Downloads & Free Reading Options - Results

An investigation is presented of video data compression applied to microgravity space experiments using High Resolution High Frame Rate Video Technology (HHVT). An extensive survey of methods of video data compression, described in the open literature, was conducted. The survey examines compression methods employing digital computing. The results of the survey are presented. They include a description of each method and assessment of image degradation and video data parameters. An assessment is made of present and near term future technology for implementation of video data compression in high speed imaging system. Results of the assessment are discussed and summarized. The results of a study of a baseline HHVT video system, and approaches for implementation of video data compression, are presented. Case studies of three microgravity experiments are presented and specific compression techniques and implementations are recommended.

Voice bandwidth compression techniques were investigated to anticipate link margin difficulties in the shuttle S-band communication system. It was felt that by reducing the data rate on each voice channel from the baseline 24 (or 32) Kbps to 8 Kbps, additional margin could be obtained. The feasibility of such an alternate voice transmission system was studied. Several factors of prime importance that were addressed are: (1) achieving high quality voice at 8 Kbps; (2) performance in the presence of the anticipated shuttle cabin environmental noise; (3) performance in the presence of the anticipated channel error statistics; and (4) minimal increase in size, weight, and power over the current baseline voice processor.

We present an off-line computational method for control allocation design. The control allocation function delta = F(z)tau = delta (sub 0) (z) mapping commanded body-frame torques to actuator commands is implicitly specified by trim condition delta (sub 0) (z) and by a robust pseudo-inverse problem double vertical line I - G(z) F(z) double vertical line less than epsilon (z) where G(z) is a system Jacobian evaluated at operating point z, z circumflex is an estimate of z, and epsilon (z) less than 1 is a specified error tolerance. The allocation function F(z) = sigma (sub i) psi (z) F (sub i) is computed using a heuristic technique for selecting wavelet basis functions psi and a constrained least-squares criterion for selecting the allocation matrices F (sub i). The method is applied to entry trajectory control allocation for a reusable launch vehicle (X-33).

Data enhancement by data compression using polynomial fitting

An apparatus for data compression employing a parallel analog processor. The apparatus includes an array of processor cells with N columns and M rows wherein the processor cells have an input device, memory device, and processor device. The input device is used for inputting a series of input vectors. Each input vector is simultaneously input into each column of the array of processor cells in a pre-determined sequential order. An input vector is made up of M components, ones of which are input into ones of M processor cells making up a column of the array. The memory device is used for providing ones of M components of a codebook vector to ones of the processor cells making up a column of the array. A different codebook vector is provided to each of the N columns of the array. The processor device is used for simultaneously comparing the components of each input vector to corresponding components of each codebook vector, and for outputting a signal representative of the closeness between the compared vector components. A combination device is used to combine the signal output from each processor cell in each column of the array and to output a combined signal. A closeness determination device is then used for determining which codebook vector is closest to an input vector from the combined signals, and for outputting a codebook vector index indicating which of the N codebook vectors was the closest to each input vector input into the array.

There are no author-identified significant results in this report.

Book Source: Digital Library of India Item 2015.192873 dc.contributor.author: Vineeta Srivastava dc.date.accessioned: 2015-07-08T01:46:03Z dc.date.available: 2015-07-08T01:46:03Z dc.date.digitalpublicationdate: 2005-08-31 dc.identifier.barcode: 1990010091258 dc.identifier.origpath: /rawdataupload/upload/0091/258 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/192873 dc.description.scannerno: 12 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 109 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher: Indian Institute Of Technology Kanpur dc.rights: Out_of_copyright dc.source.library: Indian Institute Of Technology Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Mechanical Engineering In General. Nuclear Technology. Electrical Engineering. Machinery dc.title: Performance Comparison Of Multispectral Data Compression Techniques

