Automatic Test Pattern Generation using Grover’s Algorithm

Quantum computing is an exciting new field in the intersection of computer science, physics and mathematics. It refines the central concepts from Quantum mechanics into its least difficult structures, peeling away the complications from the physical world. Any combinational circuit that has only one stuck at fault can be tested by applying a set of inputs that drive the circuit to verify the output response. The outputs of that circuit will be different from the one desired if the faults exist. This project describes a method of generating test patterns using the Boolean satisfaction method. First, the Boolean formula is constructed to express the Boolean difference between a fault-free circuit and a faulty circuit. Second, the Boolean satisfaction algorithm is applied to the formula in the previous step. The Grover algorithm is used to solve the Boolean satisfaction problem. The Boolean Satisfiability problem for Automatic Test Pattern Generation(ATPG) is implemented on IBM Quantum Experience. The Python program initially generates the boolean expression from the file and converts it into Conjunctive Normal Form(CNF) which is passed on to Grover Oracle and runs on IBM simulator and produces excellent results on combinational circuits for test pattern generation with a quadratic speedup. Grover’s Algorithm on this problem has a run time of O(√N).

Digital System Design

The main objective of this chapter is to study and design various combinational circuits like Verification of Boolean Expression, Multiplexer, Demultiplexer Circuits, Code Converters circuits using LabVIEW tools. This chapter will make the user more comfortable towards learning of Design of Digital Systems. The various types of Boolean Expressions like SOP and POS, Combinational circuits like Adder circuit (Half adder and full adder), Subtractor circuit (Half Subtractor, Full Subtractor), some code converters like Binary to Gray and Gray to Binary, BCD to Gray and Gray to BCD and also Sequential circuits with D flip flop is also being carried out using this LabVIEW.

Mission-based reliability prediction in component-based systems

This paper develops a framework for the extraction of a reliability block diagram in component-based systems for reliability prediction with respect to specific missions. A mission is defined as a composition of several high-level functions occurring at different stages and for a specific time during the mission. The high-level functions are decomposed into lower-level functions, which are then mapped to their corresponding components or component assemblies. The reliability block diagram is obtained using functional decomposition and function-component association. Using the reliability block diagram and the reliability information on the components such as failure rates, the reliability of the system carrying out a mission can be estimated. The reliability block diagram is evaluated by converting it into a logic (Boolean) expression. A modeling language created using the Generic Modeling Environment (GME) platform is used, which enables modeling of a system and captures the functional decomposition and function-component association in the system. This framework also allows for real-time monitoring of the system performance where the reliability of the mission can be computed over time as the mission progresses. The uncertainties in the failure rates and operational time of each high-level function are also considered which are quantified through probability distributions using the Bayesian framework. The dependence between failures of components are also considered and are quantified through a Bayesian network (BN). Other quantities of interest such as mission feasibility and function availability can also be assessed using this framework. Mission feasibility analysis determines if the mission can be accomplished given the current state of components in the system, and function availability provides information whether the function will be available in the future given the current state of the system. The proposed methodology is demonstrated using a radio-controlled (RC) car to carry out a simple surveillance mission.

Switching function based on hypergraphs with algorithm and python programming

According to Boolean logic, a disjunctive normal form (DNF) is a canonical normal form of a logical formula consisting of a disjunction of conjunctions (it can also be described as an OR of AND’s). For each table an arbitrary T.B.T is given (total binary truth table) Boolean expression can be written as a disjunctive normal form. This paper considers a notation of a T.B.T, introduces a new concept of the hypergraphable Boolean functions and the Boolean functionable hypergraphs with respect to any given T.B.T. This study defines a notation of unitors set on switching functions and proves that every T.B.T corresponds to a minimum Boolean expression via unitors set and presents some conditions on a T.B.T to obtain a minimum irreducible Boolean expression from switching functions. Indeed, we generate a switching function in different way via the concept of hypergraphs in terms of Boolean expression in such a way that it has a minimum irreducible Boolean expression, for every given T.B.T. Finally, an algorithm is presented. Therefore, a Python programming(with complete and original codes) such that for any given T.B.T, introduces a minimum irreducible switching expression.

Combining solutions of the optimum satisfiability problem using evolutionary tunneling

The optimum satisfiability problem involves determining values for Boolean variables to satisfy a Boolean expression, while maximizing the sum of coefficients associated with the variables chosen to be true. Existing literature has identified a tabu search heuristic as the best method to deal with hard instances of the problem. This paper combines the tabu search with a simple evolutionary heuristic based on the idea of tunneling between local optima. When combining a set of solutions, variables with common values in all solutions are identified and fixed. The remaining free variables in the problem may be decomposed into several independent subproblems, so that parts of the solutions combined can be extracted and combined in an improved solution. This solution can be further improved by applying the tabu search in an improvement stage. The value of the new heuristic is demonstrated in extensive computational experiments on both existing and new test instances.

Impact of Spiritual Meditation on Drug Addiction Recovery and Wellbeing: A Systematic Review

It is well recognized throughout the history that religiosity, spirituality and the meditation practice have significant effects to the physical and mental health. Hence, this paper interested to evaluate the effectiveness of spiritual meditation on drug addiction recovery and its effects on psychological and mental health aspects. Three main databases in medicinal and psychology field were screened to identify the eligible studies which are PubMed, Cochrane and Scopus by using Boolean expression. Fourteen papers were included in this review. Standardized mean differences were calculated based on the intergroup mean difference and standard deviation followed by Cochran’s Q and I2 determination for heterogeneity analysis. The mean differences were statistically pooled in the meta-analysis and presented as a forest plot. The risk of bias was high for each study and assessed using the Jadad scale. The meta-analysis showed significant differences in across studies for addiction related outcome (I2=27%, 95% CI: -1.703, -0.454), anxiety (I2=0%, 95% CI: -0.874, -0.634) and stress (I2=100%, 95% CI: -0.874, -0.634). In general, spiritual meditation may promote the addiction recovery as well as improve the psychological and mental health outcomes by reducing the depression, anxiety and stress symptoms. In conclusion, randomized control trial on spiritual meditation gave positive impact on the addiction behavior as well as mental health and clarifies its reliability on addiction therapy problems.International Journal of Human and Health Sciences Vol. 04 No. 04 October’20 Page : 237-250