In this Innovative Practice Work in Progress, we present a Graphical User Interface (GUI)-based tool that uses the photoplethysmograph (PPG) to demonstrate fundamental signal processing concepts such as periodicity, noise, jitter, and peak selection, offering students an interactive learning experience. Extraction of the heart rate from the PPG has several steps, some appropriate for sophomore-level students in the signals and systems course, and others better suited for senior-level students in the digital signal processing course. For example, sophomore-level students can be exposed to concepts such as periodicity and statistical averages, which they see in signals and systems and introductory statistics courses. More complex topics such as peak selection and digital filtering are better suited to senior-level students. This GUI was deployed in a sophomore-level signals and systems course in the spring semester of 2024 to help students understand concepts related to statistics and periodicity. The GUI will be similarly deployed in a senior-level digital signal processing course in the fall semester of 2024. Assessment results show that the GUI helped students improve learning across multiple concept areas, and survey questions showed that students found the GUI to be helpful.

In this Innovative Practice WIP paper, we present a comparison study of large language models (LLMs) to see which would respond best to questions posed in classes. While the ultimate goal of the project is to develop classroom chatbots using LLMs, the first step, presented here, is to evaluate different models for multiple topic areas and select a few of them for further development. In this preliminary work, we cover two subject areas: signal processing and differential equations. We take slightly different approaches to each area so we can get a better understanding of the range of capabilities of the LLMs. For signal processing, we use custom open-source LLMs and the free version of OpenAI's ChatGPT 3.5. All models we use here are free. However, significant coding and processing power is required to implement the models in this method. On the other hand, for differential equations, we compare the paid version of ChatGPT 4.0 and compare that with a custom GPT, also from the paid version of ChatGPT. In this case, there is no requirement for coding skills. However, a monthly fee is required. We evaluated the LLMs by testing them on question batteries of different difficulty levels. We found that all the models function well when the questions are simple and drawn directly from the source material. However, as the difficulty level of the questions increases, the “better” models in terms of training and parameters perform better, making a case for better training, but both ways of training come with costs. The results from this evaluation will be presented at the conference. Future work involves further development of these models into more interactive chatbots and their deployment in classrooms for preliminary evaluation.

Modern face recognition (FR) models, particularly their convolutional neural network based implementations, often raise concerns regarding privacy and ethics due to their "black-box" nature. To enhance the explainability of FR models and the interpretability of their embedding space, we introduce in this paper three novel techniques for discovering semantically meaningful feature directions (or axes). The first technique uses a dedicated facial-region blending procedure together with principal component analysis to discover embedding space direction that correspond to spatially isolated semantic face areas, providing a new perspective on facial feature interpretation. The other two proposed techniques exploit attribute labels to discern feature directions that correspond to intra-identity variations, such as pose, illumination angle, and expression, but do so either through a cluster analysis or a dedicated regression procedure. To validate the capabilities of the developed techniques, we utilize a powerful template decoder that inverts the image embedding back into the pixel space. Using the decoder, we visualize linear movements along the discovered directions, enabling a clearer understanding of the internal representations within face recognition models. The source code will be made publicly available.

Advances in deep learning and convolutional neural networks (ConvNets) have driven remarkable face recognition (FR) progress recently. However, the black-box nature of modern ConvNet-based face recognition models makes it challenging to interpret their decision-making process, to understand the reasoning behind specific success and failure cases, or to predict their responses to unseen data characteristics. It is, therefore, critical to design mechanisms that explain the inner workings of contemporary FR models and offer insight into their behavior. To address this challenge, we present in this paper a novel template-inversion approach capable of reconstructing high-fidelity face images from the embeddings (templates, feature-space representations) produced by modern FR techniques. Our approach is based on a novel Deep Face Decoder (DFD) trained in a regression setting to visualize the information encoded in the embedding space with the goal of fostering explainability. We utilize the developed DFD model in comprehensive experiments on multiple unconstrained face datasets, namely Visual Geometry Group Face dataset 2 (VGGFace2), Labeled Faces in the Wild (LFW), and Celebrity Faces Attributes Dataset High Quality (CelebA-HQ). Our analysis focuses on the embedding spaces of two distinct face recognition models with backbones based on the Visual Geometry Group 16-layer model (VGG-16) and the 50-layer Residual Network (ResNet-50). The results reveal how information is encoded in the two considered models and how perturbations in image appearance due to rotations, translations, scaling, occlusion, or adversarial attacks, are propagated into the embedding space. Our study offers researchers a deeper comprehension of the underlying mechanisms of ConvNet-based FR models, ultimately promoting advancements in model design and explainability.

