Motion segmentation using curve fitting on Lagrangian particle trajectories

In this paper we present a segmentation algorithm to extract foreground object motion in a moving camera scenario without any preprocessing step such as tracking selected features, video alignment, or foreground segmentation. By viewing it as a curve fitting problem on advected particle trajectories, we use RANSAC to find the polynomial that best fits the camera motion and identify all trajectories that correspond to the camera motion. The remaining trajectories are those due to the foreground motion. By using the superposition principle, we subtract the motion due to camera from foreground trajectories and obtain the true object-induced trajectories. We show that our method performs on par with state-of-the-art technique, with an execution time speed-up of 10x-40x. We compare the results on real-world datasets such as UCF-ARG, UCF Sports and Liris-HARL. We further show that it can be used toper-form video alignment.

Polymer optoelectronic structures for retinal prosthesis

This commentary highlights the effectiveness of optoelectronic properties of polymer semiconductors based on recent results emerging from our laboratory, where these materials are explored as artificial receptors for interfacing with the visual systems. Organic semiconductors based polymer layers in contact with physiological media exhibit interesting photophysical features, which mimic certain natural photoreceptors, including those in the retina. The availability of such optoelectronic materials opens up a gateway to utilize these structures as neuronal interfaces for stimulating retinal ganglion cells. In a recently reported work entitled ``A polymer optoelectronic interface provides visual cues to a blind retina,'' we utilized a specific configuration of a polymer semiconductor device structure to elicit neuronal activity in a blind retina upon photoexcitation. The elicited neuronal signals were found to have several features that followed the optoelectronic response of the polymer film. More importantly, the polymer-induced retinal response resembled the natural response of the retina to photoexcitation. These observations open up a promising material alternative for artificial retina applications.

ELLIPSE FITTING USING FINITE RATE OF INNOVATION PRINCIPLES

We address the problem of parameter estimation of an ellipse from a limited number of samples. We develop a new approach for solving the ellipse fitting problem by showing that the x and y coordinate functions of an ellipse are finite-rate-of-innovation (FRI) signals. Uniform samples of x and y coordinate functions of the ellipse are modeled as a sum of weighted complex exponentials, for which we propose an efficient annihilating filter technique to estimate the ellipse parameters from the samples. The FRI framework allows for estimating the ellipse parameters reliably from partial or incomplete measurements even in the presence of noise. The efficiency and robustness of the proposed method is compared with state-of-art direct method. The experimental results show that the estimated parameters have lesser bias compared with the direct method and the estimation error is reduced by 5-10 dB relative to the direct method.

Ellipse Fitting Using the Finite Rate of Innovation Sampling Principle

Standard approaches for ellipse fitting are based on the minimization of algebraic or geometric distance between the given data and a template ellipse. When the data are noisy and come from a partial ellipse, the state-of-the-art methods tend to produce biased ellipses. We rely on the sampling structure of the underlying signal and show that the x- and y-coordinate functions of an ellipse are finite-rate-of-innovation (FRI) signals, and that their parameters are estimable from partial data. We consider both uniform and nonuniform sampling scenarios in the presence of noise and show that the data can be modeled as a sum of random amplitude-modulated complex exponentials. A low-pass filter is used to suppress noise and approximate the data as a sum of weighted complex exponentials. The annihilating filter used in FRI approaches is applied to estimate the sampling interval in the closed form. We perform experiments on simulated and real data, and assess both objective and subjective performances in comparison with the state-of-the-art ellipse fitting methods. The proposed method produces ellipses with lesser bias. Furthermore, the mean-squared error is lesser by about 2 to 10 dB. We show the applications of ellipse fitting in iris images starting from partial edge contours, and to free-hand ellipses drawn on a touch-screen tablet.

Alternative search strategies for a BSM resonance fitting the ATLAS diboson excess

We study an s-channel resonance R as a viable candidate to fit the diboson excess reported by ATLAS. We compute the contribution of the similar to 2 TeV resonance R to semileptonic and leptonic final states at the 13 TeV LHC. To explain the absence of an excess in the semileptonic channel, we explore the possibility where the particle R decays to additional light scalars X, X or X, Y. A modified analysis strategy has been proposed to study the three-particle final state of the resonance decay and to identify decay channels of X. Associated production of R with gauge bosons has been studied in detail to identify the production mechanism of R. We construct comprehensive categories for vector and scalar beyond-standard-model particles which may play the role of particles R, X, Y and find alternate channels to fix the new couplings and search for these particles.

Photocatalytic and Thermal Degradation of Poly(methyl methacrylate), Poly(butyl acrylate), and Their Copolymers

The photocatalytic and thermal degradations of poly(methyl methacrylate), poly(butyl acrylate), and their copolymers of different compositions were studied. The photocatalytic degradation was investigated in o-dichlorobenzene in the presence of two different catalysts, namely, Degussa P-25 and combustion synthesized nanotitania (CSN-TiO2). The samples were analyzed by using gel permeation chromatography (GPC) to obtain the molecular weight distributions (MWDs) as a function of reaction time. Experimental data indicated that the photodegradation of these polymers occurs by both random and chain end scission. A continuous distribution kinetic model was used to determine the degradation rate coefficients by fitting the experimental data with the model. Both the random and specific rate coefficients of the copolymers decreased with increasing percentage of butyl acrylate (BA). Thermal degradation of the copolymers was investigated by thermo-gravimetry. The normalized weight loss profiles for the copolymers showed that the thermal stability of the copolymers increased with mole percentage of BA in the copolymer (PMMABA). The Czawa method was used to determine the activation energies at different conversions. At low acrylate content in the copolymer, the activation energy depends on conversion, indicating multiple degradation mechanisms. At high acrylate content in the copolymer, the activation energy is independent of conversion, indicating degradation by a one-step mechanism.