Motivated by the problem of the definition of a distance between two sequences of characters, we investigate the so-called learning process of typical sequential data compression schemes. We focus on the problem of how a compression algorithm optimizes its features at the interface between two different sequences $A$ and $B$ while zipping the sequence $A+B$ obtained by simply appending $B$ after $A$. We show the existence of a universal scaling function (the so-called learning function) which rules the way in which the compression algorithm learns a sequence $B$ after having compressed a sequence $A$. In particular it turns out that it exists a crossover length for the sequence $B$, which depends on the relative entropy between $A$ and $B$, below which the compression algorithm does not learn the sequence $B$ (measuring in this way the relative entropy between $A$ and $B$) and above which it starts learning $B$, i.e. optimizing the compression using the specific features of $B$. We check the scaling function on three main classes of systems: Bernoulli schemes, Markovian sequences and the symbolic dynamic generated by a non trivial chaotic system (the Lozi map). As a last application of the method we present the results of a recognition experiment, namely recognize which dynamical systems produced a given time sequence. We finally point out the potentiality of these results for segmentation purposes, i.e. the identification of homogeneous sub-sequences in heterogeneous sequences (with applications in various fields from genetic to time-series analysis).

Motivated by the problem of the definition of a distance between two sequences of characters, we investigate the so-called learning process of typical sequential data compression schemes. We focus on the problem of how a compression algorithm optimizes its features at the interface between two different sequences $A$ and $B$ while zipping the sequence $A+B$ obtained by simply appending $B$ after $A$. We show the existence of a universal scaling function (the so-called learning function) which rules the way in which the compression algorithm learns a sequence $B$ after having compressed a sequence $A$. In particular it turns out that it exists a crossover length for the sequence $B$, which depends on the relative entropy between $A$ and $B$, below which the compression algorithm does not learn the sequence $B$ (measuring in this way the relative entropy between $A$ and $B$) and above which it starts learning $B$, i.e. optimizing the compression using the specific features of $B$. We check the scaling function on three main classes of systems: Bernoulli schemes, Markovian sequences and the symbolic dynamic generated by a non trivial chaotic system (the Lozi map). As a last application of the method we present the results of a recognition experiment, namely recognize which dynamical systems produced a given time sequence. We finally point out the potentiality of these results for segmentation purposes, i.e. the identification of homogeneous sub-sequences in heterogeneous sequences (with applications in various fields from genetic to time-series analysis).

Huffman compression is a statistical, lossless, data compression algorithm that compresses data by assigning variable length codes to symbols, with the more frequently appearing symbols given shorter codes than the less. This work is a modification of the Huffman algorithm which permits uncompressed data to be decomposed into indepen- dently compressible and decompressible blocks, allowing for concurrent compression and decompression on multiple processors. We create implementations of this modified algorithm on a current NVIDIA GPU using the CUDA API as well as on a current Intel chip and the performance results are compared, showing favorable GPU performance for nearly all tests. Lastly, we discuss the necessity for high performance data compression in today's supercomputing ecosystem.

Key issues of data compression for near Earth and deep space to Earth transmission discussion group are briefly presented. Specific recommendations as made by the group are as follows: (1) since data compression is a cost effective way to improve communications and storage capacity, NASA should use lossless data compression wherever possible; (2) NASA should conduct experiments and studies on the value and effectiveness of lossy data compression; (3) NASA should develop and select approaches to high ratio compression of operational data such as voice and video; (4) NASA should develop data compression integrated circuits for a few key approaches identified in the preceding recommendation; (5) NASA should examine new data compression approaches such as combining source and channel encoding, where high payoff gaps are identified in currently available schemes; and (6) users and developers of data compression technologies should be in closer communication within NASA and with academia, industry, and other government agencies.

A process for x ray registration and differencing results in more efficient compression is discussed. Differencing of registered modeled subject image with a modeled reference image forms a differential image for compression with conventional compression algorithms. Obtention of a modeled reference image includes modeling a relatively unrelated standard reference image upon a three dimensional model, which three dimensional model is also used to model the subject image for obtaining the modeled subject image. The registration process of the modeled subject image and modeled reference image translationally correlates such modeled images for resulting correlation thereof in spatial and spectral dimensions. Prior to compression, a portion of the image falling outside a designated area of interest may be eliminated, for subsequent replenishment with a standard reference image. The compressed differenced image may be subsequently transmitted and/or stored, for subsequent decompression and addition to a standard reference image so as to form a reconstituted or approximated subject image at either remote location and/or at a later moment in time. Overall effective compression ratios of 100:1 are possible for thoracic x ray digital images.

