Compressed sensing based cyclic feature spectrum sensing for cognitive radios

Spectrum sensing is currently one of the most challenging design problems in cognitive radio. A robust spectrum sensing technique is important in allowing implementation of a practical dynamic spectrum access in noisy and interference uncertain environments. In addition, it is desired to minimize the sensing time, while meeting the stringent cognitive radio application requirements. To cope with this challenge, cyclic spectrum sensing techniques have been proposed. However, such techniques require very high sampling rates in the wideband regime and thus are costly in hardware implementation and power consumption. In this thesis the concept of compressed sensing is applied to circumvent this problem by utilizing the sparsity of the two-dimensional cyclic spectrum. Compressive sampling is used to reduce the sampling rate and a recovery method is developed for re- constructing the sparse cyclic spectrum from the compressed samples. The reconstruction solution used, exploits the sparsity structure in the two-dimensional cyclic spectrum do-main which is different from conventional compressed sensing techniques for vector-form sparse signals. The entire wideband cyclic spectrum is reconstructed from sub-Nyquist-rate samples for simultaneous detection of multiple signal sources. After the cyclic spectrum recovery two methods are proposed to make spectral occupancy decisions from the recovered cyclic spectrum: a band-by-band multi-cycle detector which works for all modulation schemes, and a fast and simple thresholding method that works for Binary Phase Shift Keying (BPSK) signals only. In addition a method for recovering the power spectrum of stationary signals is developed as a special case. Simulation results demonstrate that the proposed spectrum sensing algorithms can significantly reduce sampling rate without sacrifcing performance. The robustness of the algorithms to the noise uncertainty of the wireless channel is also shown.

Compressed sensing approach to ultra-wideband receiver design

One of the scarcest resources in the wireless communication system is the limited frequency spectrum. Many wireless communication systems are hindered by the bandwidth limitation and are not able to provide high speed communication. However, Ultra-wideband (UWB) communication promises a high speed communication because of its very wide bandwidth of 7.5GHz (3.1GHz-10.6GHz). The unprecedented bandwidth promises many advantages for the 21st century wireless communication system. However, UWB has many hardware challenges, such as a very high speed sampling rate requirement for analog to digital conversion, channel estimation, and implementation challenges. In this thesis, a new method is proposed using compressed sensing (CS), a mathematical concept of sub-Nyquist rate sampling, to reduce the hardware complexity of the system. The method takes advantage of the unique signal structure of the UWB symbol. Also, a new digital implementation method for CS based UWB is proposed. Lastly, a comparative study is done of the CS-UWB hardware implementation methods. Simulation results show that the application of compressed sensing using the proposed method significantly reduces the number of hardware complexity compared to the conventional method of using compressed sensing based UWB receiver.

DEVELOPMENT OF CONFORMABLE CNG TANK FOR AUTOMOTIVE APPLICATION

The purpose of this study is to design, develop and integrate a Compressed Natural Gas (CNG) tank that will have a conformable shape for efficient storage in a light-duty pick-up truck. The CNG tank will be a simple rectangular box geometry to demonstrate capability of non-cylindrical shapes. Using CAD drawings of the truck, a conformable tank will be designed to fit under the pick-up bed. The intent of the non-cylindrical CNG tank is to demonstrate improvement in size over the current solution, which is a large cylinder in the box of a pick-up truck. The geometry of the tank’s features is critical to its size and strength. The optimized tank design will be simulated with Finite Element Analysis (FEA) to determine critical stress regions, and appropriate design changes will be made to reduce stress concentration. Following the American National Standard Institute (ANSI) guide, different aluminum alloys will be optimized to obtain the best possible result for the CNG tank.

Characterization Of Porous Media In Proton Exchange Membrane Fuel Cell Based on Percolation Studies

Water management in the porous media of proton exchange membrane (PEM) fuel cells, catalyst layer and porous transport layers (PTL) is confronted by two issues, flooding and dry out, both of which result in improper functioning of the fuel cell and lead to poor performance and degradation. The data that has been reported about water percolation and wettability within a fuel cell catalyst layer is limited to porosimetry. A new method and apparatus for measuring the percolation pressure in the catalyst layer has been developed. The experimental setup is similar to a Hele-Shaw experiment where samples are compressed and a fluid is injected into the sample. Pressure-Wetted Volume plots as well as Permeability plots for the catalyst layers were generated from the percolation testing. PTL samples were also characterizes using a Hele-Shaw method. Characterization for the PTLs was completed for the three states: new, conditioned and aged. This is represented in a Ce-t* plots, which show a large offset between new and aged samples.