Baraniuk, Richard G.- Department of Electrical and Computer Engineering, Rice University

• An Architecture for Distributed Wavelet Analysis and Processing in Sensor Networks
• Sensing by Sampling Sparsity/Compressibility
• DISTRIBUTED MULTISCALE DATA ANALYSIS AND PROCESSING FOR SENSOR Raymond Wagner, Shriram Sarvotham, Hyeokho Choi, Richard Baraniuk
• A single-pixel terahertz imaging system based on compressed sensing Wai Lam Chan,a
• 1708 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 52, NO. 4, APRIL 2006 Let G = (V; E) be an instance of GNC, where V =
• EFFICIENT MACHINE LEARNING USING RANDOM PROJECTIONS Chinmay Hegde,r
• Sudocodes Fast Measurement and Reconstruction
• IEEE TRANSACTION ON INFORMATION THEORY, VOL. X, NO. X, MONTH 2009 1 Sparse Recovery from Combined Fusion Frame
• COMPRESSIVE IMAGE FUSION Tao Wan Nishan Canagarajah Alin Achim
• MITSUBISHI ELECTRIC RESEARCH LABORATORIES Cambridge, Massachusetts
• Compressive Sensing and Beyond New approaches to signal acquisition and processing
• Bounds for Signal to Noise Ratio in Analog to Information Converter Systems
• SUBMITTED TO IEEE TRANSACTIONS OF SPEECH AND AUDIO PROCESSING , VOL. XX, NO. XX, XXX-200X 1 Position and Trajectory Learning for Microphone
• 1-bit Compressive Sensing Petros Boufounos
• Connection-level Analysis and Modeling of Network Traffic Shriram Sarvotham, Rudolf Riedi, Richard Baraniuk
• APPLICATION OF COMPRESSIVE SENSING TO THE DESIGN OF WIDEBAND SIGNAL ACQUISITION RECEIVERS
• APPROXIMATIONAND COMPRESSION OF PIECEWISE SMOOTH IMAGES USING A WAVELETWEDGELET GEOMETRIC MODEL
• Vehicle Speed Estimation using Acoustic Wave Patterns Volkan Cevher, Member, IEEE, Rama Chellappa, Fellow, IEEE
• QUANTIZATION AND ERASURES IN FRAME REPRESENTATIONS PETROS T. BOUFOUNOS
• Recovery of Clustered Sparse Signals from Compressive Measurements
• ELEC 431 DIGITAL SIGNAL PROCESSING Spring 2010 Rice University
• Generalized Likelihood Ratio Detection for Functional MRI Signal Using Complex Data
• WAVELET TRANSFORMS FOR NONLINEAR SIGNAL PROCESSING Robert Nowak
• LETTER Communicated by Michael Lewicki Sparse Coding via Thresholding and Local Competition
• HIDDEN MARKOV MODELS FOR DNA SEQUENCING Petros Boufounos
• Spectroscopic Imaging using Terahertz Time-Domain Signals Timothy D. Dorney, Richard G. Baraniuk, Daniel M. Mittleman, and Robert D. Nowak
• A Prototype Hardware for Random Demodulation Based Compressive Analog-to-Digital Conversion
• Finite Range Scalar Quantization for Compressive Sensing
• DISTRIBUTED WAVELET TRANSFORM FOR IRREGULAR SENSOR NETWORK GRIDS Raymond Wagner, Hyeokho Choi, Richard Baraniuk #
• Test Whether Your Poster Can Stand Alone
• On The Problem of Simultaneous Encoding of Magnitude and Location Information
• Random Sampling for Analog-to-Information Conversion of Wideband Signals
• MANIFOLD-BASED APPROACHES FOR IMPROVED CLASSIFICATION Mark A. Davenport,r
• Neural Population Structures and Consequences for Neural Coding Don H. Johnson
• Efficient and Robust Compressed Sensing using Optimized Expander Graphs
• Modeling wireless sensor and actuator networks using frame theory