Performance of the NATO data compression algorithm in a noisy communication channel is superior to that of the present TDF data compression algorithm, provided that noise channel is not too harsh. Text is legible in a noise channel that has a bit error probability on the order 0.0003 (single bit errors). Multiple (burst) errors are not a problem provided the burst length does not exceed ten bits. Where actual burst length greatly exceeds the ten-bit (maximum) average expected in tactical situation noise, data compression algorithm performance may be poor, as with most data compression algorithms without a powerful noise protection algorithm. The NATO algorithm has enough compression to meet the current TDF performance specification, but its compression efficiency is lower than that of the TDF. The two algorithms have about the same complexity. (Author)

We use the traveling front approach to derive exact asymptotic results for the statistics of the number of particles in a class of directed diffusion limited aggregation models on a Cayley tree. We point out that some aspects of these models are closely connected to two different problems in computer science, namely the digital search tree problem in data structures and the Lempel-Ziv algorithm for data compression. The statistics of the number of particles studied here is related to the statistics of height in digital search trees which, in turn, is related to the statistics of the length of the longest word formed by the Lempel-Ziv algorithm. Implications of our results to these computer science problems are pointed out.

We propose `Dracula', a new framework for unsupervised feature selection from sequential data such as text. Dracula learns a dictionary of $n$-grams that efficiently compresses a given corpus and recursively compresses its own dictionary; in effect, Dracula is a `deep' extension of Compressive Feature Learning. It requires solving a binary linear program that may be relaxed to a linear program. Both problems exhibit considerable structure, their solution paths are well behaved, and we identify parameters which control the depth and diversity of the dictionary. We also discuss how to derive features from the compressed documents and show that while certain unregularized linear models are invariant to the structure of the compressed dictionary, this structure may be used to regularize learning. Experiments are presented that demonstrate the efficacy of Dracula's features.

In this paper we propose a multiresolution compression scheme applied to image and volume data. It is intended for rapid browsing in graphical files, in which some regions of interest may exist; therefore the compression algorithms focus on incorporating quick full-reconstruction procedures, both in images and volumes. This scheme is based on vector quantization of the coefficients of a wavelet decomposition. The differences in the methods lie on how vectors in the multiresolution decompositions are selected for vector quantization. We have also developed a client-server application using this scheme, which allows images with increasing levels of detail to be represented in the client system, as they are received from the server and decoded. Numerical results show the performances of all the selections made.

The quantum entropy-typical subspace theory is specified. It is shown that any mixed state with von Neumann entropy less than h can be preserved approximately by the entropy-typical subspace with entropy= h. This result implies an universal compression scheme for the case that the von Neumann entropy of the source does not exceed h.

Motivation: Data volumes generated by next-generation sequencing technolo- gies is now a major concern, both for storage and transmission. This triggered the need for more efficient methods than general purpose compression tools, such as the widely used gzip. Most reference-free tools developed for NGS data compression still use general text compression methods and fail to benefit from algorithms already designed specifically for the analysis of NGS data. The goal of our new method Leon is to achieve compression of DNA sequences of high throughput sequencing data, without the need of a reference genome, with techniques derived from existing assembly principles, that possibly better exploit NGS data redundancy. Results: We propose a novel method, implemented in the software Leon, for compression of DNA sequences issued from high throughput sequencing technologies. This is a lossless method that does not need a reference genome. Instead, a reference is built de novo from the set of reads as a probabilistic de Bruijn Graph, stored in a Bloom filter. Each read is encoded as a path in this graph, storing only an anchoring kmer and a list of bifurcations indicating which path to follow in the graph. This new method will allow to have compressed read files that also already contain its underlying de Bruijn Graph, thus directly re-usable by many tools relying on this structure. Leon achieved encoding of a C. elegans reads set with 0.7 bits/base, outperforming state of the art reference-free methods. Availability: Open source, under GNU affero GPL License, available for download at http://gatb.inria.fr/software/leon/

