Some new results on signal reconstruction from Fourier transform magnitude spectrum

We address the problem of signal reconstruction from Fourier transform magnitude spectrum. The problem arises in many real-world scenarios where magnitude-only measurements are possible, but it is required to construct a complex-valued signal starting from those measurements. We present some new general results in this context and show that the previously known results on minimum-phase rational transfer functions, and recoverability of minimum-phase functions from magnitude spectrum, form special cases of the results reported in this paper. Some simulation results are also provided to demonstrate the practical feasibility of the reconstruction methodology.

Vibration spectrum analyzer using stretched membranes of polymer piezoelectrics for sensor networks

The key problem tackled in this paper is the development of a stand-alone self-powered sensor to directly sense the spectrum of mechanical vibrations. Such a sensor could be deployed in wide area sensor networks to monitor structural vibrations of large machines (e. g. aircrafts) and initiate corrective action if the structure approaches resonance. In this paper, we study the feasibility of using stretched membranes of polymer piezoelectric polyvinlidene fluoride for low-frequency vibration spectrum sensing. We design and evaluate a low-frequency vibration spectrum sensor that accepts an incoming vibration and directly provides the spectrum of the vibration as the output.

Infrared spectrum of formamide in the solid phase

Infrared spectra of solid formamide are reported as a function of temperature. Solid formamide samples were prepared at 30 K and then annealed to higher temperatures (300 K) with infrared transmission spectra being recorded over the entire temperature range. The NH2 vibrations of the formamide molecule were found to be particularly very sensitive to temperature change. The IR spectra revealed a phase change occurring in solid formamide between 155 and 165 K. Spectral changes observed above and below the phase transition may be attributed to a rearrangement between formamide dimers and the formation of polymers is proposed at higher temperatures.

Generalized eigenvalue decomposition of the field autocorrelation in correlation diffusion of photons in turbid media

We propose a novel numerical method based on a generalized eigenvalue decomposition for solving the diffusion equation governing the correlation diffusion of photons in turbid media. Medical imaging modalities such as diffuse correlation tomography and ultrasound-modulated optical tomography have the (elliptic) diffusion equation parameterized by a time variable as the forward model. Hitherto, for the computation of the correlation function, the diffusion equation is solved repeatedly over the time parameter. We show that the use of a certain time-independent generalized eigenfunction basis results in the decoupling of the spatial and time dependence of the correlation function, thus allowing greater computational efficiency in arriving at the forward solution. Besides presenting the mathematical analysis of the generalized eigenvalue problem on the basis of spectral theory, we put forth the numerical results that compare the proposed numerical method with the standard technique for solving the diffusion equation.

Least squares QR-based decomposition provides an efficient way of computing optimal regularization parameter in photoacoustic tomography

A computationally efficient approach that computes the optimal regularization parameter for the Tikhonov-minimization scheme is developed for photoacoustic imaging. This approach is based on the least squares-QR decomposition which is a well-known dimensionality reduction technique for a large system of equations. It is shown that the proposed framework is effective in terms of quantitative and qualitative reconstructions of initial pressure distribution enabled via finding an optimal regularization parameter. The computational efficiency and performance of the proposed method are shown using a test case of numerical blood vessel phantom, where the initial pressure is exactly known for quantitative comparison. (C) 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)

Group testing based spectrum hole search using a simple sub-nyquist sampling scheme

In this paper, we consider the problem of finding a spectrum hole of a specified bandwidth in a given wide band of interest. We propose a new, simple and easily implementable sub-Nyquist sampling scheme for signal acquisition and a spectrum hole search algorithm that exploits sparsity in the primary spectral occupancy in the frequency domain by testing a group of adjacent subbands in a single test. The sampling scheme deliberately introduces aliasing during signal acquisition, resulting in a signal that is the sum of signals from adjacent sub-bands. Energy-based hypothesis tests are used to provide an occupancy decision over the group of subbands, and this forms the basis of the proposed algorithm to find contiguous spectrum holes. We extend this framework to a multi-stage sensing algorithm that can be employed in a variety of spectrum sensing scenarios, including non-contiguous spectrum hole search. Further, we provide the analytical means to optimize the hypothesis tests with respect to the detection thresholds, number of samples and group size to minimize the detection delay under a given error rate constraint. Depending on the sparsity and SNR, the proposed algorithms can lead to significantly lower detection delays compared to a conventional bin-by-bin energy detection scheme; the latter is in fact a special case of the group test when the group size is set to 1. We validate our analytical results via Monte Carlo simulations.