In this innovative practice work-in-progress paper, we hypothesize that in engineering areas such as signal processing, tools such as ChatGPT do not threaten academic integrity in the classroom. We believe that if questions and problems are suitably posed, ChatGPT can assist, but cannot provide solutions. To test this hypothesis, we ask two questions: (a) How can ChatGPT be used to assist students in a signal processing class? and (b) How can the class itself be designed to leverage what ChatGPT has to offer? To answer these questions, we deploy ChatGPT in three different scenarios: (1) In a graduate level course to explore the use cases and limitations of the tool; (2) In summer REU cohorts to study the attitudes of students before and after one-hour workshops; and (3) in undergraduate signal processing courses where students will be exposed to generative AI tools over an entire semester. Surveys and discussions with the students will be analyzed and results will be presented at the conference. With the three separate activities across different time scales and student levels, our results can be used to generate guidelines for instructors to incorporate generative AI tools in their classes.

Keystroke dynamics has been shown to be a promising method for user authentication based on a user's typing rhythms. Over the years, it has seen increasing applications such as in preventing transaction fraud, account takeovers, and identity theft. However, due to the variable nature of keystroke dynamics, a user's typing patterns may vary on a different keyboard or in a different keyboard language setting, which may affect the system accuracy. In other words, an algorithm modeled with data collected using a mechanical keyboard may perform significantly differently when tested with an ergonomic keyboard. Similarly, an algorithm modeled with data collected in one language may perform significantly differently when tested with another language. Hence, there is a need to study the impact of multiple keyboards and multiple languages on keystroke dynamics performance. This motivated us to develop two free-text keystroke dynamics datasets. The first is a multi-keyboard keystroke dataset comprising of four (4) physical keyboards - mechanical, ergonomic, membrane, and laptop keyboards - and the second is a bilingual keystroke dataset in both English and Chinese languages. Data were collected from a total of 86 participants using a non-intrusive web-based keylogger in a semi-controlled setting. To the best of our knowledge, these are the first multi-keyboard and bilingual keystroke datasets, as well as the data collection software, to be made publicly available for research purposes. The usefulness of our datasets was demonstrated by evaluating the performance of two state-of-the-art free-text algorithms.

Facebook has become an important part of our daily life. From knowing the status of our relatives, showing off a new car, to connecting with a high school classmate, abundant personally identifiable information (PII) are made visible to others by posts, images and news. However, this free flow of information has also created significant cyber-security challenges that make us vulnerable to social engineering and cyber crimes. To confront these challenges, we propose a new behavioral biometric that verifies a user based on his or her widget interaction behavior when using Facebook. Specifically, we monitor activities on the user's Facebook account using our own logging software and verify the user's claimed identity by binary classifiers trained with two algorithms (SVM-rbf and the GBM- Gradient Boosting Machines). Our novel dataset consists of eight users over a month of data collection with an average of 2.95k rows of data per user. We convert these activities data into meaningful features such as day-of-week, hour-of-day, and widget types and duration of mouse staying on a widget. The performance shows that our novel widget interaction modality is promising for authentication. The SVM-rbf classifiers achieve a mean Equal Error Rate (EER) and mean Accuracy (ACC) of 3.91% and 97.79%, while the GBM classifiers a mean EER and ACC of 2.76% and 97.88%, respectively. In addition, we perform an ablation study to understand the impact of individual features on authentication performance. The importance of features are ranked in the descending order of hour-of-day, day-of-week, and widget types and duration.