• Distributed Wavelet DeNoising for Sensor Networks Raymond Wagner
• WYNER-ZIV CODING OF MULTISPECTRAL IMAGES FOR AIRBORNE PLATFORMS Shantanu Rane, Yige Wang, Petros Boufounos and Anthony Vetro
• arXiv:0901.0760v1[cs.LG]7Jan2009 A Theoretical Analysis of Joint Manifolds
• HIGH-RESOLUTION NAVIGATION ON NON-DIFFERENTIABLE IMAGE MANIFOLDS Michael B. Wakin,r
• Neurally Plausible Sparse Coding via Competitive Algorithms Christopher J. Rozell1
• Distributed Compressed Sensing of Jointly Sparse Signals
• 0 2000 4000 6000 time (1 unit=500ms)
• Wavelet-domain Approximation and Compression of Piecewise Smooth Images
• Optimal Stimulus Coding by Neural Populations using Rate Codes Don H. Johnson and Will Ray
• Compression of Higher Dimensional Functions Containing Smooth Discontinuities
• Appl. Phys. B 68, 10851094 (1999) / DOI 10.1007/s003409900011 Applied Physics B
• IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. X, NO. XX, XXXXXX 200X 1 Causal Compensation for Erasures in Frame
• Distributed Compressed Sensing Dror Baron, Michael B. Wakin, Marco F. Duarte,
• DISTRIBUTED MULTISCALE DATA ANALYSIS AND PROCESSING FOR SENSOR Raymond Wagner, Shriram Sarvotham, Hyeokho Choi, Richard Baraniuk
• WD1 (Invited) 3:3Qpm-4:OOpm
• A Hidden Markov Tree Model for the Complex Wavelet Transform
• MULTISCALE EDGE GRAMMARS FOR COMPLEX WAVELET TRANSFORMS
• Domain Decomposition Methods for Linear Inverse Problems with Sparsity Constraints
• Compressive Sensing DNA Microarrays Wei Dai, Mona A. Sheikh, Olgica Milenkovic, and Richard G. Baraniuk
• Multiscale Geometric Image Processing Justin K. Romberg, Michael B. Wakin, and Richard G. Baraniuk
• Annual Logging Symposium, June 19-23, 2010 Formation Evaluation in the Bakken Complex Using Laboratory Core Data
• Geometric methods for wavelet-based image compression Michael Wakin, Justin Romberg, Hyeokho Choi, Richard Baraniuk
• On the Complexity Distribution of Sphere Decoding
• Analog-to-Information Conversion via Random Demodulation
• Representation and Compression of Multi-Dimensional Piecewise Functions Using Surflets
• Spectral Compressive Sensing Marco F. Duarte, Member, IEEE, and Richard G. Baraniuk, Fellow, IEEE
• Learning with Compressible Priors Volkan Cevher
• CAAM 335: MATRIX ANALYSIS Course Notes: Spring 2011
• The Price of Privacy and the Limits of LP Decoding Cynthia Dwork
• FAST COMPRESSIVE SAMPLING WITH STRUCTURALLY RANDOM MATRICES Thong T. Do
• How Open is Open Education? Richard Baraniuk
• Challenges and Opportunities for the Open Education Movement
• Peer Review Anew: Three Principles and a
• Connexions is a non-profit publishing project that brings textbooks and other learning materials into the Internet Age. Connexions makes high-quality educational content available to anyone,
• A wise man can learn from anyone, even from a fool, but a fool can
• Eighteen Ways to Attack a Problem Ronald N. Bracewell1
• The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition
• DIGITAL VISION Digital Object Identifier 10.1109/MSP.2007.914731
• Deterministic Constructions of Compressed Sensing Ronald A. DeVore
• A Simple Proof for Recoverability of 1-Minimization: Go Over or Under?
