Strategies to recruit and retain underrepresented students in the STEM fields are as diverse as the students they seek to engage. The community college pipeline is well recognized as a source for both nontraditional and underrepresented students for engineering programs. The Transfer Initiative for Engineering Scholars (TIES) addresses the unique needs of this cohort who transfer as juniors from the community college environment. TIES is currently in the fourth year of receiving funding from NSF's S-STEM program and providing financial support to eligible students in an environment committed to smooth transitions, multiple options for student support services, community building with peers and faculty, leadership development, and mentor relationships. Many of the support activities build upon previously existing retention programs, but new components, such as leadership seminars, community building, and industry mentoring, were developed specifically for TIES participants. Among the several components of the program, those that contribute to a supportive community environment were seen by the students as extremely important for a successful transition to Georgia Tech's undergraduate electrical engineering and computer engineering programs.

Many current radar-based human detection systems employ some type of Doppler or Fourier-based processing, followed by spectrogram and gait analysis to classify detected targets. However, Fourier-based techniques inherently assume a linear variation in target phase over the aperture, whereas human targets have a highly nonlinear phase history. This mismatch leads to significant loss in SNR and integration gain. In this paper, an Enhanced Optimized Non-Linear Phase (EnONLP) detector is proposed that employs a dictionary to store possible target returns generated from the human model for each combination of parameter values. An orthogonal matching pursuit algorithm is used to compute a sparse approximation to the radar return that is optimal in the least squares sense. Performance of the EnONLP algorithm is compared to that of a parameter-estimation based algorithm and conventional, fully-adaptive STAP.

While the ongoing information technology (IT) revolution is providing us with tremendous educational opportunities, educators and IT researchers face numerous obstacles and pedagogical questions. The Georgia Institute of Technology (Georgia Tech) has a long history in engineering education research and has developed and designed many different tools for instructor authoring, course capturing, indexing, and retrieval. Special attention has been applied to the design and deployment of distributed learning environments. This paper describes the environment and challenges facing Georgia Tech as it expands its campus worldwide while maintaining integration of faculty and students across these campuses. The focus is primarily on the problems of synchronous delivery to multiple sites with a description of how technology is currently being deployed in Georgia Tech's distance-learning (DL) classrooms, as well as technology that is under development for the DL classroom of the future.

This paper describes a feedback algorithm for tracking the dominant subspaces of continuously time-varying channels in multiantenna communication systems. The nature of the problem is quantization of subspaces. It is well known that subspaces can be mathematically modeled as points in a Grassmann manifold. We model the variations between the dominant subspaces of channels at adjacent time instants to be along geodesics in the Grassmann manifold. Instead of quantizing the subspaces themselves, we propose to quantize the geodesic trajectory connecting two subspaces. More specifically, we quantize a key entity that characterizes a geodesic arc: the velocity matrix, which resembles angular speed in a one-dimensional complex space. Two techniques are proposed for quantizing the velocity matrix of the geodesic. In the first, a 1-bit feedback is utilized to indicate the preferred sign of a random velocity matrix of the geodesic. In the other, the velocity matrix is quantized using a Gaussian vector quantization codebook. Numerical results show that the performance of the proposed 1-bit feedback algorithm is better than a previously proposed Grassmannian subspace packing scheme at low-to-medium Doppler frequencies and better than a gradient sign feedback scheme at all Doppler frequencies. In our simulations, the Gaussian vector quantization algorithm is always better than the 1-bit feedback algorithm.

Many previously proposed communications systems based on chaos disregard common channel distortions and fail to work under realistic channel conditions. A sequential equalization algorithm based on the Viterbi algorithm has shown good performance to combat channel distortions. However, the amount of computation needed for the sequential equalization algorithm increases sequentially with the number of channel parameters and the precision of the estimated chaotic sequence. A lower complexity, iterative equalization algorithm for chaotic communication systems is developed. The trellis size is greatly reduced by implementing the channel equalization and chaotic demodulation separately, but connecting them iteratively. The proposed algorithms are simulated under both time-invariant and time-varying channel distortions. Bit-error rate graphs are illustrated for each case.

