The importance of mathematics and science education in today's modern, technology-driven society cannot be understated. This paper describes the history of and ongoing efforts in curricular and educational technology development for the infinity project, a joint effort between educators, administrators, and industry leaders to establish an engineering curriculum at the high school level. Several issues are considered, including the choice and design of the technology platform used in the curriculum, the curricular and technology development timeline, and the design examples chosen to illuminate important engineering concepts within the curriculum. We also provide a list of key best practices and challenges in developing novel pre-college engineering curricula for future engineers.

In wireless communications, it is often desirable to merge bit decisions from multiple receivers to improve overall link performance. It is well known that in order to fuse bit decisions optimally from a network of receivers, precise knowledge of receiver bit error rates (BERs) is needed. This information, however, is rarely available in practice. We present an iterative procedure for blindly estimating receiver BERs to enable near optimal blind fusion of bit decisions in a multi-receiver network. We show that the solution of the estimation problem is a structured eigenvalue task and propose a modified power method procedure to perform it. We prove that the desired solution is a stable point of the algorithm and the algorithm is locally stable. Furthermore, we show, via simulations, that the technique results in excellent performance in nearly all practical operating scenarios.

Many blind source separation (BSS) methods are based on minimization of entropy-based criteria. Gradient-based methods have been observed to converge slowly when applied to such criteria. In this paper, we argue that the non-convex “bird's beak” shape of such criteria is the reason why gradient BSS methods perform poorly. To overcome these limitations, we propose a novel local parameter optimization method based upon curve fitting to a density-based entropy measure. Simulations show that the novel method can be used as an effective refinement procedure for an existing BSS method.

The Infinity Project is a joint effort between university educators, high school teachers, administrators, and industrial leaders to establish an engineering curriculum that is taught within the regular high school day. The curriculum teaches students about the design of technology-driven systems and motivates them to learn fundamental concepts of mathematics, science, and engineering. The curriculum currently consists of (1) a course text [1], (2) integrated laboratory exercises with real-time signal processing hardware, (3) summer teacher training institutes, and (4) a web community portal for information sharing (www.infinity-project.org). In this paper, we briefly describe three real-time signal processing laboratory exercises—SketchWave, Coin Counter, and Video Steganography—that are used within the course to both teach and excite students about engineering design, fundamental scientific and mathematical concepts, and the power of problem solving. These representative exercises are drawn from over 200 laboratory worksheets developed for the course and available online for participating schools.

This paper outlines the goals, structure, and technology elements of the INFINITY Project, a joint effort between university educators, industrial partners, and civic leaders to introduce a signal-processing-based engineering curriculum at the high school educational level. Implementation issues of the program are addressed, including class textbook and laboratory creation, teacher training, and online classroom support. Initial responses to the effort from teachers and students at fourteen different high schools are highly positive, and plans for further expansion of the program are given.

We propose a blind signal separation algorithm (CLUE) that uses the sum of the individual code lengths of the extracted signals as a measure of the separation performance. The new technique combines a widely-available universal data compression routine with any single-parameter search procedure. Unlike previous approaches, the proposed method is model-free and does not rely on the moment values of the signals for its separation performance. An example shows the algorithm's efficiency in separating mixtures of image, audio, and text data.

Distance education is becoming an increasingly important means of reaching a wider variety of traditional and non-traditional students and of developing unique educational partnerships between universities. In an effort to assist engineering departments just beginning to pursue distance education, the authors reflect on an innovative four year experiment in Internet based distance education involving students and faculty from the University of Colorado at Boulder, George Mason University, Rice University, Cornell University, and Sandia National Labs. The educational and research benefits to both the students and faculty are discussed in detail. In addition, the weaknesses and limitations of this experiment are also addressed. It is hoped that these comments and observations will benefit other institutions beginning to pursue similar distance education programs.

Noise and interference on communications channels can often deviate significantly from that predicted by Gaussian models. This unfortunate reality can degrade the system performance to unacceptable levels. Empirical universal receivers have been developed which achieve asymptotic optimality over a very wide range of channels, and are thus useful in such situations. However, these receivers require training or pilot signals from the channel, which in turn reduces the effective bandwidth of the channel. In this paper, we demonstrate a "training-free" empirical receiver which is shown to perform significantly better than reasonably trained universal receivers and nearly as well as the globally optimal receiver. Importantly this receiver is capable of deriving the necessary training information directly from the transmitted communication signal, and thus requires no bandwidth overhead. Applications to QPSK reception are discussed thoroughly.

