Richard L. Tutwiler

High frequency piezoelectric MEMS ultrasound transducers

High-frequency ultrasound array transducers using piezoelectric thin films on larger structures are being developed for high-resolution imaging systems. The increase in resolution is achieved by a simultaneous increase in operating frequency (30 MHz to about 1 GHz) and close coupling of the electronic circuitry. Two different processing methods were explored to fabricate array transducers. In one implementation, a xylophone bar transducer was prototyped, using thin film PbZr0.52Ti0.48O3 (PZT) as the active piezoelectric layer. In the other, the piezoelectric transducer was prepared by mist deposition of PZT films over electroplated Ni posts. Because the PZT films are excited through the film thickness, the drive voltages of these transducers are low, and close coupling of the electronic circuitry is possible. A complementary metal-oxide-semiconductor (CMOS) transceiver chip for a 16-element array was fabricated in 0.35-mum process technology. The ultrasound front-end chip contains beam-forming electronics, receiver circuitry, and analog-to-digital converters with 3-Kbyte on-chip buffer memory.

Automatic Feature Extraction and Text Recognition From Scanned Topographic Maps

A system for automatic extraction of various feature layers and recognition of the text content of scanned topographic maps is presented here. Linear features which are often intersecting with the text are first extracted using a novel line representation method and a set of directional morphological operations. Other graphical objects are then removed in several stages to obtain a text-only image. A custom defect model is subsequently used to create an artificial training set for a Hidden Markov Model-based character recognition engine. Finally, the recovered text is recognized using this multifont segmentation-free optical character recognition (OCR). Extensive testing is conducted to assess the performance of different stages of the proposed system. Furthermore, our custom OCR is shown to achieve a 94% recognition rate for the extracted text, thereby outperforming a commercial OCR used as a benchmark.

CMOS Ultrasound Transceiver Chip for High-Resolution Ultrasonic Imaging Systems

The proposed CMOS ultrasound transceiver chip will enable the development of portable high resolution, high-frequency ultrasonic imaging systems. The transceiver chip is designed for close-coupled MEMS transducer arrays which operate with a 3.3-V power supply. In addition, a transmit digital beamforming system architecture is supported in this work. A prototype chip containing 16 receive and transmit channels with preamplifiers, time-gain compensation amplifiers, a multiplexed analog-to-digital converter with 3 kB of on-chip SRAM, and 50-MHz resolution time delayed excitation pulse generators has been fabricated. By utilizing a shared A/D converter architecture, the number of A/D converter and SRAM is cut down to one, unlike typical digital beamforming systems which need 16 A/D converters for 16 receive channels. The chip was fabricated in a 0.35-mum standard CMOS process. The chip size is 10 mm2, and its average power consumption in receive mode is approximately 270 mW with a 3.3-V power supply. The transceiver chip specifications and designs are described, as well as measured results of each transceiver component and initial pulse-echo experimental results are presented.

Automatic Classification of Athletes With Residual Functional Deficits Following Concussion by Means of EEG Signal Using Support Vector Machine

There is a growing body of knowledge indicating long-lasting residual electroencephalography (EEG) abnormalities in concussed athletes that may persist up to 10-year postinjury. Most often, these abnormalities are initially overlooked using traditional concussion assessment tools. Accordingly, premature return to sport participation may lead to recurrent episodes of concussion, increasing the risk of recurrent concussions with more severe consequences. Sixty-one athletes at high risk for concussion (i.e., collegiate rugby and football players) were recruited and underwent EEG baseline assessment. Thirty of these athletes suffered from concussion and were retested at day 30 postinjury. A number of task-related EEG recordings were conducted. A novel classification algorithm, the support vector machine (SVM), was applied as a classifier to identify residual functional abnormalities in athletes suffering from concussion using a multichannel EEG data set. The total accuracy of the classifier using the 10 features was 77.1%. The classifier has a high sensitivity of 96.7% (linear SVM), 80.0% (nonlinear SVM), and a relatively lower but acceptable selectivity of 69.1% (linear SVM) and 75.0% (nonlinear SVM). The major findings of this report are as follows: 1) discriminative features were observed at theta, alpha, and beta frequency bands, 2) the minimal redundancy relevance method was identified as being superior to the univariate -test method in selecting features for the model calculation, 3) the EEG features selected for the classification model are linked to temporal and occipital areas, and 4) postural parameters influence EEG data set and can be used as discriminative features for the classification model. Overall, this report provides sufficient evidence that 10 EEG features selected for final analysis and SVM may be potentially used in clinical practice for automatic classification of athletes with residual brain functional abnormalities following a concussion episode.

Improved Multi Angled Parallelism for separation of text from intersecting linear features in scanned topographic maps

Separation of the text and graphics layers in maps with dense and overlapping sets of features (e.g. topographic maps) is a challenging problem. Multi Angled Parallelism (MAP) provides an efficient tool to detect miscellaneous linear features using directional morphological operations and higher order feature representation. However, in its original formulation sides of characters, short lines, and parts of lines that pass through characters are often misclassified. This paper presents an improvement over MAP to automatically extract complete line networks with arbitrary orientation and curvature even when they pass through characters with minimal impact on the text content. The resulting text only image can then be processed for text grouping, reorientation, and recognition. The proposed algorithm does not rely on heuristics and can be easily adapted to work with maps of various scales and sources and other line drawing images by adjusting only a small number of parameters.

