E-commerce security has recently been an emerging topic due to the escalation in credit card fraud and stolen user accounts. In general, the security bridge and privacy leakage occur on the side of e-commerce companie...
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E-commerce security has recently been an emerging topic due to the escalation in credit card fraud and stolen user accounts. In general, the security bridge and privacy leakage occur on the side of e-commerce companies due to various factors such as flaws in the design of their storage systems. The stored information of users increases the risk on privacy bridge and to remedy such risks e-commerce companies are forced to make costly investments. The security threats also enforce the development of robust security protocols and methods in digital commerce systems. The current protocols and methods generally bring extra communication and computation costs to all parties involving in the e-commerce system and the security risk on the side of e-commerce companies still remains. In this paper, we propose a Secure E-commerce Scheme (SES) which alleviates the security threats on the side of e-commerce companies and reduces communication costs for all parties. The proposed secure e-commerce protocol, SES, is implemented, analyzed and compared to two well-known schemes;Secure Electronic Transaction (SET) and 3D Secure.
One method of attacking an imbedded invisible watermark is to create a derivative image that is geometrically distorted relative to the original. One attack, developed at Cambridge University, is called "StirMark...
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One method of attacking an imbedded invisible watermark is to create a derivative image that is geometrically distorted relative to the original. One attack, developed at Cambridge University, is called "StirMark."(1) image-distorting methods modify images so subtly that the changes are essentially unnoticeable to a viewer. However, their effect on invisible watermarks can be devastating, rendering them unextractable. In this paper, an automated countermeasure to image-distorting attacks will be described. Employing an unmarked copy of the original image as a reference, the possible distortion in a suspect image is first detected by the method, then measured, and finally reversed, producing a restored image approximately geometrically aligned with the original. Using a robust invisible watermarking method described previously by one of the authors to produce a watermarked image, "StirMark" to distort the watermarked image, and a copy of the original unmarked Image for reference, the restoration method is demonstrated to be sufficient by showing successful extraction of the imbedded watermark from a restored image. (C) 2000 SPIE and IS&T. [S1017-9909(00)00904-1].
We describe ordered subsets (OS) algorithms applied to regularized expectation-maximization (EM) algorithms for emission tomography. Our reconstruction algorithms are based on a maximum a posteriori approach, which al...
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We describe ordered subsets (OS) algorithms applied to regularized expectation-maximization (EM) algorithms for emission tomography. Our reconstruction algorithms are based on a maximum a posteriori approach, which allows us to incorporate a priori information in the form of a regularizer to stabilize the unstable EM algorithm. In this work, we use two-dimensional smoothing splines as regularizers. Our motivation for using such regularizers stems from the fact that, by relaxing the requirement of imposing significant spatial discontinuities and using instead quadratic smoothing splines, solutions are easier to compute and hyperparameter calculation becomes less of a problem. To optimize our objective function, we use the method of iterated conditional modes, which is useful for obtaining convenient closed-form solutions. In this case, step sizes or line-search algorithms necessary for gradient-based descent methods are also avoided. We finally accelerate the resulting algorithm using the OS principle and propose a principled way of scaling smoothing parameters to retain the strength of smoothing for different subset numbers. Our experimental results indicate that our new methods provide quantitatively robust results as well as a considerable acceleration. (C) 2003 SPIE and IST.
Understanding seasonal snow cover dynamics is critical for management of hydrological regimes, habitat availability for wildlife species, forest fire risk assessment and recreational demands. Although data products pr...