Third generation wireless systems plan to use wideband code-division multiple access (CDMA) technologies for their communication schemes. Unlike current standards, such as IS-95, these new systems will transmit both voice and data information at much higher rates than currently offered today. To support these high data rates, the new wideband CDMA systems (WCDMA and cdma2000) will make use of shorter user codes and more aggressive power control schemes. As a consequence, the multiuser interference at the receiver will be strongly non-Gaussian and thus degrade the performance of the standard receiver. To combat this, we present a novel "type-based" receiver which outperforms the current industrial gold standard (RAKE receiver) in most reasonable operational scenarios. This type-based receiver is asymptotically optimal with respect to any form of multiuser interference and any form of background noise, mimics the performance of multiuser detectors without having to know the interfering users' chip sequences, doesn't suffer from the "near-far" problem, and matches the computational complexity of the RAKE receiver. These properties make this new receiver ideally suited for both indoor and outdoor CDMA based cellular systems operating in interference limited, multipath environments.

Since practicing engineers work in multidisciplinary teams, it is important that universities provide as many teaming experiences as possible. In this paper, we present some of the advantages and disadvantages of traditional teaming approaches. We then present issues related to virtual teaming-the teaming of students from geographically distributed locations. Virtual teaming adds a new dimension to the teaming experiences that universities can provide to students to better equip them for the environment in which they will work in research positions and in industry. Experiences with a three-year program in virtual teaming between the University of Colorado and George Mason University are presented.

The problem of distinguishing reliably between signaling formats in the presence of noise, interference, unknown dispersive channel conditions, as well as timing and frequency mismatches is addressed. Methods based on a combination of blind equalization and universal classification are presented and their performance is assessed through simulations.

This paper describes the ongoing efforts of the participants of the Signal Processing Education consortium (SPEC) in bringing the most up-to-date technologies to the teaching of undergraduate digital signal processing. In particular, we describe herein the use of new multimedia technologies for distributed teaming of undergraduates and faculty to both take advantage of the diverse faculty expertise and to mimic the increasingly common practice of distributed teaming in industry.

Competitive pressures in the global marketplace have forced companies to form teams from the best talent available irrespective of their geographical location. As it comes online, the National information Infrastructure will be increasingly used to support such interactions. American companies are far ahead of the universities in realizing systems to support such geographically distributed interactions. Universities must catch up by exposing their students to such design environments. In addition, universities should help define and evaluate network-based information dissemination systems by serving as testbeds for new interactive strategies. The paper presents initial results in a distance teaming experiment at the University of Colorado, George Mason University, and Rice University.

For many modern detection systems, analytic expressions for the probability of false alarm and/or probability of error are not available. Monte Carlo simulations have often been used as a direct means of estimating these quantities. A technique known as importance sampling can provide a significant reduction in the variance of such estimates. A number of importance sampling biasing z densities have been shown to be effective for problems of interest. A method for further improving the accuracy of importance sampling estimates is presented. This technique utilises additional information available in the simulation to adapt the standard importance sampling estimate based upon specific realizations of the biased inputs. This new methodology is easily implemented and results in an appreciable improvement in the accuracy of the estimator in all examples considered.>

The problem of designing and analyzing suboptimal detectors via statistical distance measures is considered. As a preliminary result, we show that only the minimum and maximum probability of error are valid measures of discrimination between the input statistics. This result would seem then to imply that the use of distance measures in this context can be inappropriate. However, to overcome this apparent obstacle, we demonstrate explicit relationships between various f-divergences and the loss in performance of an arbitrary detector relative to the optimal detector. In particular, we establish both upper and lower bounds on the performance loss of a suboptimal detector in terms of the "distance" between the pertinent statistics of both the optimal and suboptimal detectors. While designing detectors by minimizing these upper bounds can be an elusive task, in many practical cases, the lower bound presented herein holds with equality. In this case, minimizing the separation of the output statistics of the detector with respect to a particular f-divergence equivalently minimizes the resulting probability of error of the detector. To facilitate design, other researchers have established conditions under which one may design arbitrary detection strategies with respect to a specific f-divergence (Kullback-Leibler distance being a principal example). We extend this approach by deriving necessary and sufficient conditions under which one may design detection strategies with respect to an arbitrarily chosen f-divergence. Thus when these conditions are met, one may optimize a detector with respect to the most analytically tractable distance measure to obtain the minimum probability of error detector over a selected class of detection strategies. Examples demonstrating the utility of this theory for the problem of designing optimal linear detectors and optimal signal sets are presented.>