We propose a blind multiuser detector based on Monte Carlo Markov chain (MCMC) techniques. The detector exploits mutually orthogonal complementary sequences to distinguish between transmitting users and space-time codes to take advantage of the available spatial diversity. We propose a partitioning scheme for the symbol draws in the MCMC algorithm that reduces the complexity without any degradation in performance. The detector's performance is simulated in an iterative receiver that utilizes an outer coder. The simulations display some loss in coding gain because of the blind nature of the system; however, diversity gain is preserved.

The paper describes a low rate feedback algorithm for conveying partial channel state information - specifically, the dominant row subspace of the channel matrix - from the receiver to the transmitter in a continuously time-varying multiple-antenna environment. Since subspaces are points on a complex Grassmann manifold, variations in subspaces are treated as a piecewise geodesic process on the manifold. The receiver feeds back one bit to indicate the preferred sign of a random velocity matrix of the geodesic. Numerical results show that the performance of the proposed algorithm is better than the Grassmannian subspace packing approach at low-to-medium Doppler frequency and always better than the previously proposed gradient sign feedback scheme.

OFDM transmitter diversity systems that do not required a cyclic prefix have recently been proposed as means to improve bandwidth efficiency. For these systems, knowledge of the channel parameters is required at the receivers for interference cancellation, diversity combining, and decoding. In this paper, we propose a low complexity pilot-symbol-assisted channel estimator for OFDM transmitter diversity systems without a cyclic prefix. The pilot symbols are constructed to be periodic within an OFDM symbol interval in order to avoid ISI and ICI and to be non-overlapping in frequency to allow for the simultaneous sounding of the multiple diversity channels. The time-varying channel responses are then tracked by interpolating a set of estimates obtained through periodically transmitted pilot symbols. The effectiveness and limitations of the proposed estimator are verified by computer simulations.

In this paper, we examine the convergence behavior of finite alphabet (FA) beamformers. The two most popular implementations of FA beamformers for digital communication signals are the iterative least-squares with projection (ILSP) and the minimum mean-square error (MMSE) beamformer. To facilitate the analysis, for the binary communications case, we derive closed-form expressions for the mean weight vector, signal-to-noise ratio, and signal-to-interference ratio for both the ILSP and MMSE beamformers in terms of the bit-error rate (BER) performance at each iteration. Next, we generalize the analysis for the M-ary pulse-amplitude modulation and M-ary phase-shift keying cases. We show that both ILSP and MMSE beamformers have previously unreported bias terms in the array response vectors which are functions of the BER for each iteration. Furthermore, as the BER becomes arbitrarily small, we show that our solutions converge to the well-known asymptotic expressions widely published in the literature. Next, we provide a geometric interpretation of the effects of the noise bias vector in terms of angles between subspaces. Based on our analysis, we were able to develop necessary and sufficient conditions for convergence in the mean. We conclude with Monte-Carlo simulations to validate our analysis.

Connexions is a new approach to authoring, teaching, and learning that aims to fully exploit modern information technology. Available free of charge to anyone under open-content and open-source licenses, Connexions offers custom-tailored, current course material, is adaptable to a wide range of learning styles, and encourages students to explore the links among courses and disciplines. In contrast to the traditional process of textbook writing and publishing, Connexions fosters world-wide, cross-institution communities of authors, instructors, and students, who collaboratively and dynamically fashion “modules” from which courses are constructed. We believe the ideas and philosophy embodied by Connexions have the potential to change the very nature of textbook writing and publishing, producing a dynamic, interconnected educational environment that is pedagogically sound, both time and cost efficient, and fun. This paper overviews the philosophy and technology behind Connexions and describes a nascent community developing material for DSP education.