This article is from Sensors (Basel, Switzerland) , volume 13 . Abstract Airborne Light Detection and Ranging (LIDAR) has become a mainstream technology for terrain data acquisition and mapping. High sampling density of LIDAR enables the acquisition of high details of the terrain, but on the other hand, it results in a vast amount of gathered data, which requires huge storage space as well as substantial processing effort. The data are usually stored in the LAS format which has become the de facto standard for LIDAR data storage and exchange. In the paper, a hardware accelerated compression of LIDAR data is presented. The compression and decompression of LIDAR data is performed by a dedicated FPGA-based circuit and interfaced to the computer via a PCI-E general bus. The hardware compressor consists of three modules: LIDAR data predictor, variable length coder, and arithmetic coder. Hardware compression is considerably faster than software compression, while it also alleviates the processor load.

This article is from Sensors (Basel, Switzerland) , volume 13 . Abstract Airborne Light Detection and Ranging (LIDAR) has become a mainstream technology for terrain data acquisition and mapping. High sampling density of LIDAR enables the acquisition of high details of the terrain, but on the other hand, it results in a vast amount of gathered data, which requires huge storage space as well as substantial processing effort. The data are usually stored in the LAS format which has become the de facto standard for LIDAR data storage and exchange. In the paper, a hardware accelerated compression of LIDAR data is presented. The compression and decompression of LIDAR data is performed by a dedicated FPGA-based circuit and interfaced to the computer via a PCI-E general bus. The hardware compressor consists of three modules: LIDAR data predictor, variable length coder, and arithmetic coder. Hardware compression is considerably faster than software compression, while it also alleviates the processor load.

Article from BYTE magazine, March 1991

Analog-to-digital converter-pulse height analyzer for data reduction from balloon measurements

Advances in audio data compression are largely driven by the need to conserve transmission rate or bandwidth, while maintaining the ability to accurately reconstruct the signal at the receiver. This report examines data compression methods with an emphasis on techniques for the compression of audio data. An overview of data compression schemes is presented to provide the background for a performance comparison between selected versions of data compression systems featuring adaptive differential pulse code modulation (ADPCM) schemes. Two different types of data compression systems are investigated; IIR and FIR impulse implementations. A modification to the basic ADPCM system using a modular function is implemented. The modular operation results in a smaller size codebook and prevents data expansion when the source is not matched to the code. This modification is utilized for both types of ADPCM coders compared. To complete the compression system Huffman coding, is employed to encode and decode the compressed data to and from binary form.

Book Source: Digital Library of India Item 2015.228892 dc.contributor.author: Suyog Moogi dc.date.accessioned: 2015-07-10T15:51:54Z dc.date.available: 2015-07-10T15:51:54Z dc.date.digitalpublicationdate: 0000-00-00 dc.identifier.barcode: 5990010117795 dc.identifier.origpath: /rawdataupload/upload/0117/797 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/228892 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 54 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher: Iit Kanpur dc.rights: Out_of_copyright dc.source.library: I I T Kanpur dc.subject.classification: Electrical Engineering dc.title: Ecg Data Compression Using Adaptive Wavelet Packets

Includes bibliographical references (p. [283]-288) and index

Image compression is performed to reduce the bit rate for image transmission and the required storage in the case of image archival. Typically, it involves a transformation of the image to reduce the correlation and redundancy between pixels. The wavelet transform has gained wide acceptance as a tool to decorrelate most image sources. The transformed image is decomposed at different scales of details and approximation. Such decomposition facilitates image compression and progressive transmission. In this study, a system and method for image compression is presented based on the wavelet transformation. A threshold is determined adaptively based on the desired compression ratio and only significant coefficients are retained.

