James A. Storer
`
`Professor of Computer Science
`
`Computer Science Department
`Brandeis University
`Waltham, MA 02453
`storer@brandeis.edu
`
`Education:
`
`Ph.D. Princeton University, computer science (1979)
`MA. Princeton University, computer science (1977)
`BA. Cornell University, mathematics and computer science (1975)
`
`Employment History:
`Brandeis University, Professor of Computer Science (1993 — present)
`Brandeis University, Associate Professor of Computer Science (1986 — 1992)
`Harvard University, Visiting Professor of Computer Science (1987 — 1988)
`Brandeis University, Assistant Professor of Computer Science (1981 - 1986)
`Bell Laboratories at Murray Hill, MTS (1979 - 1981)
`
`Research Interests:
`
`Computer algorithms, communications and internet related computing, data compression and
`archiving (including text, images, video, and multi—media), storage and processing of large
`data sets, image retrieval, object recognition, text, image, and video processing, parallel
`computing, applications of deep learning to image analysis.
`
`Professional Activities:
`
`0 In 1991 I founded the Annual IEEE Data Compression Conference (DCC), the first major
`international conference devoted entirely to data compression, and have served as the
`conference chair since then.
`
`0 I am a member of the ACM and IEEE Computer Society. I routinely serve as referee for
`papers submitted to journals (JACM, SICOMP, Theoretical CS, J. Algorithms, Algorithms,
`Signal Processing, JPDC, Acta Inf., Algorithmicia, IPL, IPM, Theoretical CS, J. Alg.,
`Networks, IEEE J. Rob. & Aut., IEEE Trans. Inf. Theory, IEEE Trans. Comp., IEEE
`Trans. Image Proc., Proceedings of the IEEE, IBM J. of R&D, JCSS, etc.). I have served as
`an editor for Information Processing and Management, Journal of Visual Communication
`and Image Representation, and the Proceedings of the IEEE. I have served as a program
`committee member
`for various
`conferences,
`including IEEE Data Compression
`Conference, Combinatorial Pattern Matching (CPM), International Conference on String
`Processing and Information Retrieval (SPIRE), Conference on Information and Knowledge
`Management (CIKM), Sequences and Combinatorial Algorithms on Words, DAGS.
`
`o I consult in the areas of computer algorithms, data compression and communications
`(including text, image, and video), data storage and backup, cell phone, digital camera, and
`DVR technology, image and video processing, information and image retrieval, including
`providing expert services for computing technology related litigation.
`
`0 I have obtained patents and SBIR funding to engage in research projects (such as high
`speed data compression hardware)
`that were not possible within the university
`environment, but which complemented my academic research and provided practical
`experience on which to base research directions.
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`Books
`
`Hyperspectal Data Compression
`
`G. Motta, F. Rizzo, and J. A. Storer, Editors
`Springer—Verlag, www.springer.com, November 2006
`(425 pages, 6" x 9", hard—bound)
`ISBN: 0—387—28579—2
`
`This book provides a survey of recent results in the field of compression of remote sensed 3D
`data, with a particular interest in hyperspectral imagery. This material is intended to be of
`interest to researchers in a variety of areas, including multi dimensional data compression,
`remote sensing, military and aerospace image processing, homeland security, archival of large
`volumes of scientific and medical data, target detection, and image classification.
`
`The interest in remote sensing applications and platforms (including airborne and spaceborne)
`has grown dramatically in recent years. Remote sensing technology has
`shifted from
`panchromatic data (a wide range of wavelengths merged into a single response),
`through
`multispectral (a few possibly overlapping bands in the visible and infrared range with spectral
`width of 100—200nm each), to hyperspectral imagers and ultraspectral sounders, with hundreds or
`thousands of bands. In addition, the availability of airborne and spaceborne sensors has increased
`considerably, followed by the widespread availability of remote sensed data in different research
`environments, including defense, academic, and commercial.