In a multiuser chaotic communications scheme, each user's information is modulated with a chaotic system and then transmitted independently over a common communication channel. The receiver sees the superposition of the distorted transmitted signals with additive noise. In this paper, a novel channel equalization algorithm is proposed for multiuser chaotic communications systems where each user's information is encoded using symbolic dynamics. The goal is to recover the chaotic signal being sent by each individual user so that its information can be estimated. This paper begins with the symbolic dynamics representation of chaotic systems being exploited to enable the representation of the multiuser chaotic system with an equivalent trellis diagram. Then, the trellis diagram is expanded to accommodate FIR channel distortions. Finally, the Viterbi algorithm is used to obtain maximum-likelihood estimates of the transmitted sequences. The performance of this algorithm in terms of bit-error rate (BER) is simulated for different channel distortions.

Space-time coded orthogonal frequency division multiplexing (OFDM) transmitter diversity techniques have been shown to provide efficient means of achieving near optimal diversity gain in frequency-selective fading channels. However, these known techniques all require a cyclic prefix to be added to the transmitted symbols, resulting in significant bandwidth expansion. In this paper, bandwidth efficient space-time and space-frequency block-coded OFDM transmitter diversity techniques are proposed that exploit spatial diversity to eliminate the need for a cyclic prefix.

The recent rapid increase in applications of DSP has generated a need for technical personnel with a DSP background. Until recently, most DSP education has been at the graduate level, so many practicing engineers are developing a need for additional DSP education. At Georgia Institute of Technology, we are delivering an online continuing education course that is targeted at these engineers. The 12-week course is comprised of three interconnected tracks in DSP system theory, real-time implementation principles, and laboratory assignments with MATLAB and the latest fixed-point TI DSP chips. The course was designed after a needs analysis performed by Texas Instruments training specialists. It is intended specifically to provide a full semester's course in a way that is adapted to the schedules and circumstances of practicing engineers. We describe both the content of the course and the technology for delivering it online.

Orthogonal frequency division multiplexing (OFDM) transmitter diversity techniques have been shown to be efficient means of achieving near optimal diversity gain in frequency-selective fading channels. For these systems, knowledge of the channel parameters is required at the receivers for diversity combining and decoding. In this paper, we propose a low complexity, bandwidth efficient, pilot-symbol-assisted channel estimator for multiple transmitter OFDM systems. The pilot symbols are constructed to be non-overlapping in frequency to allow for the simultaneous sounding of the multiple channels. The time-varying channel responses are tracked by interpolating a set of estimates obtained through periodically transmitted pilot symbols. The effectiveness and limitations of the proposed estimator are verified by simulations.

A transmitter diversity technique for wireless communications over frequency selective fading channels is presented. The proposed technique utilizes orthogonal frequency division multiplexing (OFDM) to transform a frequency selective fading channel into multiple flat fading subchannels on which space-frequency processing is applied. Simulation results verify that in a slow fading environment the proposed space-frequency OFDM (SF-OFDM) transmitter diversity technique has the same performance as a previously reported space-time OFDM (ST-OFDM) transmitter diversity system but shows better performance in the more difficult fast fading environments. Other implementation advantages of SF-OFDM over the ST-OFDM transmitter diversity technique are also discussed.

A variety of communications systems based on chaos and nonlinear dynamics have previously been proposed. However, most of these algorithms fail to work under realistic channel conditions. This paper presents a channel equalization scheme for chaotic communication systems based on a family of archetypal chaotic maps. The symbolic-dynamic representation of these maps is exploited to allow a straightforward and efficient implementation. Equalizer filter coefficients are updated using appropriately modified versions of decision-directed and decision-feedback equalization algorithms with adaptation based on the NLMS algorithm.

Combined monostatic clutter (MSC) and terrain scattered interference (TSI) pose a difficult challenge for adaptive radar processing. Mitigation techniques exist for each interference alone but are insufficient for their combined effects. Current approaches separate the problem into two stages where TSI is suppressed first and then MSC. The problem with this cascade approach is that during the initial TSI suppression stage, the MSC becomes corrupted. In this paper an innovative technique is introduced for achieving a significant improvement in cancellation performance for both MSC and TSI, even when the jammer appears in the mainbeam. The majority of the interference rejection, both TSI and MSC, is accomplished with an MSC filter, with further TSI suppression accomplished via an additional tapped reference beam. Simultaneous optimization of the MSC filter weights and reference beam weights yields the desired processor. Performance results using Mountain-top data demonstrate the superiority of the proposed processor over existing processors.

