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Fidelity has been a critical concern of researchers throughout the history of driving simulation. Understanding the limits of a driving simulation system is a prerequisite for conducting valid driving simulator studies. This paper proposes a novel and interdisciplinary methodology to ensure validity of studies using driving simulators (primarily for traffic control devices and other object detection tasks) based on the visual limits of human sensory and perceptual capabilities, and the characteristics of raster graphics. This methodology decomposes the perceptual issues of a stimulus into perceptual issues of different visual properties like luminance, hue, or text of the stimulus. By systematically analyzing the mechanism of human vision in driving simulators, the perceptual principle is proposed to ensure perceivable visual details in human-in-the-loop driving simulation systems. Additionally, the graphic principle is proposed to ensure perceivable features of a target object in the virtual driving environment. Both principles quantify the minimum requirements of visual fidelity with two measurements: angular resolution and matrix dimensions. The enriched results from existing pertinent studies are analyzed and organized to yield support of both principles. This research focuses on the minimum requirements for four factors; namely the visual acuity of drivers, the specifications of display systems, the configurations of graphics systems, and the design of virtual scenarios, as well as the relationship among all these factors to assess the visual fidelity in driving simulation systems. Within the realm of human perception, this work can provide criteria for proper design, calibration, and usage of driving simulators. [ABSTRACT FROM AUTHOR]

An MeV-SIMS microprobe has been constructed based on primary beam collimation by a conical glass capillary. Molecular surface mapping is obtained by raster scanning of the sample with a piezo-driven XY stage. The angular divergence of the collimated beam is smaller than 0.3 mrad, which allows a relatively large distance between capillary exit and sample without a significant loss in lateral resolution. With a capillary of 5 μm outlet diameter a lateral resolution of approximately 10 μm has been observed at a working distance of 50 mm. Capillary collimation can be obtained in a very simple and inexpensive fashion and is virtually independent of the used beam particle properties over a large range of ion mass and energy. [ABSTRACT FROM AUTHOR]

We consider two NP-hard open dimension nesting problems for which a set of items has to be packed without overlapping into a two-dimensional bin in order to minimize one or both dimensions of this bin. These problems are faced by real-life applications, such as textile, footwear and automotive industries. Therefore, there is a need for specialized systems to help in a decision making process. Bearing this in mind, we derive new concepts as the no-fit raster, which can be used to check overlapping between any two-dimensional generic-shaped items. We also use a biased random key genetic algorithm to determine the sequence in which items are packed. Once the sequence of items is determined, we propose two heuristics based on bottom-left moves and the no-fit raster concept, which are in turn used to arrange these items into the given bin observing the objective criteria. As far as we know, the problem with two-open dimensions is being solved for the first time in the context of nesting problems and we present the first whole quadratic model for this problem. Computational experiments conducted on benchmark instances from the literature (some from the textile industry and others including circles, convex, and non-convex polygons) show the competitiveness of the approaches developed as they were able to calculate the best results for 74.14% of the instances. It can be observed that these results show new directions in terms of solving nesting problems whereby approaches can be coupled in existing intelligent systems to support decision makers in this field. [ABSTRACT FROM AUTHOR]

The field of computational cognitive neuroscience (CCN) builds and tests neurobiologically detailed computational models that account for both behavioral and neuroscience data. This article leverages a key advantage of CCN–namely, that it should be possible to interface different CCN models in a plug-and-play fashion–to produce a new and biologically detailed model of perceptual category learning. The new model was created from two existing CCN models: the HMAX model of visual object processing and the COVIS model of category learning. Using bitmap images as inputs and by adjusting only a couple of learning-rate parameters, the new HMAX/COVIS model provides impressively good fits to human category-learning data from two qualitatively different experiments that used different types of category structures and different types of visual stimuli. Overall, the model provides a comprehensive neural and behavioral account of basal ganglia-mediated learning. [ABSTRACT FROM AUTHOR]

