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Provides instructions and guidelines to prospective authors who wish to submit manuscripts. [ABSTRACT FROM AUTHOR]

Glaciated regions are known to be particularly sensitive to climate change. Historical archives of glacier volume change are important, as they provide context for present-day changes. Although photogrammetric archives exist for many regions, their usefulness is often limited by a lack of contemporary ground control. High quality digital elevation models (DEMs) underpin a range of change analysis activities. This paper presents a costeffective solution which utilizes Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEMs as control for the scaling and orientation of archival data sets. Instead of relying upon ground-control points, a robust surface matching algorithm is employed to automatically determine the transformation required to register two overlapping DEMs. Through application to the Slakbreen glacier system in Svalbard, Norway, the strategy is assessed by first matching an ASTER DEM to a fixed lidar reference surface. This demonstrates that ASTER DEMs are effectively correct in scale, supporting their use as a control surface. The second stage of the research implements this by matching an aerial photogrammetric DEM to an ASTER reference surface. Resultant volumetric and annual elevation change rates are compared to those derived from lidar data, which are considered in this paper as a truth data set. ASTER-based matching produced a mean annual elevation change rate of -4.12 ma-1, compared to a value of -4.11 ma-1 derived from the iidar data. In volumetric terms, this equates to a difference of 0.6%. A major advantage of this approach is the near-global coverage offered by ASTER data and the opportunity that this presents for remote glacial change analysis over regional extents. [ABSTRACT FROM AUTHOR]

The article presents message from the editor-in-chief of the "IEEE Transactions on Industrial Electronics." Since June 1, 2004, this journal has used Manuscript Central for Web-based manuscript submission and the paper review process. In its mission to maintain a consistent and high-quality publication process, the IEEE Industrial Electronics Society follows a strict policy on the lengths of both submitted manuscripts and final papers. Submission length restrictions not only encourage authors to submit concise papers that readers will appreciate, but they help to keep the review process timely.

Differential evolution is one of the most powerful evolutionary algorithms for single objective optimization problems in the literature. Its application in the multiobjective optimization problems is also very successful, and many kinds of promising multiobjective differential evolution (MODE) algorithms have been proposed in the literature. This paper develops a new variant of MODE with two features. First, a set of personal archives are maintained to evolve the search process instead of a population with a fixed size, and a truncation procedure is used to enhance selection pressure. Second, multiple biased mutation operators incorporating the target solution quality are proposed, and an adaptive selection method is adopted to allocate the mutation operators to solutions. The proposed MODE is referred to as the MODE with personal archive and biased self-adaptive mutation selection (BiasMOSaDE). A set of 31 benchmark multiobjective problems selected from the literature are adopted to evaluate its performance. Computational results illustrate that the proposed BiasMOSaDE is competitive or even superior to several state-of-the-art MODEs in the literature. [ABSTRACT FROM AUTHOR]

This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the co-authors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under the primary entry in the Author Index. [ABSTRACT FROM PUBLISHER]

Many libraries, museums, and other organizations contain large collections of handwritten historical documents, for example, the papers of early presidents like George Washington at the Library of Congress. The first step in providing recognition/ retrieval tools is to automatically segment handwritten pages into words. State of the art segmentation techniques like the gap metrics algorithm have been mostly developed and tested on highly constrained documents like bank checks and postal addresses. There has been little work on full handwritten pages and this work has usually involved testing on clean artificial documents created for the purpose of research. Historical manuscript images, on the other hand, contain a great deal of noise and are much more challenging. Here, a novel scale space algorithm for automatically segmenting handwritten (historical) documents into words is described. First, the page is cleaned to remove margins. This is followed by a gray-level projection profile algorithm for finding lines in images. Each line image is then filtered with an anisotropic Laplacian at several scales. This procedure produces blobs which correspond to portions of characters at small scales and to words at larger scales. Crucial to the algorithm is scale selection, that is, finding the optimum scale at which blobs correspond to words. This is done by finding the maximum over scale of the extent or area of the blobs. This scale maximum is estimated using three different approaches. The blobs recovered at the optimum scale are then bounded with a rectangular box to recover the words. A postprocessing filtering step is performed to eliminate boxes of unusual size which are unlikely to correspond to words. The approach is tested on a number of different data sets and it is shown that, on 100 sampled documents from the George Washington corpus of handwritten document images, a total error rate of 17 percent is observed. The technique outperforms a state-of-the-art gap metrics word-segmentation algorithm on this collection. [ABSTRACT FROM AUTHOR]

