Michael Conner

Recursive fast algorithm and the role of the tensor product

The use of the tensor product for modeling and designing FFT algorithms is addressed. The benefit of the tensor product approach lies in the strong connection between certain tensor product constructs and important computer architectures. The scope of the tensor product approach is generalized to include a much larger class of fast recursive algorithms. This greatly enhances the versatility of the tensor product technique and brings many different algorithms to the level of understanding and flexibility enjoyed by the FFT. >

Genetic algorithms for self-spreading nodes in MANETs

We present a force-based genetic algorithm for self-spreading mobile nodes uniformly over a geographical area. Wireless mobile nodes adjust their speed and direction using a genetic algorithm, where each mobile node exchanges its genetic information of speed and direction encoded in its chromosomes with the neighboring nodes. Simulation experiments show encouraging results for the performance of our force-based genetic algorithm with respect to normalized area coverage..

Bio-inspired topology control for knowledge sharing mobile agents

We present different approaches for knowledge sharing bio-inspired mobile agents to obtain a uniform distribution of the nodes over a geographical terrain. In this application, the knowledge sharing agents in a mobile ad hoc network adjust their speed and directions based on genetic algorithms (GAs). With an analytical model, we show that the best fitness value is obtained when the number of neighbors for a mobile agent is equal to the mean node degree. The genetic information that each mobile agent exchanges with other neighboring agents within its communication range includes the nodes location, speed, and movement direction. We have implemented a simulation software to study the effectiveness of different GA-based algorithms for network performance metrics including node densities, speed, and number of generations that a GA runs. Compared to random-walk and Hill Climbing approaches, all GA-based cases show encouraging results by converging towards a uniform node distribution.

Optical Wigner distribution and ambiguity function for complex signals and images

Abstract Using a degenerate optical phase conjugation device, a new real-time coherent optical Wigner distribution and ambiguity function implementation method for both one- and two-dimensional complex signals is suggested.

A tensor product factorization of the linear convolution matrix

A tensor product representation of the Agarwal-Burns nesting scheme is presented. In addition to providing a highly compact representation of this approach to nesting, it is shown that the tensor product approach provides the structure needed to easily derive several variant schemes that are well suited for use on vector processor and multiprocessor computers. >

Estimating behavior of a GA-based topology control for self-spreading nodes in MANETs

This paper presents a dynamical system model for FGA, a force-based genetic algorithm, which is used as decentralized topology control mechanism among active running software agents to achieve a uniform spread of autonomous mobile nodes over an unknown geographical area. Using only local information, FGA guides each node to select a fitter location, speed and direction among exponentially large number of choices, converging towards a uniform node distribution. By treating a genetic algorithm (GA) as a dynamical system we can analyze it in terms of its trajectory in the space of possible populations. We use Voses theoretical model to calculate the cumulative effects of GA operators of selection, mutation, and crossover as a population evolves through generations. We show that FGA converges toward a significantly higher area coverage as it evolves.

Convergence analysis of genetic algorithms for topology control in MANETs

We describe and verify convergence properties of our forced-based genetic algorithm (FGA) as a decentralized topology control mechanism distributed among software agents. FGA uses local information to guide autonomous mobile nodes over an unknown geographical terrain to obtain a uniform node distribution. Analyzing the convergence characteristics of FGA is difficult due to the stochastic nature of GA-based algorithms. Ergodic homogeneous Markov chains are used to describe the convergence characteristics of our FGA. In addition, simulation experiments verify the convergence of our GA-based algorithm.

Markov chain models for genetic algorithm based topology control in MANETs

We analyze the convergence properties of our force based genetic algorithm(fga) as a decentralized topology control mechanism distributed among software agents. fga guides autonomous mobile agents over an unknown geographical area to obtain a uniform node distribution. The stochastic behavior of fga makes it difficult to analyze the effects of various manet characteristics over its convergence rate. We present ergodic homogeneous Markov chains to analyze the convergence of our fga with respect to changing communication range of mobile nodes. Simulation experiments indicate that the increased communication range for the mobile nodes does not result in a faster convergence.

Photon echo in direct gap semiconductor

Abstract Using statistical quantum electrodynamics techniques, we investigate photon echo in intrinsic direct transition semiconductor materials. Possible applications of this effect in ultrashort time measurements and optical digital computation are discussed.

Zak transform as an adaptive tool

In [1] we compared the short-time Fourier transform with the Gabor transform as signal processing tools and concluded in part that in many applications the Gabor transform is more compact and may require substantially less computations and storage. one part of our reasoning was based on a deconvolution algorithm [2] which computes Gabor coefficients from short-time Fourier transform samples . This algorithm has several serious defects. First, each Gabor coefficient depends on the complete set of short-time Fourier transform samples . As shown in [2], time-frequency computations are more sensitive to aliasing errors than standard Fourier analysis computations. Secondly, an important stage in the algorithm requires division by the Zak transform of the analyzing signal. In many cases, including the classical Gabor case which takes integer time-frequency shifts of the Gaussian, the Zak transform vanishes at some point, severely limiting precision and numerical stability. In this work, a new algorithm will be presented for the classical Gabor case which side steps these problems. The cost is that the short-time Fourier transform samples are taken with respect to an analyzing signal other than the Gaussian. This new analyzing signal is computed during a precomputation stage and although not as well behaved as the Gaussian is a least squareintegrable. In effect, it is constructed by a generalization of the ideas that lead to a biorthogonal [3,4] . Increasing orders of smoothness can be achieved at the price of increasing computational complexity of the processing part of the algorithm. Along these lines, the algorithm explicitly describes the