Electrical and Computer Engineering

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    1-bit Phase Shifters for Large-Antenna Full-Duplex mmWave Communications
    (IEEE, 2020) da Silva, José Mairton Barros Jr.; Sabharwal, Ashutosh; Fodor, Gábor; Fischione, Carlo
    Millimeter-wave using large-antenna arrays is a key technological component for the future cellular systems, where it is expected that hybrid beamforming along with quantized phase shifters will be used due to their implementation and cost efficiency. In this paper, we investigate the efficacy of full-duplex mmWave communication with hybrid beamforming using low-resolution phase shifters. We assume that the self-interference can be sufficiently cancelled by a combination of propagation domain and digital self-interference techniques, without any analog self-interference cancellation. We formulate the problem of joint self-interference suppression and downlink beamforming as a mixed-integer nonconvex joint optimization problem. We propose LowRes, a near-to-optimal solution using penalty dual decomposition. Numerical results indicate that LowRes using low-resolution phase shifters perform within 3% of the optimal solution that uses infinite phase shifter resolution. Moreover, even a single quantization bit outperforms half-duplex transmissions, respectively by 29% and 10% for both low and high residual self-interference scenarios, and for a wide range of practical antenna to radio-chain ratios. Thus, we conclude that 1-bit phase shifters suffice for full-duplex millimeter-wave communications, without requiring any additional new analog hardware.
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    The 2nu-SVM: A Cost-Sensitive Extension of the nu-SVM
    (2005-12-01) Davenport, Mark A.; Digital Signal Processing (http://dsp.rice.edu/)
    Standard classification algorithms aim to minimize the probability of making an incorrect classification. In many important applications, however, some kinds of errors are more important than others. In this report we review cost-sensitive extensions of standard support vector machines (SVMs). In particular, we describe cost-sensitive extensions of the C-SVM and the nu-SVM, which we denote the 2C-SVM and 2nu-SVM respectively. The C-SVM and the nu-SVM are known to be closely related, and we prove that the 2C-SVM and 2nu-SVM share a similar relationship. This demonstrates that the 2C-SVM and 2nu-SVM explore the same space of possible classifiers, and gives us a clear understanding of the parameter space for both versions.
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    3-D Local Radon Power Spectra for Seismic Attribute Extraction
    (1998-01-15) Steeghs, Philippe; Fokkema, Jacob T; Diephuis, Gerhard; Digital Signal Processing (http://dsp.rice.edu/)
    In this paper we discuss a method for volume attribute extraction that is based on a new type of local Radon power spectrum. The new algorithm results in robust and geologically meaningful volume attributes, such as volume dip and azimuth. Seismic volume attribute analysis greatly facilitates the interpretation of large 3-D seismic data volumes. However, horizon attribute maps are generally more easy to interpret than volume attribute images, which are usually time slices or cross-sections. We show that, for dip estimation, the volume attribute image is very similar to the horizon dip map.
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    3-D segmentation and volume estimation of radiologic images by a novel, feature driven, region growing technique
    (1992) Agris, Jacob Martin; de Figueiredo, Rui J. P.
    Magnetic Resonance (MR) imaging is a 3-D, multi-slice, radiological technique that acquires multiple intensities corresponding to each voxel. The transverse relaxation time, T$\sb1$, and the axial relaxation time, T$\sb2$, are two commonly obtained intensities that tend to be orthogonal. Automated segmentation of 3-D regions is very difficult because some borders may be delineated only in T$\sb1$ images, while others are delineated only in T$\sb2$ images. Classical segmentation techniques based on either global histogram segmentation or local edge detection often fail due to the non-unique and random nature of MR intensities. A 3-D, neighborhood based, segmentation method was developed based on both spatial and intensity criteria. The spatial criterion requires that only voxels connected by an edge or face to a voxel known to be in the region be considered for inclusion. Therefore, the region "grows" outward from an initial voxel. An intensity criterion that tries to balance local and global properties must also be satisfied. It determines the vector distance between the intensity of the voxel in question and a characteristic intensity for the neighboring voxels known to be in the region. Voxel intensities within a 95% confidence interval of the characteristic intensity are considered part of the region. The kernel size used to determine the characteristic intensity determines the balance between global and local properties. The segmentation terminates when no additional voxels satisfy both spatial and error criteria. Some regions, such as the brain compartments, are highly convoluted, resulting in a large number of border voxels containing a mixture of adjoining tissues. A sub-voxel estimate of the fractional composition is necessary for accurate quantification. A least-squares estimator was derived for the fractional composition of each voxel. Additionally, a maximum likelihood estimator was derived to globally estimate the fraction for all mixture voxels. This estimator is a minimum variance estimator in contrast to the least-squares estimator. The estimation methods in conjunction with the 3-D, neighborhood based, segmentation method resulted in an automated, highly accurate, quantification technique shown to be successful even for the brain compartments. Widespread applicability of these methods was further demonstrated by segmentation of kidneys in CT images.