Landscape researchers have described different options for landscape classifications and have suggested the approximate size of landscape units for various scale levels of study and the most suitable data layers (representing biophysical characteristics) for differentiating them. The literature already contains examples of evaluating data layers with regard to their information value, their correlation with one another, and so on, but less research has been done on the suitability of data layers from the perspective of the scale of landscape research. The objective of this paper is to propose a quantitative method to assess different data layers by landscape classification scale. The proposed method is based on systematic sequential multilevel division of the study area, calculating the average moderate coefficient of variation for each scale level, and comparing calculations between scale levels. It can be used to objectively determine which raster data layers are more suitable for defining large landscape units and which are more suitable for defining small ones. We tested the method for Slovenia, a small country at the junction of the Alps, Pannonian Basin, Mediterranean, and Dinaric Alps with a high landscape diversity. To test the method, we systematically divided the country into smaller units using ten differently structured grids (for the case of Slovenia, we used squares with a baseline of 1, 10, 20 km, and so on up to 100 km). For each data layer, we calculated the average moderate coefficient of variation for each division and compared it with the average coefficient of variation for the highest level, which represented the country as one whole unit. The ratios allowed us to classify the data layers into categories and assess which data layers are more important in landscape classification at a small scale, which ones are more important at a large scale, and which ones function as noise. As expected, variation diminishes with a smaller baseline in all cases, but the gradient of decreasing variation is different. We also studied the categorization of data layers using a hierarchical classification method. Following to the proposed method, it is possible to create various categories of data layers by the landscape classification scale for a certain area. The method can be used for any area and is an option for reducing subjectivity in selecting data layers for landscape analysis classification for the most general purposes (e.g., production of landscape classifications for education). [ABSTRACT FROM AUTHOR]

Point alignment is an important topic in computer vision. In order to implement cross-stitch embroidery in an automatic way, the grid points in a raster like pattern are desired to be recognized at first. In this paper, we design an algorithm to align the grid points in a raster like pattern in case of checkerboard pattern, and the proposed method was used to recognize the weave holes in a cross-stitch pattern. The method implemented in four steps. First, vertices on the boundary were extracted via removing the inner points. Next, the four corner points were detected in the extracted boundary points. Then the left-lower and left-upper points were positioned. Finally, the points alignment was implemented starting at the left-lower point from the bottom to top, left to right. The comparison experiments demonstrated that our method is robust to geometrical distortion and pose change. The method was applied to align the weave holes in a cross-stitch pattern. The results showed that the proposed method addressed its potential application to the machine weave of the cross-stitch. [ABSTRACT FROM AUTHOR]

The problem of caching shortest paths has been widely studied. All of existing methods that address this problem assume that the condition of road networks does not change with time. In this paper, we study how to refresh a cache when one edge of the underlying road network (graph) changes. A bitmap-based cache structure is proposed to store and give access to shortest paths. In the following, algorithms are developed to detect shortest paths that are affected by the change of edge. After detecting affected paths, several heuristic-based refreshment strategies are proposed to update the cache. We have conducted a series of experiments to compare the performance of proposed strategies. It shows that replacing affected shortest paths with new paths whose benefit values are the largest should be applied in the shortest path caching applications such as navigation and map services. [ABSTRACT FROM AUTHOR]

This paper presents a method for automatically constructing irregular floor plans. Given image patterns, the program produces a variety of layouts that satisfy users' geometric and topological requirements. The program extracts irregular regions from images via statistical region merging. It employs subgraph matching and simulated annealing to construct topologically feasible layouts. Methods for measuring similarities between the desired templates and irregular rooms are also reported. Applications using raster images and vector graphics are tested. Both applications generate feasible floor plans whose appearances resemble the input patterns. The results indicate that layout automation is valid without an orthogonal framework in the plane. Irregular layout synthesis allows architects to instantly implement visual preferences in early design stages. [ABSTRACT FROM AUTHOR]

Bitmap indexes are commonly used in data warehousing applications such as on-line analytic processing (OLAP). Storing the bitmaps in compressed form has been shown to be effective not only for low cardinality attributes, as conventional wisdom would suggest, but also for high cardinality attributes. Compressed bitmap indexes, such as Byte - aligned Bitmap Compression (BBC), Word - Aligned Hybrid (WAH) and several of their variants have been shown to be efficient in terms of both time and space, compared to traditional database indexes. In this paper, we propose a new technique for compressed bitmap indexing, called Super Byte - aligned Hybrid (SBH) bitmap compression, which improves upon the current state-of-the-art compression schemes. In our empirical evaluation, the query processing time of SBH was about five times faster than that of WAH, while the size of its compressed bitmap indexes was retained nearly close to that of BBC. [ABSTRACT FROM AUTHOR]

