Abstract:In this work one bit Full Adder with Ten transistors have been proposed. Reducing Power dissipation, supply voltage, leakage currents, area of chip are the most important parameters in today`s VLSI designs. The system reliability can be increased by reducing the cost, weight and physical size and it is achieved by decreasing the transistor count. Therefore the minimum power consumption target and lower area can be meet by reducing the hardware size. Digital circuits can be minimize in two methods. One is human method and another is Computational method. This paper propose one-bit Full Adder based on human method with ten transistors and simulation for the designed circuits were also performed with 4-bit, 16-bit and 32-bit ripple carry adder. Finally the simulation analysis were compared with conventional and proposed Adder in terms of total power consumption, delay, area and power delay product.

In this paper a new computationally simple scheme is proposed for model order reduction to design a Lag-Lead compensator for higher order linear discrete systems. Artificial Bee Colony (ABC) algorithm is employed to obtain a successful reduced order model for an Original higher order discrete system via Bi-linear transformation. ABC algorithm is based on minimization of Integral Square Error (ISE) between Original and reduced order models pertaining to unit step input. A Lag-Lead compensator is designed for the reduced order model with desired performance specifications and the designed compensator is cascaded with the Original higher order model to improve the stability .

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The Rapidly-exploring Random Tree (RRT) has been widely used to solve path planning problems and well suited to lots of problem domains for its probabilistically complete. However, it is not so rapid in changing environments, troubled with moving obstacles and difficult regions. In this paper, a variant of RRT is proposed which is called obstacle guided RRT (OG-RRT), aiming to plan a path in changing environments efficiently. By preserving a group of invalid configurations blocked by obstacles, an entropy value is introduced to label every state in the tree with region classification information. Then a differentiation strategy is adopted to the framework for extending. Finally, with recording the change between invalid and valid nodes, a fuzzy estimation for obstacles' movements and an opportunistic strategy for reusing information from previous queries will be used to replan a solution fast. In plentiful experiments, OG-RRT is very effective in changing environment.

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