Metrological data from sample surfaces can be obtained by using a variety of profilometry methods. Atomic Force Microscopy (AFM), which relies on contact inter-atomic forces to extract topographical images of a sample, is one such method that can be used on a wide range of surface types, with possible nanometer range resolution. However, AFM images are commonly distorted by convolution, which reduces metrological accuracy. This type of distortion is more significant when the sample surface contains high aspect ratio features such as lines, steps or sharp edges - structures commonly found in semiconductor devices and applications. Aiming at mitigating these distortions and recovering metrology soundness, we introduce a novel image deconvolution scheme based on the principle of stereo imaging. Multiple images of a sample, taken at different angles, allow for separation of convolution artifacts from true topographic data. As a result, perfect sample reconstruction and probe shape estimation can be achieved in certain cases. Additionally, shadow zones, which are areas of the sample that cannot be probed by the AFM, are greatly reduced. Most importantly, this technique does not require a priori probe characterization. It also reduces the need for slender or sharper probes, which, on one hand, induce less convolution distortion but, on the other hand, are more prone to wear and damage, thus decreasing overall system reliability. / Singapore-MIT Alliance (SMA)

This study aims to investigate the evoked and the induced activity in 40 Hz auditory responses. The 40 Hz activity, also called Pb or P50 or P1 component, has a latency of 50ms and belongs to the category of MLRs (Middle latency responses), which occur right after Auditory Brainstem Responses (ABRs) between 15 and 80ms. Its importance is related to possible clinical applications such as anesthesia, schizophrenia and auditory development. In addition to this, evoked and induced activities at 40 Hz might play an important role in cognitive processing. Trains of right ear rarefaction clicks at the mean rate of 39.1 Hz, intensity of 50dB and duration of 100 were used to elicit the above mentioned activities. Three different sequences have been used: steady state, low jittered and medium jittered. Low jittered sequence has been the main sequence used to study the 40 Hz activity. The advantage of using this sequence is the fact that a deconvolution analysis can be performed and also the fact that it does not differ too much from the standard 40Hz steady-state sequence and this means that a resonance at 40 Hz can still be obtained. Ten healthy subjects (8 males and 2 females; ages ranging from 25 to 47), with no history of audiological or neurological hearing impairment were recorded. Informed consent was signed according to approved IRB protocols. All recordings were done in a sound-proof chamber (Acoustic Systems, Inc.) with subjects lying on a bed comfortably. The subjects were not asked to perform any tasks, but just to passively listen to the acoustical stimuli. Evoked and induced activities were recorded in response to the above mentioned acoustic stimuli. The deconvolution analysis showed that the peak of activity occurs around 152ms. Wavelets analysis has confirmed this observation and has also unveiled and induced activity in the low beta range. This induced activity seems to be strictly related to the evoked activity, as it seems to occur around 390ms, which corresponds to the situation where the 40 Hz evoked activity enters a steady state condition, which lasts until the last acoustic stimulus has been applied. The latter observation is again in accordance with the literature, where it is reported that the 40Hz evoked activity could reflect the initial coactivation of neural assemblies representing specific stimulus features. A change in such stimulus features could be reflected as induced oscillations occurring in the middle beta range (16-22 Hz).

We propose an efficient iterative curvelet-regularized deconvolution algorithm that exploits continuity along reflectors in seismic images. Curvelets are a new multiscale transform that provides sparse representations for images (such as seismic images) that comprise smooth objects separated by piece-wise smooth discontinuities. Our technique combines conjugate gradient-based convolution operator inversion with noise regularization that is performed using non-linear curvelet coefficient shrinkage (thresholding). The shrinkage operation leverages the sparsity of curvelets representations. Simulations demonstrate that our algorithm provides improved resolution compared to the traditional Wiener-based deconvolution approach.

Seismic transitions of the subsurface are typically considered as zero-order singularities (step functions). According to this model, the conventional deconvolution problem aims at recovering the seismic reflectivity as a sparse spike train. However, recent multiscale analysis on sedimentary records revealed the existence of accumulations of varying order singularities in the subsurface, which give rise to fractional-order discontinuities. This observation not only calls for a richer class of seismic reflection waveforms, but it also requires a different methodology to detect and characterize these reflection events. For instance, the assumptions underlying conventional deconvolution no longer hold. Because of the bandwidth limitation of seismic data, multiscale analysis methods based on the decay rate of wavelet coefficients may yield ambiguous results. We avoid this problem by formulating the estimation of the singularity orders by a parametric nonlinear inversion method.

The Barzilai-Borwein (BB) method for unconstrained optimization has attracted attention for its "chaotic" behaviour and fast convergence on image deconvolution problems. However, images with large areas of darkness, such as those often found in astronomy or microscopy, have been shown to benefit from approaches which impose a nonnegativity constraint on the pixel values. We present a new adaptation of the BB method which enforces a nonnegativity constraint by projecting the solution onto the feasible set, but allows for infeasible iterates between projections. We show that this approach results in faster convergence than the basic Projected Barzilai-Borwein (PBB) method, while achieving better quality images than the unconstrained BB method. We find that the new method also performs comparably to the Gradient Projection-Conjugate Gradient (GPCG) method, and in most test cases achieves a lower restoration error, despite being a much simpler algorithm.

No description available.