Mainbeam jamming poses a particularly difficult challenge for conventional monopulse radars. In such cases spatially adaptive processing provides some interference suppression when the target and jammer are not exactly co-aligned, but the resulting array pattern is too distorted to be suitable for monopulse processing. The presence of coherent multipath in the form of terrain scattered interference (TSI) is normally considered a nuisance source of interference. However, it can also be exploited to suppress mainbeam jamming with space-time processing. Here we present a method for incorporating space-time processing into monopulse processing to yield a space-time monopulse processor with distortionless spatial array patterns that can achieve far better mainbeam jamming cancellation and target angle estimation than has been previously possible. Performance results for the monopulse processor are obtained for Mountaintop data containing a jammer and TSI, that demonstrate a dramatic improvement in performance over conventional monopulse and spatially adaptive monopulse.

Techniques often used in the area of adaptive array signal processing are applied to the multiuser detection problem. The results of this effort include a robust detector, suitable for use in the presence of modeling errors, and a reduced-rank detector with improved transient behavior relative to full-rank detectors. Algorithm performance is presented in the form of bit-error-rate (BER) curves and least mean square (LMS)-like learning curves.

In airborne radar applications, hot clutter refers to terrain-reflected jammer multipath interference which is spatially distributed throughout the mainbeam and sidelobes of the radar receiver. Most current cancellation methods use a single reference beam to cancel the mainbeam hot clutter interference. However these cancellers are computationally intensive and suffer from blind interval effects due to the size of the temporal window required for effective mitigation. We propose a full beamspace canceller implemented as a generalized sidelobe canceller for hot clutter mitigation. The proposed canceller exploits the spatial nature of the interference by utilizing delayed replicas of the jammer signal at all azimuth angles to significantly reduce the temporal extent of the canceller. By trading off temporal for spatial degrees of freedom, the full beamspace approach is able to drastically reduce the size of the temporal window required, achieving effective cancellation with fewer total degrees of freedom and a significant reduction in the blind interval. Results comparing the proposed full beamspace and the single-beam cancellers are reported using data collected in the Mountaintop program.

A multisensor interference cancellation scheme for CDMA communication systems is presented. The algorithm consists of constrained output power minimization at each sensor followed by optimal combining of the sensor outputs. Knowledge of the desired user's code is used to specify the constraint for minimizing the output power at each sensor of the array. If the codes (and timing) of other users (i.e., interferers) are known, they may be incorporated as additional constraints to improve receiver performance. Optimal combining of the individual sensor outputs is achieved by employing the LMS algorithm in tracking mode for the adaptation. The receiver is blind in the sense that only knowledge of the desired user's code (not the actual transmitted bit sequence) and associated timing is necessary. Algorithm performance results for Rayleigh fading channels are presented in the form of signal to interference-plus-noise ratios and bit error rate curves.

In this paper we introduce a common framework for evaluating the performance of multiple weight, partially adaptive space-time adaptive processing (STAP) algorithms in terms of composite weight vectors. We then evaluate the performance of these STAP algorithms using synthetic and Mountain Top (MT) data (for airborne early warning radar) and address some limitations of high dimensional STAP algorithms in a nonstationary clutter environment. As part of the evaluation, we also familiarize the reader with the MT database and address important issues in processing the data.

An iterative method for reducing noise in contaminated chaotic signals is proposed. This method estimates the deviation of the observed signal from the nearest noise-free signal satisfying the system dynamics in order to get a noise-reduced (or enhanced) signal. To calculate the deviation we minimize a cost function composed of two parts: one containing information that represents how close the enhanced signal is to the observed signal and another including constraints that fit the dynamics of the system. This method has a simple structure and is flexible in the choice of the parts of the cost function. The proposed method is compared with Farmer's method which is known to have good performance in mild signal-to-noise ratios but has a more complex structure.