Numerous archives and collections of past documents have become available recently thanks to mass scale digitization and preservation efforts. Libraries, national archives, and other memory institutions have started opening up their collections to interested users. Yet, searching within such collections usually requires knowledge of appropriate keywords due to different context and language of the past. Thus, non-professional users may have difficulties with conceptualizing suitable queries, as, typically, their knowledge of the past is limited. In this paper, we propose a novel approach for the temporal correspondence detection task that requires finding terms in the past which are semantically closest to a given input present term. The approach we propose is based on vector space transformation that maps the distributed word representation in the present to the one in the past. The key problem in this approach is obtaining correct training set that could be used for a variety of diverse document collections and arbitrary time periods. To solve this problem, we propose an effective technique for automatically constructing seed pairs of terms to be used for finding the transformation. We test the performance of proposed approaches over short as well as long time frames such as 100 years. Our experiments demonstrate that the proposed methods outperform the best-performing baseline by 113 percent for the New York Times Annotated Corpus and by 28 percent for the Times Archive in MRR on average, when the query has a different literal form from its temporal counterpart. [ABSTRACT FROM PUBLISHER]

This paper addresses an issue of erasure-coded data archival, where ( k+r, k ) erasure codes are employed to archive rarely accessed replicas. The traditional synchronous encoding process neither leverages the existence of replicas, nor handles encoding operations in a decentralized manner. To overcome these drawbacks, we exploit pipelined encoding processes to boost the data archival performance on storage clusters. First, we propose two data layouts called \mathbf {[D\,+\,P]_{cd}} and \mathrm {[3X]_ {cd}} by applying a chained-declustering mechanism to both Mirrored RAID-5 and triplication redundancy groups. Second, in light of the \text [D + P]cd and \mathrm {[3X]cd} layouts, we design two archiving schemes named \mathbf DP and \mathbf 3X , which exhibit the following three salient features: (i) exploiting data locality—two or three local blocks are read by each involved node for encoding; (ii) decentralized computation load—encoding operations are distributed among k nodes; and (iii) parallel archival processing—two or three encoding pipelines are simultaneously deployed to generate parity blocks. We implement both the DP and 3X schemes and three existing solutions (i.e., SynE, DE, and RapidRAID) in a real-world storage cluster. Experimental results show that our archival schemes outperform the other three solutions in terms of archiving time by a factor of at least 3.41 in a nine-node storage cluster. The experiments strongly indicate that the performance bottleneck of SynE lies in its block-receiving stage; it is disk I/O rather than network traffic that dominates archiving time for both the DE and RapidRAID schemes. [ABSTRACT FROM PUBLISHER]

Very high resolution synthetic aperture radar (SAR) sensors represent an alternative to aerial photography for delineating floods in built-up environments where flood risk is highest. However, even with currently available SAR image resolutions of 3 m and higher, signal returns from man-made structures hamper the accurate mapping of flooded areas. Enhanced image processing algorithms and a better exploitation of image archives are required to facilitate the use of microwave remote-sensing data for monitoring flood dynamics in urban areas. In this paper, a hybrid methodology combining backscatter thresholding, region growing, and change detection (CD) is introduced as an approach enabling the automated, objective, and reliable flood extent extraction from very high resolution urban SAR images. The method is based on the calibration of a statistical distribution of “open water” backscatter values from images of floods. Images acquired during dry conditions enable the identification of areas that are not “visible” to the sensor (i.e., regions affected by “shadow”) and that systematically behave as specular reflectors (e.g., smooth tarmac, permanent water bodies). CD with respect to a reference image thereby reduces overdetection of inundated areas. A case study of the July 2007 Severn River flood (UK) observed by airborne photography and the very high resolution SAR sensor on board TerraSAR-X highlights advantages and limitations of the method. Even though the proposed fully automated SAR-based flood-mapping technique overcomes some limitations of previous methods, further technological and methodological improvements are necessary for SAR-based flood detection in urban areas to match the mapping capability of high-quality aerial photography. [ABSTRACT FROM PUBLISHER]

