Browsing by Author "Rozell, Christopher John"

Now showing 1 - 4 of 4
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    All optical nanoscale sensor
    (2011-10-25) Halas, Nancy J.; Johnson, Don H.; Bishnoi, Sandra Whaley; Levin, Carly S.; Rozell, Christopher John; Johnson, Bruce R.; Rice University; United States Patent and Trademark Office
    A composition comprising a nanoparticle and at least one adsorbate associated with the nanoparticle, wherein the adsorbate displays at least one chemically responsive optical property. A method comprising associating an adsorbate with a nanoparticle, wherein the nanoparticle comprises a shell surrounding a core material with a lower conductivity than the shell material and the adsorbate displays at least one chemically responsive optical property, and engineering the nanoparticle to enhance the optical property of the adsorbate. A method comprising determining an optical response of an adsorbate associated with a nanoparticle as a function of a chemical parameter, and parameterizing the optical response to produce a one-dimensional representation of at least a portion of a spectral window of the optical response in a high dimensional vector space.
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    Analog system for computing sparse codes
    (2010-08-24) Rozell, Christopher John; Johnson, Don H.; Baraniuk, Richard G.; Olshausen, Bruno A.; Ortman, Robert Lowell; Rice University; United States Patent and Trademark Office
    A parallel dynamical system for computing sparse representations of data, i.e., where the data can be fully represented in terms of a small number of non-zero code elements, and for reconstructing compressively sensed images. The system is based on the principles of thresholding and local competition that solves a family of sparse approximation problems corresponding to various sparsity metrics. The system utilizes Locally Competitive Algorithms (LCAs), nodes in a population continually compete with neighboring units using (usually one-way) lateral inhibition to calculate coefficients representing an input in an over complete dictionary.
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    Analyzing dynamics and stimulus feature dependence in the information processing of crayfish sustaining fibers
    (2002) Rozell, Christopher John; Johnson, Don H.
    The sustaining fiber (SF) stage of the crayfish visual system converts analog stimulus representations to spike train signals. A recent theory quantifies a system's information processing capabilities and relates to statistical signal processing. To analyze SF responses to light stimuli, we extend a wavelet-based algorithm for separating analog input signals and spike output waveforms in composite intracellular recordings. We also present a time-varying RC circuit model to capture nonstationary membrane noise spectral characteristics. In our SF analysis, information transfer ratios are generally on the order of 10-4. The SF information processing dynamics show transient peaks followed by decay to steady-state values. A simple theoretical spike generator is analyzed analytically and shows general dynamic and steady-state properties similar to SFs. The information transfer ratios increase with spike rate and dynamic properties are due to direct spike generator dependence on input changes.
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    Distributed redundant representations in man-made and biological sensing systems
    (2007) Rozell, Christopher John; Johnson, Don H.
    The ability of a man-made or biological system to understand its environment is limited by the methods used to process sensory information. In particular, the data representation is often a critical component of such systems. Neural systems represent sensory information using distributed populations of neurons that are highly redundant. Understanding the role of redundancy in distributed systems is important both to understanding neural systems and to efficiently solving many modern signal processing problems. This thesis makes contributions to understanding redundant representations in distributed processing systems in three specific areas. First, we explore the robustness of redundant representations by generalizing existing results regarding noise-reduction to Poisson process modulation. Additionally, we characterize how the noise-reduction ability of redundant representation is weakened when we enforce a distributed processing constraint on the system. Second, we explore the task of managing redundancy in the context of distributed settings through the specific example of wireless sensor and actuator networks (WSANs). Using a crayfish reflex behavior as a guide, we develop an analytic WSAN model that implements control laws in a completely distributed manner. We also develop an algorithm to optimize the system resource allocation by adjusting the number of bits used to quantize messages on each sensor-actuator communication link. This optimal power scheduling yields several orders of magnitude in power savings over uniform allocation strategies that use a fixed number of bits on each communication link. Finally, we explore the flexibility of redundant representations for sparse approximation. Neuroscience and signal processing both need a sparse approximation algorithm (i.e., representing a signal with few non-zero coefficients) that is physically implementable in a parallel system and produces smooth coefficient time-series for time-varying signals (e.g., video). We present a class of locally competitive algorithms (LCAs) that minimize a weighted combination of mean-squared error and a coefficient cost function. LCAs produce coefficients with sparsity levels comparable to centralized algorithms while being more realistic for physical implementation. The resultant LCA coefficients for video sequences are more regular (i.e., smoother and more predictable) than the coefficients produced by existing algorithms.