Word based multiple dictionary scheme for text compression with application to 2D bar code

Error-Correcting Code (ECC) provides robust readability to both linear and two-dimensional (2D) barcodes, particularly for localized damage. Many ECC approaches, however, are based on assumptions about the types of damage or the communication channel used. As the applications for barcodes rapidly evolve with the increasing ubiquity of mobile cameras, an evaluation of the trade off between ECC and simply increasing the size of the bar code modules is required. This article compares the impact of simply changing the module size versus using error correction–which exhausts a percentage of the symbol area without adding payload data. For three typical nondestructive types of damage–the print-scan cycle, low quality printing, and blurring–investigated here, there is compelling support for increasing the size of the barcode modules and foregoing ECC. Freeing barcodes from the need for ECC provides an additional advantage: namely, the barcodes can be scrambled to be unreadable under the defined barcode specification without a change in size or appearance. This is in contrast to the use of ECC, for which only a small minority of rearrangements of the data in the barcode would be "decodable." Thus, without the use of ECC, it is much harder for a would-be counterfeiter or other dishonest party to determine the scrambling approach. Additionally, we can create 2D barcodes that are not "readable" using commercially available reading software, except where so desired. These results are discussed in light of destructive damage and for different applications of 2D bar codes.

With the resolution enhancement techniques such as OPC (Optical Proximity Correction) and SRAF (Sub-Resolution Assist Features), the size of layout data have grown significantly. It is quite common now to find layout files that are tens of GBs in size. Unlike GDSII which can store data hierarchically, mask data formats such as MEBES are essentially flat and more voluminous. Moreover, polygonal data present in layout data files is fractured, thereby increasing the data volume before getting stored in MEBES data format. This results in huge MEBES files. As per the ITRS roadmap of 2005, for a 45nm half-pitch node that is expected to be in use by 2010, the mask data volume for a single layer is expected to reach up to 825 GB. Storing and transferring such large mask data are issues for which the mask industry needs solutions. Historically, MEBES is the most prevalent EB format in the industry. Moreover, in many Mask Data Preparation (MDP) flows, the MEBES format is being used as the de-facto standard for specifying the fractured EB data even though the final target EB machine might be different. In this paper we present techniques for lossless reversible compression of MEBES data, i.e., when the compressed file is decompressed, the generated uncompressed file matches the original MEBES file bit- by-bit. By applying these compression techniques a compression ratio of 5X to 15X can be obtained. In practice, compressing MEBES files is usually a one-time task, but decompression of compressed files is expected to be done multiple times as every time a compressed MEBES file needs processing, it has to be decompressed. MEBES is essentially an efficient data format and the geometries are stored compactly. As a result the compression/decompression techniques described in this paper are quite computation intensive in order to achieve higher compression ratio. This in turn leads to higher CPU time for compression/decompression compared to generic compressors such as gzip. However, as the format-specific compressors produce higher compression ratios, the disk I/O time for compression and decompression is expected to be less compared to the generic compressors such as gzip and gunzip. In spite of this, the format-specific decompressor is usually 3-5X times slower than the generic decompressor such as gunzip. Since decompression is expected to be done more frequently as compared to compression, speeding up decompression is highly desirable. We present a compression technique for MEBES data which enables multiple process threads to decompress the compressed MEBES data. With the multi-core multiprocessor machines becoming quite inexpensive and common, the multi-threaded decompression is expected to perform close to the disk I/O time on such machines. The paper details out the techniques, experimental results in terms of comparative compression ratios, compression and decompression speed using single threads and multiple threads. The possibility of getting higher compression ratios as well as higher or comparable decompression speed makes it more practical to use format specific reversible compression schemes rather than using generic compressors. Even though, the paper focuses on compression and decompression of MEBES, it can be easily extended to the compression of GDSII [1].