• A Simple Proof for Recoverability of 1-Minimization (II): the Nonnegativity Case
• A REMARK ON COMPRESSED SENSING B.S. Kashin and V.N.Temlyakov
• Stability and instance optimality for Gaussian measurements in compressed sensing
• COMBINING GEOMETRY AND COMBINATORICS: A UNIFIED APPROACH TO SPARSE SIGNAL RECOVERY
• On Theory of Compressive Sensing via 1-Minimization: Simple Derivations and Extensions
• On the size of incoherent systems J.L. Nelson a,,V.N. Temlyakov b
• Construction of a Large Class of Deterministic Sensing Matrices that Satisfy a Statistical Isometry
• Circulant and Toeplitz Matrices in Compressed Sensing Holger Rauhut
• Reed Muller Sensing Matrices and the LASSO Robert Calderbank and Sina Jafarpour
• COMPRESSED SENSING WITH SEQUENTIAL OBSERVATIONS D. M. Malioutov, S. Sanghavi, A. S. Willsky
• COMPRESSIVE WIRELESS ARRAYS FOR BEARING ESTIMATION OF SPARSE SOURCES IN ANGLE DOMAIN
• A Bayesian Analysis of Compressive Sensing Data Recovery in Wireless Sensor Networks
• Sparse Signal Recovery Using Markov Random Fields Volkan Cevher
• Recovery of Compressible Signals in Unions of Subspaces
• 1-Bit Compressive Sensing Petros T. Boufounos, Richard G. Baraniuk
• Optical imaging using binary sensors Aurelien Bourquard,1, Francois Aguet,2 and Michael Unser1
• Iterative Thresholding Algorithms Massimo Fornasier
• CAAM Technical Report TR07-07 A Fixed-Point Continuation Method for 1-Regularized
• 600 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 45, NO. 3, MARCH 1997 Sparse Signal Reconstruction from
• 760 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 51, NO. 3, MARCH 2003 Subset Selection in Noise Based on Diversity
• Sparse Signal Reconstruction via Iterative Support Detection Yilun Wang and Wotao Yin
• Practical Compressive Sensing with Toeplitz and Circulant Matrices
• GROUP SPARSE OPTIMIZATION BY ALTERNATING DIRECTION METHOD WEI DENG, WOTAO YIN, AND YIN ZHANG
• Encoding the p Ball from Limited Measurements Emmanuel Cand`es and Justin Romberg
• Weighted Euclidean Superimposed Codes for Integer Compressed Sensing
• ADAPTIVE ALGORITHMS FOR SPARSE NONLINEAR CHANNEL ESTIMATION Nicholas Kalouptsidis#
• Random Projections for Manifold Learning Chinmay Hegde
• IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 52, NO. 8, AUGUST 2004 2153 Sparse Bayesian Learning for Basis Selection
• Perspectives on Sparse Bayesian Learning David Wipf, Jason Palmer, and Bhaskar Rao
• Adaptive compressed image sensing based on wavelet-trees GE Healthcare,
• A single-pixel terahertz imaging system based on compressed sensing Wai Lam Chan,a
• Faster Imaging with Randomly Perturbed, Undersampled Spirals and |L|_1 Reconstruction , J. H. Lee1
• k-t SPARSE: High frame rate dynamic MRI exploiting spatio-temporal sparsity , J. M. Santos1
• oELSEVIER Electroencephalographyand clinicalNeurophysiology95 (1995)231-251 Neuromagnetic source imaging with FOCUSS: a recursive weighted
• IOP PUBLISHING PHYSICS IN MEDICINE AND BIOLOGY Phys. Med. Biol. 54 (2009) 27912805 doi:10.1088/0031-9155/54/9/014
• Validation System of MR Image Overlay and Other Needle Insertion Techniques
• Chinmay Hegde Information
• DYNAMIC RANGE AND COMPRESSIVE SENSING ACQUISITION RECEIVERS John R. Treichler
• 2580 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 10, OCTOBER 2010 Joint Manifolds for Data Fusion
• COMPRESSIVE SENSING OF A SUPERPOSITION OF PULSES Chinmay Hegde and Richard G. Baraniuk
• IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 4, NO. 2, APRIL 2010 445 Signal Processing With Compressive Measurements
• Compressive Sensing of Streams of Pulses Chinmay Hegde and Richard G. Baraniuk
• Model-Based Compressive Sensing for Signal Ensembles