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Understanding seasonal snow cover dynamics is critical for management of hydrological regimes, habitat availability for wildlife species, forest fire risk assessment and recreational demands. Although data products provided at 500 m spatial resolution by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor provide important and readily available information on snow cover, capturing snow dynamics at finer spatial resolutions remains problematic due to the lack of high temporal and spatial resolution data, which limits the number of available observations each year. In this paper we present a new approach to create a daily time-series of 30-m snow observations (called SNOWARP), derived from daily MODIS Normalised Difference Snow Index (NDSI) snow cover data to capture the temporal dynamics of snow cover and Dynamic Time Warping (DTW) to re-order historical Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM + ), and Operational Land Imager (OLI) observations to account for inter-annual variability. The SNOWARP product was produced for 2000-2018 for an area of Western Alberta (approximately 30,000 km(2)) and was calibrated against a network of time-lapse cameras and snow pillows. Results indicate the RMSE of the SNOWARP fractional product ranges from 31.3%-68.3%, while F score of the SNOWARP binary product ranges from 87.7% - 98.6% when compared to ground truth data. Capturing fractional snow cover at a fine spatial and temporal scale is important due to the spatial heterogeneity of snow cover, particularly in mountainous regions with implications for biodiversity assessment and monitoring. SNOWARP demonstrates a novel method to increasing the temporal resolution of Landsat-derived snow cover data, providing valuable insights on regional snow cover dynamics for use in a range of applications.
Bayesian methods have formed the core of the development of reconstruction algorithms for emission tomography. In particular, there has been considerable interest in edge-preserving prior models, which are associated ...
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Bayesian methods have formed the core of the development of reconstruction algorithms for emission tomography. In particular, there has been considerable interest in edge-preserving prior models, which are associated with smoothing penalty functions that are nonquadratic functions of nearby pixel differences. In spite of several advantages of nonconvex prior models, their use in routine applications has been hindered by several factors, such as the computational expense due to the nonconvexity of penalty functions and the difficulty in the selection of hyperparameters. We note here that, by choosing a penalty function which is nonquadratic but is still convex, both the problem of nonconvexity involved in some nonquadratic priors and the edge-oversmoothing problem of conventional quadratic priors may be avoided. In this paper, we use a class of two-dimensional smoothing splines with low (first) and high (second) spatial derivatives applied to convex-nonquadratic (CNQ) penalty functions. To compare quantitative performance of our new priors, we use the quantitation of bias/variance and total squared error over noise trials. Our numerical results show that a linear combination of first- and second-order spatial derivatives applied to CNQ penalty functions improves reconstructions in terms of total squared error. (C) 2000 SPIE and IS&T. [S1017-9909(00)00202-6].
The key to successful morphological image processing is the selection of structuring elements. There are a myriad of algorithms for a multitude of imaging applications, but in each and every instance, algorithm perfor...
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The key to successful morphological image processing is the selection of structuring elements. There are a myriad of algorithms for a multitude of imaging applications, but in each and every instance, algorithm performance depends on the structuring elements. The classical approach to morphological processing is to have a human being, or a group of human beings, use intuition and an understanding of the goals to design algorithms based on erosions, openings, hit-or-miss transforms, and other basic morphological operators. Some of the methods are reviewed that have been developed for automatic algorithm design, where morphological operators are designed based on sample data, structural decomposition, and criteria set by the imaging scientist. (C) 2004 SPIE and IST.
This study aimed to validate a 7-sensor inertial measurement unit system against optical motion capture to estimate bilateral lower -limb kinematics. Hip, knee, and ankle sagittal plane peak angles and range of motion...
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This study aimed to validate a 7-sensor inertial measurement unit system against optical motion capture to estimate bilateral lower -limb kinematics. Hip, knee, and ankle sagittal plane peak angles and range of motion (ROM) were compared during bodyweight squats and countermovement jumps in 18 participants. In the bodyweight squats, left peak hip flexion (intraclass correlation coefficient [ICC] = .51), knee extension (ICC = .68) and ankle plantar flexion (ICC = .55), and hip (ICC = .63) and knee (ICC = .52) ROM had moderate agreement, and right knee ROM had good agreement (ICC = .77). Relatively higher agreement was observed in the countermovement jumps compared to the bodyweight squats, moderate to good agreement in right peak knee flexion (ICC = .73), and right (ICC = .75) and left (ICC = .83) knee ROM. Moderate agreement was observed for right ankle plantar flexion (ICC = .63) and ROM (ICC = .51). Moderate agreement (ICC > .50) was observed in all variables in the left limb except hip extension, knee flexion, and dorsiflexion. In general, there was poor agreement for peak flexion angles, and at least moderate agreement for joint ROM. Future work will aim to optimize methodologies to increase usability and confidence in data interpretation by minimizing variance in system-based differences and may also benefit from expanding planes of movement.