Using full motion 3D Flash LIDAR video for target detection, segmentation, and tracking

It is now the case that well-performing flash LIDAR focal plane array devices are commercially available. Such devices give us the ability to measure and record registered 3D point cloud sequences at video frame rates. For target detection and tracking applications this allows the processes of structure from motion or multi-view stereo reconstruction to be circumvented. This allows us to construct simple and robust real-time 3D detection and tracking systems. The goal of this work is to demonstrate for the first time a proof-of-concept system using a commercial 3D Flash LIDAR camera. The system will accomplish the detection, segmentation, and tracking of human sized objects using a combination of fundamental point cloud processing algorithms. With marginal refinement efforts the result of this work is directly applicable to perimeter surveillance and site security.

Extended character defect model for recognition of text from maps

Topographic maps contain a small amount of text compared to other forms of printed documents. Furthermore, the text and graphical components typically intersect with one another thus making the extraction of text a very difficult task. Creating training sets with a suitable size from the actual characters in maps would therefore require the laborious processing of many maps with similar features and the manual extraction of character samples. This paper extends the types of defects represented by Bairds document image degradation model in order to create pseudo randomly generated training sets that closely mimic the various artifacts and defects encountered in characters extracted from maps. Two Hidden Markov Models are then trained and used to recognize the text. Tests performed on extracted street labels show an improvement in performance from 88.4% when only the original Bairds model is used to a character recognition rate of 93.2% when the extended defect model is used for training.

Contour Line Recognition & Extraction from Scanned Colour Maps Using Dual Quantization of the Intensity Image

Automatic separation of the different layers of information in maps poses an immense challenge due to the heavy interconnectedness of these layers. This process is further complicated by the problem of mixed color pixels and aliasing induced by the scanning process. In this paper we present a new semiautomatic method to extract contour lines from scanned color images of topographic maps. In the proposed method, contour lines are removed from the image using a novel algorithm based on quantization of the intensity image followed by contrast limited adaptive histogram equalization. Unlike other interactive map feature extraction methods, in the proposed algorithm the user is involved in only one simple step of the feature extraction process and no prior knowledge about the underlying image processing steps is required. This method is incorporated as a .NET API plugin into ArcGIS (a commercially available Geographic Information System (GIS)) and its performance is tested on a number of graphics rich map samples of various sources.

Hyper-spectral content aware resizing

Image resizing is performed for many reasons in image processing. Often, it is done to reduce or enlarge an image for display. It is also done to reduce the bandwidth needed to transmit an image. Most image resizing algorithms work based on principles of spatial or spatial frequency interpolation. One drawback to these algorithms is that they are not image content aware and can fail to preserve relevant features in an image, especially during size reduction. Recently, a content aware image resizing algorithm, called seam carving, was developed. In this paper we discuss an extension of the seam carving algorithm to hyper-spectral imagery. For a hyper-spectral image with an MxN field of view and with P spectral layers, our algorithm identifies a one pixel wide path through the image field of view containing a minimum of information and then removes it. This process is repeated until the image size is reduced to the desired dimension. Information content is assessed using normalized spatial power metrics. Several such metrics have been tested with varying results. The resulting carved hyper-spectral image has the minimum reduction in information for the resizing based upon energy metrics used to quantify information. We will present the results of seam carving applied to imagery sets of: three spectra RGB imagery from a standard still camera, two spectra imagery generated synthetically, and three spectra imagery captured with VNIR, SWIR, and LWIR cameras.

Multilayer transfer matrix characterization of complex materials with scanning acoustic microscopy

A multilayer structured thin film system, such as a biomedical thin film, MEMS (Micro Electric Mechanical System)/NEMS (Nano Electric Mechanical System) devices, and semiconductors, is widely used in various fields of industries. To non-destructively evaluate the multilayer structured thin film system, a mechanical scanning acoustic reflection microscope has been well recognized as a useful tool in recent years. Especially, the V(z) curve method with the scanning acoustic microscope is used to characterize the very small area of the system. In this study, V(z) curve simulation software for simulating transducer output when we transmit an ultrasound wave into the specimen has been developed. In the software, the Thompson-Haskell transfer matrix method is applied to solve for the reflectance function. All input and output interfaces incorporated in a GUI interface for users’ convenience. Surface acoustic wave velocities are calculated from the simulated V(z) curves. For the precise calculation advanced signal processing techniques are utilized. The surface acoustic wave velocity is compared to that from an experiment with a bulk solid. We also tested the simulation’s thickness sensitivity by simulating models with different thickness in nanoscale. A series of experiments with multilayered solids are carried out and the results are compared with the simulation results. It was the first time a comparison of analytical versus experimental for V(z) curves for multilayered system were performed. For the multilayered specimen, silicon (100) is used as a substrate. Titanium (thickness: 10 nanometer) and platinum (thickness: 100 nanometer) are deposited respectively.