New directions in computing and algorithms has lead to some new applications that have tolerance to imprecision. Although, These applications are creating large volumes of data which exceeds the capability of today's computing systems. Therefore, researchers are trying to find new techniques to alleviate this crisis. Approximate Computing is one promising technique that uses a trade off between precision and efficiency of computing. Acceleration is another solution that uses specialized logics in order to do computations in a way that is more power efficient. Another technique is Data compression which is used in memory systems in order to save capacity and bandwidth.

A non-adaptive predictor, a nonuniform quantizer, and a multi-level Huffman coder are incorporated into a differential pulse code modulation system for coding and decoding broadcast video signals in real time.

We investigate the use of the Multiple Optimised Parameter Estimation and Data compression algorithm (MOPED) for data compression and faster evaluation of likelihood functions. Since MOPED only guarantees maintaining the Fisher matrix of the likelihood at a chosen point, multimodal and some degenerate distributions will present a problem. We present examples of scenarios in which MOPED does faithfully represent the true likelihood but also cases in which it does not. Through these examples, we aim to define a set of criteria for which MOPED will accurately represent the likelihood and hence may be used to obtain a significant reduction in the time needed to calculate it. These criteria may involve the evaluation of the full likelihood function for comparison.

Mathematical data compression model for satellite measurement histogram transmission

Data compressor processor concept envisions a simplified system for telemetry communications. It is simultaneously a zero-order processor and a floating aperture, a variable aperture, and a binary integer aperture with a decoded buffet fullness counter.

Data compressor processor concept envisions a simplified system for telemetry communications. It is simultaneously a zero-order processor and a floating aperture, a variable aperture, and a binary integer aperture with a decoded buffet fullness counter.

Soon after space and Earth science data is collected, it is stored in one or more archival facilities for later retrieval and analysis. Since the purpose of the archival process is to keep an accurate and complete record of data, any data compression used in an archival system must be lossless, and protect against propagation of error in the storage media. A browse capability for space and Earth science data is needed to enable scientists to check the appropriateness and quality of particular data sets before obtaining the full data set(s) for detailed analysis. Browse data produced for these purposes could be used to facilitate the retrieval of data from an archival facility. Quick-look data is data obtained directly from the sensor for either previewing the data or for an application that requires very timely analysis of the space or Earth science data. Two main differences between data compression techniques appropriate to browse and quick-look cases, are that quick-look can be more specifically tailored, and it must be limited in complexity by the relatively limited computational power available on space platforms.

Data compression has been proposed for several flight missions as a means of either reducing on board mass data storage, increasing science data return through a bandwidth constrained channel, reducing TDRSS access time, or easing ground archival mass storage requirement. Several issues arise with the implementation of this technology. These include the requirement of a clean channel, onboard smoothing buffer, onboard processing hardware and on the algorithm itself, the adaptability to scene changes and maybe even versatility to the various mission types. This paper gives an overview of an ongoing effort being performed at Goddard Space Flight Center for implementing a lossless data compression scheme for space flight. We will provide analysis results on several data systems issues, the performance of the selected lossless compression scheme, the status of the hardware processor and current development plan.

The expression 'data compression' has broad meaning and may encompass any or all of the following: data compaction, bandwidth compression, redundancy removal, redundancy reduction, adaptive sampling, parameter extraction, optimal estimation, and possibly a few other techniques. In general, data compression denotes operations which are performed to reduce the quantity of data prior to transmission, but which still preserve the minimum data elements of a measurement continuum such that the original information may be reconstructed within established limits of error. This document is intended as a single source document describing available techniques for reducing the quantity of data processed and for providing meaningful presentation. It includes mathematical, statistical, and graphical techniques which have been used successfully.

This report describes in detail the operation and capabilities of the TIESPAD unit. This unit was tested with AN/SPS-10 surface search radar and an AN/SPS-30 air search radar, and with an AN/FPS-93 air search radar via a microwave relay. These brief tests show that the TIESPAD unit does effectively help the operator to detect and track moving targets that would otherwise not be detected on a standard AN/SPS-type indicator.