In this innovative practice work-in-progress paper, we compare two different methods to teach machine learning concepts to undergraduate students in Electrical Engineering. While machine learning is now being offered as a senior-level elective in several curricula, this does not mean all students are exposed to it. Exposure to the concepts and practical applications of machine learning will assist in the creation of a workforce ready to tackle problems related to machine learning, currently a “hot topic” in industry. To this end, the authors are working on introducing Electrical Engineering students to machine learning in a required, Junior-level, Signals and Systems course. The main challenge with teaching machine learning in a junior-level course involves requiring students to appreciate the linkages between complex concepts in linear algebra, statistics, and optimization. While it can be argued that Junior-level students should have seen concepts in some of these topics, requiring them to apply these topics together is a challenge. Therefore, in order to assist students to better grasp these concepts, we provide them with hands-on activities, since immersive experiences will help students appreciate the practical uses of machine learning. In a previous approach, authors held stand-alone workshops where students in the class were given Android apps for data collection, followed by different sets of hands-on activities. While this approach showed promise, several students indicated that the stand-alone workshop lacked context. To alleviate these concerns, in the Fall semester of 2020, the authors tried a different approach. Students were provided hands-on activities side-by-side with regular course content enabling links to be made with machine learning throughout the course and providing better context to the content being presented. Preliminary assessments indicate that this approach promotes student learning. While students prefer the proposed side-by-side teaching approach, numerical comparisons show that the workshop approach may be more effective for student learning, indicating that further work in this area is required.

A main challenge in target localization arises from the lack of reliable distance measures. This issue is especially pronounced in indoor settings due to the presence of walls, floors, furniture, and other dynamically changing conditions, such as the movement of people and goods, varying temperature and air flows. Here, we develop a new computational framework to estimate the location of a target without the need for reliable distance measures. The method, which we term Ordinal UNLOC, uses only ordinal data obtained from comparing the signal strength from anchor pairs at known locations to the target. Our estimation technique utilizes rank aggregation, function learning as well as proximity-based unfolding optimization. As a result, it yields accurate target localization for common transmission models with unknown parameters and noisy observations that are reminiscent of practical settings. Our results are validated by both numerical simulations and hardware experiments.

In this innovative practice work-in-progress paper, we discuss novel methods to teach machine learning concepts to undergraduate students. Teaching machine learning involves introducing students to complex concepts in statistics, linear algebra, and optimization. In order for students to better grasp concepts in machine learning, we provide them with hands-on exercises. These types of immersive experiences will expose students to the different stages of the practical uses of machine learning. The data collection apparatus is based on applications (apps) developed for the Android platform. Due to the accessible nature of the app and the exercises based on the app, this approach is useful for students across all majors.We provide the students with three different sets of activities, the first of which will introduce the basics of machine learning with specially designed artificial datasets. The second and third activities involve data collection, modeling, training, and testing, as applied to machine learning algorithms. The second activity will involve collecting touch/swipe data on mobile devices from students as they use a touch logger app. The third activity uses the Reflections app to collect cross-correlation data from rooms with different purposes. These hands-on activities guide the students through every step of the machine learning process. Student learning is assessed for each activity by holding workshops for undergraduate students. A workshop with the first activity outlining the basics of machine learning was given in the fall of 2018 and significant student learning was demonstrated. Workshops for the second and third activities are planned for the fall semester of 2019. Results from these workshops will be presented at the conference.

Mars, the most Earth-like of the planets, has motivated scientists to search for life there. Presently, plans are in the works for landing humans on the Red planet between 2025 and the 2030s. Young students of today may have an opportunity to travel to Mars and /or serve as future astronauts. Therefore, it is important to provide them with enjoyable and rewarding learning experiences about space travel and the planet Mars. This paper describes successful STEM activities that use innovative e-learning to simulate components of real Mars missions.