The choice of the proper resolution in landslide susceptibility mapping is a worth considering issue. If, on the one hand, a coarse spatial resolution may describe the terrain morphologic properties with low accuracy, on the other hand, at very fine resolutions, some of the DEM-derived morphometric factors may hold an excess of details. Moreover, the landslide inventory maps are represented throughout geospatial vector data structure, therefore a conversion procedure vector-to-raster is required. This work investigates the effects of raster resolution on the susceptibility mapping in conjunction with the use of different algorithms of vector-raster conversion. The Artificial Neural Network technique is used to carry out such analyses on two Sicilian basins. Seven resolutions and three conversion algorithms are investigated. Results indicate that the finest resolutions do not lead to the highest model performances, whereas the algorithm of conversion data may significantly affect the ANN training procedure at coarse resolutions. [ABSTRACT FROM AUTHOR]

Estimating the cardinality of data streams over a sliding window is an important problem in many applications, such as network traffic monitoring, web access log analysis and database. The problem becomes more difficult in large-scale data streams when time and space complexity is taken into account. In this paper, we present a novel randomized data structure to address the problem. The significant contributions are as follows: (1) A space-efficient counter vector sketch (CVS) are proposed, which extends the well-known bitmap sketch to sliding window settings. (2) Based on the CVS, a random update mechanism is introduced, whereby a small fixed number of entries are randomly chosen from CVS in a step and then updated. This means that the update procedure just costs constant time. (3) Furthermore, estimating cardinality by CVS just needs one-pass scan of the data. (4) Finally, a theoretical analysis is given to show the accuracy of CVS-based estimators. Our comprehensive experiments confirm that the CVS-based schema attains high accuracy, and demonstate its time efficiency in comparison with the Timestamp Vector (TSV) and the auxiliary indexing method. [ABSTRACT FROM AUTHOR]

In raster scanning applications of atomic force microscopies, precisely tracking periodic triangular trajectories is the major objective of nanopositioning stages. Considering the fact of periodic operations, the repetitive control technique becomes promising and has been recently developed to reduce tracking errors. In our new experiments, it is found that, with triangular reference input, the hysteresis nonlinearity mainly affects the system at the odd harmonics of the input signal. In this sense, an odd-harmonic repetitive control (ORC) strategy is proposed to handle the hysteresis nonlinearity, with the hysteresis treated as the odd-harmonic periodic disturbance. Therefore, it avoids the modeling and inverting of the complex hysteresis nonlinearity. Another benefit of the developed ORC strategy is that it can also account for the tracking errors caused by the linear dynamics effect. To verify the effectiveness of the ORC strategy, real-time experiments are performed on a custom-built piezo-actuated nanopositioning stage. Experimental results show that the developed ORC strategy achieves precise tracking of 1562.5-Hz triangular trajectory with the hysteresis nonlinearity mitigated to a negligible level, which demonstrates the feasibility and effectiveness of the developed ORC strategy on hysteresis compensation during high-speed raster scanning. [ABSTRACT FROM AUTHOR]

Block Truncation Coding (BTC) image compression technique is capable of extraordinary computational efficiency for high-framerate configurations under the trend of high image resolution. However, the recent development such as the Dot-Diffused BTC (DDBTC) method still encounters various challenges of visual artifacts, including impulsive noise, false contours and blocking effects. In this study, a new method, the Near-Aperiodic DDBTC (NADDBTC) technique, is proposed to analyze the tradeoff and balance between computational efficiency and visual artifacts. It is mainly because of the quantization levels and the co-optimized Class Matrix (CM) and Diffused Matrix (DM) during image compression procedure. In addition, the tiling method for bitmap generation is optimized for aperiodic compressed results. Experimental results show that this method is capable of excellent image quality and visual perception, as well as processing efficiency, at a level similar to the DDBTC technique by taking advantage of parallel processing in dot diffusion. [ABSTRACT FROM AUTHOR]

This paper presents an important step towards the standardization of research works on Optical Character Recognition in Persian language. It describes the formations of a standard handwritten database, including isolated digits, isolated signs, multi-digit numbers, numerical strings, courtesy amounts, and postal codes. In this regard, binary images of 72,180 samples were extracted from the designed forms. These forms were filled by 180 writers selected from different ages, genders, and jobs. Then these forms were scanned at 300 dpi with a high-speed scanner. Finally, forms are segmented into samples and are stored in bitmap format. This database is named PHOND, Persian Handwritten Optical Numbers & Digits, and it is available to the research community. Comparisons with the previous related databases illustrate the advantages of PHOND against other databases. Different experiments are done using PHOND database and the results are compared with other research works in handwritten recognition. [ABSTRACT FROM AUTHOR]

