Hybrid Approaches in Microwave Imaging Using Quantitative Time- and Frequency-Domain Algorithms

A method for photoacoustic tomography is presented that uses line integrals over the acoustic wave field from a photoacoustic source for the reconstruction of a three-dimensional image. The line integrals are acquired with an optical line sensor based on a Mach–Zehnder interferometer. Image reconstruction is a two-step process. In the first step data from a scan of the line outside the object are used to reconstruct a linear projection of the source distribution. In the second step the inverse linear Radon transform is applied to multiple projections taken at different directions. This study focuses on the first step comparing two different open scan curves of the line detector around the object and corresponding two-dimensional reconstruction algorithms. An open curve, such as a 180° arc or an ‘L’ formed by two lines, establishes a limited view problem for which limitations exist concerning the stability of the reconstruction. Using experimental data from phantoms, time domain direct and iterative reconstruction algorithms as well as frequency domain (FD) algorithms are compared. The results indicate that although the image quality for all algorithms is similar, the FD algorithms offer much faster reconstruction speed, whereas the time domain algorithms allow for a refinement of image quality by including special correction procedures.

Convenience and safety are the key words for the automotive driving and various sensor technologies have been studied for enhanced perception of driving environments. In frequency modulated continuous wave (FMCW) radar systems, single antenna is enough for range and velocity detection of multiple targets. Multiple array antenna is needed for estimating direction of arrival(DoA). Using DoA estimation algorithm in time domain as in the conventional systems, it is difficult to distinguish vehicles lie in the same angle. In order to facilitate the enhanced angle estimation, DoA estimation algorithm is applied in frequency domain. In this paper, the method for applying multiple signal classification(MUSIC) algorithm in frequency domain is suggested and the performance is analyzed.

In this work, a method is presented to extend the convolutional perfectly matched layer (C-PML) to simulate acoustic wave propagation in elastic media with a second-order equation formulation. This non-physical layer is used at the computational edge of a finite element method algorithm in frequency domain, and a pseudo-spectral algorithm in time domain, as an absorbing boundary condition (ABC) to truncate unbounded media. Numerical results show that the C-PML ABC attenuates the outgoing surface waves more effectively than classical PML ABC as proposed by Berenger [J. Comput. Phys. 114, 195-200 (1994)] for electromagnetic waves in the case of oblique incidence, where the PML method suffers from large spurious reflections. Moreover, a modification of the proposed C-PML formulation is also discussed in order to stabilize the absorbing layer in anisotropic solids where numerical instabilities can appear.

Reed-Solomon decoders can be designed using time or frequency domain algorithms. The main advantage of the time domain method is its regular computational structure, an attractive feature for a flexible VLSI implementation. The main disadvantage of this is the relatively high computation count, hence it is important to find means of reducing the computational complexity of the time domain method. Several modifications to the time domain Reed-Solomon decoding algorithm are described for reducing its computational complexity by about 60%, as well as regular VLSI architectures for implementing the time domain algorithm. The paper concludes with a discussion of the advantages and disadvantages of the time domain decoding technique.

A novel approach of blind constant modulus algorithm (CMA) for single carrier frequency domain equalization is proposed to solve the application of CMA implemented in frequency domain, based on the constant modulus property from frequency domain to time domain. It is illustrated that this method has lower complexity than time-domain subspace algorithm has in broad wireless systems. And compared with the frequency-domain algorithm in [5], the complexity of the new idea is greatly reduced as well. In this research, the transformation of constant modulus algorithm from time domain to frequency domain is obtained under the assumption that the source signal amplitude remains 1. Therefore, the validity of equation (8) results in the conclusion that the performance of symbol error of the method would not be influenced by the transformation from time domain to frequency domain. Simulation results demonstrate it.

Sine-fit algorithms for real signals have many application areas including frequency, harmonics and amplitude estimation in power grids, ADC and DAC characterization. There are numerous methods for the estimation of parameters of sinusoidal signals, due to importance. This paper compares the performance of two outstanding algorithms, in which one of them is implemented in the time domain and the other in the frequency domain. Time domain algorithm is the well-known multi-harmonic sine-fit and frequency domain algorithm is the windowed IpDFT method. Performances of these algorithms are compared, advantages and disadvantages of the methods will be given.

