A Generalized Approach to Multiresolution Complex SAR Signal Processing

Multiresolution synthetic aperture radar (SAR) image formation has been proven to be beneficial in a variety of applications such as improved imaging and target detection as well as speckle reduction. SAR signal processing traditionally carried out in the Fourier domain has inherent limitations in the context of image formation at hierarchical scales. We present a generalized approach to the formation of multiresolution SAR images using biorthogonal shift-invariant discrete wavelet transform (SIDWT) in both range and azimuth directions. Particularly in azimuth, the inherent subband decomposition property of wavelet packet transform is introduced to produce multiscale complex matched filtering without involving any approximations. This generalized approach also includes the formulation of multilook processing within the discrete wavelet transform (DWT) paradigm. The efficiency of the algorithm in parallel form of execution to generate hierarchical scale SAR images is shown. Analytical results and sample imagery of diffuse backscatter are presented to validate the method.

Functional genes and gene array analysis as tools for monitoring hydrocarbon biodegradation

Bioremediation, which is the exploitation of the intrinsic ability of environmental microbes to degrade and remove harmful compounds from nature, is considered to be an environmentally sustainable and cost-effective means for environmental clean-up. However, a comprehensive understanding of the biodegradation potential of microbial communities and their response to decontamination measures is required for the effective management of bioremediation processes. In this thesis, the potential to use hydrocarbon-degradative genes as indicators of aerobic hydrocarbon biodegradation was investigated. Small-scale functional gene macro- and microarrays targeting aliphatic, monoaromatic and low molecular weight polyaromatic hydrocarbon biodegradation were developed in order to simultaneously monitor the biodegradation of mixtures of hydrocarbons. The validity of the array analysis in monitoring hydrocarbon biodegradation was evaluated in microcosm studies and field-scale bioremediation processes by comparing the hybridization signal intensities to hydrocarbon mineralization, real-time polymerase chain reaction (PCR), dot blot hybridization and both chemical and microbiological monitoring data. The results obtained by real-time PCR, dot blot hybridization and gene array analysis were in good agreement with hydrocarbon biodegradation in laboratory-scale microcosms. Mineralization of several hydrocarbons could be monitored simultaneously using gene array analysis. In the field-scale bioremediation processes, the detection and enumeration of hydrocarbon-degradative genes provided important additional information for process optimization and design. In creosote-contaminated groundwater, gene array analysis demonstrated that the aerobic biodegradation potential that was present at the site, but restrained under the oxygen-limited conditions, could be successfully stimulated with aeration and nutrient infiltration. During ex situ bioremediation of diesel oil- and lubrication oil-contaminated soil, the functional gene array analysis revealed inefficient hydrocarbon biodegradation, caused by poor aeration during composting. The functional gene array specifically detected upper and lower biodegradation pathways required for complete mineralization of hydrocarbons. Bacteria representing 1 % of the microbial community could be detected without prior PCR amplification. Molecular biological monitoring methods based on functional genes provide powerful tools for the development of more efficient remediation processes. The parallel detection of several functional genes using functional gene array analysis is an especially promising tool for monitoring the biodegradation of mixtures of hydrocarbons.

Receptor guanylyl cyclase C (GC-C): regulation and signal transduction

Receptor guanylyl cyclase C (GC-C) is the target for the gastrointestinal hormones, guanylin, and uroguanylin as well as the bacterial heat-stable enterotoxins. The major site of expression of GC-C is in the gastrointestinal tract, although this receptor and its ligands play a role in ion secretion in other tissues as well. GC-C shares the domain organization seen in other members of the family of receptor guanylyl cyclases, though subtle differences highlight some of the unique features of GC-C. Gene knock outs in mice for GC-C or its ligands do not lead to embryonic lethality, but modulate responses of these mice to stable toxin peptides, dietary intake of salts, and development and differentiation of intestinal cells. It is clear that there is much to learn in future about the role of this evolutionarily conserved receptor, and its properties in intestinal and extra-intestinal tissues.

Jet Engine Health Signal Denoising Using Optimally Weighted Recursive Median Filters

The removal of noise and outliers from health signals is an important problem in jet engine health monitoring. Typically, health signals are time series of damage indicators, which can be sensor measurements or features derived from such measurements. Sharp or sudden changes in health signals can represent abrupt faults and long term deterioration in the system is typical of gradual faults. Simple linear filters tend to smooth out the sharp trend shifts in jet engine signals and are also not good for outlier removal. We propose new optimally designed nonlinear weighted recursive median filters for noise removal from typical health signals of jet engines. Signals for abrupt and gradual faults and with transient data are considered. Numerical results are obtained for a jet engine and show that preprocessing of health signals using the proposed filter significantly removes Gaussian noise and outliers and could therefore greatly improve the accuracy of diagnostic systems. [DOI: 10.1115/1.3200907].