Frame aggregation is one of the IEEE802.11n wireless networks features. In frame aggregation multiple frames are combined and then sent. However sending a large number of frames in one aggregation frame in an error-prone environment will result in massive frame loss and retransmission. Thus a prior knowledge about the state of the channel will help in selecting the proper aggregation size that would utilize the channel bandwidth and improve the system performance. In this paper, we introduce an algorithm that adjusts the aggregation size based on the ACK bitmap without adding any extra headers or messages for determining the network conditions. Simulation results show that the Acknowledgment Bitmap can be used as an indicator of the network conditions to create adaptive frame aggregation schemes. [ABSTRACT FROM AUTHOR]

Due to the advancement in cloud computing technology, the research on database outsourcing has been actively done. In database outsourcing, because a service provider might be untrusted or compromised, two issues of data security emerge: data confidentiality and data integrity. Many data transformation schemes were widely studied for preserving data confidentiality, but they are vulnerable to data leakage problem because they do not consider data distribution when encrypting original data. Meanwhile, several query authentication schemes were proposed to verity data integrity, but they suffer from transmission overhead of verification data. In this paper, we propose a privacy-aware data transformation and a query authentication scheme which guarantees the data confidentiality and the query result integrity of sensitive data. To solve the original data leakage problem, we propose a bitmap-based data transformation scheme with anchor selection based on data distribution. We also devise an algebraic coding-based hash index that transforms a query to a bitmap and retrieves the anchor information efficiently. To reduce the transmission overhead of verification data, we propose a query result authentication index that stores an encrypted signature for each anchor, which is an concatenated hash digest of cluster data. Through performance evaluation, we show that our scheme outperforms the existing method up to 15 times, in terms of query processing time and verification overhead. [ABSTRACT FROM AUTHOR]

Data warehousing is the crucial part of business intelligence applications. The data warehouse physical design is a hard task due to a large number of possible choices involved. The bitmap join indexes selection problem is crucial in the data warehouse physical design. All proposed approaches to solve the bitmap join indexes selection problem are based on statistics such as data mining or meta-heuristics such as genetic algorithm and particle swarm optimization. In the present work, we propose a new approach based on mixed-integer linear programming for solving the bitmap join indexes selection problem. Several experiments are performed to demonstrate the effectiveness of the proposed approach and the results are compared to the well known approaches that are best so far: the data mining, the genetic algorithm and particle swarm optimization based approaches. The mixed-integer linear programming is found to be faster and more effective than the genetic algorithm, particle swarm optimization and data mining approaches for solving the bitmap join indexes selection problem. [ABSTRACT FROM AUTHOR]

Various applications, like for instance algorithm design, mission planning, geo-referencing, change detection, Automatic Target Recognition (ATR) or SAR data analysis, rely on simulated synthetic aperture radar (SAR) images. However, there are different SAR simulation techniques with different advantages and disadvantages. Depending on the needs of a certain application, a suitable SAR simulation technique has to be used. This paper compares three SAR image simulation approaches, RaySAR, CohRaS®, and SARViz, showing their similarities and differences. RaySAR and CohRaS® are two ray tracing based SAR image simulators. RaySAR is based on the open-source software POV-Ray, while CohRaS® is developed as SAR simulator from scratch. The third simulator, SARViz, is a real-time SAR simulator based on the rasterization approach. The geometrical features of the three simulators are compared and the differences and different applications are analyzed. [ABSTRACT FROM AUTHOR]