Snow fall and melt events are complex meteorological phenomena that help chart the effects of climate change and impact many critical environmental processes including hydrologic and biogeographic systems. Daily snow ...
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Snow fall and melt events are complex meteorological phenomena that help chart the effects of climate change and impact many critical environmental processes including hydrologic and biogeographic systems. Daily snow maps, derived from MODIS imagery, provide managers and researchers with vital snow cover information, but only at spatial scales of 500 m or more. Finer resolution time series maps, however, retain large temporal gaps, particularly during recurrent cloud cover. This paper's authors have developed the novel algorithm MODSAT-NDSI to harness the strengths of both coarse and finer spatial resolution imagery by fusing MODIS and Landsat normalized difference snow index (NDSI) data. Daily 30 m snow cover maps were thus generated for 2000 - 2017 with an overall accuracy of 90%, using 33 validation sites distributed throughout south-central British Columbia. Snow cover trends were analyzed across stratified elevation bands and land cover types, revealing that snow cover persists under lower elevation forests for an average of 23.5 d longer than in adjacent open areas during spring. We conclude that the MODSAT-NDSI approach captures temporal and spatial advantages of freely available snow cover datasets and can be modified to suit a variety of novel investigations relating to snow cover or other spectral indices.
The design of a gamut mapping algorithm (GMA) is always a compromise between preserving different competing aspects such as color, contrast, and lightness. A natural requirement of a GMA is that the algorithmic treatm...
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The design of a gamut mapping algorithm (GMA) is always a compromise between preserving different competing aspects such as color, contrast, and lightness. A natural requirement of a GMA is that the algorithmic treatment of this competition has to avoid any additional artifacts such as discontinuities or loss of contrast. In this work, several common gamut mapping algorithms are studied from this aspect, resulting in the observation that problems with geometric discontinuities are widespread. For the assessment of the phenomena induced by local mapping properties, an algorithmic test is developed and applied. This new test supports both the quality check of existing as well as the development of new GMAs. (c) 2006 SPIE and IS&T.
Compression of three-dimensional (3D) or four-dimensional medical image data has now become imperative for clinical picture archiving and communication systems (PACS), telemedicine, and telepresence networks. While lo...
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Compression of three-dimensional (3D) or four-dimensional medical image data has now become imperative for clinical picture archiving and communication systems (PACS), telemedicine, and telepresence networks. While lossless compression is often desired, lossy compression techniques are gaining acceptance for medical applications, provided that clinically important information can be preserved in the coding process. We present a coherently three-dimensional method for volumetric image compression using 3D wavelet transform and 3D zerotree coding. First the volumetric image data is decomposed using 3D separable wavelet filterbanks. In this study, we adopt a three-level decomposition to form a 22-band multiresolution pyramid of an octree. Then, to exploit the dependencies among the subband coefficients resulting from 3D wavelet decomposition, a 3D zerotree coding scheme is utilized. To take advantage of the near-Laplacian distributions of the subband coefficients and the efficiency of zerotree coding, a pseudouniform quantization is adopted. The proposed volumetric image compression scheme is applied to two sets of real CT medical data. Significant coding gains have been achieved which demonstrate the effectiveness of the proposed volumetric image compression scheme for medical as well as other applications. (C) 1998 SPIE and IS&T. [S1017-9909(98)02703-2].
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