• EXACT SIGNAL RECOVERY FROM SPARSELY CORRUPTED MEASUREMENTS
• Sparse Geodesic Paths Mark A. Davenport and Richard G. Baraniuk
• BioMed Central Page 1 of 2
• Kronecker Compressive Sensing Marco F. Duarte, Member, IEEE, and Richard G. Baraniuk, Fellow, IEEE
• Near-Optimal Bayesian Localization via Incoherence and Sparsity
• DISTRIBUTED TARGET LOCALIZATION VIA SPATIAL SPARSITY Volkan Cevher
• The Smashed Filter for Compressive Classification and Target Recognition
• MULTISCALE RANDOM PROJECTIONS FOR COMPRESSIVE CLASSIFICATION Marco F. Duarte,r
• CONTROLLING FALSE ALARMS WITH SUPPORT VECTOR MACHINES Mark A. Davenport, Richard G. Baraniuk
• Coherent Image Processing using Quaternion Wavelets Wai Lam Chan, Hyeokho Choi, Richard G. Baraniuk
• TO APPEAR IN IEEE JOURNAL OF SOLID-STATE CIRCUITS 1 ASIC Implementation of Soft-Input Soft-Output
• 290 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 26. NO. 2, FEBRUARY 2008 Soft-Output Sphere Decoding: Algorithms and
• Matrix Decomposition Architecture for MIMO Systems: Design and Implementation Trade-offs
• Sparse Signal Recovery Using Markov Random Fields Volkan Cevher
• Exact Signal Recovery from Sparsely Corrupted Measurements through the Pursuit of Justice
• TEXAS HOLD 'EM ALGORITHMS FOR DISTRIBUTED COMPRESSIVE SENSING
• DNA ARRAY DECODING FROM NONLINEAR MEASUREMENTS BY BELIEF PROPAGATION
• Compressed Sensing DNA Microarrays ECE, Rice University TREE0706
• DISTRIBUTED BEARING ESTIMATION VIA MATRIX COMPLETION Andrew Waters1
• Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors
• Democracy in Action: Quantization, Saturation, and Compressive Sensing
• Low Complexity Efficient Raw SAR Data Compression Shantanu Ranea, Petros Boufounosa, Anthony Vetroa and Yu Okadab
• Average Case Analysis of Sparse Recovery from Combined Fusion Frame Measurements
• Greedy Sparse Signal Reconstruction From Sign Measurements
• A Simple Proof that Random Matrices are Democratic Mark A. Davenport, Jason N. Laska, Petros T. Boufounos,
• Compressed Sensing for Fusion Frames Petros Boufounosa, Gitta Kutyniokb, and Holger Rauhutc
• Post-Silicon Timing Characterization by Compressed Sensing
• SPARSE SIGNAL RECONSTRUCTION FROM NOISY COMPRESSIVE MEASUREMENTS USING CROSS VALIDATION
• GENERATING BINARY PROCESSES WITH ALL-POLE SPECTRA Petros Boufounos
• Signal Processing for DNA Sequencing Petros T. Boufounos
• Sparse Signal Reconstruction from
• Quantization and Erasures on Frame Representations Petros Boufounos
• Sharp thresholds for high-dimensional and noisy recovery
• Efficient Compressive Sensing with Deterministic Guarantees Using Expander
• NECESSARY AND SUFFICIENT CONDITIONS FOR SPARSITY PATTERN RECOVERY 1 Necessary and Sufficient Conditions for
• Compressed Sensing over the Grassmann Manifold: A Unified Analytical Framework
• COMPRESSIVE SENSING BY RANDOM CONVOLUTION JUSTIN ROMBERG
• Instance-optimality in Probability with an 1-Minimization Decoder
• arXiv:0911.0736v1[math.NA]4Nov2009 A Simple Proof that Random Matrices are Democratic
• In Situ Compressive Sensing Lawrence Carin, Dehong Liu, and Bin Guo
• Recovery of Jointly Sparse Signals from Few Random Projections
• Sparse Event Detection in Wireless Sensor Networks using Compressive Sensing
• Forward sequential algorithms far best basis S.F.Cotter, J.Adler, B.D.Raa and K.Kreutz-Dclgado
• RLS-WEIGHTED LASSO FOR ADAPTIVE ESTIMATION OF SPARSE SIGNALS Daniele Angelosante
• Orthogonal Super Greedy Algorithm and Applications in Compressed Sensing
• Information-theoretic bounds on sparsity recovery in the high-dimensional and noisy setting
• Performance Limits of Matching Pursuit Algorithms Department of Electrical and Computer Eng.