`
`Remote sensed data present special challenges in the acquisition, transmission, analysis, and
`storage process. Perhaps most significant is the information extraction process. In most cases
`accurate analysis depends on high quality data, which comes with a price tag: increased data
`volume. For example,
`the NASA JPL’s Airborne Visible/Infrared Imaging Spectrometer
`(AVIRIS, http://aviris.jpl.nasa.gov) records the visible and the near-infrared spectrum of the
`reflected light of an area 2 to 12 kilometers wide and several kilometers long (depending on the
`duration of the flight) into hundreds of non overlapping bands. The resulting data volume
`typically exceeds 500 Megabytes per flight and it is mainly used for geological mapping, target
`recognition, and anomaly detection. On the other hand, ultraspectral sounders such as the NASA
`JPL’s Atmospheric Infrared Sounder (AIRS, http://www—airs.jpl.nasa.gov), which has recently
`become a reference in compression studies on this class of data, records thousands of bands
`covering the infrared spectrum and generates more than 12 Gigabytes of data daily. The major
`application of this sensor is the acquisition of atmospheric parameters such as temperature,
`moisture, clouds, gasses, dust concentrations, and other quantities to perform weather and
`climate forecast.
`
`Chapter 1 addresses compression architecture and reviews and compares compression methods.
`Chapter 2 through 4 focus on lossless compression (where the decompressed image must be bit
`for bit identical to the original). Chapter 5 (contributed by the editors) describes a lossless
`algorithm based on vector quantization with extensions to near lossless and possibly lossy
`compression for efficient browsing and pure pixel classification. Chapters 6 deals with near
`lossless compression while Chapter 7 considers lossy techniques constrained by almost perfect
`classification. Chapters 8 through 12 address lossy compression of hyperspectral imagery, where
`there is a tradeoff between compression achieved and the quality of the decompressed image.
`Chapter 13 examines artifacts that can arise from lossy compression.
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`An Introduction to Data Structures and Algorithms
`James A. Storer
`
`Birkhauser/ Springer, www.springer.com, February 2002
`(600 pages, 7" x 10", hard—bound)
`ISBN 0—8176—4253—6, ISBN 3—7643—4253—6
`
`A highly a highly accessible format presents algorithms with one page displays that will appeal
`to both students and teachers of computer science. The thirteen chapters include: Models of
`Computation (including Big O notation), Lists (including stacks, queues, and linked lists),
`Induction and Recursion, Trees (including self—adjusting binary search trees), Algorithms Design
`Techniques, Hashing, Heaps (including heapsort and lower bounds on sorting by comparisons),
`Balanced Trees (including 2—3 trees, red—black trees, and AVL trees), Sets Over a Small
`Universe (including on—the—fly array initialization,
`in—place permutation, bucket sorting, bit—
`vectors, and the union—find data structure), Discrete Fourier Transform (including an
`introduction to complex numbers, development of the FFT algorithm, convolutions, the DFT on
`an array of reals, the discrete cosine transform, computing the DCT with a DFT of n/2 points,
`2D DFT and DCT, and an overview of JPEG image compression), Strings (including
`lexicographic sorting, KMP / BM / Rabin—Karp / Shift—And string matching, regular expression
`pattern matching, tries, suffix tries, edit distance, Burrows—Wheeler transform, text compression
`examples), Graphs (including DFS / BFS, biconnected and strongly connected components,
`spanning trees, topological sort, Euler paths, shortest paths, transitive closure, path finding, flow,
`matching, stable marriage, NP—complete graph problems), Parallel Models of Computation
`(including the PRAM, generic PRAM simulation, the hypercube/CCC/butterfly, the mesh, and
`hardware area—time tradeoffs).
`
`0 Concepts are expressed clearly, in a single page, with the least amount of notation, and
`without the "clutter" of the syntax of a particular programming language; algorithms are
`presented with self—explanatory "pseudo—code".