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    3D Geometry Coding using Mixture Models and the Estimation Quantization Algorithm
    (2002-09-01) Lavu, Sridhar; Lavu, Sridhar; Digital Signal Processing (http://dsp.rice.edu/)
    3D surfaces are used in applications such as animations, 3D object modeling and visualization. The geometries of such surfaces are often approximated using polygonal meshes. This thesis aims to compress 3D geometry meshes by using an algorithm based on normal meshes and the Estimation-Quantization (EQ) algorithm. Normal meshes are multilevel representations where finer level vertices lie in a direction normal to the local surface and therefore compress the vertex data to one scalar value per vertex. A mixture distribution model is used for the wavelet coefficients. The EQ algorithm uses the local neighborhood information and Rate-Distortion optimization to encode the wavelet coefficients. We achieve performance gains of 0.5-1dB compared to the zerotree coder for normal meshes.
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    3D microfabrication of single-wall carbon nanotube/polymer composites by two-photon polymerization lithography
    (Elsevier, 2013) Ushiba, Shota; Shoji, Satoru; Masui, Kyoko; Kuray, Preeya; Kono, Junichiro; Kawata, Satoshi
    We present a method to develop single-wall carbon nanotube (SWCNT)/polymer composites into arbitrary three-dimensional micro/nano structures. Our approach, based on two-photon polymerization lithography, allows one to fabricate three-dimensional SWCNT/polymer composites with a minimum spatial resolution of a few hundreds nm. A near-infrared femtosecond pulsed laser beam was focused onto a SWCNT-dispersed photo resin, and the laser light solidified a nanometric volume of the resin. The focus spot was three-dimensionally scanned, resulting in the fabrication of arbitrary shapes of SWCNT/polymer composites. SWCNTs were uniformly distributed throughout the whole structures, even in a few hundreds nm thick nanowires. Furthermore, we also found an intriguing phenomenon that SWCNTs were self-aligned in polymer nanostructures, promising improvements in mechanical and electrical properties. Our method has great potential to open up a wide range of applications such as micro- and nanoelectromechanical systems, micro/nano actuators, sensors, and photonics devices based on CNTs.
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    802.11b Operating in a Mobile Channel: Performance and Challenges
    (2003-09-20) Steger, Christopher; Radosavljevic, Predrag; Frantz, Patrick; Center for Multimedia Communications (http://cmc.rice.edu/)
    In the past, the worlds of wireless voice and data transmission have been largely disjoint. Voice traffic has been carried over circuit-switched cellular links, and data has been largely restricted to packet-switched wireless LANs. Now, as consumers demand higher bandwidth connections without sacrificing mobility and traffic transitions from primarily voice to data, service providers must produce what is essentially a ubiquitous wireless LAN. To this end, we have studied the effects of a mobile channel on current generation 802.11 A, B, and G wireless LAN cards to see how readily they can be applied to more challenging environments. Not surprisingly, current WLAN technology suffers from significantly degraded performance when subjected to the rigors of a mobile channel. We created emulated bi-directional peer-to-peer links in which we were able to manipulate individual channel parameters. By isolating individual propagation effects and testing several different implementations of the standards, we have discovered which channel parameters have the most significant impact on performance. For instance, the large delay spreads typical of an outdoor channel seem to produce the most deleterious effect on throughput in 802.11b. We use our observations to evaluate the viability of direct-sequence spread-spectrum systems (similar to 802.11b) versus that of OFDM systems (like 802.11a and 802.11g). Then we offer suggestions for how future systems should be adapted in order to manage these effects, and we project the ultimate limitations and possibilities for subsequent 802.11-like systems.
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    A behavioral approach to positive interpolation
    (2005) Mayo, Andrew; Antoulas, Athanasios C.
    We study interpolation by positive functions from a behavioural point of view. In particular, by considering the notion of mirror image data, the interpolation problem with passivity constraint is transformed into an unconstrained behavioural modeling one. It will be shown that the generating system for this problem has to be unitary with respect to an indefinite matrix. Using this approach, several results in the theory of interpolation by positive functions are derived in a very natural manner. The use of generating systems leads in a natural way to the recent results obtained by Byrnes et al concerning parametrizing the set of interpolants by the spectral zeros. We then apply the same approach to interpolation on the boundary.