• Compressed Sensing Reconstruction via Belief Propagation
• AN ARCHITECTURE FOR COMPRESSIVE IMAGING Michael B. Wakin, Jason N. Laska, Marco F. Duarte, Dror Baron
• Application of "Compressed Sensing" for Rapid MR Imaging
• IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. XX, NO. XX, JANUARY XXXX 1 Highly Undersampled Magnetic Resonance Image
• PRACTICAL NONCONVEX COMPRESSIVE SENSING RECONSTRUCTION OF HIGHLY-ACCELERATED 3D PARALLEL MR ANGIOGRAMS
• E-mail k2i@rice.edu | Office 713-348-5823 | Ken Kennedy Institute for Information Technology Rice University MS-39 | 6100 Main St | Houston, TX 77005 | www.k2i.rice.edu
• Ken Kennedy Institute for Information Technology I Proposal Solicitation
• DIGITAL VISION Digital Object Identifier 10.1109/MSP.2007.914729
• A Negative Result Concerning Explicit Matrices With The Restricted Isometry Property
• Analysis of the Statistical Restricted Isometry Property for Deterministic Sensing Matrices Using
• On the incoherence of noiselet and Haar bases Tomas Tuma, Paul Hurley
• On performance of greedy algorithms Vladimir N. Temlyakova
• IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. XX, NO. XX, JANUARY XXXX 1 Relaxed Conditions for Sparse Signal Recovery
• IEEE TRANSACTIONS ON SIGNAL PROCESSING 1 Recovering sparse signals with a certain family of
• June 19, 2008 18:38 World Scientific Review Volume -9in x 6in Bookchapter Physics-Based Channel Models and Fundamental Limits
• Average Case Analysis of Multichannel Sparse Recovery Using Convex Relaxation
• Accelerated Projected Gradient Method for Linear Inverse Problems with Sparsity Constraints
• IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 53, NO. 7, JULY 2005 2477 Sparse Solutions to Linear Inverse Problems
• A Fast Reconstruction Algorithm for Deterministic Compressive Sensing using Second Order
• Subspace Pursuit for Compressive Sensing: Closing the Gap Between Performance and Complexity
• Compressed sensing by inverse scale space and curvelet thresholding
• Affine Scaling Transformation Algorithms for Harmonic Retrieval in a Compressive Sampling Framework.
• RECONSTRUCTION IN COMPRESSIVE SENSING USING AFFINE SCALING TRANSFORMATIONS WITH VARIABLE-p DIVERSITY MEASURE
• Sublinear Compressive Sensing Reconstruction via Belief Propagation Decoding
• IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. XX, NO. XX, MONTH 2010 1 An adaptive greedy algorithm with application to
• SPARLS: The Sparse RLS Algorithm Behtash Babadi, Nicholas Kalouptsidis and Vahid Tarokh
• Weighted Superimposed Codes and Constrained Integer Compressed Sensing
• IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 57, NO. 3, MARCH 2009 1233 of the first summand. Taking (1) into account, we can distinguish two
• Optimized Projections for Compressed Sensing Michael Elad
• Compressed Learning: Universal Sparse Dimensionality Reduction and Learning in the Measurement
• Bayesian Compressive Sensing Shihao Ji, Ya Xue, and Lawrence Carin
• 2140 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 54, NO. 6, JUNE 2006 Oversampled A/D Conversion and Error-Rate
• 1053-5888/08/$25.002008IEEE IEEE SIGNAL PROCESSING MAGAZINE [83] MARCH 2008 DIGITAL VISION
• Rapid MR Imaging with "Compressed Sensing" and Randomly Under-Sampled 3DFT Trajectories , D. L. Donoho2
• Magnetic Resonance in Medicine 61:103116 (2009) k-t FOCUSS: A General Compressed Sensing Framework
• Accelerated Three-Dimensional Upper Airway MRI Using Compressed Sensing
• Coding vs. Packet Retransmission over Noisy Channels
