Signal Processing Commons^™

Ambiqual – A Full Reference Objective Quality Metric For Ambisonic Spatial Audio, Miroslaw Narbutt, Andrew Allen, Jan Skoglund, Michael Chinen, Andrew Hines

Conference papers

Streaming spatial audio over networks requires efficient encoding techniques that compress the raw audio content without compromising quality of experience. Streaming service providers such as YouTube need a perceptually relevant objective audio quality metric to monitor users’ perceived quality and spatial localization accuracy. In this paper we introduce a full reference objective spatial audio quality metric, AMBIQUAL, which assesses both Listening Quality and Localization Accuracy. In our solution both metrics are derived directly from the B-format Ambisonic audio. The metric extends and adapts the algorithm used in ViSQOLAudio, a full reference objective metric designed for assessing speech and audio quality. …

A Redundant Monitoring System For Human Welder Operation Using Imu And Vision Sensors, Rui Yu

Theses and Dissertations--Electrical and Computer Engineering

In manual control, the welding gun’s moving speed can significantly influence the welding results and critical welding operations usually require welders to concentrate consistently in order to react rapidly and accurately. However, human welders always have some habitual action which can have some subtle influence the welding process. It takes countless hours to train an experienced human welder. Using vision and IMU sensor will be able to set up a system and allow the worker got more accurate visual feedback like an experienced worker.

The problem is that monitor and measuring of the control process not always easy under a …

Self-Image Multimedia Technologies For Feedforward Observational Learning, Nkiruka M. A. Uzuegbunam

Theses and Dissertations--Electrical and Computer Engineering

This dissertation investigates the development and use of self-images in augmented reality systems for learning and learning-based activities. This work focuses on self- modeling, a particular form of learning, actively employed in various settings for therapy or teaching. In particular, this work aims to develop novel multimedia systems to support the display and rendering of augmented self-images. It aims to use interactivity (via games) as a means of obtaining imagery for use in creating augmented self-images. Two multimedia systems are developed, discussed and analyzed. The proposed systems are validated in terms of their technical innovation and their clinical efficacy in …

Speaker And Gender Identification Using Bioacoustic Data Sets, Neenu Jose

Theses and Dissertations--Electrical and Computer Engineering

Acoustic analysis of animal vocalizations has been widely used to identify the presence of individual species, classify vocalizations, identify individuals, and determine gender. In this work automatic identification of speaker and gender of mice from ultrasonic vocalizations and speaker identification of meerkats from their Close calls is investigated. Feature extraction was implemented using Greenwood Function Cepstral Coefficients (GFCC), designed exclusively for extracting features from animal vocalizations. Mice ultrasonic vocalizations were analyzed using Gaussian Mixture Models (GMM) which yielded an accuracy of 78.3% for speaker identification and 93.2% for gender identification. Meerkat speaker identification with Close calls was implemented using Gaussian …

Testing Of Recursive And Non-Recursive Algorithms For Real-Time Phasor And Frequency Estimations In Power Systems, Dhruv Sharma

Dissertations, Master's Theses and Master's Reports

Steady-state performance of various recursive and non-recursive algorithms are tested in this report according to the test signals given in the IEEE Standard C37.118.1-2011. Phase magnitude and phase angle of the power grid signals have been estimated using Discrete Fourier Transform (non-recursive), Discrete Fourier Transform (recursive), Least Square, and Wavelet Transform Algorithms. Frequency estimation is performed using Discrete Fourier Transform, Weighted Least Square, and Zero Crossing methods. These algorithms are evaluated in LabView software and tested by generating test signals in a Simulink model. Furthermore, Total Vector Error (TVE) is calculated using dynamic test signals as per the IEEE Standard …

Video Frame Reduction In Autonomous Vehicles, Gaurav R. Bagwe

Dissertations, Master's Theses and Master's Reports

Camera sensors are emerging in many applications such as Smart Buildings and autonomous driving. The Data generated by multiple cameras in a smart building and autonomous driving applications is usually transmitted through an edge box to a cloud terminal. This transmitted information requires a considerable channel bandwidth, which is not available through current communication standards. The report proposes a Camera Sensor Frame Reduction method to decrease the required channel bandwidth for applications such as autonomous driving.

Here, we propose a method that incorporates cross frame similarity measurement method to reduce the redundant frames and decrease the data rate of each …

Study Of Obstacle Effect On The Gpsr Protocol And A Novel Intelligent Greedy Routing Protocol For Vanets, Ravikumar Chilmula

Dissertations, Master's Theses and Master's Reports

In recent years, connected vehicle technologies have been developed by automotive companies, academia, and researchers as part of Intelligent Transportation Systems (ITS). This group of stakeholders continue to work on these technologies to make them as reliable and cost-effective as possible. This attention is because of the increasing connected vehicles safety-related, entertainment, and traffic management applications, which have the potential to decrease the number of road accidents, save fuel and time for millions of daily commuters worldwide.

Vehicular Ad-Hoc Network (VANET), which is a subgroup of Mobile Ad-Hoc Network (MANET), is being developed and implemented in vehicles as the critical …

Offline And Online Density Estimation For Large High-Dimensional Data, Aref Majdara

Dissertations, Master's Theses and Master's Reports

Density estimation has wide applications in machine learning and data analysis techniques including clustering, classification, multimodality analysis, bump hunting and anomaly detection. In high-dimensional space, sparsity of data in local neighborhood makes many of parametric and nonparametric density estimation methods mostly inefficient.

This work presents development of computationally efficient algorithms for high-dimensional density estimation, based on Bayesian sequential partitioning (BSP). Copula transform is used to separate the estimation of marginal and joint densities, with the purpose of reducing the computational complexity and estimation error. Using this separation, a parallel implementation of the density estimation algorithm on a 4-core CPU is …

Target Detection, Tracking, And Localization Using Multi-Spectral Image Fusion And Rf Doppler Differentials, Casey D. Demars

Dissertations, Master's Theses and Master's Reports

It is critical for defense and security applications to have a high probability of detection and low false alarm rate while operating over a wide variety of conditions. Sensor fusion, which is the the process of combining data from two or more sensors, has been utilized to improve the performance of a system by exploiting the strengths of each sensor. This dissertation presents algorithms to fuse multi-sensor data that improves system performance by increasing detection rates, lowering false alarms, and improving track performance. Furthermore, this dissertation presents a framework for comparing algorithm error for image registration which is a critical …