Performance of the NATO data compression algorithm in a noisy communication channel is superior to that of the present TDF data compression algorithm, provided that noise channel is not too harsh. Text is legible in a noise channel that has a bit error probability on the order 0.0003 (single bit errors). Multiple (burst) errors are not a problem provided the burst length does not exceed ten bits. Where actual burst length greatly exceeds the ten-bit (maximum) average expected in tactical situation noise, data compression algorithm performance may be poor, as with most data compression algorithms without a powerful noise protection algorithm. The NATO algorithm has enough compression to meet the current TDF performance specification, but its compression efficiency is lower than that of the TDF. The two algorithms have about the same complexity. (Author)

The feasibility of using a sequency-ordered Fast Walsh transform algorithm in a transformation-type ECG data compression scheme was demonstrated. The relationship of the mean square error between the original and reconstructed ECG signals as a function of the number of Walsh coefficients retained and the number of bits used to represent each coefficient was determined. A finite impulse response digital low-pass filter was tested as a means of improving the diagnostic characteristics of the reconstructed ECG waveforms. Suggestions for further work in this area are included. (Author)

This thesis proposes a new analysis/synthesis procedure for speech and image compression. The algorithm applies the discrete wavelet transform to subject data in order to obtain a set of multiresolution wavelet coefficients. The wavelet coefficients are then encoded by using the generalized Lloyd algorithm. The statistical properties of the wavelet coefficients are utilized to determine the number of resolution levels as well as the codebook size at each resolution level. Coding results show that the new procedure provides a significant improvement in the quality of the reproduced data. The tested data includes speech, image, and transient signals. Wavelet transforms, Vector quantization, Speech coding, Image coding.

This project provides an integrated design approach of a portable video on demand system capable of delivering high quality image and video in a wireless communication environment. The research focuses on both the algorithm and circuit design techniques developed for implementing a low power video compression/decompression system at power levels that are two orders of magnitude below existing solutions. The three main technological challenges in realizing such a system are high compression efficiency for transmission bandwidth reduction, error resiliency to minimize the effects of channel error, and low power implementation for portability.

Buckling data review for stiffened cylinder failures during axial compression and bending

Image segmentation can be a key step in data compression and image analysis. However, the segmentation results produced by most previous approaches to region growing are suspect because they depend on the order in which portions of the image are processed. An iterative parallel segmentation algorithm avoids this problem by performing globally best merges first. Such a segmentation approach, and two implementations of the approach on NASA's Massively Parallel Processor (MPP) are described. Application of the segmentation approach to data compression and image analysis is then described, and results of such application are given for a LANDSAT Thematic Mapper image.

Image segmentation can be a key step in data compression and image analysis. However, the segmentation results produced by most previous approaches to region growing are suspect because they depend on the order in which portions of the image are processed. An iterative parallel segmentation algorithm avoids this problem by performing globally best merges first. Such a segmentation approach, and two implementations of the approach on NASA's Massively Parallel Processor (MPP) are described. Application of the segmentation approach to data compression and image analysis is then described, and results of such application are given for a LANDSAT Thematic Mapper image.

This report summarizes a 24 month investigation on video data reduction method applicable to RPV's. Areas investigated included interframe reduction, two-dimensional transform and hybrid coding using Walsh and Fourier transforms and DPCM coding, and adaptive techniques using Hadamard transforms. Included are numerous photographs of low altitude target runs showing the effects of channel errors and coding degradation. A bibliography of up to date references on image processing is also included.

In this thesis, a hybrid lossless compression model is tested which employs a combination of both a lossy compression method and one or more lossless image compression methods to produce an overall lossless image compression. The hybrid model decomposes the original image into a browse and a residual image. The hybrid model is tested and evaluated using various combinations of lossy and lossless image compression methods. The lossy compression method used in the model is JPEG (Joint Photographic Experts (Group) . The lossless compression methods used are Huffman, Arithmetic, LZW, lossless JPEG. and Diagonal coding. The compression results achieved using the hybrid compression model are compared to the compression achieved using the corresponding direct lossless compression. Additionally, the hybrid model is evaluated as to the advantages that the decomposition of the image into browse and residual images provide to the user. Image compression, Data compression, Hybrid compression, Lossless, Lossy, Browse, Residual.

