Jafar Saniie

Model-based estimation of ultrasonic echoes. Part I: Analysis and algorithms

The patterns of ultrasonic backscattered echoes represent valuable information pertaining to the geometric shape, size, and orientation of the reflectors as well as the microstructure of the propagation path. Accurate estimation of the ultrasonic echo pattern is essential in determining the object/propagation path properties. In this study, we model ultrasonic backscattered echoes in terms of superimposed Gaussian echoes corrupted by noise. Each Gaussian echo in the model is a nonlinear function of a set of parameters: echo bandwidth, arrival time, center frequency, amplitude, and phase. These parameters are sensitive to the echo shape and can be linked to the physical properties of reflectors and frequency characteristics of the propagation path. We address the estimation of these parameters using the maximum likelihood estimation (MLE) principle, assuming that all of the parameters describing the shape of the echo are unknown but deterministic. In cases for which noise is characterized as white Gaussian, the MLE problem simplifies to a least squares (LS) estimation problem. The iterative LS optimization algorithms when applied to superimposed echoes suffer from the problem of convergence and exponential growth in computation as the number of echoes increases. In this investigation, we have developed expectation maximization (EM)-based algorithms to estimate ultrasonic signals in terms of Gaussian echoes. The EM algorithms translate the complicated superimposed echoes estimation into isolated echo estimations, providing computational versatility. The algorithm outperforms the LS methods in terms of independence to the initial guess and convergence to the optimal solution, and it resolves closely spaced overlapping echoes.

Model-based estimation of ultrasonic echoes. Part II: Nondestructive evaluation applications

For Part I see ibid., vol.48, no.3, pp.787-802 (2001). Accurate estimation of the ultrasonic echo pattern leading to the physical property of the object is desirable for ultrasonic NDE (nondestructive evaluation) applications. In Part I of this study, we have presented a generalized parametric ultrasonic echo model, composed of a number of Gaussian echoes corrupted by noise, and algorithms for accurately estimating the parameters. In Part II of this study, we explore the merits of this model-based estimation method in ultrasonic applications. This method produces high resolution and accurate estimates for ultrasonic echo parameters, i.e., time of flight (TOF) amplitude, center frequency, bandwidth, and phase. Furthermore, it offers a solution to the deconvolution problem for restoration of the target response, i.e., ultrasonic reflection and transmission properties of materials, from the backscattered echoes. The model-based estimation method makes deconvolution possible in the presence of significant noise. It can also restore closely spaced overlapping echoes beyond the resolution of the measuring system. These properties of the estimation method are investigated in various ultrasonic applications such as transducer pulse-echo wavelet estimation, subsample time delay estimation, and thickness sizing of thin layers.

A successive parameter estimation algorithm for chirplet signal decomposition

In ultrasonic imaging systems, the patterns of detected echoes correspond to the shape, size, and orientation of the reflectors and the physical properties of the propagation path. However, these echoes often are overlapped due to closely spaced reflectors and/or microstructure scattering. The decomposition of these echoes is a major and challenging problem. Therefore, signal modeling and parameter estimation of the nonstationary ultrasonic echoes is critical for image analysis, target detection, arid object recognition. In this paper, a successive parameter estimation algorithm based on the chirplet transform is presented. The chirplet transform is used not only as a means for time-frequency representation, but also to estimate the echo parameters, including the amplitude, time-of-arrival, center frequency, bandwidth, phase, and chirp rate. Furthermore, noise performance analysis using the Cramer Rao lower bounds demonstrates that the parameter estimator based on the chirplet transform is a minimum variance and unbiased estimator for signal-to-noise ratio (SNR) as low as 2.5 dB. To demonstrate the superior time-frequency and parameter estimation performance of the chirplet decomposition, ultrasonic flaw echoes embedded in grain scattering, and multiple interfering chirplets emitted by a large, brown bat have been analyzed. It has been shown that the chirplet signal decomposition algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements. Numerical and analytical results show that the algorithm is efficient and successful in high-fidelity signal representation

Ultrasonic data compression via parameter estimation

Ultrasonic imaging in medical arid industrial applications often requires a large amount of data collection. Consequently, it is desirable to use data compression techniques to reduce data and to facilitate the analysis and remote access of ultrasonic information. The precise data representation is paramount to the accurate analysis of the shape, size, and orientation of ultrasonic reflectors, as well as to the determination of the properties of the propagation path. In this study, a successive parameter estimation algorithm based on a modified version of the continuous wavelet transform (CWT) to compress and denoise ultrasonic signals is presented. It has been shown analytically that the CWT (i.e., time/spl times/frequency representation) yields an exact solution for the time-of-arrival and a biased solution for the center frequency. Consequently, a modified CWT (MCWT) based on the Gabor-Helstrom transform is introduced as a means to exactly estimate both time-of-arrival and center frequency of ultrasonic echoes. Furthermore, the MCWT also has been used to generate a phase/spl times/bandwidth representation of the ultrasonic echo. This representation allows the exact estimation of the phase and the bandwidth. The performance of this algorithm for data compression and signal analysis is studied using simulated arid experimental ultrasonic signals. The successive parameter estimation algorithm achieves a data compression ratio of (1 5N/J), where J is the number of samples and N is the number of echoes in the signal. For a signal with 10 echoes and 2048 samples, a compression ratio of 96% is achieved with a signal-to-noise ratio (SNR) improvement above 20 dB. Furthermore, this algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements ranging from 10 to 60 dB for composite signals having SNR as low as -10 dB.