The performance of ultrasonic imaging methods developed in time domain and frequency domain algorithms is investigated in this study. The sectorial synthetic aperture focusing technique (S–SAFT) outputs the flaw image by superposing the sector–scan images obtained at various measurement points in the time domain. The inverse scattering imaging method (ISIM) is based on the elastodynamic inversion in the frequency domain and reconstructs the mathematical function which represents the flaw shape. The advantages and usabilities of these methods are discussed, taking into account the practical application to the NDE field.

In this work, a solar photovoltaic (PV) array-integrated unified power quality conditioner (PV-UPQC) system is presented for power quality (PQ) improvement. Major task in the control of UPQC is generation of reference signal. The control algorithms in time domain (TD) and frequency domain (FD) do not consider DC offset present in the input signal and this got affected in the reference signal generation. A control algorithm based on modified second-order generalized integrator (MSOGI) with DC offset elimination capability is presented in this work. This MSOGI is integrated with conventional pq-theory-based control for extracting in phase fundamental and quadrature fundamental component of distorted input signal and for eliminating DC offset present in the input signal. The dynamic performance of the system is evaluated under various dynamic conditions like grid voltage sag and swell and change in solar irradiation in MATLAB Simulink. The proposed system improves power quality and generates clean energy from PV array and also enables operation of the system in the absence of grid. The total harmonic distortion (THD) of system is limited according to the IEEE-519 and IEEE-1159 standards.KeywordsPower qualityPV-UPQCSeries VSCShunt VSCDC offsetMSOGI

Magnetic Resonance Sounding (MRS) equipment for groundwater exploration has high receiving sensitivity, and it is vulnerably affected by high-level power-line harmonic when worked near the city and village. In order to effectively extract the weak signal, variable step-size LMS algorithm in frequency domain is studied to eliminate noise based on the principle of adaptive noise cancellation. Simulation results show that the algorithm can increase the signal to noise ratio more than 10dB, and the fitting error of parameters is about 10% in the condition that the frequency offset between signals and power-line harmonic is greater than 7Hz and with different SNR and water content. Therefor the variable step-size LMS algorithm in frequency domain can obtained better de-noising effect and parameter extraction accuracy.

In these recent years, Compressive Sensing (CS) is becoming an attractive topic in the field of Information Theory. It is widely used in several area including networking, image processing and digital camera. In particular, image reconstruction based on small number of measured components is known as the most useful application. In this paper, SL0 algorithm is specially used for the reconstruction process. It significantly decrease the processing time by utilizing a matrix in which the number of row is much smaller than number of column. Therefore, SL0 is known as one of the fastest and most accurate algorithm in CS. However due to ill-posed condition, if the prior information of the original image is undetermined, the reconstruction procedure of SL0 is much affected by the noise. Unfortunately, the investigation for solving this SL0 ill-posed condition is very limited therefore SL0 is not widely applied in many application. Consequently, this paper proposes a novel regularization technique for SL0 algorithm in the frequency domain. In order to reduce and constraint the space of reconstructed image, the frequency domain Tikhonov regularization technique is employed. It is shown that the quality of the reconstructed image is much better compared to the traditional algorithm under the noisy environment. The experimental result is exclusively simulated for 3 images: Lena, Sussie and Cameraman under both Gaussian and Non-Gaussian noise models (such as AWGN, Poisson noise, Salt & Pepper noise and Speckle noise) at different noise powers.

Spectrum sensing is the key and premise of cognitive radio. The current spectrum sensing methods have some failures and limitations, such as sensitivity to noise uncertainty and failure in some special modulation types. In order to cope with these problems and improve the detection performance, we propose a blind spectrum sensing method based on Petrosian's algorithm in frequency domain. It calculates the fractal dimension by Petrosian's method in frequency domain, and compares the fractal dimension with the threshold to determine whether the primary users exist or not. Monte Carlo simulations show that the proposed method can blind sense spectrum effectively. It outperforms energy detection, box dimension method and Higuchi dimension method with about 1 dB, 25 dB and 25 dB improvements respectively, and consumes much shorter detection times than them. Furthermore, it can overcome some failures and limitations of some other spectrum sensing methods, such as sensitivity to noise uncertainty and failure in some special modulation types, and can be applied to fast blind spectrum sensing in situations of noise uncertain.