One of the goals of telemedicine is to enable remote visualization and browsing of medical volumes. There is a need to employ scalable compression schemes and efficient client-server models to obtain interactivity and an enhanced viewing experience. First, we present a scheme that uses JPEG2000 and JPIP (JPEG2000 Interactive Protocol) to transmit data in a multi-resolution and progressive fashion. The server exploits the spatial locality offered by the wavelet transform and packet indexing information to transmit, in so far as possible, compressed volume data relevant to the clients query. Once the client identifies its volume of interest (VOI), the volume is refined progressively within the VOI from an initial lossy to a final lossless representation. Contextual background information can also be made available having quality fading away from the VOI. Second, we present a prioritization that enables the client to progressively visualize scene content from a compressed file. In our specific example, the client is able to make requests to progressively receive data corresponding to any tissue type. The server is now capable of reordering the same compressed data file on the fly to serve data packets prioritized as per the client's request. Lastly, we describe the effect of compression parameters on compression ratio, decoding times and interactivity. We also present suggestions for optimizing JPEG2000 for remote volume visualization and volume browsing applications. The resulting system is ideally suited for client-server applications with the server maintaining the compressed volume data, to be browsed by a client with a low bandwidth constraint.

PDF417-based card carrying the much information is an international defacto standard for verification, registration, or other data management of the government issued card. In this paper, the PDF417 symbol (a two-dimensional (2D) bar code) is divided

Data Matrix is a 2D barcode with high information density and wide applicability. When printed in documents or used for labeling products, the barcode may be subject to deformations. This paper looks at the recognition of Data Matrix barcodes is under scaling, rotation and cylindrical warping. The results suggest that the readability of Data Matrix barcodes is immune to rotation transformations and responds well to scaling, while it is more seriously impaired by cylindrical warping.

In this paper, a landmark based localization system using a Kinect sensor is proposed and evaluated with the implemented system for precise and autonomous navigation of low cost robots. The proposed localization method finds the positions of landmark on the image plane and the depth value using color and depth images. The coordinates transforms are defined using the depth value. Using coordinate transformation, the position in the image plane is transformed to the position in the body frame. The ranges between the landmarks and the Kinect sensor are the norm of the landmark positions in body frame. The Kinect sensor position is computed using the tri-lateral whose inputs are the ranges and the known landmark positions. In addition, a new matching method using the pin hole model is proposed to reduce the mismatch between depth and color images. Furthermore, a height error compensation method using the relationship between the body frame and real world coordinates is proposed to reduce the effect of wrong leveling. The error analysis are also given to find out the effect of focal length, principal point and depth value to the range. The experiments using 2D bar code with the implemented system show that the position with less than 3cm error is obtained in enclosed space(3,500mm×3,000mm×2,500mm)

Packages are expected to carry ever more information in a limited space. One solution to this problem is automatic identification, i.e. adding elements to the package that can be linked to electronic information systems. The aim of this study was to find the best way to produce additional business for the value chain of packages via existing technologies for automatic identification.Automatic identification can give benefits in the form of cost savings, new business opportunities, additional value to existing business and increased customer loyalty to all players in the value chain. Available technologies are 2D bar codes, digital watermarks, image recognition, fibre matrix, radiofrequency identification tags and magnetic codes.Pilot tests and user studies showed that additional services offered via automatic identification should include detailed product data, recipes, nutrient needs and user instructions and should match user demand. Obstacles to use are costs, time consumption and complexity.In Japan, mobile barcodes are part of everyday life: well known to consumers and used on a flat‐rate basis. From the beginning, the introduction was based on consumer needs, not on early profit. Today, it operates on a win–win principle, with benefits for all the players in the value chain, and several traditional printers have created completely new service concepts for their customers. Copyright © 2011 John Wiley & Sons, Ltd.

Companies can optimize their supply chain if they exchange item-level data, i.e. specific data about each item gathered with the help of Radio Frequency Identification or 2D bar codes. However, business critical information might be inferred from this data, e.g. strategic relations or best practices. Therefore, companies are reluctant to share item-level data. In this paper we discuss requirements for an encryption scheme for exchanging item-level data by storing it in a cloud-based data repository. We envision a system that allows the data owner to enforce access control on an item-level. And data should remain confidential even against the cloud service provider.