• 2005 Conference on Information Sciences and Systems, The Johns Hopkins University, March 1618, 2005 Non-Asymptotic Performance of Symmetric Slepian-Wolf Coding
• cross-traffic T 2T 3T 4T time
• A Network Application Programming Interface for Data Processing in Sensor Networks
• A MULTISCALE DATA REPRESENTATION FOR DISTRIBUTED SENSOR NETWORKS Raymond Wagner, Shriram Sarvotham, Richard Baraniuk
• A multiscale data representation for distributed sensor networks: Proofs of basic characteristics and error bounds
• RICE UNIVERSITY Distributed Image Compression in Camera Networks
• Image Super-Resolution for Improved Automatic Target Recognition
• DISTRIBUTED IMAGE COMPRESSIONFOR SENSORNETWORKS USING CORRESPONDENCEANALYSISAND SUPER-RESOLUTION
• SIAM J. DISCRETE MATH. c 2007 Society for Industrial and Applied Mathematics Vol. 21, No. 1, pp. 199219
• Under United States copyright law, transfer of copyright from the author(s) must be explicitly stated in writing to enable the publisher to distribute or publish the work to the fullest extent. The following copyright transfer
• 1 Dynamical Systems Given A Rnn
• 15-Sep-04 1 How to Prepare and PresentHow to Prepare and Present
• Presentation Critique for ___________________ Avg Good Great
• Rapid FPGA Delay Characterization Using Clock Synthesis and Sparse Sampling Mehrdad Majzoobi, Eva Dyer, Ahmed Elnably, and Farinaz Koushanfar
• IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 56, NO. 9, SEPTEMBER 2010 4395 Analysis of Orthogonal Matching Pursuit
• Rice University Random Observations on Random Observations
• Acoustic Sensor Network Design for Position Volkan Cevher
• 2752 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 55, NO. 6, JUNE 2007 Regression Level Set Estimation Via Cost-Sensitive
• The Smashed Filter for Compressive Classification and Target Recognition
• RICE UNIVERSITY Error Control for Support Vector Machines
• LEARNING MINIMUM VOLUME SETS WITH SUPPORT VECTOR MACHINES Mark A. Davenport, Richard G. Baraniuk
• Detection and Estimation with Compressive Measurements
• Theory and Implementation of an Analog-to-Information Converter
• Analog-to-Information Conversion via Random Demodulation
• A New Compressive Imaging Camera Architecture using Optical-Domain Compression
• Quantization of Sparse Representations Petros Boufounos, Richard Baraniuk
• Compressive Sensing Recovery of Spike Trains Using A Structured Sparsity Model
• Random Projections for Manifold Learning: Proofs and Analysis
• COMPRESSIVE SENSING FOR SENSOR CALIBRATION Volkan Cevher and Richard Baraniuk
• PERFORMANCE LIMITS FOR JOINTLY SPARSE SIGNALS VIA GRAPHICAL MODELS
• WAVELET-DOMAIN COMPRESSIVE SIGNAL RECONSTRUCTION USING A HIDDEN MARKOV TREE MODEL
• Theoretical performance limits for jointly sparse signals
• IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 15, NO. 5, MAY 2006 1071 Wavelet-Domain Approximation and Compression
• Analysis of the DCS one-stage Greedy Algorothm for Common Sparse Supports
• Fast Reconstruction of Piecewise Smooth Signals from Incoherent Projections
• Joint Sparsity Models for Distributed Compressed Sensing
• Compressing Piecewise Smooth Multidimensional Functions Using Surflets: Rate-Distortion Analysis
• Phase and Magnitude Perceptual Sensitivities in Nonredundant Complex Wavelet Representations
• Compressive Phase Retrieval Matthew L. Moravec,r Justin K. Romberg,g and Richard G. Baraniukr
• Geometric Toolsfor Image Compression Michael Wukin,Justin Romberg, Hyeokho Choi, Richard Buruniuk
• 1056 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 10, NO. 7, JULY 2001 Bayesian Tree-Structured Image Modeling Using
