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Antonin Svoboda

Bio: Antonin Svoboda is an academic researcher. The author has contributed to research in topics: Stone's representation theorem for Boolean algebras & Maximum satisfiability problem. The author has an hindex of 2, co-authored 2 publications receiving 34 citations.

Papers
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Journal ArticleDOI
TL;DR: This paper shows how to make tests of the activity proving the existence of algorithmic elements, their synthesis and the design of corresponding block diagrams of the system.
Abstract: A system observed from the outside as a black box displays certain activity which can be described (and stored) as variations of the input and output variables of the system during its observation. Such information stored for adequately large intervals of time represents samples of activity ready for statistical analysis. Any time-invariant obtained as a result of this investigation represents an element of behavior of the system which can be explained by the existence of corresponding elements of the internal structure of the black box. The resulting abstract model of the internal structure is algorithmic when the elements of behavior take on the form of a law predicting the future activity from some stored information about the past activity. Not all systems are found to be algorithmic. The activity of any part of an electronic digital system with a central clocking can be defined by Boolean variables arranged in suitable timetables. Any time-invariant Boolean equation holding between these variables represents an element of the system's behavior but does not necessarily contain any algorithmic element of the system. In this paper we show how to make tests of the activity proving the existence of algorithmic elements, their synthesis and the design of corresponding block diagrams of the system.

10 citations


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Book
07 Apr 2011
TL;DR: The aim of this monograph is to clarify the role of mathematics and computer technology in the development of systems science and to provide some examples of how these roles have changed in recent years.
Abstract: 1: Introduction.- 1.1. Systems Science.- 1.2. Systems Problem Solving.- 1.3. Hierarchy of Epistemological Levels of Systems.- 1.4. The Role of Mathematics.- 1.5. The Role of Computer Technology.- 1.6. Architecture of Systems Problem Solving.- Notes.- 2: Source and Data Systems.- 2.1. Objects and Object Systems.- 2.2. Variables and Supports.- 2.3. Methodological Distinctions.- 2.4. Discrete Versus Continuous.- 2.5. Image Systems and Source Systems.- 2.6. Data Systems.- Notes.- Exercises.- 3: Generative Systems.- 3.1. Empirical Investigation.- 3.2. Behavior Systems.- 3.3. Methodological Distinctions.- 3.4. From Data Systems to Behavior Systems.- 3.5. Measures of Uncertainty.- 3.6. Search for Admissible Behavior Systems.- 3.7. State-Transition Systems.- 3.8. Generative Systems.- 3.9. Simplification of Generative Systems.- 3.10. Systems Inquiry and Systems Design.- Notes.- Exercises.- 4: Structure Systems.- 4.1. Wholes and Parts.- 4.2. Systems, Subsystems, Supersystems.- 4.3. Structure Source Systems and Structure Data Systems.- 4.4. Structure Behavior Systems.- 4.5. Problems of Systems Design.- 4.6. Identification Problem.- 4.7. Reconstruction Problem.- 4.8. Reconstructability Analysis.- 4.9. Simulation Experiments.- 4.10. Inductive Reasoning.- 4.11. Inconsistent Structure Systems.- Notes.- Exercises.- 5: Metasystems.- 5.1. Change versus Invariance.- 5.2. Primary and Secondary Systems Traits.- 5.3. Metasystems.- 5.4. Metasystems versus Structure Systems.- 5.5. Multilevel Metasystems.- 5.6. Identification of Change.- Notes.- Exercises.- 6: Complexity.- 6.1. Complexity in Systems Problem Solving.- 6.2. Three Ranges of Complexity.- 6.3. Measures of Systems Complexity.- 6.4. Bremermann's Limit.- 6.5. Computational Complexity.- 6.6. Complexity Within GSPS.- Notes.- Exercises.- 7: Goal-Oriented Systems.- 7.1. Primitive, Basic, and Supplementary Concepts.- 7.2. Goal and Performance.- 7.3. Goal-Oriented Systems.- 7.4. Structure Systems as Paradigms of Goal-Oriented Behavior Systems.- 7.5. Design of Goal-Oriented Systems.- 7.6. Adaptive Systems.- 7.7. Autopoietic Systems.- Notes.- Exercises.- 8: Systems Similarity.- 8.1. Similarity.- 8.2. Similarity and Models of Systems.- 8.3. Models of Source Systems.- 8.4. Models of Data Systems.- 8.5. Models of Generative Systems.- 8.6. Models of Structure Systems.- 8.7. Models of Metasystems.- Notes.- Exercises.- 9: GSPS: Architecture, USE, Evolution.- 9.1. Epistemological Hierarchy of Systems: Formal Definition.- 9.2. Methodological Distinctions: A Summary.- 9.3. Problem Requirements.- 9.4. Systems Problems.- 9.5. GSPS Conceptual Framework: Formal Definition.- 9.6. Overview of GSPS Architecture.- 9.7. GSPS Use: Some Case Studies.- 9.8. GSPS Evolution.- Notes.- Exercises.- Appendices.- A: List of Symbols.- B: Glossary of Relevant Mathematical Terms.- C: Some Relevant Theorems.- D: Refinement Lattices.- E: Classes of Structures Relevant to Reconstructability Analysis.- References.- Author Index.