Particle swarm optimization (PSO) has been recently adopted to solve constrained optimization problems. In this paper, a cultural-based constrained PSO is proposed to incorporate the information of the objective function and constraint violation into four sections of the belief space, specifically normative knowledge, spatial knowledge, situational knowledge, and temporal knowledge. The archived information facilitates communication among swarms in the population space and assists in selecting the leading particles in three different levels: personal, swarm, and global levels. Comprehensive comparison of the proposed heuristics over a number of benchmark problems with selected state-of-the-art constraint-handling techniques demonstrates that the proposed cultural framework helps the multiple-swarm PSO to perform competitively with respect to selected designs. [ABSTRACT FROM PUBLISHER]

The article presents information on the International Magnetics Conference 2005 held in Nagoya, Japan. The conference was of outstanding scientific and technical quality. Altogether there were 1058 presentations given at the conference. These were strictly selected by the Program Committee from the 1527 digests and nominations that were submitted. After the conference, a total of 817 manuscripts were submitted for consideration for publication in this volume. This was a much heavier burden than anticipated for the editors of Intermag 2005. In order to avoid any delays, two publications teams were set up: Higashi and Nishi.

Recently, various multiobjective particle swarm optimization (MOPSO) algorithms have been developed to efficiently and effectively solve multiobjective optimization problems. However, the existing MOPSO designs generally adopt a notion to "estimate" a fixed population size sufficiently to explore the search space without incurring excessive computational complexity. To address the issue, this paper proposes the integration of a dynamic population strategy within the multiple-swarm MOPSO. The proposed algorithm is named dynamic population multiple-swarm MOPSO. An additional feature, adaptive local archives, is designed to improve the diversity within each swarm. Performance metrics and benchmark test functions are used to examine the performance of the proposed algorithm compared with that of five selected MOPSOs and two selected multiobjective evolutionary algorithms. In addition, the computational cost of the proposed algorithm is quantified and compared with that of the selected MOPSOs. The proposed algorithm shows competitive results with improved diversity and convergence and demands less computational cost. [ABSTRACT FROM AUTHOR]

The problem of determining the script and language of a document image has a number of important applications in the field of document analysis, such as indexing and sorting of large collections of such images, or as a precursor to optical character recognition (OCR). In this paper, we investigate the use of texture as a tool for determining the script of a document image, based on the observation that text has a distinct visual texture. An experimental evaluation of a number of commonly used texture features is conducted on a newly created script database, providing a qualitative measure of which features are most appropriate for this task. Strategies for improving classification results in situations with limited training data and multiple font types are also proposed. [ABSTRACT FROM AUTHOR]

Earth observation data have increased significantly over the last decades with satellites collecting and transmitting to Earth receiving stations in excess of 3 TB of data a day. This data acquisition rate is a major challenge to the existing data exploitation and dissemination approaches. The lack of content- and semantic-based interactive information searching and retrieval capabilities from the image archives is an impediment to the use of the data. In this paper, we describe a framework we have developed [Intelligent Interactive Image Knowledge Retrieval (I³ KR)] that is built around a concept-based model using domain-dependant ontologies. In this framework, the basic concepts of the domain are identified first and generalized later, depending upon the level of reasoning required for executing a particular query. We employ an unsupervised segmentation algorithm to extract homogeneous regions and calculate primitive descriptors for each region based on color, texture, and shape. We initially perform an unsupervised classification by means of a kernel principal components analysis method, which extracts components of features that are nonlinearly related to the input variables, followed by a support vector machine classification to gene rate models for the object classes. The assignment of concepts in the ontology to the objects is achieved automatically by the integration of a description logics-based inference mechanism, which processes the interrelationships between the properties held in the specific concepts of the domain ontology. The framework is exercised in a coastal zone domain. [ABSTRACT FROM AUTHOR]

