Posts by Collection

An Android app for spatial acoustic analysis as a learning tool

Published in IEEE Frontiers in Education Conference (FIE), 2016

Abstract: 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.

Download here

UNLOC: Optimal unfolding localization from noisy distance data

Published in Sampling Theory in Signal and Image Processing (STSIP): Special Issue on Harmonic Analysis and Inverse Problems, 2017

Abstract: Target localization is an important problem in signal processing and sensor networks, with many application areas including security (E911, first responders), consumer electronics (location awareness in malls and hospitals), and health monitoring (location-aware patient care). In this paper, we formulate target localization as an inverse problem: given the locations of a set of anchors and noisy distance measures to a target, the localization problem is to estimate the (unknown) location of the target. We propose to solve the localization problem using an unfolding-based optimization. We show that the corresponding stress optimization, despite being a nonlinear problem (quadratic objective function with quadratic constraints), yields a global optimum that can be approximated using an efficient iterative algorithm. We term our computational approach as UNLOC (unfolding-based localization) and benchmark its effectiveness on both synthetic data and labgenerated experimental data. The proposed localization technique generally produces accurate target location, and the quality of localization can be further improved by an appropriate choice of weights in the objective function of optimization.

Download here

Teaching ranging and localization using Bluetooth on Android Devices

Published in IEEE Frontiers in Education Conference (FIE), 2017

Abstract: 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.

Download here

Localization using wireless signals

Published in US Patent, 2018

Abstract: A method for localization of a target device within an environment, the method comprising the steps of: (i) receiving, by a target device, a plurality of wireless signals transmitted from one or more anchor devices within the environment; (ii) determining, by a target device, a received signal strength indication for at least some of the received plurality of wireless signals; (iii) estimating, based on the determined received signal strength indications, a distance from the target device to each of the one or more anchor devices from which a wireless signal was received; and (iv) estimating, based on the estimated distances, a location of the target device within the environment.

Download here

Time-of-Flight (ToF) Cameras for Underwater Situational Awareness

Published in OCEANS MTS/IEEE SEATTLE, 2019

Abstract: Recently developed commercial Time-of-Flight (ToF) cameras have been used to accurately and reliably measure scene depth with high resolution in applications such as automotive LiDAR. There is a desire to adapt this technology for applications in underwater environments. In this work, we establish a methodology for using modified commercial ToF cameras in turbid water. We express the need for hardware and software modifications to the camera and demonstrate initial results in the efficacy of the camera in an underwater test scenario. We include ToF camera imagery taken under a variety of water conditions to understand the performance limitations of this technology as a function of water clarity. Target detection results from preliminary laboratory test tank experiments are presented for two different classifiers, each of which achieves high accuracy for a certain range of water conditions.

Download here

Denoising of The Distance Data from Underwater Time-of-Flight (ToF) Cameras

Published in Unpublished, 2020

Abstract: In underwater sensing, there is a demand for high-speed, high-resolution optical range imaging of objects in turbid water. Our prior work adapted commercially available timeof-flight (ToF) hardware to function in the unique optical conditions imposed by the water medium-namely, high levels of signal absorption and scattering. In this paper, we propose an algorithm to denoise the distance data captured with a ToF camera in turbid water conditions. To this effect, we use a 2-parameter scale-space generated by the iterative nonlocal means filter to denoise the DCS data. We then use these denoised DCS data to reconstruct the denoised distance image. We test the algorithm on data obtained from a simulated optical channel as well as real experimental ToF data. Compared with the local denoising methods, the results obtained with the proposed algorithm provide demonstrably improved distance images.

Download here

Statistical Methods for Fast LOS Detection for Ranging and Localization

Published in International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), 2020

Abstract: Received signal strength indication (RSSI) data is often used for ranging and localization algorithms, where the data may be obtained using Bluetooth Low Energy (BLE) radios. In the BLE protocol, when a device is in advertising mode, it is possible to obtain RSSI values. However, these RSSI values are noisy and can often fluctuate due to multipath effects, which reduces the accuracy and reliability of ranging and localization. Additionally, the effectiveness of RSSI ranging degrades when there is an absence of line of sight (LOS) or when devices are in rich scattering environments. Therefore, the detection of LOS plays a very significant role in indoor localization and room reconstruction. In this paper, we present algorithms to detect whether there is LOS present between a transmit-receive pair being used for ranging. Our focus in this paper is fast detection with a minimum number of samples. We use measurements such as the energy distance and Mahalanobis distance, and benchmark our results against the Neyman-Pearson detector. Numerical simulations are used to validate our algorithms.

Download here

Teaching Signal Processing Applications using an Android Echolocation App

Published in ASEE Computers in Education Journal, 2021

Abstract: 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.

Download here

Restoration of time-of-flight (ToF) underwater images using TV regularization

Published in SPIE, Ocean Sensing and Monitoring XIII, 2021

Abstract: This paper investigates a total variation (TV) regularization image processing algorithm to restore underwater range images taken with a modified commercial time-of-flight (ToF) camera. The ToF camera illuminator was modified to support 532 nm flood illumination for underwater operation. This approach can produce highresolution amplitude and range images while rejecting a significant amount of ambient light. However, scattering due to the water turbidity adversely impacts image quality by introducing high amounts of image noise and image blurring that affect both the amplitude and range images. The TV regularization algorithm is applied to experimental images taken in a small test tank in the presence of a scattering agent to simulate a range of practical turbidities. Algorithm details are provided, and baseline and processed images are presented. The processed images demonstrate image restoration that retains the downrange edge features of the object being imaged is possible for a range of practical turbidities.

Download here

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.