An adaptive space-time processing system for dynamic spatial channels in a CDMA mobile communications network is presented. The proposed system consists of an adaptive sensor array that estimates the desired directions and waveforms, followed by adaptive linear equalizers that refine the waveform estimates by compensating for the mobile radio uplink channel. Capacity gains are achieved through code reuse, which is made possible by performing the spatial processing after the codes have been decorrelated with one another. Simulation results for a two sensor array in a variety of interference scenarios are presented in the form of bit error rate curves.

This paper describes a novel method for detecting nonstationary signals in colored noise. A first order complex autoregressive, or AR(1), signal model is used which restricts the application of the detector to low order signals, i.e., those which are well modeled by a low order AR process and have only a single spectral peak. The detector assumes the noise covariance is stationary and known. The likelihood function is estimated in the frequency domain because the model simplifies, and the nonstationary frequency estimate can be obtained by an algorithm which approximates the Viterbi algorithm. The AR model parameters are then used to form the appropriate covariance matrix and the approximate likelihood is calculated. Therefore, the detector uses efficient approximations to approximate the generalized likelihood ratio test (GLRT). Simulation results are shown to compare the detector with the known signal likelihood ratio test.>

The extreme sensitivity of a chaotic system's steady state response to small changes in its initial conditions makes long term prediction of the evolution of such a system difficult, if not impossible. In the framework of parameter estimation, it is shown how this sensitivity can hinder attempts to determine model parameters that will reproduce a target chaotic time sequence. Specifically, a waveform error minimization technique based on gradient descent optimization is not well suited for estimating the parameters of a strongly chaotic system. A modification of this minimization procedure that avoids some of the obstacles present when estimating the parameters of a chaotic system is proposed.>

Applying the random coefficient model to array processing, specifically in the design of a beamformer for direction finding, is described. This model is similar to the autoregressive (AR) model, except the coefficients are allowed to change with time instead of remaining constant, thus allowing the beamformer to better model any additive noise or signal correlations in the observations. Through the use of a binary hypothesis test, it is shown that random coefficient models better fit typical array data than do AR models. A Kalman filter is presented that has the array observations as inputs and the parameters of the random coefficient model as outputs. A beamformer based on the random coefficient model is derived that is similar to the constant coefficient linear predictive beamformer. The two beamformers are compared and it is shown that the random coefficient beamformer outperforms the CCLP beamformer.>

Array processing algorithms generally assume that the received signal, composed of both narrowband signals and noise, is Gaussian. This is not true in general. In the context of the narrowband array processing problem, the authors develop robust methods to estimate accurately the spatial correlation matrix. This estimate is robust against amplitude distribution of the noise, be it Gaussian or not, and takes into account sensor placement. Given this estimate, existing array processing algorithms designed for Gaussian circumstances can be used on non-Gaussian problems. The resulting algorithms can be used in the presence of any type of second-order noise process and perform nearly as well as existing algorithms do with Gaussian noise.>

In many situations some information about the structure of the covariance matrix of a random process is known beyond the fact that it is symmetric and positive definite; for instance, the matrix is frequently Toeplitz. Many people have considered the structured covariance matrix estimation problem for Gaussian processes. However, in actual practice, random signals are seldom, if ever, Gaussian. By using a generalization to processes with known non-Gaussian densities, the authors demonstrate how to find the maximum-likelihood estimate of complex Toeplitz covariance matrices and then evaluate the use of this estimate in some passive array beamforming algorithms. There is substantial improvement in the performance of these bearing estimation algorithms when the authors' estimate is used, especially when non-Gaussian noise is present. >

Knowing the number of sources in the acoustic field of a narrowband passive array can be very useful when determining the bearing of these sources relative to the array; many of the "high resolution" bearing estimation algorithms assume that the number of sources is known. The sphericity test is a well-known algorithm for estimating the number of sources from the eigenvalues of the spatial correlation matrix of the array. A new sphericity test statistic is proposed whose distribution approaches the asymptotic chi-squared distribution much faster than previously proposed statistics and, consequently, weaker sources are more readily detected. The capability of this algorithm to detect closely spaced sources is examined and compared to the resolution capabilities of well-known bearing estimation algorithms.