The appropriate placement for mathematics courses for first-year engineering students is an important factor in ensuring the success of engineering students in terms of retention and graduation rates. However, often the advisors only are able to look at the cutoffs of a few factors when making their recommendations. We propose that modeling offers advisors a more effective method to integrate multiple factors in order to make the most appropriate recommendation for first year math courses to entering students and help ensure student success. Results to be reported in this study are effectiveness of predicting engineering students' success in Calculus I courses using regression models, significant predictors to Calculus I grades, the actual recommendations that students received from advisors for appropriate calculus placement, whether students took the advisors recommendations in choosing a calculus placement, and the relationship between the model and what advisors are actually advising. The results of this study will provide information that helps to find the most effective means of advising to help ensure student success.

Wavelet packet-division multiplexing (WPDM) is a high-capacity, flexible, and robust multiple-signal transmission technique in which the message signals are waveform coded onto wavelet packet basis functions for transmission. We derive an expression for the probability of error for a WPDM scheme in the presence of both impulsive and Gaussian noise sources and demonstrate that WPDM can provide greater immunity to impulsive noise than both a time-division multiplexing scheme and an orthogonal frequency-division multiplexing scheme.

In this paper, we present an entropy based approach for DOA estimation in Gaussian and non-Gaussian environments. The DOA estimates are obtained by minimizing an entropy measure of the array data in the noise subspace. We show that the entropy approach leads to the MAP algorithm under the Gaussian assumption. Under the non-Gaussian assumption, we apply the varimax norm as an information measure. An intuitive consistency analysis is also performed. Computer simulations are used to demonstrate the effectiveness of the proposed approach.

In this paper, two types of adaptive FREquency SHift filters are proposed. One is LMS adaptive FRESH filter. The other is blind adaptive FRESH filter. By exploiting the spectral correlation of cyclostationary signals, these adaptive filters can separate the signals which overlap in both frequency and time domain. Theoretical development and simulations of these filters are given in this paper. The results show that for signals which spectrally overlap, the adaptive FRESH filters can perform very well while ordinary adaptive filters fail.

Derives a new set of basis functions which have high energy concentration in both time and frequency. The approach is based on transforming a signal energy maximization problem into an equivalent eigenvalue problem for a certain integral operator, and then showing that the eigenvectors of this integral operator form a complete orthonormal basis. This basis set is in some sense optimal since each basis function has the highest energy concentration within a certain subspace of /spl Lscr//sup 2/. Furthermore, this basis can be employed to analyze and design communication signals for which the energy concentration within the essential duration and essential bandwidth is important and some other optimum characteristics would also be required.>