`
`0 Each chapter starts with an introduction and ends with chapter notes and exercises that
`promote further learning.
`
`is not treated as a separate chapter, but is used
`0 Sorting, often perceived as rather technical,
`in many examples (including bubble sort, merge sort, tree sort, heap sort, quick sort, and
`several parallel algorithms). Lower bounds on sorting by comparisons are included with
`the presentation of heaps in the context of lower bounds for comparison based structures.
`
`0 Chapters 1-4 focus on elementary concepts, the exposition unfolding at a slower pace.
`Sample exercises with solutions are also provided. These chapters assume a reader with
`only some basic mathematics and a little computer programming experience. An
`introductory college-level course on data structures may be based on Chapters 1 -4 and the
`first half of Chapters 5 (algorithms design), 6 (hashing), and 12 (graphs).
`
`0 Chapters 5—13 progress at a faster pace. The material is suitable for undergraduates or
`graduates who need only review Chapters 1—4. A more advanced course on the design and
`analysis of algorithms may be based on these chapters.
`
`0 Chapter 13 on parallel models of computation is something of mini—book itself. The idea is
`to further teach fundamental concepts in the design of algorithms by exploring exciting
`models
`of
`computation,
`including
`the
`PRAM,
`generic
`PRAM simulation,
`HC/CCC/Butterfly,
`the mesh, and parallel hardware area—time tradeoffs (with many
`examples). A sampling of this chapter can be a fun way to end a course based on earlier
`portions of the book. In addition, a seminar style course can spends its first half covering
`this chapter in detail and then study papers from the literature.
`
`0 Apart from classroom use, this book serves as an excellent reference text on the subject of
`data structures, algorithms, and parallel algorithms. Its page—at—a—time format makes it easy
`to review material that the reader has studied in the past.
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`Data Compression: Methods and Theory
`James A. Storer
`
`Computer Science Press (a subsidiary of W. H. Freeman Press), 1988
`(419 pages, 6" x 9", hard—bound)
`ISBN 0—88175—161—8
`
`The first two chapters contain introductory material on information and coding theory. The
`remaining four chapters cover some of my data compression research performed in the
`period 1977 - 1987 (including substantial material that has not been reported elsewhere).
`Chapter 3 considers on-line textual substitution methods that employ "learning" heuristics to
`adapt to changing data characteristics. Chapter 4 considers massively parallel algorithms for
`on-line methods and their VLSI implementations. Chapter 5 considers off-line methods
`(including the NP-completeness of certain methods). Chapter 6 addresses program size
`(Kolmogorov) complexity. The appendices present source code and empirical results.
`
`Image and Text Compression
`
`James A. Storer, Editor
`Kluwer Academic Press (part of Springer), 1992
`(354 pages, 6" x 9", hard—bound)
`ISBN 0—7923—9243—4
`
`This is an edited volume of papers by leading researchers in the field; topics include: vector
`quantization, fractals, optical algorithms, arithmetic coding, context modeling, LZ methods,
`massively parallel hardware (the chapter I contributed), bounds on Huffman codes, coding
`delay, and 2D lossless compression. Also included is a 75 page bibliography of data
`compression research that I compiled specifically for this book.
`
`Proceedings Compression and Complexity
`
`B. Carpentieri, A. De Santis, U. Vaccaro, and J. A. Storer, Editors
`IEEE Computer Society Press, 1998
`(400 pages, 6" x 9", hard-bound)
`ISBN 0-8186-8132—2
`
`This is an edited volume of the papers presented at the International Conference on
`Compression and Complexity of Sequences, held in Positano, Italy in 1997.
`
`Papers address the theoretical aspects of data compression and its relationship to problems on
`sequences, and include contributions from the editors.