518 citations

Journal ArticleDOI
TL;DR: It is demonstrated that a multiple-output incompletely specified switching function is reaeized if a ≤ relation is satisfied between the corresponding charchteristic functions, which leads to a new unified outlook on functional decomposition as used in modular synthesis procedures.
Abstract: A methodology based on the theory of Boolean equations has been developed which permits a unified approach to the analysis and synthesis of combinational logic circuits. The type of circuits covered by the approach includes both the classical loopless combinational networks as well as those that contain closed feedback loops and thus have internally a sequential character. To that end, a general multiple-output circuit represented by a Mealy-type machine is studied using characteristic equations (functions) that describe its internal structure. It is shown how behavioral properties of the circuit are reflected through the sosutions of these equations. Moreover, it is demonstrated that a multiple-output incompletely specified switching function is reaeized if a ≤ relation is satisfied between the corresponding charchteristic functions. This leads to a new unified outlook on functional decomposition as used in modular synthesis procedures. Although the building modules are allowed to be sequential circuits, it is shown under which conditions the feedback loops are redundant with respect to the realization of a given output characteristic function, and thus the existence conditions of nondegenerate combinational circuits with loops are stated.

70 citations

Journal ArticleDOI
TL;DR: An efficient tabular method is presented to solve switching equations based on the use of a Boolean algebra, and the compactness and simplicity of the method are outstanding, and it is straightforward to implement for computer processing.
Abstract: Several problems in switching theory such as automatic test pattern generation, can be exactly and elegantly investigated by using switching equations. An efficient tabular method is presented to solve these switching equations. The solutions of a given equation are compacted into a table, and a Boolean algebra is defined for these tables. The proposed procedure is based on the use of this Boolean algebra. The compactness and simplicity of the method are outstanding, and it is straightforward to implement for computer processing. The complexity of the procedure is computed, and some experimental results for a set of benchmark equations are provided in order to point out the effectiveness of this method. >

21 citations

Journal ArticleDOI
TL;DR: This paper describes a method for constructing reduced solutions, i.e., general solutions involving the fewest parameters possible for some equations, using fewer than n parameters.
Abstract: The family of solutions for a Boolean equation is commonly represented in a single formula involving arbitrary Boolean parameters. It is well known that n parameters suffice to construct a general solution for an equation in n unknowns. For some equations, however, a general solution may be constructed using fewer than n parameters. This paper describes a method for constructing reduced solutions, i.e., general solutions involving the fewest parameters possible.

20 citations

Journal ArticleDOI
TL;DR: An on-line testing procedure for constructing a test set for identifying a specific fault in a circuit to within an equivalence class is outlined, which eliminates the need for precalculating a fault dictionary.
Abstract: This paper deals with the problem of identifying multiple stuck-type hardware failures in combinational switching networks. Our work is an extension of that of Poage, and Bossen and Hong, and we employ the cause-effect equation for representing faulty circuit behavior. We introduce the concept of solving simultaneous equations over check point variables. These check point solutions are studied in detail. From the solutions one can calculate the function realized by a faulty circuit. We outline an on-line testing procedure for constructing a test set for identifying a specific fault in a circuit to within an equivalence class. This procedure eliminates the need for precalculating a fault dictionary, which, in many instances, can be quite advantageous. We also outline how to apply these techniques to the following problems: 1) identifying redundancy; 2) determining the set of faults not detected by an arbitrary test set; and 3) constructing a complete fault dictionary.

19 citations