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    A closed-loop model of the ovine cardiovascular system
    (2003) Qian, Junhui; Clark, John W., Jr.
    The conscious sheep is an important large animal model for the study of human cardiovascular and cardiopulmonary system. In this study we develop a closed-loop mathematical model of its cardiovascular system. A distributed approach is taken in describing the systemic circulation, which is divided into cerebral, coronary, foreleg, thoracic, abdominal, and hind-limb circulations. Nonlinear aspects of the systemic venous system are described, which include nonlinear pressure-volume characteristics of small and large veins and pressure-operated valves in large veins. The complete integrated model mimics typical steady-state hemodynamic data in the supine position. It is also used to predict the blood volume shifts and hemodynamic changes that accompany standing up. These include the short-term neurally mediated cardiovascular response to the orthostatic stress. Additional studies predict the circulatory response to an increased afterload (balloon inflation) presented to the right ventricle. This model is further used to predict the response of the ovine cardiovascular system to the implantation of the PAL (Para-corporeal Artificial Lung device and to test the putative effectiveness of different PAL device designs.
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    A coding theoretic approach to image segmentation
    (2001) Ndili, Unoma Ifeyinwa; Nowak, Robert D.
    Using a coding theoretic approach, we achieve unsupervised image segmentation by implementing Rissanen's concept of Minimum Description Length (MDL) for estimating piecewise homogeneous regions in images. MDL offers a mathematical foundation for balancing brevity of descriptions against their fidelity to the data by penalizing overly complex representations. Our image model is a Gaussian random field whose mean and variance functions are piecewise constant. The image pixels are conditionally independent and Gaussian, given the mean and variance functions. Our model is aimed at identifying regions of constant intensity (mean) and texture (variance). We adopt a multi-scale encoding approach to the segmentation problem, and develop two different schemes. One algorithm is based on an adaptive (greedy) rectangular partitioning, while the second algorithm is an optimally-pruned wedgelet-decorated dyadic partitioning scheme. We compare the two algorithms with the more common signal plus constant noise schemes, which account for variations in mean only. We explore applications of our algorithms on Synthetic Aperture Radar (SAR) imagery. Based on our segmentation scheme, we implement a robust Constant False alarm Rate (CFAR) detector towards Automatic Target Recognition (ATR) on Laser Radar (LADAR) and Infra-Red (IR) images.
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    A Compressive Phase-Locked Loop
    (2011) Schnelle, Stephen; Baraniuk, Richard G.
    We develop a new method for tracking narrowband signals acquired through compressive sensing, called the compressive sensing phase-locked loop (CS-PLL). The CS-PLL enables one to track oscillating signals in very large bandwidths using a small number of measurements. Not only does the CS-PLL potentially operate below the Nyquist rate, it can extract phase and frequency information without the computational complexity normally associated with compressive sensing signal re-construction. The CS-PLL has a wide variety of applications, including but not limited to communications, phase tracking, robust control, sensing, and FM demodulation. In particular we emphasize the advantages of using this system in wideband surveillence systems. Our design modifies classical PLL designs to operate with CS-based sampling systems. Performance results are shown for PLLs operating on both real and complex data. In addition to explaining general performance tradeoffs, implementations using several different CS sampling systems are explored.
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    A Context-Aware Trust Framework for Resilient Distributed Cooperative Spectrum Sensing in Dynamic Settings
    (IEEE, 2017) Vosoughi, Aida; Cavallaro, Joseph R.; Marshall, Alan
    Cognitive radios enable dynamic spectrum access where secondary users (SUs) are allowed to operate on the licensed spectrum bands on an opportunistic noninterference basis. Cooperation among the SUs for spectrum sensing is essential for environments with deep shadows. In this paper, we study the adverse effect of insistent spectrum sensing data falsification (ISSDF) attack on iterative distributed cooperative spectrum sensing. We show that the existing trust management schemes are not adequate in mitigating ISSDF attacks in dynamic settings where the primary user (PU) of the band frequently transitions between active and inactive states. We propose a novel context-aware distributed trust framework for cooperative spectrum sensing in mobile cognitive radio ad hoc networks (CRAHN) that effectively alleviates different types of ISSDF attacks (Always-Yes, Always-No, and fabricating) in dynamic scenarios. In the proposed framework, the SU nodes evaluate the trustworthiness of one another based on the two possible contexts in which they make observations from each other: PU absent context and PU present context. We evaluate the proposed context-aware scheme and compare it against the existing context-oblivious trust schemes using theoretical analysis and extensive simulations of realistic scenarios of mobile CRAHNs operating in TV white space. We show that in the presence of a large set of attackers (as high as 60% of the network), the proposed context-aware trust scheme successfully mitigates the attacks and satisfy the false alarm and missed-detection rates of 10−2 and lower. Moreover, we show that the proposed scheme is scalable in terms of attack severity, SU network density, and the distance of the SU network to the PU transmitter.
