Matching Items (3)

Filtering by

Clear all filters

135912-Thumbnail Image.png

Multi-Static Space-Time-Frequency Channel Modeling

Description

Radio communication has become the dominant form of correspondence in modern society. As the demand for high speed communication grows, the problems associated with an expanding consumer base and limited spectral access become more difficult to address. One communications system

Radio communication has become the dominant form of correspondence in modern society. As the demand for high speed communication grows, the problems associated with an expanding consumer base and limited spectral access become more difficult to address. One communications system in which people commonly find themselves is the multiple access cellular network. Users operate within the same geographical area and bandwidth, so providing access to every user requires advanced processing techniques and careful subdivision of spectral access. This is known as the multiple access problem. This paper addresses this challenge in the context of airborne transceivers operating at high altitudes and long ranges. These operators communicate by transmitting a signal through a target scattering field on the ground without a direct line of sight to the receiver. The objective of this investigation is to develop a model for this communications channel, identify and quantify the relevant characteristics, and evaluate the feasibility of using it to effectively communicate.

Contributors

Agent

Created

Date Created
2015-12

137100-Thumbnail Image.png

Maximum Entropy Surrogation in Multiple Channel Signal Detection

Description

Multiple-channel detection is considered in the context of a sensor network where data can be exchanged directly between sensor nodes that share a common edge in the network graph. Optimal statistical tests used for signal source detection with multiple noisy

Multiple-channel detection is considered in the context of a sensor network where data can be exchanged directly between sensor nodes that share a common edge in the network graph. Optimal statistical tests used for signal source detection with multiple noisy sensors, such as the Generalized Coherence (GC) estimate, use pairwise measurements from every pair of sensors in the network and are thus only applicable when the network graph is completely connected, or when data are accumulated at a common fusion center. This thesis presents and exploits a new method that uses maximum-entropy techniques to estimate measurements between pairs of sensors that are not in direct communication, thereby enabling the use of the GC estimate in incompletely connected sensor networks. The research in this thesis culminates in a main conjecture supported by statistical tests regarding the topology of the incomplete network graphs.

Contributors

Agent

Created

Date Created
2014-05

147972-Thumbnail Image.png

Audio Waveform Sample SVD Compression and Impact on Performance

Description

Lossy compression is a form of compression that slightly degrades a signal in ways that are ideally not detectable to the human ear. This is opposite to lossless compression, in which the sample is not degraded at all. While lossless

Lossy compression is a form of compression that slightly degrades a signal in ways that are ideally not detectable to the human ear. This is opposite to lossless compression, in which the sample is not degraded at all. While lossless compression may seem like the best option, lossy compression, which is used in most audio and video, reduces transmission time and results in much smaller file sizes. However, this compression can affect quality if it goes too far. The more compression there is on a waveform, the more degradation there is, and once a file is lossy compressed, this process is not reversible. This project will observe the degradation of an audio signal after the application of Singular Value Decomposition compression, a lossy compression that eliminates singular values from a signal’s matrix.

Contributors

Agent

Created

Date Created
2021-05