• Adding Linguistic Constraints to Document Image Decoding: Comparing the Iterated Complete Path and Stack Algorithms
• A WAVELET-BASED STATISTICAL MODEL FOR IMAGE RESTORATION Yi Wan and Robert D. Nowak
• WAVELET-BASED DENOISING USING HIDDEN MARKOV MODELS M. Jaber Borran and Robert D. Nowak
• A NEW BAYESIAN MODEL AVERAGING FRAMEWORK FOR WAVELET-BASED SIGNAL PROCESSING
• Quasi-Circular Rotation Invariance in Image Denoising Yi Wan and Robert D. Nowak
• 666 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 8, NO. 5, MAY 1999 Wavelet-Domain Filtering
• A Bayesian Multiscale Approach to Joint Image Restoration and Edge Detection
• 1852 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 47, NO. 7, JULY 1999 Wavelet-Based Transformations
• Adaptive Wavelet Transforms via Lifting Roger Claypoole,y
• Multiscale Modeling and Estimation of Poisson Processes with Application to Photon-limited Imaging
• LIFTING CONSTRUCTIONOF NON-LINEAR WAVELETTRANSFORMS Roger L. c`laypoole, JI:and Richard G.Baraniuk Robert D. Nowak
• ADAPTIVE WAVELET TRANSFORMS VIA LIFTING RogerL. Claypoole,JI:and Richard G.Baraniuk Robert D.Nowak
• Hidden MarkovModels for Wavelet-BasedSignalProcessing Matthew S.Crouse and Richard G. Baraniuk * Robert D.Nowak
• Wavelet Domain Filtering for Nuclear Medicine Imaging Robert D. Nowak,', David J. Nowak,2 Richard G. B a r a n i ~ k , ~and Robert S. Hellman4
• VOLTERRA FILTER IDENTIFICATION USING PENALIZED LEAST SQUARES
• Tensor Product Basis Approximations for Volterra Filters Robert D. Nowak , Student Member, IEEE, Barry D. Van Veeny, Member, IEEE,
• Wavelet-Based Decompositions for Nonlinear Signal Processing Robert D. Nowak and Richard G. Baraniuk
• Sparse coding via thresholding and local competition in neural circuits
• LOCALLY COMPETITIVE ALGORITHMS FOR SPARSE APPROXIMATION Christopher Rozell, Don Johnson, Richard Baraniuk
• Favorable Recording Criteria for Spike Sorting Mona A. Sheikh and Don H. Johnson
• ANALYSIS OF NOISE REDUCTION IN REDUNDANT EXPANSIONS UNDER DISTRIBUTED PROCESSING REQUIREMENTS
• signal processing education IEEE SIGNAL PROCESSING MAGAZINE10 SEPTEMBER 2004
• Toward a Theory of Information Processing Sinan Sinanovic and Don H. Johnson
• AN INFORMATION PROCESSING APPROACH TO DISTRIBUTED DETECTION Michael A. Lexa and Don H. Johnson
• Scanning Our Past Origins of the Equivalent Circuit Concept: The Current-Source Equivalent
• Information Processing During Transient Responses in the Crayfish Visual System
• When does interval coding occur? Don H. Johnson and Raymon M. Glantz
• Dialogue Concerning Neural Coding and Information Theory Don H. Johnson
• Scanning Our Past Origins of the Equivalent Circuit Concept: The Voltage-Source Equivalent
• ASYMPTOTIC RATES OF THE INFORMATION TRANSFER RATIO Sinan Sinanovic and Don H. Johnson
• Variable-Rate Coding with Feedback for Universal Communication Systems
• Probability Assignments with Worst-Case Coding Length Constraints
• Multiscale Connection-levelAnalysis of Network Traffic Shrirarn Sarvotham, RudolfRiedi ami Richard Baraniuk
• DIMACS WORKSHOP ON INTERNET AND WWW MEASUREMENT, MAPPING AND MODELING, 2002 1 Connection-Level Modeling of Network Traffic
• Probe Design for Compressive Sensing DNA Microarrays
• BLIND ERROR-FREE DETECTION OF TRANSFORM-DOMAIN WATERMARKS Mona A. Sheikh and Richard G. Baraniuk
• DESIGNING COMPRESSIVE SENSING DNA MICROARRAYS Mona A. Sheikh,
• On the impact of variability on the buffer dynamics in IP networks
• MULTISCALE IMAGE DISPARITY ESTIMATION USING THE QUATERNION WAVELET TRANSFORM