This work in progress paper describes software that enables online machine learning experiments in an undergraduate DSP course. This software operates in HTML5 and embeds several digital signal processing functions. The software can process natural signals such as speech and can extract various features, for machine learning applications. For example in the case of speech processing, LPC coefficients and formant frequencies can be computed. In this paper, we present speech processing, feature extraction and clustering of features using the K-means machine learning algorithm. The primary objective is to provide a machine learning experience to undergraduate students. The functions and simulations described provide a user-friendly visualization of phoneme recognition tasks. These tasks make use of the Levinson-Durbin linear prediction and the K-means machine learning algorithms. The exercise was assigned as a class project in our undergraduate DSP class. The description of the exercise along with assessment results is described.

The central focus of this work-in-progress is to investigate the following: (1) What do the lesson plans created by teachers reveal about their understanding of science and engineering practices? (2) Will including programming exercises in all lesson plans improve STEM skills in general, and coding skills in particular? And (3) Will integrating science and engineering practices in high school lesson plans improve student retention in STEM and STEM-related areas? To answer these questions, we develop project-based lessons and mobile app-based laboratories that incorporate science and engineering practices. Additionally, with these apps, we: a) enable and motivate students to learn STEM topics by immersing themselves in interactive apps, b) include lesson content and electronic labs delivered using the latest mobile technology and platforms, and c) provide teacher training to better improve content delivery. We will design innovative Science, Technology, Engineering and Math (STEM) lesson plans and immersive labs, to create a transformative educational experience for high school students. The lessons and labs will be deployed with the help of mobile applications (apps) that will use multi-sensory and multimodal inputs and outputs to interact with students, and be designed to align with the newly adopted 2017 New York State Science Learning Standards.

This paper describes the use of a space usage determination algorithm for teaching signal processing and machine learning concepts to undergraduate electrical engineering and computer science students. An Android device transmits a high-frequency signal in an unknown space. The device determines the reflective properties of this unknown space by analyzing the received signal. Based on the features extracted from this signal, the app measures distances and determines how the space can be utilized for various application such as libraries, conference rooms, or laboratories. The application and related algorithms use concepts such cross-correlation, feature extraction, learning/training algorithms, and discrimination/decision making. These concepts are typically covered in undergraduate classes such as Digital Signal Processing, Control Systems, and Probability and Statistics; and graduate-level classes such as Pattern Recognition and Detection and Estimation Theory. The app is used to create compelling demonstrations and immersive exercises to teach basic concepts related to signal processing and machine learning. Undergraduate student hands-on workshops and outreach activities are planned to evaluate the effectiveness of this approach. Assessment results will be presented at the conference.

Several web-based signal processing simulation packages for education have been developed in a Java environment. Although this environment has provided convenience and accessibility using standard browser technology, it has recently become vulnerable to cyber-attacks and is no longer compatible with secure browsers. In this paper, we describe our efforts to transform our award-winning J-DSP online laboratory by rebuilding it on an HTML5 framework. Along with a new simulation environment, we have redesigned the interface to enable several new functionalities and an entirely new educational experience. These new features include functions that enable real-time interfaces with sensor boards and mobile phones. The Web 4.0 HTML5 technology departs from older Java interfaces and provides an interactive graphical user interface (GUI) enabling seamless connectivity and both software and hardware experiences for students in DSP classes.

This paper describes the use of Bluetooth hardware for localization and signal processing education on Android smart-phones and tablets. The localization algorithm uses the Received Signal Strength Indcation (RSSI) value of transmitting devices in order to triangulate their position. The concepts that are featured in the use of this technology have classroom relevant content such as multilateration (a matrix problem in linear algebra), wave properties and interactions (physics), statistics relating to laboratory data, and engineering application concepts (such as software development and coding). These concepts can be taught through classroom demonstrations and interaction. Preliminary data from in-class activities demonstrate the effectiveness of the app for teaching concepts in localization and ranging. Further in-class activities and workshops are planned.

