regarding development of BIST for high-performance pipe-lined designs. These problems can be divided into three groups covering: (1) design of benchmark circuits to represent a special class of objects to be tested (high-performance pipe-lined architectures), (2) the new methods for testing of this class of objects (BIST), and (3) the new methods for evaluating the quality of test solutions (fault simulation). In order to find the relations between different design decisions and their corresponding testability characteristics the benchmark suite was formed of eight circuits with the same functionality, but different structures. The thesis describes the structural characteristics of the circuits and provides an overview and the discussion of their testability characteristics. A new approach for self-testing of digital systems with pipe-lined architectures is also proposed. This is a new at-speed functional BIST methodology for these architectures. The key aspects include using inherent functionality of the system to generate test sequences and usage of MISR monitors for testing. This also leads to exploration of the potential of digitized analog signals to be used as a test-sequences for at-speed BIST. Along with that a novel evaluation environment to transfer sequential fault simulation task into a set of combinational subtasks is developed. Its goal is to speed up the process of BIST design. The methodology is evaluated in a case-study, using the benchmarks proposed previously and the results are presented and discussed. The combinational fault simulation using parallel pattern exact critical path tracing is extended to run on multi-core systems. The parallelism is achieved in three dimensions: faults, patterns and model. The fault and pattern parallelism are achieved on each single core and the model is divided between different cores to make a fault reasoning concurrently. The experimental results using ISCAS and ITC benchmarks are presented and discussed. Finally the novel method for observability improvement inside the sequential circuits is presented. It is shown that using only two rules to insert MISR monitors enables combinational fault simulator to be used for simulation of sequential circuits. The results of theoretical experiments to estimate performance benefits of such a method is also presented and discussed.

Methods for Coopetition and Retention Analysis: An Application to University Management

Abstract: The goal of the paper is to study how the strictly optimal solutions of community detection, based on similarity matrices, depend on the parameter of the distance threshold setting method, applied beforehand. In order to detect communities, we apply the oft-used modularity metric and arrive at strict optimality by linear programming, solving an np-hard problem. The distance threshold method is used, making the matrix more and more sparse, and thus the best value of the threshold is determined, by analyzing the number of subsequent clusters detected. Our method is applied on educational coopetition data in the business school of TUT, with four specializations, out of which we sample 36 students, each time selecting from a pair of specializations. Since the optimal number of clusters tends to be four, for any two-fold sampling, we detect a natural division within each specialization as well, the reason for which is a matter for further study. As a result, coopetition the simultaneous competition and cooperation is measured between the departments of the business school. The average grade of the students is a proxy for the competitive score of the department. The traditional conductance is used as a proxy for the cooperative score of the department. For our data, the optimal value for the threshold in community detection is 0.07, this way enough noise has been removed from the data, but not too many values, so that vital information is retained. Thus, we most often obtain our goal of detecting four clusters, in the two-fold sampling, effectively displaying the usefulness of fine-tuning the distance threshold while evaluating it by the strictly optimal community detection. Keywords— coopetition, metrics, social network analysis, linear programming, threshold method, education I. INTRODUCTORY OVERVIEW OF THE NUMERICAL