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Ion channel sensors have several applications including DNA sequencing, biothreat detection, and medical applications. Ion channel sensors mimic the selective transport mechanism of cell membranes and can detect a wide range of analytes at the molecule level. Analytes are sensed through changes in signal patterns. Papers in the literature have described different methods for ion channel signal analysis. In this paper, the authors describe a series of new graphical tools for ion channel signal analysis which can be used for research and education. The paper focuses on the utility of these tools in biosensor classes. Teaching signal processing and machine learning for ion channel sensors is challenging because of the multidisciplinary content and student backgrounds which include physics, chemistry, biology, and engineering. The paper describes graphical ion channel analysis tools developed for an online simulation environment called J-DSP. The tools are integrated and assessed in a graduate bio-sensor course through computer laboratory exercises.

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 modern and past climate change. The tutorials use published data to examine the relationship between 20th century atmospheric CO[subscript 2] 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.

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.

Reliable and rapid non-invasive testing has become essential for COVID-19 diagnosis and tracking statistics. Recent studies motivate the use of modern machine learning (ML) and deep learning (DL) tools that utilize features of coughing sounds for COVID-19 diagnosis. In this paper, we describe system designs that we developed for COVID-19 cough detection with the long-term objective of embedding them in a testing device. More specifically, we use log-mel spectrogram features extracted from the coughing audio signal and design a series of customized deep learning algorithms to develop fast and automated diagnosis tools for COVID-19 detection. We first explore the use of a deep neural network with fully connected layers. Additionally, we investigate prospects of efficient implementation by examining the impact on the detection performance by pruning the fully connected neural network based on the Lottery Ticket Hypothesis (LTH) optimization process. In general, pruned neural networks have been shown to provide similar performance gains to that of unpruned networks with reduced computational complexity in a variety of signal processing applications. Finally, we investigate the use of convolutional neural network architectures and in particular the VGG-13 architecture which we tune specifically for this application. Our results show that a unique ensembling of the VGG-13 architecture trained using a combination of binary cross entropy and focal losses with data augmentation significantly outperforms the fully connected networks and other recently proposed baselines on the DiCOVA 2021 COVID-19 cough audio dataset. Our customized VGG-13 model achieves an average validation AUROC of 82.23% and a test AUROC of 78.3% at a sensitivity of 80.49%. [ABSTRACT FROM AUTHOR]

Solar array management and photovoltaic (PV) fault detection is critical for optimal and robust performance of solar plants. PV faults cause substantial power reduction along with health and fire hazards. Traditional machine learning solutions require large, labeled datasets which are often expensive and/or difficult to obtain. This data can be location and sensor specific, noisy, and resource intensive. In this paper, we develop and demonstrate new semi supervised solutions for PV fault detection. More specifically, we demonstrate that a little-known area of semi-supervised machine learning called positive unlabeled learning can effectively learn solar fault detection models using only a fraction of the labeled data required by traditional techniques. We further introduce a new feedback enhanced positive unlabeled learning algorithm that can increase model accuracy and performance in situations such as solar fault detection when few sensor features are available. Using these algorithms, we create a positive unlabeled solar fault detection model that can match and even exceed the performance of a fully supervised fault classifier using only 5% of the total labeled data. [ABSTRACT FROM AUTHOR]

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]

Abstract In this paper, localization using narrowband communication signals are considered in the presence of fading channels with time of arrival measurements. When narrowband signals are used for localization, due to existing hardware constraints, fading channels play a crucial role in localization accuracy. In a location estimation formulation, the Cramer–Rao lower bound for localization error is derived under different assumptions on fading coefficients. For the same level of localization accuracy, the loss in performance due to Rayleigh fading with known phase is shown to be about 5dB compared to the case with no fading. Unknown phase causes an additional 1dB loss. The maximum likelihood estimators are also derived. In an alternative distributed detection formulation, each anchor receives a noisy signal from a node with known location if the node is active. Each anchor makes a decision as to whether the node is active or not and transmits a bit to a fusion center once a decision is made. The fusion center combines all the decisions and uses a design parameter to make the final decision. We derive optimal thresholds and calculate the probabilities of false alarm and detection under different assumptions on the knowledge of channel information. Simulations corroborate our analytical results. [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]