An Android-based eModule app has been designed and developed for science, technology, engineering, and mathematics (STEM) education. The eModule consists of: (1) an Android demonstration of echolocation; (2) a set of notes describing the functionality of the app, the basics of echolocation, and its application to advanced signal processing systems such as RADAR, LIDAR, and SONAR; (3) quizzes to test the concepts introduced by the demonstration and the notes; and (4) companion videos. The eModule is, therefore, a holistic teaching and learning app that can be used across various grade levels including K-12, undergraduate signals and systems, and graduate DSP education.The app, “Reflections”, provides students a means to determine distances to objects while allowing them the ability to manipulate signal envelopes, signal shapes, signal types, and frequency constraints. The intuitive graphical user interface, combined with notes, videos and quizzes, creates a rich educational environment to help educate users with the fundamental concepts of signals, systems, and digital signal processing. In addition to its role in STEM education, the app has potential use in low-visibility environments and for spatial acoustic analysis. Preliminary assessments strongly support the effectiveness of the eModule as an education tool signals and systems and DSP classes.

An Android app has been developed to assist in the education of individuals in a science, technology, engineering, and mathematics (STEM) course of study. The Android Reflection Application provides students a means to determine distances to objects while allowing them the ability to manipulate signal envelopes, signal shapes, signal types, and frequency constraints. The convenient and intuitive graphical user interface immerses the user into a richly educational environment allowing for the solidification of fundamental concepts regarding digital signal processing (DSP). In addition to the educational benefits, this application is also being applied to spatial acoustic analysis and assistance in low-visibility. This feature will allow users to determine the best use for a given space whether it is a quiet study room or a room better suited for conference meetings. The effectiveness of this application has not yet been formally tested but suggests a positive result.

Traditional STEM education models in electrical engineering and computer science rely on structured classes, laboratories, and textbooks to transfer key concepts. Even though this process meets most of the ABET objectives, it does not respond well to current workforce needs that require widely accessible programs that will provide a large pool of graduates with STEM backgrounds, analytical and programming skills, critical thinking, and leadership abilities. In this work in progress paper, we describe our efforts to motivate students to pursue studies in STEM areas. We accomplish this by creating and disseminating modules that demonstrate how math and engineering theory enable modern applications such as those embedded in wireless devices.

Signal processing algorithms, software, and hardware are being used in several fields including non-engineering areas such as arts and media. Students in these fields and particularly in the new Digital Culture major at Arizona State University (ASU) use signal processing tools in several of their projects and artistic endeavors. Yet the blind use of these DSP tools in other disciplines, without understanding their properties has been a long-standing problem. In fact, the broader issue is the disconnect between engineers that develop tools and artists that use them to design the next generation digital art applications. In that context, ASU has formed the Arts Media and Engineering (AME) School and more recently, the multidisciplinary undergraduate Digital Culture degree granting program. In order to provide formal training in signal processing to students that are non-Electrical Engineering majors, we piloted a new course titled Signal Processing for Digital Culture. This course, which is being offered online, teaches non-majors some of the basics of signal processing and covers several applications. The only prerequisite to the course is general sophomore calculus. This new online course contains several topics and is focused on an approach that teaches concepts by connecting theory to compelling applications. Future plans include introducing this course at Clarkson University as a Knowledge Area course open to students from all majors.

Audio signal modeling and simulation is important in several coding, noise removal, and recognition applications. This paper focuses on implementing models for loudness estimation and their use in estimating parameters on iOS mobile devices (iPhones and iPads). We briefly address estimating excitation patterns and loudness through auditory models. These loudness estimation and other algorithms were implemented in the award winning educational iOS app iJDSP for performing DSP simulations on mobile devices. The modules were introduced to graduate students in the general signal processing area, to evaluate their effectiveness as teaching tools. The evaluation process involved giving the students a pre-quiz, guiding them through hands-on activities on the iOS app, and finally, a post-quiz. Assessments results were positive with noticeable improvement of student understanding of topics such as spectrograms and linear predictive coding.

This paper introduces diffusion adaptation strategies over distributed networks with nonlinear transmissions, motivated by the necessity for bounded transmit power. Local information is exchanged in real-time with neighboring nodes in order to estimate a common parameter vector via constrained nonlinear transmissions, using an adaptive learning algorithm. We propose nonlinear diffusion strategies for such an adaptive estimation. We will study convergence properties of the proposed algorithm in the mean and the mean-square sense. Simulations support the performance analysis and show that the proposed algorithm performs close to the linear case with the added advantage of power savings.

