Phase shifting structured light illumination for range sensing involves projecting a set of grating patterns where accuracy is determined, in part, by the number of stripes. However, high pattern frequencies introduce ambiguities during phase unwrapping. This paper proposes a process for embedding a period cue into the projected pattern set without reducing the signal-to-noise ratio. As a result, each period of the high frequency signal can be identified. The proposed method can unwrap high frequency phase and achieve high measurement precision without increasing the pattern number. Therefore, the proposed method can significantly benefit real-time applications. The method is verified by theoretical and experimental analysis using prototype system built to achieve 120 fps at 640 480 resolution.

Active stereo vision is a method of 3D surface scanning involving the projecting and capturing of a series of light patterns where depth is derived from correspondences between the observed and projected patterns. In contrast, passive stereo vision reveals depth through correspondences between textured images from two or more cameras. By employing a projector, active stereo vision systems find correspondences between two or more cameras, without ambiguity, independent of object texture. In this paper, we present a hybrid 3D reconstruction framework that supplements projected pattern correspondence matching with texture information. The proposed scheme consists of using projected pattern data to derive initial correspondences across cameras and then using texture data to eliminate ambiguities. Pattern modulation data are then used to estimate error models from which Kullback-Leibler divergence refinement is applied to reduce misregistration errors. Using only a small number of patterns, the presented approach reduces measurement errors versus traditional structured light and phase matching methodologies while being insensitive to gamma distortion, projector flickering, and secondary reflections. Experimental results demonstrate these advantages in terms of enhanced 3D reconstruction performance in the presence of noise, deterministic distortions, and conditions of texture and depth contrast.

OF DISSERTATION FAULT IDENTIFICATION ON ELECTRICAL TRANSMISSION LINES USING ARTIFICIAL NEURAL NETWORKS Transmission lines are designed to transport large amounts of electrical power from the point of generation to the point of consumption. Since transmission lines are built to span over long distances, they are frequently exposed to many different situations that can cause abnormal conditions known as electrical faults. Electrical faults, when isolated, can cripple the transmission system as power flows are directed around these faults therefore leading to other numerous potential issues such as thermal and voltage violations, customer interruptions, or cascading events. When faults occur, protection systems installed near the faulted transmission lines will isolate these faults from the transmission system as quickly as possible. Accurate fault location is essential in reducing outage times and enhancing system reliability. Repairing these faulted elements and restoring the transmi...

Accurate detection and segmentation of suspicious regions within the complex and irregular tissues of the breast, as depicted with ultrasonic B scans, typically require human analysis and decision making. Tissue characterization methods for classifying suspicious regions often depend on identifying and then accurately segmenting these regions. Motivated by an ultimate goal to automate this critical identification and segmentation step for tissue characterization problems, this work examines ultrasonic signal characteristics between various regions of breast tissue broadly classified as normal tissue and breast lesions. This paper introduces a nonparametric model based on order statistics ͑OS͒ estimated from multiresolution ͑MR͒ decompositions of energy-normalized subregions. Experimental results demonstrate the classification performance of the OS-based features extracted from the tumor and normal tissue regions in multiple scans from 84 patients, which resulted in a total of 204 tumor regions ͑from 43 malignant and 161 benign͒ and 816 normal tissue regions. Performance results indicate that OS-based features achieved an area under the receiveroperator characteristic curve of 91% in the discrimination between breast lesions and surrounding normal tissues.

Breast cancer diagnosis through ultrasound tissue characterization was studied using receiver operating characteristic (ROC) analysis of combinations of acoustic features, patient age, and radiological findings. A feature fusion method was devised that operates even if only partial diagnostic data are available. The ROC methodology uses ordinal dominance theory and bootstrap resampling to evaluate and confidence intervals in simple as well as paired data analyses. The combined diagnostic feature had an of 0.96 with a confidence interval of [0 93 0 99] at a significance level of 0.05. The combined features show statistically significant improvement over prebiopsy radiological findings. These results indicate that ultrasound tissue characterization, in combination with patient record and clinical findings, may greatly reduce the need to perform biopsies of benign breast lesions.