Determination of coefficient of consolidation from early stage of log t plot

The standard curve-fitting methods, Casagrande's log t method and Taylor's root t method, for the determination of the coefficient of consolidation use the later part of the consolidation curve and are influenced by secondary compression effects. Literature shows that secondary compression is concurrent with primary consolidation and that its effect is to decrease the value of the coefficient of consolidation. If the early part of the time-compression data is used, the values obtained will be less influenced by secondary compression effects. A method that uses the early part of the log t plot is proposed in this technical note. As the influence of secondary compression is reduced, the value obtained by this method is greater than that yielded by both the standard methods. The permeability values computed from C-v (obtained from the proposed method) rue more in agreement with the measured values than the standard methods showing that the effects of secondary compression are minimized. Time-compression data for a shorter duration is sufficient for the determination of C-v if the coefficient of secondary compression is not required.

Structural, optical and electrical properties of sulfur-incorporated amorphous carbon films

Amorphous carbon-sulfur (a-C:S) composite films were prepared by vapor phase pyrolysis technique. The structural changes in the a-C:S films were investigated by electron microscopy. A powder X-ray diffraction (XRD) study depicts the two-phase nature of a sulfur-incorporated a-C system. The optical bandgap energy shows a decreasing trend with an increase in the sulfur content and preparation temperature. This infers a sulfur incorporation and pyrolysis temperature induced reduction in structural disorder or increase in sp (2) or pi-sites. The presence of sulfur (S 2p) in the a-C:S sample is analyzed by the X-ray photoelectron spectroscopy (XPS). The sp (3)/sp (2) hybridization ratio is determined by using the XPS C 1s peak fitting, and the results confirm an increase in sp (2) hybrids with sulfur addition to a-C. The electrical resistivity variation in the films depends on both the sulfur concentration and the pyrolysis temperature.

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.

Swarm intelligence-based solver for parameter estimation of laboratory through-diffusion transport of contaminants

Theoretical approaches are of fundamental importance to predict the potential impact of waste disposal facilities on ground water contamination. Appropriate design parameters are generally estimated be fitting theoretical models to data gathered from field monitoring or laboratory experiments. Transient through-diffusion tests are generally conducted in the laboratory to estimate the mass transport parameters of the proposed barrier material. Thes parameters are usually estimated either by approximate eye-fitting calibration or by combining the solution of the direct problem with any available gradient-based techniques. In this work, an automated, gradient-free solver is developed to estimate the mass transport parameters of a transient through-diffusion model. The proposed inverse model uses a particle swarm optimization (PSO) algorithm that is based on the social behavior of animals searching for food sources. The finite difference numerical solution of the forward model is integrated with the PSO algorithm to solve the inverse problem of parameter estimation. The working principle of the new solver is demonstrated and mass transport parameters are estimated from laboratory through-diffusion experimental data. An inverse model based on the standard gradient-based technique is formulated to compare with the proposed solver. A detailed comparative study is carried out between conventional methods and the proposed solver. The present automated technique is found to be very efficient and robust. The mass transport parameters are obtained with great precision.

Kinetic studies on thermal decomposition of polystyrene peroxide

Polystyrene peroxide has been synthesized and its decomposition has been studied by thermogravimetry and differential thermal analysis. Polystyrene peroxide has been found to decompose exothermically at about 110°C. The activation energy for the decomposition was estimated to be 30 kcal/mole both by the Jacobs and Kureishy method and by fitting the α versus time curves to the first-order kinetic equation. This suggests that the rate-controlling step in the decomposition of polystyrene peroxide is cleavage of the O---O bond.

Isomerization Dynamics In Solution In The Absence Of An Activation Barrier - Evaluation Of The Potential Parameters

We analyze recent experimental results of Sundström and Gillbro by using the theory of Bagchi, Fleming and Oxtoby. The experimental results are in good agreement with this theory, but not with the earlier theory of Förster and Hoffmann. By fitting the new experimental results to the theory, we obtain approximate estimates of the frequency of the excited surface (assumed harmonic) and the width of the sink function.

Thermal characterization of microheaters from the dynamic response

Thermal characterization of surface-micromachined microheaters is carried out from their dynamic response to electrothermal excitations. An electrical equivalent circuit model is developed for the thermo-mechanical system. The mechanical parameters are extracted from the frequency response obtained using a laser Doppler vibrometer. The resonant frequencies of the microheaters are measured and compared with FEM simulations. The thermal time constants are obtained from the electrical equivalent model by fitting the model response to the measured frequency response. Microheaters with an active area of 140 µm × 140 µm have been realized on two different layers (poly-1 and poly-2) with two different air gaps (2 µm and 2.75 µm). The effective time constants, combining thermal and mechanical responses, are in the range of 0.13–0.22 ms for heaters on the poly-1 layer and 1.9 µs–0.15 ms for microheaters on the poly-2 layer. The thermal time constants of the microheaters are in the range of a few microseconds, thus making them suitable for sensor applications that need a faster thermal response.

Stochastic Modeling of an Aircraft Traversing a Runway Using Time Series Analysis

Time series, from a narrow point of view, is a sequence of observations on a stochastic process made at discrete and equally spaced time intervals. Its future behavior can be predicted by identifying, fitting, and confirming a mathematical model. In this paper, time series analysis is applied to problems concerning runwayinduced vibrations of an aircraft. A simple mathematical model based on this technique is fitted to obtain the impulse response coefficients of an aircraft system considered as a whole for a particular type of operation. Using this model, the output which is the aircraft response can be obtained with lesser computation time for any runway profile as the input.