This study aims to develop a practical algorithm which can detect freezing of gait(FOG) in patients with Parkinson's disease(PD). Eighteen PD patients(<TEX>$68.8{\pm}11.1yrs.$</TEX>) participated in this study, and three(<TEX>$68.7{\pm}4.0yrs.$</TEX>) of them showed FOG. We suggested two time-domain algorithms(with 1-axis or 3-axes acceleration signals) and compared them with the frequency-domain algorithm in the literature. We measured the acceleration of left foot with a 3-axis accelerometer inserted at the insole of a shoe. In the time-domain method, the root-mean-square(RMS) acceleration was calculated in a moving window of 4s and FOG was defined as the periods during which RMS accelerations located within FOG range. The parameters in each algorithm were optimized for each subject using the simulated annealing method. The sensitivity and specificity were same, i.e., <TEX>$89{\pm}8%$</TEX> for the time-domain method with 1-axis acceleration and were <TEX>$91{\pm}7%$</TEX> and <TEX>$90{\pm}8%$</TEX> for the time-domain method with 3-axes acceleration, respectively. Both performances were better in the time-domain methods than in the frequency-domain method although the results were statistically insignificant. The amount of calculation in the time-domain method was much smaller than in the frequency-domain method. Therefore it is expected that the suggested time domain algorithm would be advantageous in the systematic implementation of FOG detection.

We present a general broadband approach to blind source separation (BSS) for convolutive mixtures based on second-order statistics. This avoids several known limitations of the conventional narrowband approximation, such as the internal permutation problem. In contrast to traditional narrowband approaches, the new framework simultaneously exploits the nonwhiteness property and nonstationarity property of the source signals. Using a novel matrix formulation, we rigorously derive the corresponding time-domain and frequency-domain broadband algorithms by generalizing a known cost-function which inherently allows joint optimization for several time-lags of the correlations. Based on the broadband approach time-domain, constraints are obtained which provide a deeper understanding of the internal permutation problem in traditional narrowband frequency-domain BSS. For both the time-domain and the frequency-domain versions, we discuss links to well-known, and also, to novel algorithms that constitute special cases. Moreover, using the so-called generalized coherence, links between the time-domain and the frequency-domain algorithms can be established, showing that our cost function leads to an update equation with an inherent normalization ensuring a robust adaptation behavior. The concept is applicable to offline, online, and block-online algorithms by introducing a general weighting function allowing for tracking of time-varying real acoustic environments.

In recent years inkjet technology has a wide range of application including Flat Panel Display and Semiconductors. As compared with consumer appliances, industrial inkjet requires higher specifications in terms of dot placement accuracy and droplet volume uniformity. The stability of inkjet is a major requirement and then an in-situ monitoring system for inkjet condition is strongly required to prevent inferior products. In piezo-driven inkjet heads, the piezo actuators can be used as sensors to detect jetting failures caused by air bubbles. This might be the key technology to improve the reliability of the inkjet process. In this paper, we propose the model of jetting failure detection system and point out how the modification of the inkjet driving circuit for better jetting performance influences on the detection results. The prototype of the jetting failure detection system is developed and used to validate the detection algorithm both in time and frequency domain. This prototype and detection algorithm can realize the in-situ monitoring for all jetting due to high-speed system configuration.

The dynamical properties of structures, such as natural frequencies, damping ratios and mode shapes, can be obtained by several identification methods. Some are based on the direct signal processing in a time domain; others transform response data to the frequency domain. The development of these techniques is useful in the production of more accurate structural models; they can be also used to test the level of damage in structures (or verify their strength to support new load actions) by using experimental data. There are situations where frequency domain algorithms and conventional system identification techniques fail, do not allow adequate solution of the identification problems or become trapped in a local optimum. It is in these cases where evolutionary optimization techniques are important tools for evaluating the dynamical properties of structural systems in practical applications. This article presents a methodology to determine the dynamic properties of structures knowing their response in terms of displacement, velocities or accelerations in the time domain when they are subjected to a free vibration excitation. In order to do that, a specialized evolutionary algorithm capable of adapting its parameters to the different types of registers obtained from the dynamic time response of a structure is implemented in a robust way, making this approach useful in practical situations. A distributed real genetic algorithm (DRGA) based on an island model of different subpopulations is used to adjust a simulated response signal of a building to the real response signal. Initially, computer-generated synthetic response signals are used but, in future, the approach will be validated with signals obtained from free vibration experimental tests and will be extended to other types of dynamical excitation signals. Finally, the method will be tested with data obtained from earthquake events.