Statement of Problem Telomere dynamics in the three-dimensional (3D) space of the mammalian nucleus plays an important role in the maintenance of genomic stability. However, the telomere distribution in 3D nuclear space of normal and tumor cells was unknown when the study was initiated. Methods Telomere fluorescence in situ hybridization (FISH) and 3D molecular imaging, deconvolution, and analysis were used to investigate telomere organization in normal, immortalized and tumor cells from mouse and human cell lines, and primary tissues. Results Telomeres are organized in a non-overlapping manner and in a cell-cycle dependant fashion in normal cells. In the late G2 phase of cell cycle, telomeres are assembled into a flattened sphere that is termed the telomeric disk In contrast, the telomeric disk is disrupted in the tumor cells. Moreover, telomeric aggregates (TAs) are found in tumor cells. Conditional c-Myc over-expression induces telomeric aggregation leading to the onset of breakage-bridge-fusion cycles and subsequent chromosomal abnormality. Conclusions Telomeres are distributed in a nonrandom and dynamic fashion in the 3D space of a normal cell. Telomeric aggregates are present in cells with genomic instability such as tumor cells and cells with deregulation of c-Myc. Consequently, TA can be a useful biomarker for research in cancer and other disease processes.

Statement of Problem Telomere dynamics in the three-dimensional (3D) space of the mammalian nucleus plays an important role in the maintenance of genomic stability. However, the telomere distribution in 3D nuclear space of normal and tumor cells was unknown when the study was initiated. Methods Telomere fluorescence in situ hybridization (FISH) and 3D molecular imaging, deconvolution, and analysis were used to investigate telomere organization in normal, immortalized and tumor cells from mouse and human cell lines, and primary tissues. Results Telomeres are organized in a non-overlapping manner and in a cell-cycle dependant fashion in normal cells. In the late G2 phase of cell cycle, telomeres are assembled into a flattened sphere that is termed the telomeric disk In contrast, the telomeric disk is disrupted in the tumor cells. Moreover, telomeric aggregates (TAs) are found in tumor cells. Conditional c-Myc over-expression induces telomeric aggregation leading to the onset of breakage-bridge-fusion cycles and subsequent chromosomal abnormality. Conclusions Telomeres are distributed in a nonrandom and dynamic fashion in the 3D space of a normal cell. Telomeric aggregates are present in cells with genomic instability such as tumor cells and cells with deregulation of c-Myc. Consequently, TA can be a useful biomarker for research in cancer and other disease processes.

A magnetic study over a 95 m x 150 m area of the Hickory sandstone aquifer in central Texas was carried out as part of multitechnique geophysical investigation that included ground penetrating radar (GPR), electromagnetic (EM), seismic and seimoelectric. In geophysical exploration, the magnetic method can be utilized as an alternative to more expensive methods, such as seismic or it can be used to complement other methods. In this thesis, the magnetic method is applied to estimate the location of a previously mapped fault by Texas A&M geology students, and it is used to estimate the magnetic susceptibility contrast of the targeted fault. The main challenge of this study is imaging shallow faults using the geophysical magnetic method in a fractured aquifer with widely-scattered distribution of iron bearing rocks as in the case of the Hickory sandstone aquifer. A Geometric—G858 Cesium vapor magnetometer was used to collect magnetic data. The data consisted of 19 north-south and 1 east-west lines acquired in October and November of 2012. Elementary data processing such as diurnal correction, regional correction, reduction to pole (RTP) filter, Euler deconvolution, forward modeling and inversion were employed to characterize the faulted zone. This faulted zone separates granite basement rocks from the Hickory sandstone. As a result, this study emphasizes that Euler deconvolution applied to RTP-filtered data increases the interpretability of geological and structural contacts. The results of the magnetic method have been compared to results of GPR, EM and seismoelectric methods. Understanding the magnetic mineralogy of rocks and their properties can improve the geological interpretation of magnetic surveys.

Algae can be used to treat wastewater and manure while producing a feedstock for renewable energy. Algae require nutrients to achieve their maximum growth and manure could provide those nutrients, thereby reducing the cost of algae production and the impact of manure treatment. Algae concentration during cultivation is a critical variable that is difficult to measure due to the high concentration of suspended solids present in manure. This dissertation addresses methods to measure algae concentration in the presence of manure solids. Quantifying the algae concentration gravimetrically or by optical density was unreliable due to manure solids interfering with the measurement. Cell counting to determine algae concentration was accurate but time consuming, subjective, required dilution of concentrated samples and only small sample volumes could be measured. Chlorophyll extraction was a consistent method to determine algae concentration in manure based media, but the model had to be adjusted to account for solids interference. The proposed equation predicted chlorophyll concentration from Chlorella vulgaris in dairy manure better than the reference equation. Different algae strains (Chlorella vulgaris, Cylindrocystis sp, and Scenedesmus sp.) and manure sources (dairy, beef, swine, and sheep) were used to validate the proposed equation and all combinations had a linear relationship between actual and predicted chlorophyll concentration, but not all comparisons followed a 1:1 reference line. Even with chlorophyll extraction the manure solids interfered with the chlorophyll measurement and calibrations had to be developed based on manure type. A method based on spectral deconvolution was used to quantify algae concentration in the presence of manure without chlorophyll extraction. Various manure-algae mixtures were scanned with a spectrophotometer. Algae concentration was accurately determined with the four manure sources. Measuring algae concentration required absorbance spectra from 600 to 700 nm and manure solids concentration between 280 and 350 nm. Spectral deconvolution was able to differentiate algae concentration and manure solids concentration with a Pearson coefficient of 95.3% and 99.8% respectively. This method proved to be an accurate and efficient method for estimating algae and manure solids content in unprocessed samples. A critical factor was utilizing appropriate reference spectra.