Pulsed parabolic equation model of acoustic transmission (PPEMAT)

Underwater sound is very important in the field of oceanography where it is used for remote sensing in much the same way that radar is used in atmospheric studies. One way to mathematically model sound propagation in the ocean is by using the parabolic-equation method, a technique that allows range dependent environmental parameters. More importantly, this method can model sound transmission where the source emits either a pure tone or a short pulse of sound. Based on the parabolic approximation method and using the split-step Fourier algorithm, a computer model for underwater sound propagation was designed and implemented. This computer model differs from previous models in its use of the interactive mode, structured programming, modular design, and state-of-the-art graphics displays. In addition, the model maximizes the efficiency of computer time through synchronization of loosely coupled dual processors and the design of a restart capability. Since the model is designed for adaptability and for users with limited computer skills, it is anticipated that it will have many applications in the scientific community.

Numerical calculation of diffraction coefficients from building edges using FDTD method

Finite Difference Time Domain (FDTD) Method and software are applied to obtain diffraction waves from modulated Gaussian plane wave illumination for right angle wedges and Fast Fourier Transform (FFT) is used to get diffraction coefficients in a wideband in the illuminated lit region. Theta and Phi polarization in 3-dimensional, TM and TE polarization in 2-dimensional cases are considered respectively for soft and hard diffraction coefficients. Results using FDTD method of perfect electric conductor (PEC) wedge are compared with asymptotic expressions from Uniform Theory of Diffraction (UTD). Extend the PEC wedges to some homogenous conducting and dielectric building materials for diffraction coefficients that are not available analytically in practical conditions. ^

Step and flash imprint lithography: Fabrication of patterned media for extremely high density magnetic data storage devices

Currently the data storage industry is facing huge challenges with respect to the conventional method of recording data known as longitudinal magnetic recording. This technology is fast approaching a fundamental physical limit, known as the superparamagnetic limit. A unique way of deferring the superparamagnetic limit incorporates the patterning of magnetic media. This method exploits the use of lithography tools to predetermine the areal density. Various nanofabrication schemes are employed to pattern the magnetic material are Focus Ion Beam (FIB), E-beam Lithography (EBL), UV-Optical Lithography (UVL), Self-assembled Media Synthesis and Nanoimprint Lithography (NIL). Although there are many challenges to manufacturing patterned media, the large potential gains offered in terms of areal density make it one of the most promising new technologies on the horizon for future hard disk drives. Thus, this dissertation contributes to the development of future alternative data storage devices and deferring the superparamagnetic limit by designing and characterizing patterned magnetic media using a novel nanoimprint replication process called "Step and Flash Imprint lithography". As opposed to hot embossing and other high temperature-low pressure processes, SFIL can be performed at low pressure and room temperature. Initial experiments carried out, consisted of process flow design for the patterned structures on sputtered Ni-Fe thin films. The main one being the defectivity analysis for the SFIL process conducted by fabricating and testing devices of varying feature sizes (50 nm to 1 μm) and inspecting them optically as well as testing them electrically. Once the SFIL process was optimized, a number of Ni-Fe coated wafers were imprinted with a template having the patterned topography. A minimum feature size of 40 nm was obtained with varying pitch (1:1, 1:1.5, 1:2, and 1:3). The Characterization steps involved extensive SEM study at each processing step as well as Atomic Force Microscopy (AFM) and Magnetic Force Microscopy (MFM) analysis.

A novel signal processing method for intraoperative neurophysiological monitoring in spinal surgeries

Intraoperative neurophysiologic monitoring is an integral part of spinal surgeries and involves the recording of somatosensory evoked potentials (SSEP). However, clinical application of IONM still requires anywhere between 200 to 2000 trials to obtain an SSEP signal, which is excessive and introduces a significant delay during surgery to detect a possible neurological damage. The aim of this study is to develop a means to obtain the SSEP using a much less, twelve number of recordings. The preliminary step involved was to distinguish the SSEP with the ongoing brain activity. We first establish that the brain activity is indeed quasi-stationary whereas an SSEP is expected to be identical every time a trial is recorded. An algorithm was developed using Chebychev time windowing for preconditioning of SSEP trials to retain the morphological characteristics of somatosensory evoked potentials (SSEP). This preconditioning was followed by the application of a principal component analysis (PCA)-based algorithm utilizing quasi-stationarity of EEG on 12 preconditioned trials. A unique Walsh transform operation was then used to identify the position of the SSEP event. An alarm is raised when there is a 10% time in latency deviation and/or 50% peak-to-peak amplitude deviation, as per the clinical requirements. The algorithm shows consistency in the results in monitoring SSEP in up to 6-hour surgical procedures even under this significantly reduced number of trials. In this study, the analysis was performed on the data recorded in 29 patients undergoing surgery during which the posterior tibial nerve was stimulated and SSEP response was recorded from scalp. This method is shown empirically to be more clinically viable than present day approaches. In all 29 cases, the algorithm takes 4sec to extract an SSEP signal, as compared to conventional methods, which take several minutes. The monitoring process using the algorithm was successful and proved conclusive under the clinical constraints throughout the different surgical procedures with an accuracy of 91.5%. Higher accuracy and faster execution time, observed in the present study, in determining the SSEP signals provide a much improved and effective neurophysiological monitoring process.