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No description available.

No description available.

No description available.

<p> In this thesis we develop several approaches to the problem of blind channel equalization based on second-order statistics (808). We consider the single-input singleoutput (8180) system with minimum phase channel where the received signal is sampled at the symbol rate (T-spaced equalizer). We formulate the equalizer design criterion as a simple convex optimization problem, where the equalizer can be obtained efficiently avoiding the local minima problem. </p> <p> We also extend the problem to the single-input multiple-output (8IMO) systems where the received signal is sampled at an integer multiple of the symbol rate. We formulate the problem as a convex optimization problem using the features existing in the channel matrix structure. The problem can be solved efficiently to obtain the equalizer where a global minima is guaranteed. Moreover, we modify this formulation and deduce a closed form solution to the equalizer. Although both methods are sensitive to the channel order as well as existing subspace methods, they perform better than the subspace methods when the channel matrix is close to being singular. Furthermore, we propose an efficient direct minimum mean square error (MM8E) approach to estimate the equalizer. The method does not rely on the channel order and utilizes the channel matrix structure in SIMO systems. Therefore, it outperforms existing algorithms including the previously proposed methods. However, due to the large amount of computations involved in this method we present a new algorithm that belongs to the same class with moderate computational complexity and acceptable performance loss with respect to the latter algorithm. </p> / Thesis / Master of Applied Science (MASc)

In a parallel distributed detection system each local detector makes a decision based on its own observations and transmits its local decision to a fusion center, where a global decision is made. Given fixed local decision rules, in order to design the optimal fusion rule, the fusion center needs to have perfect knowledge of the performance of the local detectors as well as the prior probabilities of the hypotheses. Such knowledge is not available in most practical cases. In this thesis, we propose a blind technique for the general distributed detection problem with multiple hypotheses. We start by formulating the optimal M-ary fusion rule in the sense of minimizing the overall error probability when the local decision rules are fixed. The optimality can only be achieved if the prior probabilities of hypotheses and parameters describing the local detector performance are known. Next, we propose a blind technique to estimate the parameters aforementioned as in most cases they are unknown. The occurrence numbers of possible decision combinations at all local detectors are multinomially distributed with occurrence probabilities being nonlinear functions of the prior probabilities of hypotheses and the parameters describing the performance of local detectors. We derive nonlinear Least Squares (LS) and Maximum Likelihood (ML) estimates of unknown parameters respectively. The ML estimator accounts for the known parametric form of the likelihood function of the local decision combinations, hence has a better estimation accuracy. Finally, we present the closed-form expression of the overall detection performance for both binary and M-ary distributed detection and show that the overall detection performance using estimated values of unknown parameters approaches quickly to that using their true values. We also investigate various impacts to the overall detection. The simulation results show that the blind algorithm proposed in this thesis provides an efficient way to solve distributed detection problems. / Thesis / Master of Applied Science (MASc)

Based on the principle of sequential lessons for teaching orienteering, the program is a center that teaches navigation to people who are blind, located in the remains of the Chesapeake and Ohio Canal in Harpers Ferry, West Virginia. Through the use of materials, light, and this program of sequential patterns, the architecture focuses on heightening the working senses of the occupants using the progression throughout the building. This connection of the body and mind to architecture creates an awareness of space, unifying a perception of place, while bridging the old life to the new. / Master of Architecture

Link adaptation is important to guarantee robust and reliable wireless communications with- out wasting valuable radio resources. This technique has become more feasible with the recent appearance of Software Defined Radios (SDRs), which allow easy reconfiguration of their parameters via software. As the environment changes over time, the transmitter needs to be able to effectively estimate its performance under different radio input parameters to be able to find a close to optimal solution. In most wireless communications, an equalizer is implemented at the receiver to estimate the channel impulse response. This estimate can be fed back to the transmitter via a feedback channel, which can in turn help generate a sub-optimal transmission solution for the current situation. In this thesis, a link adaptation method is proposed that uses Orthogonal Frequency-Division Multiplexing (OFDM) in conjunction with blind channel estimation. With the use of OFDM, it can be assumed that the frequency fading at each subcarrier is approximately flat. In addition, under the assumption that the channel is quasi-stationary, the Bit Error Rate (BER) at each subcarrier can be estimated by using the well-known BER formulas for an Additive White Gaussian Noise (AWGN) channel. However, the effect of imperfect channel estimation must also be taken into account. A novel OFDM blind channel estimator is developed. Finally, both simulations and real over-the-air results are presented. / Master of Science

No description available.

Thesis (Ph. D.)--Florida State University, 2004. / Advisor: Dr. Sandra Lewis, Florida State University, College of Education, Department of Special Education and Rehabilitation Counseling Services. Title and description from dissertation home page (viewed Sept. 22, 2004). Includes bibliographical references.