Bandwidth is a precious commodity. In order to make the best use of what is available, better modulation schemes need to be developed, or less data needs to be sent. This paper will investigate the option of sending less data via data compression. The structure and the entropy of the data determine how much lossless compression can be obtained for a given set of data. This paper shows the data structure and entropy for several actual telemetry data sets and the resulting lossless compression obtainable using data compression techniques.

Wavelets provide a new mathematical and computational approach to representing image data. A wavelet basis is a complete orthonormal system of functions in terms of which image data can be represented. The low computational complexity of the wavelet transform and the bounded support of the wavelet basis functions off high retention of information that typically corresponds to image features, and low workload for computing, compressing, and reconstituting image data. Aware conducted computational experiments to determine the contribution of wavelets to solving two classes of practical problem: 1. Position location by matching observations to a stored image. 2. Identification of objects of specific size or characteristics and sensory clutter. The results suggest that compression using wavelets preserves more than enough information to register position correctly using position reconstituted reference images that have been compressed in excess of 100:1. A variety of analysis made to develop an indication of the relationship between information loss and compression are reported.

The objective of this report is to give a brief descriptive summary of the research findings on the research project under ARO Contract DAAH04-95-1-0193, sponsored by SDIO/IST and managed by the U.S. Army Research Office. This is a three-year research grant with a one-year no-cost extension. Technical details are not included in this report, since most of the results have been reported semi-annually. The research findings can be divided into five groups. Group 1, which consists of papers 19, 20, and 21, is aimed at building the mathematical theory for our research on formulation and characterization of wavelets, splines, and other local basis functions. Group 2, which consists of papers 2, 3, 15, 16, 23, 24, and 25, is a compilation of research work on wavelet foundation, theory, methods, algorithms, and computational schemes. Group 3, which consists of papers 1, 4, and 9, is focused on the stability study and approximation order of bi-variate splines. Group 4, which consists of papers 5, 10, 11, 12, 17,18, and 22, is focused on the third type of local basis functions, namely, localized sinusoidal functions. Finally, Group 5, which consists of papers 6, 7, 8, 13, and 14, is concerned with applications, particularly on wavelet feature extraction and compression of image-like data.

This paper presents a study on the effects of various data compression methods from the viewpoint of central computer to terminal communications on a large graphics oriented timesharing system, PLATO IV. The desired goal is to increase terminal display speed without significant increase in the transmission error rates. While the major emphasis in this paper is on text transmission, some discussion of other display functions is included. Chapters provide (1) a description of the PLATO IV architecture and communications system; (2) a review of two projects involving processor based terminals; (3) an explanation of how text is currently transmitted, followed by an analysis of the average number of bits/character obtained by this method; (4) an introduction to the theoretical background for variable length or Huffman coding; (5) projected savings and overhead involved in the use of word lists to reduce the average number of bits/character used to represent text; and (6) conclusions, suggestions for future research, and discussions of projects involving text compression and other methods of improving display speed. (Author/DAG)

Performance of the NATO data compression algorithm in a noisy communication channel is superior to that of the present TDF data compression algorithm, provided that noise channel is not too harsh. Text is legible in a noise channel that has a bit error probability on the order 0.0003 (single bit errors). Multiple (burst) errors are not a problem provided the burst length does not exceed ten bits. Where actual burst length greatly exceeds the ten-bit (maximum) average expected in tactical situation noise, data compression algorithm performance may be poor, as with most data compression algorithms without a powerful noise protection algorithm. The NATO algorithm has enough compression to meet the current TDF performance specification, but its compression efficiency is lower than that of the TDF. The two algorithms have about the same complexity. (Author)