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  • Thesis

  • Authors: Jain, Ankur;  Co-Author: 2006 (Imaging technologies such as optical coherence tomography (OCT), two-photon excitation fluorescence microscopy (TPEF), and second harmonic generation (SHG) microscopy require optical scanners to transversely scan a focused laser beam onto the tissue specimen being imaged. However, for in vivo early-cancer detection of internal organs the optical scanners must be integrated into slender endoscopes. The goal of this work is to develop millimeter-sized MEMS optical scanners packaged inside endoscopes to enable endoscopic biomedical imaging. This work reports MEMS micromirrors and microlens scanners fabricated using post-CMOS micromachining processes, which can provide large scan ranges at low driving voltages. Several 1-D and 2-D micromirror scanners have been designed, fabricated and characterized. Scanning micromirrors, as large as 1.3 by 1.1 mm2, have demonstrated optical scan angles greater than 40° at actuation voltages below 20Vdc. The maximum scanning speed of these devices is in the range of 200 to 500Hz, which is adequate for real-time bio-imaging. A new electrothermal microactuator design is reported which enables large vertical displacements (LVD). This LVD microactuator uses two sets of electrothermal bimorph thin-film beams to provide out-of-plane elevation to the micromirror, while keeping the mirror parallel to the substrate. LVD micromirrors have demonstrated large bi-directional scanning ability (>±40°) as well as large vertical piston motion (-0.5mm) at low driving voltages (<15V). A 1-D LVD micromirror has the ability to scan optical angles greater than 170° at its resonance frequency of 2.4kHz. Polymer microlenses integrated with the LVD microactuators have been developed for endoscopic optical coherence microscopy which requires microlenses to axially scan their focal planes by 0.5 to 2 mm. A modified fabrication process allows larger polymer lenses with better thermal isolation to be integrated. A maximum vertical displacement of 0.71 mm was obtained. These scanners have been packaged inside 5-mm diameter endoscopes to enable in vivo imaging. Endoscopic OCT with transverse and axial resolutions of 151.m and 1211m, respectively has been demonstrated at imaging speeds of 2 to 6 frames/second. TPEF and SHG imaging with imaging resolution greater than 1.5µm has been obtained. These results show the potential for the use of MEMS-based endoscopy for early-cancer detection.)

  • Thesis

  • Authors: Jain, Ankur;  Co-Author: 2006 (Recent years have seen a steady rise of a new class of data management systems called Data Stream Management Systems (DSMS). These systems manage rapid, high-volume data-streams with transient relations instead of static data with persistent relations. Data streams are common to applications such as network traffic and transaction monitoring systems, click-stream processors, industrial process control, and sensor networks. A DSMS operates on these continuous and time-varying data streams to facilitate on-the-fly query answering, and to support data acquisition, monitoring and analysis. In this dissertation, we present statistical stream mining solutions for effective on-line processing of streaming data. We focus research issues related to adaptive stream resource conservation and online mining in a DSMS. We have developed statistical linear and non-linear filtering techniques based on the Kalman Filter to capture temporal correlations in the streaming data. Such correlations help in stream resource conservation. We also propose techniques that capture spatial correlations between the streaming sources that further helps improving resource conservation and facilitates answering group-queries in an efficient manner. In addition to resource management and query processing, a DSMS needs to ad-dress issues related to online stream mining. Once the data stream arrives at a central server, effective mining techniques are necessary for stream analysis, before the data can be discarded. Since a stream continuously evolves with time, stream mining techniques need to be adaptive and should operate under a given memory constraint. We propose adaptive clustering solutions that use the kernel trick to capture non-linear relations in the streaming data. We also present OCODDS, a change-detection approach that can track evolutionary changes in the stream in both linear and non-linear settings. Finally, we present our techniques for effective acquisition and processing of data streams common to video sensor networks.)