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An Android application that realizes an audible version of echolocation has been implemented. Bundled within the app are videos, notes, problems, and discussion questions, thus creating an "eModule". The eModule contains material that is relevant to several educational levels including K-12 and undergraduate engineering. By using the echolocation features of the app, at the K-12 level, teachers are able to introduce labs, and science and engineering practices into their lesson plans. By using data collection and interpretation, undergraduate students can be exposed to various concepts in signals and systems, digital signal processing, and machine learning. Assessment instruments for the evaluation of the Reflections app, as well as the bundled content, have been developed. Pre/post assessment data and results of survey instruments have been collected and analyzed in an effort to assess the impact of the tool on student learning at the undergraduate level, as well as impacts on K-12 teacher interest in introducing science and engineering practices in their lesson plans. [ABSTRACT FROM AUTHOR]

The anniversary of a number of significant signal processing algorithms from the 1960s, including the least mean square algorithm and the Kalman filter, provided an opportunity at ICASSP 2019 to reflect on the links between education and innovation. This led ultimately to the proposal of some special sessions as well a panel session that would provide some insight, via a historical perspective, consideration of the current status, and an assessment of the emerging educational future. [ABSTRACT FROM AUTHOR]

In this paper, we present a unique Android-DSP (AJDSP) application which was built from the ground up to provide mobile laboratory and computational experiences for educational use. AJDSP provides a mobile intuitive environment for developing and running signal processing simulations in a user-friendly manner. It is based on a block diagram system approach to support signal generation, analysis, and processing. AJDSP is designed for use by undergraduate and graduate students and DSP instructors. Its extensive functions enable instructors and students to simulate DSP concepts including convolution, Fourier transforms, ztransform and filter design. We describe the AJDSP functions and the process of using them in computer exercises, classroom demonstrations, and undergraduate remote laboratories. We include quantitative and qualitative assessments of AJDSP at the end of this paper. We also include descriptions of outreach efforts where we used AJDSP in middle schools and high schools to demonstrate how DSP algorithms enable several applications. [ABSTRACT FROM AUTHOR]

A number of fundamental quantities in statistical signal processing and information theory can be expressed as integral functions of two probability density functions. Such quantities are called density functionals as they map density functions onto the real line. For example, information divergence functions measure the dissimilarity between two probability density functions and are useful in a number of applications. Typically, estimating these quantities requires complete knowledge of the underlying distribution followed by multidimensional integration. Existing methods make parametric assumptions about the data distribution or use nonparametric density estimation followed by high-dimensional integration. In this paper, we propose a new alternative. We introduce the concept of “data-driven basis functions”-functions of distributions whose value we can estimate given only samples from the underlying distributions without requiring distribution fitting or direct integration. We derive a new data-driven complete basis that is similar to the deterministic Bernstein polynomial basis and develop two methods for performing basis expansions of functionals of two distributions. We also show that the new basis set allows us to approximate functions of distributions as closely as desired. Finally, we evaluate the methodology by developing data-driven estimators for the Kullback-Leibler divergences and the Hellinger distance and by constructing empirical estimates of tight bounds on the Bayes error rate. [ABSTRACT FROM AUTHOR]

Modern data collection in the Earth Sciences has propelled the need for understanding signal processing and time-series analysis techniques. However, there is an educational disconnect in the lack of instruction of time-series analysis techniques in many Earth Science academic departments. Furthermore, there are no platform-independent freeware tools available for teaching Earth signals analysis. In order to address these issues, we developed the Java-Digital Signal Processing/Earth Systems Edition (J-DSP/ESE), a platform-independent software tool that can be integrated with the Earth Science university curriculum for signal processing and analysis instruction. This tool has an intuitive block-based programming environment, and students do not need be familiar with any programming language to use it. In order to demonstrate the utility of this software in an instructional environment, we developed three tutorials related to basic signal processing, and signal analysis of modem and past climate change. The tutorials use published data to examine the relationship between 20th century atmospheric CO2 and global temperature, and the relationship between ocean temperature and solar radiation over the past 300,000 y. The tutorials were administered in two workshops with different communities of students in Earth Science and electrical engineering. Our technical assessments show that the students were able to comprehend basic signal processing and analysis of climate signals using J-DSP/ESE. In the subjective assessments, a vast majority of students stated that the software was easy to learn and use, and that it significantly improved their understanding of climate change. [ABSTRACT FROM AUTHOR]

A distributed estimation problem is considered with multiple-access channels between sensors and a fusion center. The sensors phase-modulate their noisy observations before transmitting them to the fusion center, where a signal parameter is estimated. The asymptotic efficiency of this estimator is then determined by using two inequalities that relate the Fisher information and the characteristic function. A necessary and sufficient condition for equality is found for the first time in the literature. The loss in efficiency of the distributed estimation scheme relative to the centralized approach is quantified for different sensing noise distributions. It is shown that this distributed estimation system does not incur an efficiency loss if and only if the sensing noise distribution is Gaussian. [ABSTRACT FROM AUTHOR]

Current state-of-the-art approaches for biological sequence querying and alignment require preprocessing and lack robustness to repetitions in the sequence. In addition, these approaches do not provide much support for efficiently querying subsequences, a process that is essential for tracking localized database matches. We propose a query-based alignment method for biological sequences that first maps sequences to time-domain waveforms before processing the waveforms for alignment in the time-frequency plane. The mapping uses waveforms, such as Gaussian functions, with unique sequence representations in the time-frequency plane. The proposed alignment method employs a robust querying algorithm that utilizes a time-frequency signal expansion whose basis function is matched to the basic waveform in the mapped sequences. The resulting \tt WAVEQuery approach was demonstrated for both deoxyribonucleic acid (DNA) and protein sequences using the matching pursuit decomposition as the signal basis expansion. We specifically evaluated the alignment localization of \tt WAVEQuery over repetitive database segments, and we demonstrated its operation in real-time without preprocessing. We also demonstrated that \tt WAVEQuery significantly outperformed the biological sequence alignment method BLAST for queries with repetitive segments for DNA sequences. A generalized version of the \tt WAVEQuery approach with the metaplectic transform is also described for protein sequence structure prediction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We present a novel signal processing methodology to determine the direction and the strength of coupling between coupled nonlinear systems. The methodology is based on multivariate local linear prediction in the reconstructed state spaces of the observed variables from each multivariable nonlinear system. Application of the method is illustrated with systems of coupled Rossler and Lorenz oscillators in various coupling configurations. The obtained results are compared with ones produced by the use of the directed transfer function, a model-based method in the time domain. Through a surrogate analysis, it is shown that the proposed method is more reliable than the directed transfer function in identifying the direction and strength of the involved interactions. [ABSTRACT FROM AUTHOR]

Focuses on the interaction and integration of several components of a Mobile Ad-Hoc Network (MANET) using smart antenna systems. Description of adaptive signal processing algorithms; Information on the design of the antenna system; Simulation for the performance of the MANET.