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.

Cyclic automorphic graph decompositions

Chapter 1 introduces the tools and mechanics necessary for this report. Basic definitions and topics of graph theory which pertain to the report and discussion of automorphic decompositions will be covered in brief detail. An automorphic decomposition D of a graph H by a graph G is a G-decomposition of H such that the intersection of graph (D) @H. H is called the automorhpic host, and G is the automorphic divisor. We seek to find classes of graphs that are automorphic divisors, specifically ones generated cyclically. Chapter 2 discusses the previous work done mainly by Beeler. It also discusses and gives in more detail examples of automorphic decompositions of graphs. Chapter 2 also discusses labelings and their direct relation to cyclic automorphic decompositions. We show basic classes of graphs, such as cycles, that are known to have certain labelings, and show that they also are automorphic divisors. In Chapter 3, we are concerned with 2-regular graphs, in particular rCm, r copies of the m-cycle. We seek to show that rCm has a ρ-labeling, and thus is an automorphic divisor for all r and m. we discuss methods including Skolem type difference sets to create cycle systems and their correlation to automorphic decompositions. In the Appendix, we give classes of graphs known to be graceful and our java code to generate ρ-labelings on rCm.

Effects of fixed night shift work on 24 hour blood pressure regulation, state anxiety levels and total sleep time

Nearly 22 million Americans operate as shift workers, and shift work has been linked to the development of cardiovascular disease (CVD). This study is aimed at identifying pivotal risk factors of CVD by assessing 24 hour ambulatory blood pressure, state anxiety levels and sleep patterns in 12 hour fixed shift workers. We hypothesized that night shift work would negatively affect blood pressure regulation, anxiety levels and sleep patterns. A total of 28 subjects (ages 22-60) were divided into two groups: 12 hour fixed night shift workers (n=15) and 12 hour fixed day shift workers (n=13). 24 hour ambulatory blood pressure measurements (Space Labs 90207) were taken twice: once during a regular work day and once on a non-work day. State anxiety levels were assessed on both test days using the Speilberger’s State Trait Anxiety Inventory. Total sleep time (TST) was determined using self recorded sleep diary. Night shift workers demonstrated increases in 24 hour systolic (122 ± 2 to 126 ± 2 mmHg, P=0.012); diastolic (75 ± 1 to 79 ± 2 mmHg, P=0.001); and mean arterial pressures (90 ± 2 to 94 ± 2mmHg, P<0.001) during work days compared to off days. In contrast, 24 hour blood pressures were similar during work and off days in day shift workers. Night shift workers reported less TST on work days versus off days (345 ± 16 vs. 552 ± 30 min; P<0.001), whereas day shift workers reported similar TST during work and off days (475 ± 16 minutes to 437 ± 20 minutes; P=0.231). State anxiety scores did not differ between the groups or testing days (time*group interaction P=0.248), suggesting increased 24 hour blood pressure during night shift work is related to decreased TST, not short term anxiety. Our findings suggest that fixed night shift work causes disruption of the normal sleep-wake cycle negatively affecting acute blood pressure regulation, which may increase the long-term risk for CVD.

STUDY OF SPARK IGNITION ENGINE COMBUSTION MODEL FOR THE ANALYSIS OF CYCLIC VARIATION AND COMBUSTION STABILITY AT LEAN OPERATING CONDITIONS

A fundamental combustion model for spark-ignition engine is studied in this report. The model is implemented in SIMULINK to simulate engine outputs (mass fraction burn and in-cylinder pressure) under various engine operation conditions. The combustion model includes a turbulent propagation and eddy burning processes based on literature [1]. The turbulence propagation and eddy burning processes are simulated by zero-dimensional method and the flame is assumed as sphere. To predict pressure, temperature and other in-cylinder variables, a two-zone thermodynamic model is used. The predicted results of this model match well with the engine test data under various engine speeds, loads, spark ignition timings and air fuel mass ratios. The developed model is used to study cyclic variation and combustion stability at lean (or diluted) combustion conditions. Several variation sources are introduced into the combustion model to simulate engine performance observed in experimental data. The relations between combustion stability and the introduced variation amount are analyzed at various lean combustion levels.