Spectrum sensing via universal source coding

We consider nonparametric sequential hypothesis testing when the distribution under null hypothesis is fully known and the alternate hypothesis corresponds to some other unknown distribution. We use easily implementable universal lossless source codes to propose simple algorithms for such a setup. These algorithms are motivated from spectrum sensing application in Cognitive Radios. Universal sequential hypothesis testing using Lempel Ziv codes and Krichevsky-Trofimov estimator with Arithmetic Encoder are considered and compared for different distributions. Cooperative spectrum sensing with multiple Cognitive Radios using universal codes is also considered.

Evaluation of document binarization using eigen value decomposition

A necessary step for the recognition of scanned documents is binarization, which is essentially the segmentation of the document. In order to binarize a scanned document, we can find several algorithms in the literature. What is the best binarization result for a given document image? To answer this question, a user needs to check different binarization algorithms for suitability, since different algorithms may work better for different type of documents. Manually choosing the best from a set of binarized documents is time consuming. To automate the selection of the best segmented document, either we need to use ground-truth of the document or propose an evaluation metric. If ground-truth is available, then precision and recall can be used to choose the best binarized document. What is the case, when ground-truth is not available? Can we come up with a metric which evaluates these binarized documents? Hence, we propose a metric to evaluate binarized document images using eigen value decomposition. We have evaluated this measure on DIBCO and H-DIBCO datasets. The proposed method chooses the best binarized document that is close to the ground-truth of the document.

Fractional Hilbert transform extensions and associated analytic signal construction

The analytic signal (AS) was proposed by Gabor as a complex signal corresponding to a given real signal. The AS has a one-sided spectrum and gives rise to meaningful spectral averages. The Hilbert transform (HT) is a key component in Gabor's AS construction. We generalize the construction methodology by employing the fractional Hilbert transform (FrHT), without going through the standard fractional Fourier transform (FrFT) route. We discuss some properties of the fractional Hilbert operator and show how decomposition of the operator in terms of the identity and the standard Hilbert operators enables the construction of a family of analytic signals. We show that these analytic signals also satisfy Bedrosian-type properties and that their time-frequency localization properties are unaltered. We also propose a generalized-phase AS (GPAS) using a generalized-phase Hilbert transform (GPHT). We show that the GPHT shares many properties of the FrHT, in particular, selective highlighting of singularities, and a connection with Lie groups. We also investigate the duality between analyticity and causality concepts to arrive at a representation of causal signals in terms of the FrHT and GPHT. On the application front, we develop a secure multi-key single-sideband (SSB) modulation scheme and analyze its performance in noise and sensitivity to security key perturbations. (C) 2013 Elsevier B.V. All rights reserved.

Multivalent Cu-Doped ZnO Nanoparticles with Full Solar Spectrum Absorbance and Enhanced Photoactivity

Full solar spectrum absorbers are widely pursued for applications related to photocatalysis and photovoltaics. Here we report multivalent Cu-doped ZnO nanoparticles which exhibit full solar spectrum absorbance and high photoactivity. Metathesis-based, green-chemical approaches with synthesis yield of similar to 100% are used. Cu incorporation in ZnO results in an increase of average solar spectrum absorbance from a mere 0.4% to 34%. On the other hand, (Zn, Cu)0 composites result in materials with up to 64% average solar spectrum absorbance. Doped systems operate well under both visible and UV illumination. The nanomaterials prepared are characterized by using X-ray powder diffraction (XRD), scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET) surface area analysis, and X-ray photoelectron spectroscopy (XPS). Photocatalysts explored have particle sizes >= 50 nm. This is deliberately done in order to avoid the nanotoxic size regime of ZnO. Despite the large particle size and low specific surface area (<20 m(2).g(-1)), the best catalyst reported here compare favorably with recent reports on ZnO based systems. Using X-photoelectron spectroscopy and synthesis property correlations, we infer that the presence of multivalent Cu (most likely in the form of Cu1+delta) on ZnO surface is responsible for the observed photoactivity enhancement.

Scaling of pressure spectrum in turbulent boundary layers

Scaling of pressure spectrum in zero-pressure-gradient turbulent boundary layers is discussed. Spatial DNS data of boundary layer at one time instant (Re-theta = 4500) are used for the analysis. It is observed that in the outer regions the pressure spectra tends towards the -7/3 law predicted by Kolmogorov's theory of small-scale turbulence. The slope in the pressure spectra varies from -1 close to the wall to a value close to -7/3 in the outer region. The streamwise velocity spectra also show a -5/3 trend in the outer region of the flow. The exercise carried out to study the amplitude modulation effect of the large scales on the smaller ones in the near-wall region reveals a strong modulation effect for the streamwise velocity, but not for the pressure fluctuations. The skewness of the pressure follows the same trend as the amplitude modulation coefficient, as is the case for the velocity. In the inner region, pressure spectra were seen to collapse better when normalized with the local Reynolds stress (-(u'v') over bar) than when scaled with the local turbulent kinetic energy (q(2) = (u'(2)) over bar + (v'(2)) over bar + (w'(2)) over bar)

