A modified version of traditional k-means clustering algorithm applying computational geometry for initialization of cluster centers has been presented in this paper. It is well known that the quality of k-means clustering depends on its random initialization of centers. This paper shows that when the initial cluster centers are selected from the list of circumference points of the empty circles sorted in non-increasing order of their corresponding radii, it can significantly improv e the performance. The proposed algorithm is named as Empty Circles based k-means (ECKM) Clustering, which was successfully implemented with PYTHON. Extensive experimentation was carried out on various benchmark data sets that includes both artificial and real data sets having different shapes, sizes and features. In order to minimize the size of this paper, we presented the results for seven of such benchmark datasets, viz., iris, wine, hepta, flame compound, breast_cancer and DS577 although in reality we experimented for more than fifteen data sets in order to validate our ECKM. Results establish the superiority of the ECKM clustering over standard techniques.

Quantitative models built as tools for evaluating human language performance, can prove useful for both theoretical and applied areas of discourse comprehension, assessment, and education. The current paper offers a test case focused on mathematical articulation of a model and a test of that model with existing corpora. The model presented has been developed to evaluate free text answers of students based on the fuzzy indiscernibility between the semantic spaces created by the model answer and the learners’ response. The model semantic space represented as a knowledge matrix can be constructed out of one or more model answers prepared by human experts and closely resemble the knowledge of the human evaluator. The proposed model finds out the indiscernibility between the types of word usage and scores the answer based on the fuzzy indiscernibility measures stored in a graded thesaurus prepared specifically for this purpose. The results returned on experimental data correlates well with human evaluators. This simulated learner-friendly atmosphere, inspired by a teacher’s intelligent benevolence, ensures effective attainment of learning objective in e-learning environment.

This paper reports an efficient state encoding scheme for synthesis of large FSMs with enhanced BIST quality. A metric, referred to as degree-of-freedom (DOF) [5] in FSM states has been employed to quantify the BIST quality. Analysis of DOF enables efficient encoding of FSM states and gives solution to the problem of handling unreachable/ hard-to-exit/ hardto- reach state codes of an FSM. The synthesis of a large FSM is realized through decomposition. DOF analysis for the individual component sub-FSM states is done to reduce the complexity of synthesis. A scheme is proposed for encoding the sub-FSM states that significantly improves testability of the synthesized resultant FSM, displaying the terminal behavior as the original FSM.

This paper introduces a novel BIST-quality metric termed as the FiF -- FoF (Fan-in-Factor & Fan-out-Factor) defined on FSM-states. Based on the FiF -- FoF analysis, an efficient scheme is presented that ensures all state codes appear with uniform likelyhood at the present state (PS) lines during the test phase. This results in higher fault efficiency in a BIST structure. Experimental results on MCNC benchmarks show that the scheme improves fault efficiency of sequential circuits significantly, with marginal area overhead.

This paper presents an efficient BIST scheme with low power consumption for sequential circuits. The BIST structure is obtained by using a ghost-FSM. A multiobjective genetic algorithm (MOGA) is employed to optimize the twin criteria of BIST quality and power consumption of the resultant circuit simultaneously. The scheme ensures enhancement of fault coverage along with minimization of power overhead of the BISTed circuits. Experimental results on MCNC benchmarks confirm the effectiveness of the proposed scheme to produce circuits with improved fault efficiency along with lower power consumption.

Designing a BIST structure for sequential circuits is rather a complex problem as some states remain unreachable and some act as the sink under any input sequence. This paper reports an efficient scheme to provide uniform mobility, referred to as degree of freedom, in a sequential machine by enhancing the reachability as well as the emittability of the states. The uniform mobility of states ensures higher fault efficiency in a BIST structure of the circuit. Moreover, as a non-scan scheme, the technique provides lower test application time and at-speed testing.

In the domain of combinational logic synthesis, logic minimization plays a vital role in determining the area and performance of the synthesized circuit. Logic minimization based on AND-OR decomposition of functions is a well studied area. However, minimization based on AND-XOR decomposition has received relatively lesser attention. Since many real-life combinational functions are XOR dominated, a logic minimizer producing efficient AND-XOR decomposition can lead to more efficient realization of such circuits. The computer-aided design tool KGPXORMIN presented in this paper is a multilevel AND-XOR minimizer which outperforms the scheme reported by Saul (1991) by 45.77% in the literal count metric. In general, most of the real-life and benchmark circuits are a combination of OR and XOR logic. In order to have area efficient realization, we need to have an efficient minimizer capable of judicious use of OR and XOR gates. An integrated tool KGPMIN has been developed which combines the AND-XOR minimizer KGPXORMIN and well-known AND-OR minimizer MISII. Depending on the measure of dominance of OR and XOR logic, it switches from one minimizer to the other during the decomposition phase. By judicious switching from one minimizer to the other, on the average, KGPMIN outperforms MISII by 64.08% in literal count and 45.16% in absolute gate area for the MCNC combinational logic benchmarks. It also outperforms KGPXORMIN by 17.46% in literal count and 34.32% in gate area. The number of levels of the circuits synthesized with KGPMIN can be found to be comparable with the figures arrived at from the application of MISII.

Specific inherent advantages of AND-XOR networks have encouraged researchers to look for efficient minimization and synthesis tools for their realization. Among several canonical representations of AND-XOR networks, the most easily testable one is the fixed polarity Consistent Generalized Reed Muller (CGRM) form. In this paper, a Genetic Algorithm (GA) formulation of the problem of finding the polarity of the variables resulting in minimum number of product terms has been proposed. The quality of the solution obtained and the high rate of convergence have established the effectiveness of the genetic algorithm in solving this particular NP-hard problem. Further, the inherent parallelism of genetic algorithm makes the proposed scheme an ideal candidate for solving the problem in a multiprocessor environment.