An architectural floorplan is a class of drawings that reflects the layout of rooms. The difference between a floorplan and a natural image and its dual features as both a vector graphic and a raster image makes it difficult to be generated by conventional deep neural generative models. We propose an adversarial generative framework that combines vector generation and raster discrimination for residential floorplan generation tasks. The floorplan is first generated in vector format with room areas as constraints and then discriminated in raster format visually using convolutional layers. A Differentiable Renderer connects the gap between the Vector Generator and Raster Discriminator. A self-attention mechanism is utilized to capture the interrelations of rooms in each floorplan. Experiments were conducted to demonstrate the feasibility of the proposed FloorplanGAN. In addition, we evaluated the effectiveness of generation based on diverse objective metrics and a user study. The code is available here: https://github.com/luozn15/FloorplanGAN. [Display omitted] • Design the neural network framework with priors of architecture design domain knowledge. • Generating floorplans in format of vector graphics instead of raster images. • Adopting simplified differentiable render to co-train the vector generator and raster discriminator. • Experimenting with diverse evaluation methods for floorplan generation effectiveness. • Adopting attention mechanism to explicitly capture interrelations of rooms in floorplan. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we proposed multi-factors correlation (MFC) to describe the image, structure element correlation (SEC), gradient value correlation (GVC) and gradient direction correlation (GDC). At first, the RGB color space image is converted to a bitmap image and a mean color component image utilizing the block truncation coding (BTC). Then, three correlations will be used to extract the image feature. The structure elements can effectively represent the bitmap which is generated by BTC, and SEC can effectively denote the bitmap׳s structure and the correlation of the block in the bitmap. GVC and GDC can effectively denote the gradient relation, which is computed by a mean color component image. Formed by SEC, GVC and GDC, the image feature vectors can effectively represent the image. In the end, the results demonstrate that the method has better performance than other image retrieval methods in the experiment. [Copyright &y& Elsevier]

Abstract: In processing raster digital elevation models (DEMs) it is often necessary to assign drainage directions over flats—that is, over regions with no local elevation gradient. This paper presents an approach to drainage direction assignment which is not restricted by a flat's shape, number of outlets, or surrounding topography. Flow is modeled by superimposing a gradient away from higher terrain with a gradient towards lower terrain resulting in a drainage field exhibiting flow convergence, an improvement over methods which produce regions of parallel flow. This approach builds on previous work by Garbrecht and Martz (1997), but presents several important improvements. The improved algorithm guarantees that flats are only resolved if they have outlets. The algorithm does not require iterative application; a single pass is sufficient to resolve all flats. The algorithm presents a clear strategy for identifying flats and their boundaries. The algorithm is not susceptible to loss of floating-point precision. Furthermore, the algorithm is efficient, operating in O(N) time whereas the older algorithm operates in time. In testing, the improved algorithm ran 6.5 times faster than the old for a 100×100 cell flat and 69 times faster for a 700×700 cell flat. In tests on actual DEMs, the improved algorithm finished its processing 38–110 times sooner while running on a single processor than a parallel implementation of the old algorithm did while running on 16 processors. The improved algorithm is an optimal, accurate, easy-to-implement drop-in replacement for the original. Pseudocode is provided in the paper and working source code is provided in the Supplemental Materials. [Copyright &y& Elsevier]

Abstract: The watersheds are commonly delineated from digital elevation models (DEM). This approach is not efficient when an accurate DEM is not available. Furthermore, since raster-based algorithms are employed, the computations for large areas are very time consuming and even may be impractical. This article investigates delineation of the watersheds from the medial axis of river networks: If the river network is sampled by a set of points, the medial axis of the sample points provides an approximation of the catchments, whose aggregation results in the watersheds. Although this idea has been already proposed in the literature, the complexities of the medial axis extraction prevent it from being practically used. A major issue is appearing extraneous branches in the media axis due to perturbations of the sample points, which must be filtered out in a pre- or post-processing step. This article improves a Voronoi-based medial axis extraction algorithm by using labeled sample points to automatically avoid extraneous branches. The proposed method is used in four case studies to delineate the watersheds. The results illustrate that the proposed method is stable, easy to implement and robust, even in presence of significant noises and perturbations. The results also indicate that the watersheds delineated using the proposed and the DEM-based methods are reasonably comparable. [Copyright &y& Elsevier]

Abstract: With the expansion of complex geographic calculations and the increase of spatial data types involved in the spatial analysis of large areas, the need becomes urgent for fast rasterization of massive multi-source geographic vector data. A parallel scanline algorithm is proposed for rapid rasterization. It provides a systematic solution to solve the complicated situation in parallel processing (cross-processor boundaries, common boundaries, and tiny polygons), thus ensuring the accuracy of the parallel scanline algorithm. The relationship of parallel speedup with the number of processors, the data partition pattern, and the raster grid size is discussed. Massive vector geographic data (approximately 0.7 million polygons) used in the experiment were effectively processed, thereby dramatically reducing the processing time and getting good speedup. [Copyright &y& Elsevier]

