Volume 2, Issue 1

The general simulation of n-body systems often requires the simulation of pairwise interaction events between the objects. The naive method of simulating these events is an algorithm that polls each pair of objects for an interaction every time step. This algorithm has O(n 2)operations per time step in the number of objects. However, this method scales very well to multiple cores. In this paper, we propose a novel method of pairwise simulation that saves a significant amount of computation time by predicting possible future outcomes rather than reacting to them. We demonstrate the implementation of this method, as well as demonstrate that it has amortized O(n) complexity per time step. We also demonstrate an implementation to allow this algorithm to scale comparably well to multiple cores of a shared memory machine.

The emergent collective intelligence of groups of simple agents known as swarm intelligence is a new exiting way of achieving a form of artificial intelligence. This paper studies a formal model for swarm intelligence inspired by biological swarms found in nature. Software agents are used to model the individuals of a swarm. Each agent is controlled by a neural network that processes position data from the others in its visible zone given by a compound eye and in this way navigates in 3D space. An additional input parameter is used to represent the agent’s motivation to form a swarm. Simulations with different motivation parameters exhibit remarkable agent formations that can be considered as biologically plausable. Several ways to improve the model are discussed.

The most imperative process in smart antenna system is beam forming, which changes the beam pattern of an antenna for a particular angle. If the antenna does not change the position for the specified angle, then the signal loss will be very high. By considering the aforesaid drawback, here a new genetic algorithm based technique for beam forming in smart antenna system is proposed. In the proposed technique, if the angle is given as input, the maximum signal gain in the beam pattern of the antenna with corresponding position and phase angle is obtained as output. The length of the beam, interference, phase angle, and the number of patterns are the factors that are considered in our proposed technique. By using this technique, the gain of the system gets increased as well as the interference is reduced considerably. The implementation result exhibits the efficiency of the system in beam forming.

There are many ways to divide datasets into some clusters. One of most popular data clustering algorithms is K-means algorithm which uses the distance criteria for measuring the data correlation. To do that, we should know in advance the number of classes (K) and choose K data points as an initial set to run the algorithm. However, the choice of initial points is a main problem in this algorithm which may cause the algorithm to converge to a local minimum. Some other data clustering algorithms have been proposed to overcome this problem. The methods are Genetic algorithm (GA), Ant Colony Optimization (ACO), PSO algorithm, and ABC algorithms. In this paper, we employ the Stem Cells Optimization algorithm for data clustering. The algorithm was inspired by behavior of natural stem cells in the human body. We developed a new data clustering based on this new optimization scheme which has the advantages such as high convergence rate and easy implementation process. It also avoids local minimums in an intelligent manner. The experimental results obtained by using the new algorithm on different well-known test datasets compared with those obtained using other mentioned methods demonstrate the better accuracy and high speed of the new algorithm.

Recently (2011) Chen et al. found that Wang et al.’s scheme (2007) is vulnerable to impersonation attacks and parallel session attacks; and then proposed a security enhancement of Wang et al.’s scheme. Chen et al. claimed to inherit the merits and eradicate the flaws of the original scheme through their improved scheme. Unfortunately, we found that Chen et al.’s scheme inherits some flaws of the original scheme, like the known-key attack, smart card loss attack and its serious consequences. In addition, Chen et al.’s scheme is not easily reparable and is unable to provide forward secrecy. Thus Chen et al.’s scheme still has scope for security enhancement. Finally, we propose an improved scheme with better security strength. Moreover, we analyze the performance of our scheme and prove that ours is suitable for applications with high security requirements.

The Network-on-Chip (NoC) is an emerging communication technique for System-on-Chip (SoC) communications. The NoC uses multiple processors, usually targeted for embedded applications and other applications [3, 13]. Performance of the bus is degraded by the increasing number of processing elements and transaction oriented model [13]. This has attracted much attention for applying wireless network protocols as CDMA, TDMA, and dTDMA in SoC. The TDMA systems use a fixed number of timeslots. This protocol wastes bandwidth when some timeslots are allocated but not used. The dynamic TDMA (dTDMA) bus arbiter dynamically grows and shrinks the number of timeslots to match the number of active transmitters [14]. In this paper, we present a design of area-efficient switch for inter-layer communications in 3-D NoC. The arbitration logic in the switch is based on a programmable priority encoder. A 640-bit message with uniform random destination data pattern was injected per IP per machine clock cycle. We have obtained the maximum clock frequency of 2.09 GHz for 96(4 × 8 × 3) IP cores connected in a mesh topology. The presented architecture demonstrates their superior functionality in terms of speed, latency, area, and power consumption as compared with the existing implementation [14]. The maximum power consumption of the proposed area-efficient programmable arbiter is 0.625 mW. The design is synthesized using 180nm TSMC Technology.