The widespread use of digital multimedia data has increased the need for effective means of copyright protection. Watermarking has attracted much attention, as it allows the embedding of a signature in a digital document in an imperceptible manner. In practice, watermarking is subject to various attacks, intentional or unintentional, which degrade the embedded information, rendering it more difficult to detect. A very common, but not malicious, attack is quantization, which is unavoidable for the compression and transmission of digital data. The effect of quantization attacks on the nearly optimal Cauchy watermark detector is examined in this paper. The quantization effects on this detection scheme are examined theoretically, by treating the watermark as a dither signal. The theory of dithered quantizers is used in order to correctly analyze the effect of quantization attacks on the Cauchy watermark detector. The theoretical results are verified by experiments that demonstrate the quantization effects on the detection and error probabilities of the Cauchy detection scheme. [ABSTRACT FROM AUTHOR]

Nonlinear equation systems (NESs) are ubiquitous, and solving them is an important yet challenging task in numerical computation. Most of the NESs usually contain multiple roots. To locate multiple roots, the use of evolutionary algorithms attracts more attention recently. For the purpose of finding different roots within a limited computational budget in a single run simultaneously, in this article, a hybrid niching-based differential evolution with two archives, referred to as HNDE/2A, is proposed. It can be featured as: 1) two niching techniques, i.e., crowding and speciation, are combined to balance the diversity and convergence; 2) a root archive is used to save the found roots during the run. Additionally, if a root is saved into the root archive, it will be reinitialized immediately to further promote the population diversity; and 3) an inferior offspring archive is presented to utilize the useful information of the inferior offspring. To evaluate the performance of our proposal, 30 problems are chosen as the test suite. Moreover, HNDE/2A is also used to solve two real-world problems. Compared with other algorithms, HNDE/2A yields better results in terms of the root ratio and the success rate with a less computational budget. [ABSTRACT FROM AUTHOR]

The article presents information about the reviewers who review the papers published in the journal "IEEE Transactions on Electronic Devices." The author acknowledges and recognizes the individuals inside and out of the Electron Devices Society who have contributed to this effort. The author informs that many reviewers volunteer for more noble reasons than merely a sense of duty. The journal reviews over 800 manuscripts a year and publish a little less than half of those. A list of the men and women who have done their job as reviewers of these manuscripts by virtue of the timeliness, thoroughness and thoughtfulness of their comments has also been presented.

The diffusion of multimedia technology and the availability of more and more effective electronic imaging tools has driven the attention of researchers and managers of academy, industry, museums, and government bodies, working in the sector of cultural heritage, to the possibility of successfully applying image processing techniques to the analysis, restoration, archiving, and preservation of artwork. The growing interest for the application of image processing techniques for cultural heritage is testified to by several events.

The article offers information on Special Issue on Pulsed Power Science and Technology would be going to publish in October 2018 announced by IEEE Transactions on Plasma Science. Topics discussed include aim is to provide the distribution of archival quality papers detailing innovative developments in the areas of pulsed power science and technology; manuscript submitted to the Special Issue must be revised; and discusses the significance of peer review.

A digital time stamping system based on open source technologies (LINUX-UBUNTU, OpenTSA, OpenSSL, MySQL) is described in detail, including all important testing results. The system, called BALTICTIME, was developed under a project sponsored by the European Commission under the Program FP 6. It was designed to meet the requirements posed to the systems of legal and accountable time stamping and to be applicable to the hardware commonly used by the national time metrology laboratories. The BALTICTIME system is intended for the use of governmental and other institutions as well as personal bodies. Testing results demonstrate that the time stamps issued to the user by BALTICTIME and saved in BALTICTIME's archives (which implies that the time stamps are accountable) meet all the regulatory requirements. Moreover, the BALTICTIME in its present implementation is able to issue more than 10 digital time stamps per second. The system can be enhanced if needed. The test version of the BALTICTIME service is free and available at http://baltictime.pfi.lt:8080/btws/ and http://baltictime.lnmc.lv:8080/ btws/. [ABSTRACT FROM AUTHOR]

This work implements an enhanced hybrid classification method through the utilization of the naïve Bayes approach and the Support Vector Machine (SVM). In this project, the Bayes formula was used to vectorize (as opposed to classify) a document according to a probability distribution reflecting the probable categories that the document may belong to. The Bayes formula gives a range of probabilities to which the document can be assigned according to a predetermined set of topics (categories) such as those found in the "20 Newsgroups" data set for instance. Using this probability distribution as the vectors to represent the document, the SVM can then be used to classify the documents on a multidimensional level. The effects of an inadvertent dimensionality reduction caused by classifying using only the highest probability using the naïve Bayes classifier can be overcome using the SVM by employing all the probability values associated with every category for each document. This method can be used for any data set and shows a significant reduction in training time as compared to the Lsquare method and significant improvement in the classification accuracy when compared to pure naïve Bayes systems and also the TF-IDF/SVM hybrids. [ABSTRACT FROM AUTHOR]