Abstract: Labeling of connected components in an image or a raster of non-imagery data is a fundamental operation in fields of pattern recognition and machine intelligence. The bulk of effort devoted to designing efficient connected components labeling (CCL) algorithms concentrated on the domain of binary images where labeling is required for a computer to recognize objects. In contrast, in the Geographical Information Science (GIS) a CCL algorithm is mostly applied to multi-categorical rasters in order to either convert a raster to a shapefile, or for statistical characterization of individual clumps. Recently, it has become necessary to label connected components in very large, giga-cell size, multi-categorical rasters but performance of existing CCL algorithms lacks sufficient speed to accomplish such task. In this paper we present a modification to the popular two-scan CCL algorithm that enables labeling of giga-cell size, multi-categorical rasters. Our approach is to apply a divide-and-conquer technique coupled with parallel processing to a standard two-scan algorithm. For specificity, we have developed a variant of a standard CCL algorithm implemented as r.clump in GRASS GIS. We have established optimal values of data blocks (stemming from the divide-and-conquer technique) and optimal number of computational threads (stemming from parallel processing) for a new algorithm called r.clump3p. The performance of the new algorithm was tested on a series of rasters up to 160Mcells in size; for largest size test raster a speed up over the original algorithm is 74 times. Finally, we have applied the new algorithm to the National Land Cover Dataset 2006 raster with cells. Labeling this raster took 39h using two-processors, 16 cores computer and resulted in 221,718,501 clumps. Estimated speed up over the original algorithm is 450 times. The r.clump3p works within the GRASS environment and is available in the public domain. [Copyright &y& Elsevier]

Abstract: This paper describes a visual stimulus generator (VSImG) capable of displaying a gray-scale, 256×256×8bitmap image with a frame rate of 500Hz using a boustrophedonic scanning technique. It is designed for experiments with motion-sensitive neurons of the fly’s visual system, where the flicker fusion frequency of the photoreceptors can reach up to 500Hz. Devices with such a high frame rate are not commercially available, but are required, if sensory systems with high flicker fusion frequency are to be studied. The implemented hardware approach gives us complete real-time control of the displacement sequence and provides all the signals needed to drive an electrostatic deflection display. With the use of analog signals, very small high-resolution displacements, not limited by the image’s pixel size can be obtained. Very slow image displacements with visually imperceptible steps can also be generated. This can be of interest for other vision research experiments. Two different stimulus files can be used simultaneously, allowing the system to generate X–Y displacements on one display or independent movements on two displays as long as they share the same bitmap image. [Copyright &y& Elsevier]

Abstract: Microstructure based forming models using statistically representative microstructural input provide the most accurate predictions for early localization and failure during complex forming operations. However, the sheer size and complexity of the three dimensional (3D) microstructural data from real materials makes it hard to implement that data in current finite element models. In this report, a technique to create a vectorized 3D microstructure suitable for input into finite element codes is developed and applied to represent the distribution of particles of different phases found in continuous cast (CC) AA5754 sheets, which tend to have heterogeneous particle distributions with particles of several phases in different shapes and sizes (from 0.2μm to 10μm) and distributed at random, in stringers and along the “centerline”. The technique consists of a 3D reconstruction of the true microstructure by performing serial sections and conversion of the 3D raster image to the vector image. A 3D mesh is generated automatically using Unigraphics and Hypermesh from real particle field measurements, which can be imported to any FE code. The vectorized microstructure is validated by comparison with the reconstructed images of particle distribution data. [ABSTRACT FROM AUTHOR]

We present a variation to the well studied SVD based image compression technique. Our variation can be viewed as a preprocessing step in which the input image is permuted as per a fixed, data independent permutation, after which it is fed to the standard SVD algorithm. Likewise, our decompression algorithm can be viewed as the standard SVD algorithm followed by a postprocessing step which applies the inverse permutation. On experimenting with standard images we show that our method performs substantially better than the standard method. Typically, for any given compression quality, our method needs about 30% fewer singular values and vectors to be retained. We also present a bit allocation scheme and show that our method also performs better than the more familiar discrete cosine transform (DCT). We show that the original SVD algorithm as well as our variation, can be viewed as instances of the Karhunen–Loeve transform (KLT). In fact, we observe that there is a whole family of variations possible by choosing different parameter values while applying the KLT. We present heuristic arguments to show that our variation is likely to yield the best compression of all these. We also present experimental evidence, which appears to justify our analysis. [Copyright &y& Elsevier]