Multi-GNSS precise point positioning with raw single-frequency and dual-frequency measurement models

The emergence of multiple satellite navigation systems, including BDS, Galileo, modernized GPS, and GLONASS, brings great opportunities and challenges for precise point positioning (PPP). We study the contributions of various GNSS combinations to PPP performance based on undifferenced or raw observations, in which the signal delays and ionospheric delays must be considered. A priori ionospheric knowledge, such as regional or global corrections, strengthens the estimation of ionospheric delay parameters. The undifferenced models are generally more suitable for single-, dual-, or multi-frequency data processing for single or combined GNSS constellations. Another advantage over ionospheric-free PPP models is that undifferenced models avoid noise amplification by linear combinations. Extensive performance evaluations are conducted with multi-GNSS data sets collected from 105 MGEX stations in July 2014. Dual-frequency PPP results from each single constellation show that the convergence time of undifferenced PPP solution is usually shorter than that of ionospheric-free PPP solutions, while the positioning accuracy of undifferenced PPP shows more improvement for the GLONASS system. In addition, the GLONASS undifferenced PPP results demonstrate performance advantages in high latitude areas, while this impact is less obvious in the GPS/GLONASS combined configuration. The results have also indicated that the BDS GEO satellites have negative impacts on the undifferenced PPP performance given the current “poor” orbit and clock knowledge of GEO satellites. More generally, the multi-GNSS undifferenced PPP results have shown improvements in the convergence time by more than 60 % in both the single- and dual-frequency PPP results, while the positioning accuracy after convergence indicates no significant improvements for the dual-frequency PPP solutions, but an improvement of about 25 % on average for the single-frequency PPP solutions.

Atrial Electric Signal During Sinus Rhythm in Lone Paroxysmal Atrial Fibrillation

Atrial fibrillation (AF) is the most common tachyarrhythmia and is associated with substantial morbidity, increased mortality and cost. The treatment modalities of AF have increased, but results are still far from optimal. More individualized therapy may be beneficial. Aiming for this calls improved diagnostics. Aim of this study was to find non-invasive parameters obtained during sinus rhythm reflecting electrophysiological patterns related to propensity to AF and particularly to AF occurring without any associated heart disease, lone AF. Overall 240 subjects were enrolled, 136 patients with paroxysmal lone AF and 104 controls (mean age 45 years, 75% males). Signal measurements were performed by non-invasive magnetocardiography (MCG) and by invasive electroanatomic mapping (EAM). High-pass filtering techniques and a new method based on a surface gradient technique were adapted to analyze atrial MCG signal. The EAM was used to elucidate atrial activation in patients and as a reference for MCG. The results showed that MCG mapping is an accurate method to detect atrial electrophysiologic properties. In lone paroxysmal AF, duration of the atrial depolarization complex was marginally prolonged. The difference was more obvious in women and was also related to interatrial conduction patterns. In the focal type of AF (75%), the root mean square (RMS) amplitudes of the atrial signal were normal, but in AF without demonstrable triggers the late atrial RMS amplitudes were reduced. In addition, the atrial characteristics tended to remain similar even when examined several years after the first AF episodes. The intra-atrial recordings confirmed the occurrence of three distinct sites of electrical connection from right to left atrium (LA): the Bachmann bundle (BB), the margin of the fossa ovalis (FO), and the coronary sinus ostial area (CS). The propagation of atrial signal could also be evaluated non-invasively. Three MCG atrial wave types were identified, each of which represented a distinct interatrial activation pattern. In conclusion, in paroxysmal lone AF, active focal triggers are common, atrial depolarization is slightly prolonged, but with a normal amplitude, and the arrhythmia does not necessarily lead to electrical or mechanical dysfunction of the atria. In women the prolongation of atrial depolarization is more obvious. This may be related to gender differences in presentation of AF. A significant minority of patients with lone AF lack frequent focal triggers, and in them, the late atrial signal amplitude is reduced, possibly signifying a wider degenerative process in the LA. In lone AF, natural impulse propagation to LA during sinus rhythm goes through one or more of the principal pathways described. The BB is the most common route, but in one-third, the earliest LA activation occurs outside the BB. Susceptibility to paroxysmal lone AF is associated with propagation of the atrial signal via the margin of the FO or via multiple pathways. When conduction occurs via the BB, it is related with prolonged atrial activation. Thus, altered and alternative conduction pathways may contribute to pathogenesis of lone AF. There is growing evidence of variability in genesis of AF also within lone paroxysmal AF. Present study suggests that this variation may be reflected in cardiac signal pattern. Recognizing the distinct signal profiles may assist in understanding the pathogenesis of AF and identifying subgroups for patient-tailored therapy.

