Browsing by Subject "Computational sensing"
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Item Open Access Computational Mass Spectrometry(2015) Chen, Evan XuguangConventional mass spectrometry sensing has isomorphic nature, which means measure the input mass spectrum abundance function by a resemble of delta function to avoid ambiguity. However, the delta function nature of traditional mass spectrometry sensing approach imposes trade-offs between mass resolution and throughput/mass analysis time. This dissertation proposes a new field of mass spectrometry sensing which combines both computational signal processing and hardware modification to break the above trade-offs. We introduce the concept of generalized sensing matrix/discretized forward model in mass spectrometry filed. The presence of forward model can bridge the cap between sensing system hardware design and computational sensing algorithm including compressive sensing, feature/variable selection machine learning algorithms, and stat-of-art inversion algorithms.
Throughout this dissertation, the main theme is the sensing matrix/forward model design subject to the physical constraints of varies types of mass analyzers. For quadrupole ion trap systems, we develop a new compressive and multiplexed mass analysis approach mutli Resonant Frequency Excitation (mRFE) ejection which can reduce mass analysis time by a factor 3-6 without losing mass spectra specificity for chemical classification. A new information-theoretical adaptive sensing and classification framework has proposed on quadrupole mass filter systems, and it can significantly reduces the number of measurements needed and achieve a high level of classification accuracy. Furthermore, we present a coded aperture sector mass spectrometry which can yield a order-of-magnitude throughput gain without compromising mass resolution compare to conventional single slit sector mass spectrometer.
Item Open Access Sampling and Signal Estimation in Computational Optical Sensors(2007-12-14) Shankar, MohanComputational sensing utilizes non-conventional sampling mechanisms along with processing algorithms for accomplishing various sensing tasks. It provides additional flexibility in designing imaging or spectroscopic systems. This dissertation analyzes sampling and signal estimation techniques through three computational sensing systems to accomplish specific tasks. The first is thin long-wave infrared imaging systems through multichannel sampling. Significant reduction in optical system thickness is obtained over a conventional system by modifying conventional sampling mechanisms and applying reconstruction algorithms. In addition, an information theoretic analysis of sampling in conventional as well as multichannel imaging systems is also performed. The feasibility of performing multichannel sampling for imaging is demonstrated using an information theoretic metric. The second system is an application of the multichannel system for the design of compressive low-power video sensors. Two sampling schemes have been demonstrated that utilize spatial as well as temporal aliasing. The third system is a novel computational spectroscopic system for detecting chemicals that utilizes the surface plasmon resonances to encode information about the chemicals that are tested.Item Open Access Wavefront Engineering and Computational Sensing with Acoustic Metamaterials(2017) Xie, YangboAcoustic metamaterials are a family of engineered materials that can be designed to possess flexible acoustic properties. They are composed of subwavelength periodic structures that can be homogenized as effective materials within the designed frequency bands. Acoustic wave controlling devices with spatially inhomogeneous or/and anisotropic acoustic properties can be designed with metamaterials. The early versions of acoustic metamaterials generally share several drawbacks that limit their applications: relatively high loss, narrow bandwidth, as well as difficulty in fabricating multiple samples with uniform properties. In this work, we approach these issues with a family of geometry-based acoustic metamaterials and demonstrate several devices based on these building blocks with various wave manipulation functionalities. The presented acoustic metamaterial-based devices are categorized into two kinds. The first kind of devices, including negative refraction prism, planar acoustic lenses, beam-steering metasurfaces and phase acoustic holograms, control the propagation or the states of existence of acoustic waves. The second kind focuses on a reciprocal process—instead of controlling the forward propagation, the sensing signals are modulated with randomized resonant metamaterials to realize computation sensing.
Our research approach is summarized as follows: firstly, we designed various metamaterial unit cells as the building blocks, adding to the existing unit cell library. Particularly, a family of labyrinthine or space-coiling unit cells provide access to a broader materials parameters space previously inaccessible by conventional spring-mass model-based unit cell designs. Second, with the extended unit cell library, we designed thin planar wave modulation devices, including acoustic lenses and metasurfaces that can bend the acoustic beam as predicted by the Generalized Snell’s Law. Third, we extend the spatially inhomogeneous modulation from 1D to 2D by designing computer generated phase holograms. Last but not least, a metamaterial-based compressive sensor is designed and demonstrated for the localization of multiple audio sources and the separation of overlapping audio signals.