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`Proceedings of the Data Compression Conference
`
`James A. Storer, Co—Chair
`IEEE Computer Society Press
`1991 — present (approximately 500 pages hard—bound)
`
`I have chaired DCC since it was founded in 1991; starting in 2013 the conference leadership
`has been expanded; I am currently co-chair (with M. Marcellin, formally committee chair).
`
`The DCC proceedings are co—edited with the DCC program committee chair(s), which over
`the years has been J. Reif (1991), M. Cohn (1992—2006), M. Marcellin (2007—2012), Ali
`Bilgin & Joan Serra—Sagrista (2013—present).
`
`Each volume has ten page extended abstracts of the presentations at technical sessions and
`one page abstracts of presentations at the posters session. The call for papers states that
`topics of interest include but are not limited to:
`
`An international forum for current work on data compression for text, images,
`video, audio, and related areas. Topics of interest include but are not limited to:
`Lossless and lossy compression algorithms for specific types of data (text,
`images, multi-spectral and hyper-spectral images, palette images, video, speech,
`music, maps, instrument and sensor data, space data, earth observation data,
`graphics, 3D representations, animation, bit-maps, etc.), source coding,
`text
`compression,
`joint
`source-channel
`coding, multiple
`description
`coding,
`quantization theory, vector quantization (VQ), multiple description VQ,
`compression algorithms that employ transforms (including DCT and wavelet
`transforms), bi-level image compression, gray scale and color image compression,
`video compression, movie compression, geometry compression, speech and audio
`compression, compression of multi-spectral and hyper-spectral data, compression
`of science, weather, and space data, source coding in multiple access networks,
`parallel compression algorithms and hardware,
`fractal based compression
`methods, error resilient compression, adaptive compression algorithms, string
`searching and manipulation used in compression applications, closest-match
`retrieval in compression applications, browsing and searching compressed data,
`content based retrieval employing compression methods, minimal length encoding
`and applications to learning, system issues relating to data compression (including
`error control, data security, indexing, and browsing), medical imagery storage and
`transmission, compression of web graphs and related data structures, compression
`applications and issues for computational biology, compression applications and
`issues for the internet, compression applications and issues for mobile computing,
`applications of compression to file distribution and software updates, applications
`of compression to files storage and backup systems, applications of compression
`to data mining, applications of compression to information retrieval, applications
`of compression to image retrieval, applications of compression and information
`theory to human—computer
`interaction (HCI), data compression standards
`including the JPEG, JPEGZOOO, MPEG (MPEG1, MPEG2, MPEG4, MPEG7,
`etc .), H.xxx, and G.xxx families.
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`Patents
`
`In-Place Difi‘erential File Compression
`
`United States patent number: 7,079,051
`INVENTORS: James A. Storer and Dana Shapira
`FILED: March 18, 2004
`GRANTED: July 18, 2006
`(23 claims, 6 of them independent)
`
`Addresses in—place differential file compression methods that can be used in software update
`and backup systems.
`
`Method and Apparatus for Data Compression
`
`United States patent number: 5,379,036
`INVENTOR: James A. Storer
`
`FILED: April 1, 1992
`GRANTED: January 1, 1995
`(23 claims, 3 of them independent)
`
`Addresses high speed parallel algorithms and hardware for data compression.
`
`System for Dynamically Compressing and Decompressing Electronic Data
`
`United States patent number: 4,876,541
`INVENTOR: James A. Storer
`
`FILED: October 15, 1987
`GRANTED: October 24, 1989
`(55 claims, 5 of them independent)
`
`Addresses dictionary based adaptive data compression.
`
`Computational Modeling of Rotation and Translation Capable Human Visual
`Pattern Recognition
`
`US. Provisional Patent Application No. 60/712,596
`INVENTORS: John Lisman, James A. Storer, Martin Cohn, Antonella DiLillo
`FILED: August 29, 2005
`(assigned to Brandeis University)
`
`Addresses rotation and translation invariant recognition.