The objective of this project is to develop and design mobile content for introducing engineering technology to high school students. More specifically, we intend to work on a sequence of modules that will establish connections between high school mathematics and physics to modern technologies associated with smart phones, iPods and other high-tech products. The participants of the project will use the previously developed AJDSP (for Android devices) and iJDSP (for iPhones and iPads) apps to facilitate this process. Additionally, modules have been developed that have been embedded in math classes. Anticipated benefits of the project include creating positive attitudes towards STEM areas that will help recruit high school students and minorities in engineering, math and science fields. After an initial pilot study and assessments at CDS High School, these activities will be disseminated to other high schools. In order to obtain feedback from high school students and teachers, we will hold workshops and collect assessment results. These results will also provide us assessments about the effectiveness of the project, and allow us to make modifications to the project as necessary. The project is part of TUES Phase 3 and I/UCRC RET activities.

This project aims to contribute to education research using mobile apps to demonstrate how signals and systems concepts are used in sensor network localization. An educational demonstration of sensor localization on mobile devices is described with the mobile devices acting as nodes of a sensor network. In our approach, we are developing an app that uses a modified version of time-difference of arrival (TDOA) using audio signals and commercial Android devices. At a high level, the app can be used to illustrate how triangulation can be used to localize devices, which is a concept that is used in GPS. Students are exposed to signal processing concepts such as correlation and the fast Fourier transform (FFT), and their utility in sensor localization. The use of FFT-based convolution for computing correlations is also demonstrated. Since the app has been developed for Android devices, it can be made widely available and has the added benefit of appealing to students that are eager to use educational apps on their mobile phones. Assessments will be performed to evaluate the effectiveness of the app in education. The work engages NSF REU and REV students who are involved in developing and testing the app.

Understanding global change is important for creating a sustainable environment, and is a key interest of the Earth systems science community. Here we present an educational tutorial that explores the relationship between sea level and global temperature using modern-day records and time-series analysis and the Java-DSP Earth Systems Edition (J-DSP/ESE) application. The objectives of the tutorial are to apply pre-processing steps based on signal type, perform spectral analysis and identify significant frequencies, perform coherency and cross-phase analysis between two records, and arrive at an informed understanding about the relationship between sea level and global temperature change. Preliminary student assessment indicates that students were comfortable using J-DSP/ESE, and quickly understood the signal processing concepts. The analysis reveals correlation between sea level variations and global temperature at inter-annual timescales related to the El Niño climatological phenomenon. In sum, the tutorial improved students' understanding of basic factors that influence global sustainability and habitability.

The recent sensing capabilities of mobile devices along with their interactivity and popularity in the student community can be used to create a unique learning environment in engineering education. Android Java-DSP (AJDSP) is a mobile educational application that interfaces with sensors and enables simulation and visualization of signal processing concepts. In this paper, we present the work done towards building non-invasive physiological signal monitoring tools in AJDSP through hardware interfaces to both external sensors and on-board device sensors. Examples of laboratory exercises that can be introduced in classes are presented. The proposed software tools can be used to provide intuitive understanding in wireless sensing and feature extraction to demonstrate the application of DSP to health monitoring systems. The effectiveness of the software modules in enhancing student understanding is demonstrated with the help of preliminary assessments.

In this paper, we describe a new set of software functions and associated exercises that can be used for teaching orthogonal frequency division multiplexing (OFDM) concepts in undergraduate DSP and communications courses. These tools can be used to simulate, visualize, and analyze the performance and behavior of OFDM systems by considering different input signals and communication channels. OFDM is a compelling paradigm because of its utility in WiFi and LTE. It is also a good demonstration of the use of the FFT in a communication system. We have developed the proposed set of functions as a part of the Java-DSP (J-DSP) visual programming environment. The functions can be used in undergraduate DSP and communications courses, in order to demonstrate to students, the application of DSP concepts in a communication system, as well as concepts such as FIR filter design, properties of the DFT matrix, random signals, and circular effects.