2D bar coding is one of the methods to enable the health /medical data portability for prior and existing health care record system. Several field trial of PHR (Personal Health Record) project has been carried out in Japan. For standardize CTG digital record for PHR, we proposed MFER for CTG and data expression by 2D bar code (QR code). 1) Digital CTG recording signal was formatted adding a special profile header for MFER. 2) Two types of waveform data description format for MFER ware proposed, row data mode and compressed mode. 3) Row mode contained 2ch of FHR signals at 250msec and 1ch of tocogram signals at 1 sec whereas compressed mode contained FHR of 1 sec and tocogram of 2 sec. 4) QR code format v.15 (cell size 77 × 77) of error correction level M was applied for this trial. 5) Based on the MFER proposal, experimental system of CTG QR code generator and viewer was developed. ref: QR code http://www.denso-wave.com/qrcode/index-e.html MFER http://219.127.129.30/ikiw/index.php?en Using level v15 format QR code, 600 bytes of data were encoded to one cell. One cell of QR code included 113 sec of recording. Using consecutive 16 QR cells could handle 30.2 min of serial CTG recording. Using iPad, QR coded CTG was successfully replayed. CTG recording format for EHR/PHR by MFER was proposed. Further study was required to confirm clinical feasibility, QR code format of CTG should be one of the cost beneficial way for PHR/EHR. Supporting information can be found in the online version of this abstract. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Text compression is a necessity for efficient data storage and transmission. Especially in the digital era, volumes of digital text have increased incredibly. Traditional text compression methods, including Huffman coding and Lempel-Ziv-Welch, have certain limitations regarding their adaptability and efficiency in dealing with such complexity and diversity of data. In this paper, we propose a hybrid method that combines Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) to optimize the compression of text using the broad exploration capabilities of GA and fast convergence properties of PSO. The experimental results reflect that the proposed hybrid approach of GA-PSO yields much better performance in compression ratio than the standalone methods by reducing the size to about 65% while retaining integrity in the original content. The proposed method is also highly adaptable to various text forms and outperformed other state-of-the-art methods such as the Grey Wolf Optimizer, the Whale Optimization Algorithm, and the African Vulture Optimization Algorithm. These results support that the hybrid method GA-PSO seems promising for modern text compression.

The compression of binary texts using antidictionaries is a novel technique based on the fact that some substrings (called antifactors) never appear in the text. Let sb be an antifactor where b is its last bit. Every time s appears in the text we know that the next bit is b~ and hence omit its representation. Since building the set of all antifactors is space consuming at compression time, it is customary to limit the maximum length of antifactors considered up to a constant k. Larger k yields better compression of the text but requires more space at compression time. In this paper we introduce the notion of almost antifactors, which are strings that rarely appear in the text. More formally, almost antifactors are strings that, if we consider them as antifactors and separately code their occurrences as exceptions, the compression ratio improves. We show that almost antifactors permit improving compression with a limited amount of main memory to compress. Our experiments show that they obtain the same compression of the classical algorithm using only 30%-55% of its memory space.

A universal algorithm for sequential data compression is presented. Its performance is investigated with respect to a nonprobabilistic model of constrained sources. The compression ratio achieved by the proposed universal code uniformly approaches the lower bounds on the compression ratios attainable by block-to-variable codes and variable-to-block codes designed to match a completely specified source.

<p>The projection into 2030 of the climate data volume increase brings an important challenge to the climate science community. This is particularly true for the CMIP7 that is projected to need about an Exabyte of storage capacity. Error-bounded lossy compression is explored as a potential solution to the above problem by different climate research teams. Several lossy compression schemes have been proposed leveraging different forms of decorrelation (transforms, prediction, HoSVD, DNN), quantization (linear, non-linear, vector), and encoding (dictionary-based, variable length, etc.) algorithms. Our experience with different applications shows that the compression methods often need to be customized and optimized to fit the specificities of the datasets to compress and the user requirements on the compression quality, ratio, and throughput. However, none of the existing lossy compression software for scientific data has been designed to be customizable. To address this issue, we developed SZ3, an innovative customizable, modular compression framework. SZ3 is a full C++ refactoring of SZ2 enabling the specialization, addition, or removal of each stage of the lossy compression pipeline to fit the specific characteristics of the datasets to compress and the use-case requirements. This extreme flexibility allows adapting SZ3 to many different use-cases, from ultra-high compression for visualization to ultra-high-speed compression between the CPU (or GPU) and the memory. Thanks to its unique set of features: customization, high compression ratio, high compression throughput, and excellent accuracy preservation, SZ3 won a 2021 R&D100 award. In this presentation, we present SZ3 and a new data prediction-based decorrelation method that significantly improves the compression ratios for climate datasets over the state-of-the-art lossy compressors, while preserving the same data accuracy. Experiments based on CESM datasets show that SZ3 can lead to up to 300% higher compression ratios than SZ2 with the same compression error bound and similar compression throughput.</p>

Note on the greedy parsing optimality for dictionary-based text compression

Impact of Developer Choices on Energy Consumption of Software on Servers