`
`Mechanical puzzle with hinge elements, rope elements, and knot elements
`
`United States Patent 8,393,623
`INVENTOR: James A. Storer
`
`FILED: October 29, 2009
`GRANTED: March 12, 2013
`(8 claims, 3 of them independent)
`
`Addresses designs for a mechanical puzzle that may be realized as a puzzle game.
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`Papers
`
`"Edit Distance with Multiple Block Operations", to appear, The Computer Journal (coauthored
`with Mira Gonen and Dana Shapira).
`
`"Improved Training of Convolutional Filters", Conference on Computer Vision and Pattern
`Recognition (CVPR) 2019, presented as both a poster and a full oral presentation.
`(coauthored with A. Prakash, D. Florencio, C. Zhang).
`
`"Compact Representations of Dynamic Video Background Using Motion Sprites", IEEE Data
`Compression Conference (DCC) 2019, 438—447 (coauthored with S. Garber, A. Prakash,
`R. Marcus, A. DiLillo).
`
`"Deflecting Adversarial Attacks with Pixel Deflection", Proceedings Conference on Computer
`Vision and Pattern Recognition (CVPR) 2018, 857 1—85 80; presented as both a poster
`and a spotlight oral presentation (coauthored with A. Prakash, N. Moran, S. Garber, A.
`DiLillo).
`
`“Robust Discriminative Localization Maps”, Proceedings Workshop on Computer Vision
`Challenges and Opportunities for Privacy and Security (CV—COPS—CVPR) 2018, Poster
`ID 4093 (coauthored with A. Prakash, N. Moran, S. Garber, A. DiLillo).
`
`"Protecting JPEG Images Against Adversarial Attacks", Proceedings IEEE Data Compression
`Conference (DCC) 2018, 139—148 (coauthored with A. Prakash, N. Moran, S. Garber,
`A. DiLillo).
`
`"A Two Tier Approach To Blackboard Video Lecture Summary", Proceedings Frontiers in
`Education Conference (FIE), Indianapolis, IN, 2017, 1—9 (coauthored with S. Garber, L.
`Milekic, A. Prakash, N. Moran, A. DiLillo).
`
`"Visual Lecture Summary Using Intensity Correlation Coefficient", Proceedings Irish Machine
`Vision and Image Processing Conference (IMVIP), Maynooth University, Ireland, 2017,
`68—75 (coauthored with S. Garber, L. Milekic, A. Prakash, N. Moran, A. DiLillo); see:
`http ://eprints .maynoothuniversity .ie/8 841/ 1 /IMVIP20 17_Proceedings .pdf
`
`"Semantic Perceptual Image Compression using Deep Convolution Networks", Proceedings
`IEEE Data Compression Conference (DCC) 2017, 250—259 (coauthored with A.
`Prakash, N. Moran, S. Garber, A. DiLillo).
`
`"Highway Networks for Visual Question Answering", Conference on Computer Vision and
`Pattern Recognition (CVPR), VQA Workshop, 2016 (coauthored with A. Prakash).
`
`"Accurate Location in Urban Areas", CPVIR Workshop 2013 (coauthored with K. Thomas).
`
`"Compression-Based Tools for Navigation with an Image Database", Algorithms 5, 2012, 1-17
`(coauthored with A. DiLillo, A. Daptardar, K. Thomas, G. Motta)
`
`"Edit Distance With Block Deletions", Algorithms 4, 2011, 40-60 (coauthored with D. Shapira).
`
`"Applications of Compression to Content Based Image Retrieval and Object Recognition",
`Proceedings International Conference On Data Compression, Communication, and
`Processing (CPP 2011), Palinuro, Italy, 179 — 189 (coauthored with Antonella Di Lillo,
`Ajay Daptardar, Giovanni Motta, and Kevin Thomas)
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`"A Rotation And Scale Invariant Descriptor For Shape Recognition", Proceedings International
`Conference On Image Processing (ICIP), Hong Kong, 2010, MA—L9: 1, 257—260
`(coauthored with A. DiLillo and G. Motta).