• 2004 International Conference on Image Processing (ICIP) QUATERNION WAVELETS FOR IMAGEANALYSIS AND PROCESSING
• DIRECTIONAL HYPERCOMPLEX WAVELETS FOR MULTIDIMENSIONAL SIGNAL ANALYSIS AND PROCESSING
• Terahertz multistatic reflection imaging Timothy D. Dorney
• Appl. Phys. B 67, 379390 (1998) Applied Physics B
• An Alternating Direction Algorithm for Matrix Completion with Nonnegative Factors
• Pareto Frontiers of Sensor Networks for Localization Volkan Cevher
• Statistical Research: Some Advice for Beginners Michael HAMADA and Randy SITTER
• Appl. Comput. Harmon. Anal. 25 (2008) 133147 www.elsevier.com/locate/acha
• Stability of l1 minimisation in compressed sensing P. Wojtaszczyk
• OPEN-CONTENT SIGNAL PROCESSINGLABORATORIES
• The Compressive Multiplexer for Multi-Channel Compressive Sensing J. P. Slavinsky,r
• A Geometric Hidden Markov Tree Wavelet Model Justin Romberg, Michael Wakin, Hyeokho Choi, Richard Baraniuk
• COMPRESSED SENSING FOR MULTI-VIEW TRACKING AND 3-D VOXEL RECONSTRUCTION
• RICE UNIVERSITY Distributed MultiScale Data Processing for Sensor
• MINIMAX SUPPORT VECTOR MACHINES Mark A. Davenport, Richard G. Baraniuk
• Imaging with Terahertz Pulses Timothy Dorney, Jon Johnson, Daniel Mittleman, Richard Baraniuk
• Compressive Sensing (CS) [Cands/Romberg/Tao, Donoho, 2005]
• Analyzing the robustness of redundant population codes in sensory and feature
• Compressive Sampling for Streaming Signals with Sparse Frequency Content
• SCALABLE INFERENCE AND RECOVERY FROM COMPRESSIVE MEASUREMENTS Mark A. Davenport and Richard G. Baraniuk
• Signal Estimation Using Wavelet-MarkovModels Matthew S. Crouse and Richard G. Baraniuk * Robert D. Nowak
• BAYESIAN WAVELET-DOMAIN IMAGE MODELING USING HIDDEN MARKOV TREES
• Implementation Models for Analog-to-Information Conversion via Random Sampling
• Wyner-Ziv Image Coding from Random Projections Shoulie Xie, Susanto Rahardja, and Zhengguo Li
• Sigma Delta Quantization for Compressive Sensing Petros Boufounos and Richard G. Baraniuk
• ACM SIGCOMM INTERNET MEASUREMENT WORKSHOP 2001 1 Connection-level Analysis and Modeling
• THE EMBEDDED TRIANGLES ALGORITHM FOR DISTRIBUTED ESTIMATION IN SENSOR NETWORKS
• Proof of Lemmas for Bearing Estimation via Matrix Completion
• arXiv:0902.4045v1[cs.IT]24Feb2009 Sparse Recovery of Positive Signals with Minimal Expansion
• 2006 Nature Publishing Group A high-performance braincomputer interface
• TO APPEAR IN IEEE TRANSACTIONS ON INFORMATION THEORY 1 Soft-Input Soft-Output Single Tree-Search
• arXiv:0901.3403v1[cs.IT]22Jan2009 Distributed Compressive Sensing
• Variable-Rate Universal Slepian-Wolf Coding with Feedback
• Sparse Weighted Euclidean Superimposed Coding for Integer Compressed Sensing
• Examining methods for estimating mutual information in spiking neural systems
• TO APPEAR IN IEEE JOURNAL OF SOLID-STATE CIRCUITS 2011 1 Design and Implementation of a Parallel
• VARIABLE-P AFFINE SCALING TRANSFORMATION ALGORITHMS FOR IMPROVED COMPRESSIVE SENSING.
• The Multiscale Structure of Non-Differentiable Image Manifolds
• October 1, 2001 / Vol. 26, No. 19 / OPTICS LETTERS 1513 Terahertz reflection imaging using Kirchhoff migration
• IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 58, NO. 1, JANUARY 2010 269 Bayesian Compressive Sensing Via
• Coherent Multiscale Image Processing using Quaternion Wavelets
• A Wideband Compressive Radio Receiver M.A. Davenport, S.R. Schnelle, J.P. Slavinsky, R.G. Baraniuk
• Fidelity analysis of mechanically aided copying/enlarging of Jan van Eyck's
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