GMM based Bayesian approach to speech enhancement in signal transform domain

Considering a general linear model of signal degradation, by modeling the probability density function (PDF) of the clean signal using a Gaussian mixture model (GMM) and additive noise by a Gaussian PDF, we derive the minimum mean square error (MMSE) estimator. The derived MMSE estimator is non-linear and the linear MMSE estimator is shown to be a special case. For speech signal corrupted by independent additive noise, by modeling the joint PDF of time-domain speech samples of a speech frame using a GMM, we propose a speech enhancement method based on the derived MMSE estimator. We also show that the same estimator can be used for transform-domain speech enhancement.

Signal set design for full-diversity low-decoding-complexity differential scaled-unitary STBCs

The problem of designing high rate, full diversity noncoherent space-time block codes (STBCs) with low encoding and decoding complexity is addressed. First, the notion of g-group encodable and g-group decodable linear STBCs is introduced. Then for a known class of rate-1 linear designs, an explicit construction of fully-diverse signal sets that lead to four-group encodable and four-group decodable differential scaled unitary STBCs for any power of two number of antennas is provided. Previous works on differential STBCs either sacrifice decoding complexity for higher rate or sacrifice rate for lower decoding complexity.

Response time measurement in flow induced signal generation on semiconductors

Measurable electrical signal is generated when a gas flows over a variety of solids, including doped semiconductors, even at the modest speed of a few meters per second. The underlying mechanism is an interesting interplay of Bernoulli's principle and the Seebeck effect. The electrical signal depends on the square of Mach number (M) and is proportional to the Seebeck coefficient (S) of the solids. Here we present experimental estimate of the response time of the signal rise and fall process, i.e. how fast the semiconductor materials respond to a steady flow as soon as it is set on or off. A theoretical model is also presented to understand the process and the dependence of the response time on the nature and physical dimensions of the semiconductor material used and they are compared with the experimental observations. (c) 2007 Elsevier B.V. All rights reserved.

A genetic algorithm with characteristic amplification through multiple geographically isolated populations and varied fitness landscapes

This paper proposes a new approach, wherein multiple populations are evolved on different landscapes. The problem statement is broken down, to describe discrete characteristics. Each landscape, described by its fitness landscape is used to optimize or amplify a certain characteristic or set of characteristics. Individuals from each of these populations are kept geographically isolated from each other Each population is evolved individually. After a predetermined number of evolutions, the system of populations is analysed against a normalized fitness function. Depending on this score and a predefined merging scheme, the populations are merged, one at a time, while continuing evolution. Merging continues until only one final population remains. This population is then evolved, following which the resulting population will contain the optimal solution. The final resulting population will contain individuals which have been optimized against all characteristics as desired by the problem statement. Each individual population is optimized for a local maxima. Thus when populations are merged, the effect is to produce a new population which is closer to the global maxima.

A genetic algorithm with characteristic amplification through multiple geographically isolated populations and varied fitness landscapes

This paper proposes a new approach, wherein multiple populations are evolved on different landscapes. The problem statement is broken down, to describe discrete characteristics. Each landscape, described by its fitness landscape is used to optimize or amplify a certain characteristic or set of characteristics. Individuals from each of these populations are kept geographically isolated from each other Each population is evolved individually. After a predetermined number of evolutions, the system of populations is analysed against a normalized fitness function. Depending on this score and a predefined merging scheme, the populations are merged, one at a time, while continuing evolution. Merging continues until only one final population remains. This population is then evolved, following which the resulting population will contain the optimal solution. The final resulting population will contain individuals which have been optimized against all characteristics as desired by the problem statement. Each individual population is optimized for a local maxima. Thus when populations are merged, the effect is to produce a new population which is closer to the global maxima.

Signal set design for full-diversity low-decoding-complexity differential scaled-unitary STBCs

The problem of designing high rate, full diversity noncoherent space-time block codes (STBCs) with low encoding and decoding complexity is addressed. First, the notion of g-group encodable and g-group decodable linear STBCs is introduced. Then for a known class of rate-1 linear designs, an explicit construction of fully-diverse signal sets that lead to four-group encodable and four-group decodable differential scaled unitary STBCs for any power of two number of antennas is provided. Previous works on differential STBCs either sacrifice decoding complexity for higher rate or sacrifice rate for lower decoding complexity.