`
`"Shape Recognition, With Applications To A Passive Assistant", Proceedings PErvasive
`Technologies Related to Assistive Environments (PETRA) Samos, Greece, June 23—25; to
`appear ACM International Conference Proceedings Series (coauthored with A. DiLillo,
`G. Motta, and K. Thomas), 2010.
`
`"Shape Recognition Using Vector Quantization", Proceedings Data Compression Conference,
`IEEE Computer Society Press, March 2010, 484—493 (coauthored with A. Di Lillo and
`G. Motta).
`
`"Network Aware Compression Based Rate Control for Printing Systems", Proceedings 10th
`International Symposium on Pervasive Systems (ISPAN 2009), Kaohsiung, Taiwan,
`December 2009, 123—128 (coauthored with Chih—Yu Tang).
`
`"VQ Based Image Retrieval Using Color and Position Features", Proceedings Data Compression
`Conference, IEEE Computer Society Press, March 2008, 432—441 (coauthored with A.
`Daptardar).
`
`"Multiresolution Rotation—Invariant Texture Classification Using Feature Extraction in the
`Frequency Domain and Vector Quantization", Proceedings Data Compression
`Conference, IEEE Computer Society Press, March 2008, 452—461 (coauthored with A.
`DiLillo and G. Motta).
`
`"Edit Distance with Move Operations", Journal of Discrete Algorithms 5:2, June 2007, 380—392
`(coauthored with D. Shapira).
`
`"Texture Classification Based on Discriminative Feature Extracted in the Frequency Domain",
`Proceedings IEEE International Conference on Image Processing (ICIP), September
`2007, II .53—II.56 (coauthored with A. DiLillo and G. Motta).
`
`"Supervised Segmentation Based on Texture Signature Extracted in the Frequency Domain",
`Third Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), June
`2007 (coauthored with A. DiLillo and G. Motta).
`
`"Texture Classification Using VQ with Feature Extraction Based on Transforms Motivated by
`the Human Visual System", Proceedings Data Compression Conference, IEEE Computer
`Society Press, 392, 2007 (coauthored with A. DiLillo and G. Motta).
`
`"Reduced Complexity Content-Based Image Retrieval using Vector Quantization", Proceedings
`Data Compression Conference, IEEE Computer Society Press, 342-351, 2006
`(coauthored with A. H. Daptardar).
`
`"In-Place Differential File Compression", Computer Journal 48:6, 677-691, November 2005
`(coauthored with D. Shapira).
`
`"Compression of Hyper/Ultra—Spectral Data ", Proceedings of SPIE, Optics and Photonics,
`Satellite Data Compression, Communication and Archiving, Jul. 2005 , Vol. 5889, pp.
`588908—1——588908—10 (coauthored with G. Motta and F. Rizzo).
`
`"Content—Based Image Retrieval Via Vector Quantization", In Advances in Visual Computing -
`Springer Lecture Notes in Computer Science 3804/2005 (ISBN 3—540—30750—8), 502—
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`509; also appeared in International Symposium on Visual Computing, December 5—7,
`2005 (coauthored with A. Daptardar).
`
`"Low Complexity Lossless Compression of Hyperspectral Imagery via Linear Prediction", IEEE
`Signal Processing Letters 12:2, 138—141, 2005 (coauthored with F. Rizzo, B. Carpentieri,
`and G. Motta).
`
`"Overlap and Channel Errors in Adaptive Vector Quantization for Image Coding", Information
`Sciences 171:1—3, 125—143, 2005 (coauthored with B. Carpentieri and F. Rizzo).
`
`"Compression of Hyperspectral Imagery", Proceedings International Conference on E-Business
`and Telecommunications (ICETE), Sebutal, Portugal, 2004; an extended version of this
`paper is a chapter in the book E-Business and Telecommunication Networks, Edited by J.