A VARIABLE SWITCHING FREQUENCY PWM TECHNIQUE FOR INVERTER-FED INDUCTION MOTOR TO ACHIEVE SPREAD SPECTRUM

Voltage Source Inverter (VSI) fed induction motors are widely used in variable speed applications. For inverters using fixed switching frequency PWM, the output harmonic spectra are located at a few discrete frequencies. The ac motordrives powered by these inverters cause acoustic noise. This paper proposes a new variable switching frequency pwm technique and compares its performance with constant switching frequency pwm technique. It is shown that the proposed technique leads to spread spectra of voltages and currents. Also this technique ensures that no lower order harmonics are present and the current THD is comparable to that of fixed switching frequency PWM and is even better for higher modulation indices.

Fourier spectrum based extraction of an equivalent trap state density in indium gallium zinc oxide transistors

Segregating the dynamics of gate bias induced threshold voltage shift, and in particular, charge trapping in thin film transistors (TFTs) based on time constants provides insight into the different mechanisms underlying TFTs instability. In this Letter we develop a representation of the time constants and model the magnitude of charge trapped in the form of an equivalent density of created trap states. This representation is extracted from the Fourier spectrum of the dynamics of charge trapping. Using amorphous In-Ga-Zn-O TFTs as an example, the charge trapping was modeled within an energy range of Delta E-t approximate to 0.3 eV and with a density of state distribution as D-t(Et-j) = D-t0 exp(-Delta E-t/kT) with D-t0 = 5.02 x 10(11) cm(-2) eV(-1). Such a model is useful for developing simulation tools for circuit design. (C) 2014 AIP Publishing LLC.

Thermal Decomposition of Propargyl Alcohol: Single Pulse Shock Tube Experimental and ab Initio Theoretical Study

Thermal decomposition of propargyl alcohol (C3H3OH), a molecule of interest in interstellar chemistry and combustion, was investigated using a single pulse shock tube in the temperature ranging from 953 to 1262 K. The products identified include acetylene, propyne, vinylacetylene, propynal, propenal, and benzene. The experimentally observed overall rate constant for thermal decomposition of propargyl alcohol was found to be k = 10((10.17 +/- 0.36)) exp(-39.70 +/- 1.83)/RT) s(-1) Ab initio theoretical calculations were carried out to understand the potential energy surfaces involved in the primary and secondary steps of propargyl alcohol thermal decomposition. Transition state theory was used to predict the rate constants, which were then used and refined in a kinetic simulation of the product profile. The first step in the decomposition is C-O bond dissociation, leading to the formation of two important radicals in combustion, OH and propargyl. This has been used to study the reverse OH propargyl radical reaction, about which there appears to be no prior work. Depending on the site of attack, this reaction leads to propargyl alcohol or propenal, one of the major products at temperatures below 1200 K. A detailed mechanism has been derived to explain all the observed products.

Approximation of Spatio-Temporal Random Processes Using Tensor Decomposition

A new representation of spatio-temporal random processes is proposed in this work. In practical applications, such processes are used to model velocity fields, temperature distributions, response of vibrating systems, to name a few. Finding an efficient representation for any random process leads to encapsulation of information which makes it more convenient for a practical implementations, for instance, in a computational mechanics problem. For a single-parameter process such as spatial or temporal process, the eigenvalue decomposition of the covariance matrix leads to the well-known Karhunen-Loeve (KL) decomposition. However, for multiparameter processes such as a spatio-temporal process, the covariance function itself can be defined in multiple ways. Here the process is assumed to be measured at a finite set of spatial locations and a finite number of time instants. Then the spatial covariance matrix at different time instants are considered to define the covariance of the process. This set of square, symmetric, positive semi-definite matrices is then represented as a third-order tensor. A suitable decomposition of this tensor can identify the dominant components of the process, and these components are then used to define a closed-form representation of the process. The procedure is analogous to the KL decomposition for a single-parameter process, however, the decompositions and interpretations vary significantly. The tensor decompositions are successfully applied on (i) a heat conduction problem, (ii) a vibration problem, and (iii) a covariance function taken from the literature that was fitted to model a measured wind velocity data. It is observed that the proposed representation provides an efficient approximation to some processes. Furthermore, a comparison with KL decomposition showed that the proposed method is computationally cheaper than the KL, both in terms of computer memory and execution time.