Localization accuracy is crucial in sensor networks. A wireless sensor network (WSN) with M anchors and one node is considered in this paper. The estimation is based on time of arrival (TOA) in the presence of fading channels. The Cramer-Rao lower bound (CRLB) for localization error in the presence of fading is derived under different scenarios. Firstly, fading coefficients are considered as unknown random parameters with a prior distribution. The ML estimator for this case is also derived. If the distribution of fading is unknown to the estimator then the modified CRLB (MCRLB) is applied and shown to be equal to the CRLB in the absence of fading. This is used to conclude that fading always deteriorates the CRLB in localization. It is shown that there is a loss of about 5dB in CRLB due to Rayleigh fading.

In this paper, we present a graphical-programming application to support signal processing education on the Android operating system. This application features a simulation environment and a palette of DSP functions, which will allow students to perform laboratories using Android smartphones and tablets. In order to demonstrate the application of the software in a classroom setting, a number of laboratories which incorporate the proposed functionalities have been developed. A set of assessments designed to evaluate the effectiveness of the software is also presented.

The use of mobile devices and tablets in engineering education has been gaining lot of interest, due to its interactive capabilities and its ability to stimulate student interest. On the other hand, this technology can also enable instructors to broaden the scope of their curriculum and increase student participation. In this paper, we describe an interactive application to perform signal processing simulations on iOS devices such as the iPhone and the iPad. Furthermore, we describe two laboratory exercises to introduce continuous/discrete convolution and filter design. The exercises and the proposed application will be evaluated by students of an undergraduate DSP course at Arizona State University during Fall 2011. Finally, we describe the planned assessment methodology which will enable us to provide prescriptive recommendations for using i-JDSP in DSP courses.

A distributed detection problem over fading Gaussian multiple-access channels is considered. Sensors observe a phenomenon and transmit their observations to a fusion center using the amplify and forward scheme. The fusion center has multiple antennas with different channel models considered between the sensors and the fusion center, and different cases of channel state information are assumed at the sensors. The performance is evaluated in terms of the error exponent for each of these cases, where the effect of multiple antennas at the fusion center is studied. When there is channel information at the sensors, the gain in error exponent due to having multiple antennas at the fusion center is shown to be limited to a factor of 8/π for Rayleigh fading channels between the sensors and the fusion center, and independent of the number of antennas at the fusion center. Simple practical schemes and numerical methods using semidefinite relaxation techniques are presented that utilize the limited possible gains available. Simulations are used to establish the accuracy of the results.

Handheld devices are increasingly finding more applications in STEM education. In this paper, we present the design of an interactive signal processing simulation software operating on both the iPhone OS (iOS) and Android platforms. This object-oriented application is called i-JDSP and is conceptually based on the award-winning Java-DSP (J-DSP) simulation environment. The i-JDSP app offers a user-friendly visual programming interface and provides users with a compelling multi-touch programming experience. It supports basic signal processing simulation functions such as the FFT, filtering, frequency response, pole-zero plots, and sound recording and playback. Initial assessments have been promising and we believe that this new attractive smartphone interface will make signal processing education among undergraduate students more appealing.

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.

We consider a distributed detection problem over fading multiple-access channels. Sensors observe a phenomenon and transmit their observations to a fusion center using the amplify-and-forward scheme. The fusion center has multiple antennas and uses the transmissions from the sensors to run a detection algorithm. The channels are Ricean fading, and the sensors have no channel information. The performance is evaluated in terms of error exponent and compared with the AWGN channels case. The benefit of having multiple antennas at the fusion center is also quantified.

We consider a wireless sensor network for distributed estimation over Rayleigh fading channels. The sensors transmit their observations over fading channels to a fusion center, where a source parameter is estimated. Since the sensor transmissions add incoherently over a multiple access channel, we consider partial channel knowledge at the sensors to improve performance. We calculate the variance of the estimate when the channel phase is quantized uniformly and fed back to the sensors. We show that as few as 3 bits of feedback is sufficient for a loss in performance of about 5%. We also show that the performance is robust in the presence of feedback errors.