Angular similarity measures have been utilized by several database applications to define semantic similarity between various data types such as text documents, time-series, images, and scientific data. Although similarity searches based on Euclidean distance have been extensively studied in the database community, processing of angular similarity searches has been relatively untouched. Problems due to a mismatch in the underlying geometry as well as the high dimensionality of the data make current techniques either inapplicable or their use results in poor performance. This brings up the need for effective indexing methods for angular similarity queries. We first discuss how to efficiently process such queries and propose effective access structures suited to angular similarity measures. In particular, we propose two classes of access structures, namely, Angular-sweep and Cone-shell, which perform different types of quantization based on the angular orientation of the data objects. We also develop query processing algorithms that utilize these structures as dense indices. The proposed techniques are shown to be scalable with respect to both dimensionality and the size of the data. Our experimental results on real data sets from various applications show two to three orders of magnitude of speedup over the current techniques. [ABSTRACT FROM AUTHOR]

Digital archives protect important data collections from failures by making multiple copies at other archives, so that there are always several good copies of a collection. In a cooperative replication network, sites "trade" space, so that each site contributes storage resources to the system and uses storage resources at other sites. Here, we examine bid trading a mechanism where sites conduct auctions to determine who to trade with. A local site wishing to make a copy of a collection announces how much remote space is needed, and accepts bids for how much of its own space the local site must "pay" to acquire that remote space. We define a spectrum of trading scenarios, ranging from a network of archives and digital libraries that trust each other, to a scenario where sites do as they please, including breaking the rules. Then, we focus on developing techniques for the scenarios where sites trust each other, although we discuss issues that may arise if sites are greedy or malicious. We examine the best policies for determining when to call auctions and how much to bid, as well as the effects of "maverick" sites that behave differently than other sites. Simulations of auction and trading sessions indicate that bid trading can allow sites to achieve higher reliability than the alternative: a system where sites trade equal amounts of space without bidding. [ABSTRACT FROM AUTHOR]

A new model for the correction of flicker in archived film sequences is formulated from first principles. The model takes into account the Hurter--Driffield Density versus log Exposure characteristic to estimate image intensity errors caused by exposure inconsistencies. The model is shown to be highly nonlinear and can accommodate typical grayscale manipulations performed during film-to-video transfers. Motivated by the above model, an algorithm for the estimation of a correction profile across the available grayscale is developed. This profile is obtained from histogrammed gray-level differences between a reference and a degraded frame and is robust with respect to intensity changes due to scene and camera motion. Our results show that the proposed algorithm is very effective toward removing flicker for a variety of archived sequences and compares favorably with other state-of-the-art techniques developed for the same purpose. Additionally, the parametric form of the profile makes it particularly suitable for metadata use and allows fast inversion of the restoration process and recovery of the original material. [ABSTRACT FROM AUTHOR]

The WEBSOM methodology, proven effective for building very large text archives, includes a method that extracts labels for each document cluster assigned to nodes in the map. However the WEBSOM method needs to retrieve all the words of all the documents associated to each node. Since maps may have more than 100,000 nodes and since the archive may contain up to seven million documents, the WEBSOM methodology needs a faster alternative method for keyword selection. Presented here is such an alternative method that is able to quickly deduce meaningful labels per node in the map. It does this just by analyzing the relative weight distribution of the SOM weight vectors and by taking advantage of some characteristics of the random projection method used in dimensionality reduction. The effectiveness of this technique is demonstrated on news document collections. [ABSTRACT FROM AUTHOR]

Describes a bottom-up method for document layout analysis. Implementation of the algorithm in the Chemical Literature Data Extraction (CLIDE) system; Kruskal's algorithm as basis of method; Significance of the method in the construction of the physical page structure; Reduction of algorithm's computational complexity to linear by the use of heuristic and path-compression.