The optimal procedure for detecting the presence of discrete-time signals in additive noise can be derived from the likelihood ratio test. When the noise has statistically independent, identically distributed components, the dependence of the detector's performance on signal characteristics can be related to the Kullback-Leibler (KL) distance between the distributions governing the hypotheses. Performance predictions based on the central limit theorem are shown to be poor approximations to the true performance. Performance of the optimal detector has long been known to increase exponentially with increasing KL distance. Symmetric noise amplitude distributions yield a symmetric dependence on the difference between the signals' amplitudes at each time index. Small-signal (locally optimal) detection performance is shown to depend on signal energy, whereas large-signal performance depends on the signal waveform. When a distance measure can be defined, performance depends on a different measure than that used in the detector with one exception (the Gaussian).>

When the statistics of the noise are non-Gaussian, analytic expressions for the probability of false alarms in detection systems are rarely available. Monte Carlo estimation techniques are therefore typically necessary. The author presents an importance sampling biasing distribution which renders exponential savings over standard Monte Carlo simulations. Two important features of this biasing strategy are that no importance sampling parameters need to be determined and no additional computations are required for implementation.>

Two simple structures employing multilayer perceptrons are proposed for demodulation of spread-spectrum signals in both synchronous and asynchronous Gaussian channels. The optimum receiver is used to benchmark the performance of the proposed receiver; in particular, it is proved to be instrumental in identifying the decision regions for the neural networks. The neutral networks are trained for the demodulation of signals via backpropagation-type algorithms. A modified backpropagation-type algorithm is introduced for single-user and multiuser detection with near-optimum performance that could have applications in other classification and pattern recognition problems. A comparative performance analysis of the three receivers, optimum, conventional, and the one employing neural networks, is carried out via Monte Carlo simulations. An importance sampling technique is employed to reduce the number of simulations necessary to evaluate the performance of these receivers in a multiuser environment. In examples given, the receiver significantly outperforms the conventional receiver.>

The problem of simulating error rates in direct sequence spread-spectrum code division multiple-access (CDMA) systems is examined. Due to the computational complexity required to simulate these systems, an importance sampling technique is developed based upon previous work by the authors. A conditional weighting function is derived such that the linear shift class of biasing densities can be employed. Results are given for a variety of detector structures and background noise distributions. It is shown that this biasing scheme can dramatically reduce the run time of realistic multiple-access simulations.>

The problem of estimating estimating expectations of functions of random vectors via simulation is investigated. Monte Carlo simulations, also known as simple averaging, have been used as a direct means of estimation. A technique known as importance sampling can be used to modify the simulation via weighted averaging in the hope that the estimate will converge more rapidly to the expected value than standard Monte Carlo simulations. A constrained optimal solution to the problem of minimizing the variance of the importance sampling estimator is derived. This is accomplished by finding the distribution which is closest to the unconstrained optimal solution in the Ali-Silvey sense (S. Ali et al., 1966). The solution from the constraint class is shown to be the least favorable density function in terms of Bayes risk against the optimal density function. Examples of constraint classes, which include epsilon -mixture, show that the constrained optimal solution can be made arbitrarily close to the optimal solution. Applications to estimating probability of error in communication systems are presented.>

Detection systems are designed to operate with optimal or nearly optimal probability of a wrong decision. Analytical solutions of the performance of these systems have been very difficult to obtain. Monte Carlo simulations are often the most tractable method of estimating performance. However, in systems with small probability of error, this technique requires very large amounts of computer time. A technique known as importance sampling substantially reduces the number of simulation trials needed, for a given accuracy, over the standard Monte Carlo method. The theory and application of the importance sampling method in Monte Carlo simulation is considered in a signal detection context. A general method of applying this technique to the optimal detection problem is given. Results show that in cases examined, the gain is approximately proportional to the inverse of the error probability. Applications of the proposed method are not limited to optimum detection systems; analysis, leading to a measure of the gain in using this biasing scheme, shows that in all optimal systems considered, less than 100 trials is needed to achieve estimates with 45% confidence, even for extremely small error probabilities.>