Abstract: Two schemes for surface construction are presented in this paper. The first one is a substantial generalization of Coons’ method, where instead of four curves four surface patches are the input data in the surface definition. The second scheme generalizes the Gordon–Coons construction of triangular patches by combining three surfaces and an appropriate correction term. In both cases the resulting surface fits one boundary curve of each constituent, and its shape is influenced not only by boundary data but also by the shapes of the given surfaces and additional shape parameters. [Copyright &y& Elsevier]

An original low cost integrated system using a planar light scanning technique in measuring the surface contour of three-dimensional objects has been investigated and an algorithm of processing the measured bit-map data into a format that could be input readily to a CAD system has been developed. Accuracy enhancement of three-dimensional object measurement is studied and a method is proposed. The method divides the scan-lines into sections and fit the data points with an appropriate curve type. Program-modules for extracting coordinates information from images, light/camera system calibration, curve fitting for scan-lines and coordinates transformation between the image plane and the light plane are also presented. [Copyright &y& Elsevier]

• Symmetry is an important aspect of nuclear shape not receiving the attention it merits. • A software, nPAsym, implemented as ImageJ plugin is presented allowing to measure nuclear shape asymmetry in digital images captured from cytologic and histologic preparations. • nPAsym has proved itself effective in distinguishing between some of the nuclear shape phenotypes. • nPAsym has a potential to become useful for both experimental research and diagnostics. : The nucleus is a complex and dynamic organelle enclosing the major part of the cell's genome. A growing body of evidence suggests that changes in the shape of this organelle can influence cell activities. The other way around, altered nuclear shape may be indicative of impaired cell function. Symmetry is an important aspect of nuclear shape not receiving the attention it merits. We address this problem by presenting a software tool allowing to quantify nuclear shape asymmetry in light microscopy images. : The software named nPAsym is written in Scala and implemented as a plugin to ImageJ making possible to use it in combination with other ImageJ tools. The plugin works with 8-bit images segmented into black nuclear masks and white background. It performs a number of operations allowing to analyze multiple objects within a single image, removing some segmentation artefacts, filtering out objects incomplete and below a specified size. The feature of interest is quantified using the notion of point asymmetry. The performance of nPAsym was tested in a small-scale study comparing nuclear shapes for cells of nodular goiter, follicular thyroid adenoma and papillary thyroid carcinoma. : We present nPAsym, the ImageJ plugin, that measures nuclear shape asymmetry. It works with digital microscopic images segmented using either a raster graphics editor or built-in ImageJ functions. nPAsym is packaged in a single.jar file and does not require installation as well as configuration. It has proved effective in distinguishing between some of the nuclear shape phenotypes. : nPAsym is the user-friendly, platform-independent and open-source software tool allowing to quantify nuclear shape asymmetry in digital images captured from cytologic and histologic preparations. It has a potential to become useful for both experimental research and diagnostics. [ABSTRACT FROM AUTHOR]

A superpoint is a host, which makes a large number of connections to distinct nodes within a short time. The emergence of superpoint is often a sign of network attacks, such as DDoS attacks and port scanning. Timely detecting superpoints plays a crucial role in defending against network attacks. In this paper, we present an algorithm for detecting superpoints based on reversible virtual bitmaps. The proposed algorithm assigns four virtual bitmaps to each source for storing its related information. Moreover, six hash functions are designed carefully and skillfully, three of which may generate uniform outputs, the other three hash functions take a combination of some special substrings from the input as their outputs. Although the algorithm does not explicitly preserve any host address, it can reconstruct IP addresses by combining some specific substrings of function values. Furthermore, we make a validation on reconstructed IP addresses to filter out non-existing IP addresses and make some bit-AND operations on virtual bitmaps of each IP address to filter out the noise, improving the accuracy of our algorithm. Experimental results demonstrate our algorithm can achieve the same accuracy as or higher accuracy than other algorithms while reducing 50% of memory consumption compared with CSE (Compact Spread Estimator) and reducing 80% of memory consumption compared with VBF (Vector Bloom Filter). [ABSTRACT FROM AUTHOR]