`Ascenso, L. Vasiu, C. Belo, and M. Saramago, Kluwer/ Springer, 317—324 (coauthored
`with B. Carpentieri, G. Motta, and F. Rizzo).
`
`"Real—Time Software Compression and Classification of Hyperspectral Images", Proceedings 11*
`SPIE International Symposium on Remote Sensing Europe, X. L. Bruzzone, Ed.,
`Maspalomas, Gran Canaria, Spain, Sept. 13—17, 2004, Vol. 5573, 182—192, (coauthored
`with B. Carpentieri, G. Motta, and F. Rizzo).
`
`"Lossless Compression of Hyperspectral Imagery: A Real Time Approach", Proceedings 11*
`SPIE International Symposium on Remote Sensing Europe, X. L. Bruzzone, Ed.,
`Maspalomas, Gran Canaria, Spain, Sept. 13—17, 2004, Vol. 5573, 262—272, (coauthored
`with B. Carpentieri, G. Motta, and F. Rizzo).
`
`"High Performance Compression of Hyperspectral Imagery with Reduced Search Complexity
`in the Compressed Domain", Proceedings Data Compression Conference, IEEE Computer
`Society Press, 2004, 479—488 (coauthored with B. Carpentieri, G. Motta and F. Rizzo).
`
`"In—Place Differential File Compression of Non—Aligned Files With Applications to File
`Distribution and Backups", Proceedings Data Compression Conference (DCC), IEEE
`Computer Society Press, 2004, 82—91 (coauthored with D. Shapira).
`
`"Report of the National Science Foundation Workshop on Information Theory and Computer
`Science Interface", produced by workshop of 20 invited participants (Chicago, Il. 2003),
`report completed and submitted to the NSF in November 2004.
`
`"Large Edit Distance with Multiple Block Operations", Proceedings Symposium on String
`Processing and Information Retrieval (SPIRE), 2003, Lecture Notes on Computer Science,
`Volume 2857/2003 ISBN: 3-540-20177-7, Springer-Verlag, 369-377 (coauthored with D.
`Shapira) .
`
`"In-Place Differential File Compression", Proceedings Data Compression Conference (DCC),
`IEEE Computer Society Press, 263-272, 2003 (coauthored with D. Shapira).
`
`"Compression of Hyperspectral Imagery", Proceedings Data Compression Conference, IEEE
`Computer Society Press, 333-342, 2003 (coauthored with G. Motta and F. Rizzo).
`
`"Partitioned Vector Quantization: Application to Lossless Compression of Hyperspectral
`Images", Proceedings International Conference on Acoustics, Speech, and Signal Processing
`(ICASSP), IMSP—P2.2—III, 241—244, 2003; also presented at IEEE International Conference
`on Multimedia and Expo (ICME2003), 2003, 553—556
`(coauthored with G. Motta and F.
`Rizzo).
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`"Generalized Edit Distance with Move Operations", Thirteenth Annual Symposium on
`Combinatorial Pattern Matching (CPM), 85—98, 2002 (coauthored with D. Shapira).
`
`"A Lossless Data Compression Algorithm for Electro—Cardiograms", Technical Report,
`Computer Science Department, Brandeis University (coauthored with G. Motta and M.
`Hosang); see also senior honors thesis of M. Hosang.
`
`"LZ—Based Image Compression", Information Sciences 135, 107—122, 2001 (coauthored with
`F. Rizzo and B. Carpentieri).
`
`"Optimal Encoding of Non—Stationary Sources",
`(coauthored with J. Reif).
`
`Information Sciences,
`
`87—105, 2001
`
`"Overlap in Adaptive Vector Quantization", Proceedings Data Compression Conference, IEEE
`Computer Society Press, 401—410, 2001 (coauthored with F. Rizzo).
`
`"Lossless Image Coding via Adaptive Linear Prediction and Classification", Proceedings of the
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`"Design and Perform