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.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

DESIGN AND DEVELOPMENT OF BODIPY-BASED FLUORESCENT PROBES FOR SENSING AND IMAGING OF CYANIDE, Zn (II) IONS, LYSOSOMAL pH AND CANCER CELLS

BODIPY (4,4-Difluoro-3a,4a-diaza-s-indacene) dyes have gained lots of attention in application of fluorescence sensing and imaging in recent years because they possess many distinctive and desirable properties such as high extinction coefficient, narrow absorption and emission bands, high quantum yield and low photobleaching effect. However, most of BODIPY-based fluorescent probes have very poor solubilities in aqueous solution, emit less than 650 nm fluorescence that can cause cell and tissue photodamages compared with bio-desirable near infrared (650-900 nm) light. These undesirable properties extremely limit the applications of BODIPY-based fluorescent probes in sensing and imaging applications. In order to overcome these drawbacks, we have developed a very effective strategy to prepare a series of neutral highly water- soluble BODIPY dyes by enhancing the water solubilities of BODIPY dyes via incorporation of tri(ethylene glycol)methyl ether (TEG) and branched oligo(ethylene glycol)methyl ether (BEG) residues onto BODIPY dyes at 1,7-, 2,6-, 3,5-, 4- and meso- positions. We also have effectively tuned absorptions and emissions of BOIDPY dyes to red, deep red and near infrared regions via significant extension of π-conjugation of BODIPY dyes by condensation reactions of aromatic aldehydes with 2,6-diformyl BODIPY dyes at 1,3,5,7-positions. Based on the foundation that we built for enhancing water solubility and tuning wavelength, we have designed and developed a series of water-soluble, BODIPY-based fluorescent probes for sensitive and selective sensing and imaging of cyanide, Zn (II) ions, lysosomal pH and cancer cells. We have developed three BODIPY-based fluorescent probes for sensing of cyanide ions by incorporating indolium moieties onto the 6-position of TEG- or BEG-modified BOIDPY dyes. Two of them are highly water-soluble. These fluorescent probes showed selective and fast ratiometric fluorescent responses to cyanide ions with a dramatic fluorescence color change from red to green accompanying a significant increase in fluorescent intensity. The detection limit was measured as 0.5 mM of cyanide ions. We also have prepared three highly water-soluble fluorescent probes for sensing of Zn (II) ions by introducing dipicoylamine (DPA, Zn ion chelator) onto 2- and/or 6-positions of BEG-modified BODIPY dyes. These probes showed selective and sensitive responses to Zn (II) ion in the range from 0.5 mM to 24 mM in aqueous solution at pH 7.0. Particularly, one of the probes displayed ratiometric responses to Zn (II) ions with fluorescence quenching at 661 nm and fluorescence enhancement at 521 nm. This probe has been successfully applied to the detection of intracellular Zn (II) ions inside the living cells. Then, we have further developed three acidotropic, near infrared emissive BODIPY- based fluorescent probes for detection of lysosomal pH by incorporating piperazine moiety at 3,5-positions of TEG- or BEG-modified BODIPY dyes as parts of conjugation. The probes have low auto-fluorescence at physiological neutral condition while their fluorescence intensities will significant increase at 715 nm when pH shift to acidic condition. These three probes have been successfully applied to the in vitro imaging of lysosomes inside two types of living cells. At the end, we have synthesized one water- soluble, near infrared emissive cancer cell targetable BODIPY-based fluorescent polymer bearing cancer homing peptide (cRGD) residues for cancer cell imaging applications. This polymer exhibited excellent water-solubility, near infrared emission (712 nm), good biocompatibility. It also showed low nonspecific interactions to normal endothelial cells and can effectively detect breast tumor cells.

EVALUATION OF INTELLIGENT COMPACTION CONTROL IN THE M-189 RECONSTRUCTION PROJECT AT IRON RIVER, MICHIGAN

This research evaluated an Intelligent Compaction (IC) unit on the M-189 highway reconstruction project at Iron River, Michigan. The results from the IC unit were compared to several traditional compaction measurement devices including Nuclear Density Gauge (NDG), Geogauge, Light Weight Deflectometer (LWD), Dynamic Cone Penetrometer (DCP), and Modified Clegg Hammer (MCH). The research collected point measurements data on a test section in which 30 test locations on the final Class II sand base layer and the 22A gravel layer. These point measurements were compared with the IC measurements (ICMVs) on a point-to-point basis through a linear regression analysis. Poor correlations were obtained among different measurements points using simple regression analysis. When comparing the ICMV to the compaction measurements points. Factors attributing to the weak correlation include soil heterogeneity, variation in IC roller operation parameters, in-place moisture content, the narrow range of the compaction devices measurement ranges and support conditions of the support layers. After incorporating some of the affecting factors into a multiple regression analysis, the strength of correlation significantly improved, especially on the stiffer gravel layer. Measurements were also studied from an overall distribution perspective in terms of average, measurement range, standard deviation, and coefficient of variance. Based on data analysis, on-site project observation and literature review, conclusions were made on how IC performed in regards to compaction control on the M-189 reconstruction project.