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Distributed wireless sensor networks (WSNs) are being proposed for various applications including defense, security, and smart stages. The introduction of hardware wireless sensors in a signal processing education setting can serve as a paradigm for data acquisition, collaborative signal processing, or simply as a platform for obtaining, processing, and analyzing real-life real-time data. In this paper, a software interface that enables the java-digital signal processing (J-DSP) visual programming environment to communicate in a two-way manner with a wireless sensor network is presented. This interface was developed by writing nesC (an extension to the C programming language for sensors) code that enables J-DSP to issue commands to multiple wireless sensor motes, activate specific transducers, and analyze data using any of the existing J-DSP signal processing functions in real time. A series of exercises were developed and disseminated to provide hardware experiences to signals and systems and signal processing undergraduate students. The hardware with the J-DSP software has been used for two semesters in the senior level digital signal processing (DSP) course at Arizona State University. The interface, the exercises, and their assessment (instruments and results) are described in the paper. Index Terms--Digital signal processing (DSP) education, Java, online education, sensors, signal processing, Web-based labs.

Pair-wise error probability (PEP) analysis for (pre) coded optimal framing, orthogonal frequency-division multiplexing (OFDM) when optimal training is employed for CSI acquisition is considered. It is observed that the use of optimal linear minimum mean square error channel estimator does not reduce the diversity but causes loss of coding gain and, more importantly, code design criteria on the presence of channel estimation error (CEE) do not change.

An interactive Web-based simulation tool called Java-DSP (J-DSP) for use in digital signal processing (DSP)-related electrical engineering courses is described. J-DSP is an object-oriented simulation environment that enables students and distance learners to perform online signal processing simulations, visualize Web-based interactive demos, and perform computer laboratories from remote locations. J-DSP is accompanied by a series of hands-on laboratory exercises that complement classroom and textbook content. The laboratories cover several fundamental concepts, including z transforms, digital filter design, spectral analysis, multirate signal processing, and statistical signal processing. Online assessment instruments for the evaluation of the J-DSP software and the associated laboratory exercises have been developed. Pre/postassessment data have been collected and analyzed for each laboratory in an effort to assess the impact of the tool on student learning. Index Terms--Assessment instruments, distance learning, interactive digital signal processing (DSP) demos, online signal processing laboratories, tools to embed digital signal processing (DSP) simulations in Web lectures, Web-based simulations.

This paper focuses on the interaction and integration of several critical components of a Mobile Ad-Hoc Network (MANET) using smart antenna systems. A MANET is a wireless network where the communicating nodes are mobile and the network topology is continuously changing. One of the central motivations for this work comes from the observed dependence of the overall network throughput on the design of the adaptive antenna system and its underlying signal processing algorithms. In fact, a major objective of this work is to study and document the overall efficiency of the network in terms of the antenna pattern and the length of the training sequence used by the beamforming algorithms. This study also considers in sufficient detail problems dealing with the choice of direction of arrival algorithm and the performance of the adaptive beamformer in the presence of antenna coupling effects. Furthermore, the paper presents strategies and algorithms to combat the effects of fading channels on the overall system. Index Terms--Adaptive antennas, mobile communications, networks, signal processing.

An educational software tool on speech coding is presented. Portions of this program are used in our senior-level DSP (digital signal processing) class at Arizona State University to expose undergraduate students to speech coding and present speech analysis/synthesis as an application paradigm for many DSP fundamental concepts. The simulation software provides an interactive environment that allows users to investigate and understand speech coding algorithms for a variety of input speech records. Time- and frequency-domain representations of input and reconstructed speech can be graphically displayed and played back on a PC equipped with a standard 16-bit sound card. The program has been developed for use in the MATLAB(1) environment and includes implementations of the FS-1015 LPC-10e, the FS-1016 CELP, the ETSI GSM, the IS-54 VSELP, the G.721 ADPCM, and the G.728 LD-CELP speech coding algorithms, integrated under a common graphical interface.

A novel algorithm is proposed for finite impulse response filtering and signal processing applications. The algorithm is derived by examining and modifying the asymptotic performance of gradient root mean square estimators, gradient estimators with dead-zone thresholds and optimal bounding ellipsoid algorithms. It is capable of operating efficiently in nonideal environments.

This paper is concerned with problems evolving around the implementation of a frequency-domain adaptive noise canceller on a fixed-point signal processor. In particular, we propose methods to improve the convergence speed and reduce the computational complexity of a constrained frequency-domain algorithm that uses a time-varying step size. In addition, we study the effects of finite word length and fixed-point arithmetic. Improvements are realized by adopting a new data reusing scheme and by applying running and pruned FFT's. Results are given using synthetic data, as well as data from noise cancellation experiments.

Autoregressive parametric spectra are estimated by modifying block covariance algorithms (BMCA) which are efficiently implemented in a frequency domain. The energy weighted combined forward and backward estimate is obtained by the development of another block modelled algorithm. The newly developed algorithm is known as the energy weighted BMCA, EWBMCA. Its performance is similar to that of Nikias and Scott's weighted covariance method.