Analysis of order statistic filters applied to ultrasonic flaw detection using split-spectrum processing

Split-spectrum processing of broadband ultrasonic signals coupled with order statistic filtering has proven to be effective in improving the flaw-to-clutter ratio of backscattered signals. It is shown that an optimal rank can be obtained with a prior knowledge of flaw-to-clutter ratio and the underlying distributions. The order statistic filter performs well where the flaw and clutter echoes have good statistical separation in a given quantile region representing a particular rank (e.g. minimum, median, maximum). Order statistic filters are analyzed for the situation in which the observations do not contain equivalent statistical information. Experimental and simulated results are presented to show how effectively the order statistic filter can utilize information contained in different frequency bands to improve flaw detection.<<ETX>>

Mobile sensor data collector using Android smartphone

In this paper, we present a system using an Android smartphone that collects, displays sensor data on the screen and streams to the central server simultaneously. Bluetooth and wireless Internet connections are used for data transmissions among the devices. Also, using Near Field Communication (NFC) technology, we have constructed a more efficient and convenient mechanism to achieve an automatic Bluetooth connection and application execution. This system is beneficial on body sensor networks (BSN) developed for medical healthcare applications. For demonstration purposes, an accelerometer, a temperature sensor and electrocardiography (ECG) signal data are used to perform the experiments. Raw sensor data are interpreted to either graphical or text notations to be presented on the smartphone and the central server. Furthermore, a Java-based central server application is used to demonstrate communication with the Android system for data storage and analysis.

System-on-chip design for ultrasonic target detection using split-spectrum processing and neural networks

Ultrasonic detection and characterization of targets concealed by scattering noise is remarkably challenging. In this study, a neural network (NN) coupled to split-spectrum processing (SSP) is examined for target echo visibility enhancement using experimental measurements with input signal-to-noise ratio around 0 dB. The SSP-NN target detection system is trainable and consequently is capable of improving the target-to-clutter ratio by an average of 40 dB. The proposed system is exceptionally robust and outperforms the conventional techniques such as minimum, median, average, geometric mean, and polarity threshold detectors. For realtime imaging applications, a field-programmable gate array (FPGA)-based hardware platform is designed for system-onchip (SoC) realization of the SSP-NN target detection system. This platform is a hardware/software co-design system using parallel and pipelined multiplications and additions for highspeed operation and high computational throughput.

Fast Chirplet Transform With FPGA-Based Implementation

This letter presents a fast chirplet transform (FCT) algorithm, a computationally efficient method, for decomposing highly convoluted signals into a linear expansion of chirplets. The FCT algorithm successively estimates the chirplet parameters in order to represent a broad range of chirplet shapes, including the broadband, narrowband, symmetric, skewed, nondispersive, or dispersive. These parameters have significant physical interpretations for radar, sonar, seismic, and ultrasonic applications. For the real-time application and embedded implementation of the FCT algorithm, an FPGA-based hardware/software co-design is developed on Xilinx Virtex-II Pro FPGA development platform. Based on the balance among the system constraints, cost, and the efficiency of estimations, the performance of different algorithm implementation schemes have been explored. The developed system-on-chip successfully exhibits robustness in the chirplet transform of experimental signals. The FCT algorithm addresses a broad range of applications including velocity measurement, target detection, deconvolution, object classification, data compression, and pattern recognition.

Ultrasonic flaw detection using discrete wavelet transform for NDE applications

In this work, we analyze signal decomposition properties of discrete wavelet transform (DWT) for enhanced ultrasonic flaw detection. In wavelet signal decomposition, a collection of time-frequency representations of the signal with different resolutions is obtained. DWT allows to utilize both time and frequency domain information for compacting and decorrelating the flaw echo from clutter echoes. In this paper, we present the performance analysis of different wavelet kernels with respect to ultrasonic NDE applications and develop the wavelet selection criteria for optimal flaw detection. Experimental results indicate that DWT based flaw detection algorithms offer flaw-to-clutter ratio enhancement of 5-12 dB when the measured flaw-to-clutter ratio is 0 dB or less. DWT flaw detection system can be implemented efficiently for real time applications using reconfigurable architecture and lifting scheme.

FPGA-Based Configurable Frequency-Diverse Ultrasonic Target-Detection System

In this paper, we present field-programmable gate-array (FPGA)-based configurable architectures that are able to perform frequency-diverse target detection for real-time ultrasonic imaging. Three design methodologies are explored including the execution of the detection algorithm on an embedded microprocessor, the creation of a dedicated hardware solution, and the use of hardware/software codesign principles. In addition to the design flow, this paper presents the impact of parameter changes on the detection-algorithm performance and FPGA implementation results. Experimental studies show that the proposed configurable systems are able to meet real-time operation requirements, and the algorithm performs robustly.