Abstruct-This paper examines the detection of regular scatterer spacings from hackscattered ultrasound using the autoregressive (AR) cepstrum. Monte Carlo simulations present a relationship between the probability of detection and the AR model order. An example using liver tissue data supports the observations made in the simulation.

This paper considers the effects of using spectral correlation in a maximum likelihood estimator (MLE) for backscattered energy corresponding to coherent reflectors embedded in media of microstructure scatterers. The spectral autocorrelation (SAC) function is analyzed for scatterer configurations based on the regularity of the interspacing distance between scatterers. It is shown that increased regularity gives rise to significant spectral correlation between different frequencies, while scatterers that are uniformly distributed throughout a resolution cell result in no significant correlation between spectral components. This implies that when a true uniform distribution for the effective scatterers exists, the power spectral density (PSD) is sufficient to characterize their echoes. However, as the microstructure scatterer distribution becomes more regular, SAC terms between different frequencies become more significant. Experimental results compare the performance of an adaptive MLE using the SAC and PSD characterizations of the grain echoes. The MLE results for 15 A-scans from stainless steel specimens with three different grain sizes indicate an average 6-dB signal-to-noise ratio (SNR) improvement in the coherent scatterer (flat-bottom hole) echo intensities for estimators using the SAC characterization, as opposed to estimators using the PSD characterization.

An ultrasonic backscattered signal from material comprised of quasiperiodic scatterers exhibit redundancy over both its phase and magnitude spectra. This paper addresses the problem of estimating mean-scatterer spacing from the backscattered ultrasound signal using spectral redundancy characterized by the spectral autocorrelation (SAC) function. Mean-scatterer spacing estimates are compared for techniques that use the cepstmm and the SAC function. A-scan models consist of a collection of regular scatterers with Gamma distributed spacings embedded in diffuse scatterers with uniform distributed spacings. The model accounts for attenuation by convolving the frequency dependent scattering centers with a time-varying system response. Simulation results indicate that SAC-based estimates converge more reliably over smaller amounts of data than cepstrum-based estimates. A major reason for the performance advantage is the use of phase information by the SAC function, while the cepstrum uses a phaseless power spectral density that is directly affected by the system response and the presence of diffuse scattering (speckle). An example of estimating the mean-scatterer spacing in liver tissue also is presented.

Abstracf-The quasiperiodicity of regularly spaced scatterers results in characteristic patterns in the spectra of backscattered ultrasonic signals from which the mean scatterer spacing can be estimated. The mean spacing has been considered for classifying certain biological tissue. This paper addresses the problem of estimating the mean scatterer spacing from backscattered ultrasound signals using the frequency-smoothed spectral autocorrelation (SAC) function. The SAC function exploits characteristic differences between the phase spectrum of the resolvable quasiperiodic scatterers and the unresolvable uniformly distributed (diffuse) scatterers to improve estimator performance over other estimators that operate directly on the magnitude spectrum. Mean scatterer spacing estimates are compared for the frequencysmoothed SAC function and a cepstral technique using an AR model. Simulation results indicate that SAC-based estimates converge more reliably over smaller amounts of data than cepstrumbased estimates. An example of computing an estimate from liver tissue scans is also presented for the SAC function and the AR cepstrum.

This paper presents an algorithm for detecting and estimating pitch in acoustic audio signals using the Generalized Spectrum (GS). A performance evaluation of a GS-based and two classical (autocorrelation-and cepstrum-based) pitch determination algorithms was conducted on a set of wavetable-synthesized musical signals. The experiment separately evaluates the tasks of pitch detection and estimation. Pitch estimation performance is presented in terms of gross pitch errors (indicating algorithm stability) and meansquared fine pitch error. Pitch detection performance is evaluated by a Receiver Operating Characteristic analysis of the detection statistics. Results demonstrate that the GS-based estimator generally performs worse than the autocorrelation and cepstrum-based methods. However, the GSbased method performed consistently better for the detection problem, especially at low signal-to-noise values.