Journal of Computer and Robotics
Volume:7 Issue: 1, Winter and Spring 2014

A major problem in information retrieval is the difficulty to define the information needs of user and on the other hand, when user offers your query there is a vast amount of information to retrieval. Different methods , therefore, have been suggested for query expansion which concerned with reconfiguring of query by increasing efficiency and improving the criterion accuracy in the information retrieval system. Accordingly in this paper, in addition to propose a new coherent categorization for approaches, we proceed to detailed identify them, and proper functional criteria to evaluate each of these approaches are suggested.

One of the interesting topics on multimedia domain is concerned with empowering computer in order to speech production. Speech synthesis is granting human abilities to the computer for speech production. Data-based approach and process-based approach are the two main approaches on speech synthesis. Each approach has its varied challenges. Unit-selection speech synthesis and statistical parametric speech synthesis are two dominant speech synthesizer techniques. The naturalness is the main challenge of all speech synthesis approaches. The Intonation, speech style and emotional state are included in naturalness factor and all of them are considered as suprasegmental features. Equipped synthesized speech with paralinguistic information is more believable from the perceptual aspect. Prosody information plays an important role on the synthesized speech quality of text to speech systems. The first purpose of modern speech synthesizer systems is text to speech conversion and the second purpose is transferring the emotional states of text in the voice form. In this paper two main speech synthesis approaches and their challenges are investigated in detail.

All practical digital circuits are usually a mixture of combinational and sequential logic. Flip–flops are essential to sequential logic therefore fuzzy flip–flops are considered to be among the most essential topics of fuzzy digital circuit. The concept of fuzzy digital circuit is among the most interesting applications of fuzzy sets and logic due to the fact that if there has to be an ultimate fuzzy computer then fuzzy circuitry is inevitable. In this research field, hardware realization of fuzzy negation, t–norms and t–conorms have been well studied in details meanwhile no formal model is introduced for more complex fuzzy circuitry such as combinational circuits, sequential circuits or memory modules. The lack of a formal model checker indicates flaws and design deficiencies are usually remain out of sight therefore validating fuzzy logic circuits was impossible to this date. In this paper we are elaborating the application of Fuzzy Program Graph in symbolic checking of fuzzy flip–flops; thus, the content is mainly focused on formal modelling of fuzzy flip–flops and investigating their correctness. To this purpose we investigated design deficiencies of a multivalued D flip–flop and found a dynamic hazard then we proposed a formal model toward fuzzy J–K flip–flops to further elaborate applications of proposed formal model and model checking approach in detecting design phase deficiencies.

One the challenging in hardware performance is to designing a high speed calculating unit. The higher of calculations speeds in a computer system  will be pointed out in terms of performance. As a result, designing a high speed calculating unit is of utmost importance. In this paper, we start design whit this knowledge that one multiplier made of several adder and one divider made of several sub tractor. Therefore, if the fast adder or fast multiplier designed, performance will be improved. In this design, a circuit is designed in a manner that without a need for transforming numbers or letters from the given bases into binary bases, the multiplication of two numbers for the same base is done in that base. Reduction in the number of conversions in the calculating unit, causes reduction in the consumption power and an increase in the operating speed of the system. In this design, a very small Data Oriented  Memory is used to save numerical and character data.

Brain-computer interfaces (BCI) record brain signals, analyze and translate them into control commands which are relayed to output devices that carry out desired actions. These systems do not use normal neuromuscular output pathways. Actually, the principal goal of BCI systems is to provide better life style for physically-challenged people which are suffered from cerebral palsy, amyotrophic lateral sclerosis, stroke, or spinal cord injury. One of the focal points in Brain-Computer Interface (BCI) systems is physiological artifacts handling. Physiological artifacts such as Electrooculography (EOG) and Electrooculography (EMG) are considered among the most important sources of physiological artifacts in BCI systems. Pre-processing is considerable step by means of next steps such as feature extraction and classification that we need clean signals without undesirable artifacts to have better classification rate. Using a linear filter to remove these artifacts is like a dime a dozen due to their acceptable results in recent BCI pre-processing researches. Although this method has different options, Forasmuch as the mu (8–13 Hz) and beta (16–25 Hz) frequency bands play a key role in classification of motor imagery we have decided to design two band pass filters with Elliptic and  Butterworth  Infinite impulse response designing methods in 8 to 40 Hz frequencies. Our results in Graz 2a dataset in BCI Competition IV indicates that, Elliptic band-pass filter has better performance for EOG removing in this specific dataset.

Identifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means algorithm, namely Persistent K-Means, which alters the convergence method of K-Means algorithm to provide more accurate clustering results than the K-means algorithm and its variants by increasing the clusters’ coherence. Persistent K-Means uses an iterative approach to discover the best result for consecutive iterations of K-Means algorithm.