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    A Data and Platform-Aware Framework For Large-Scale Machine Learning
    (2015-04-24) Mirhoseini, Azalia; Koushanfar, Farinaz; Aazhang, Behnaam; Baraniuk, Richard; Jermaine, Christopher
    This thesis introduces a novel framework for execution of a broad class of iterative machine learning algorithms on massive and dense (non-sparse) datasets. Several classes of critical and fast-growing data, including image and video content, contain dense dependencies. Current pursuits are overwhelmed by the excessive computation, memory access, and inter-processor communication overhead incurred by processing dense data. On the one hand, solutions that employ data-aware processing techniques produce transformations that are oblivious to the overhead created on the underlying computing platform. On the other hand, solutions that leverage platform-aware approaches do not exploit the non-apparent data geometry. My work is the first to develop a comprehensive data- and platform-aware solution that provably optimizes the cost (in terms of runtime, energy, power, and memory usage) of iterative learning analysis on dense data. My solution is founded on a novel tunable data transformation methodology that can be customized with respect to the underlying computing resources and constraints. My key contributions include: (i) introducing a scalable and parametric data transformation methodology that leverages coarse-grained parallelism in the data to create versatile and tunable data representations, (ii) developing automated methods for quantifying platform-specific computing costs in distributed settings, (iii) devising optimally-bounded partitioning and distributed flow scheduling techniques for running iterative updates on dense correlation matrices, (iv) devising methods that enable transforming and learning on streaming dense data, and (v) providing user-friendly open-source APIs that facilitate adoption of my solution on multiple platforms including (multi-core and many-core) CPUs and FPGAs. Several learning algorithms such as regularized regression, cone optimization, and power iteration can be readily solved using my APIs. My solutions are evaluated on a number of learning applications including image classification, super-resolution, and denoising. I perform experiments on various real-world datasets with up to 5 billion non-zeros on a range of computing platforms including Intel i7 CPUs, Amazon EC2, IBM iDataPlex, and Xilinx Virtex-6 FPGAs. I demonstrate that my framework can achieve up to 2 orders of magnitude performance improvement in comparison with current state-of-the-art solutions.
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    A distinct population of heterogeneously color-tuned neurons in macaque visual cortex
    (AAAS, 2021) Nigam, Sunny; Pojoga, Sorin; Dragoi, Valentin
    Color is a key feature of natural environments that higher mammals routinely use to detect food, avoid predators, and interpret social signals. The distribution of color signals in natural scenes is widely variable, ranging from uniform patches to highly nonuniform regions in which different colors lie in close proximity. Whether individual neurons are tuned to this high degree of variability of color signals is unknown. Here, we identified a distinct population of cells in macaque visual cortex (area V4) that have a heterogeneous receptive field (RF) structure in which individual subfields are tuned to different colors even though the full RF is only weakly tuned. This spatial heterogeneity in color tuning indicates a higher degree of complexity of color-encoding mechanisms in visual cortex than previously believed to efficiently extract chromatic information from the environment. Diverse color tuning in V4 receptive fields points to its possible role in encoding complex color stimuli in natural environment. Diverse color tuning in V4 receptive fields points to its possible role in encoding complex color stimuli in natural environment.
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    A dual acousto-optic laser scanning microscope system for the study of dendritic integration: Design, construction, and preliminary results
    (2003) Iyer, Vijay; Saggau, Peter
    Recent research has highlighted the vital role played by dendrites in effecting the computational properties of single neurons in the central nervous system (CNS). An ultraviolet (UV) acousto-optic laser scanning microscope system was developed that enables UV laser pulses to be delivered to multiple user-selected sites in the microscope's specimen plane with high spatial (<10mum) and temporal (<20mus) resolution. By employing "caged" neurotransmitters, the system can effect physiologically realistic spatio-temporal patterns of "synaptic" stimulation to the dendrites of a single cultured neuron. This system was combined with a previously developed acousto-optic laser scanning system for fast, multi-site optical recording of electrical activity (Bullen et al. 1999). This combination---the "Dual Scanner"---allows the study of important dendritic questions such as the underlying mechanisms of spatial and temporal summation. This thesis describes several current outstanding questions of dendritic integration, the design and construction of the system, and some promising preliminary results.