Query: Chronovolumes: A Direct Rendering Technique for Visualizing Time-Varying Data

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Real-time decompression and visualization of animated volume data <=SEP=> Interactive exploration of animated volume data is required by many application, but the huge amount of computational time and storage space needed for rendering does not allow the visualization of animated volumes by now. In this paper we introduce an algorithm running at interactive frame rates using 3d wavelet transforms that allows for any wavelet, motion compensation techniques and various encoding schemes of the resulting wavelet coefficients to be used. We analyze different families and orders of wavelets for compression ratio and the introduced error. We use a quantization that has been optimized for the visual impression of the reconstructed volume independent of the viewing. This enables us to achieve very high compression ratios while still being able to reconstruct the volume with as few visual artifacts as possible. A further improvement of the compression ratio has been achieved by applying a motion compensation scheme to exploit temporal coherency. Using these scheme we are capable of decompressing each volume of our animation at interactive frame rates, while visualizing these decompressed volumes on a single PC. We also present a number of improved visualization algorithms for high quality display using OpenGL hardware running at interactive frame rates on a standard PC.

Interactive volume rendering using multi-dimensional transfer functions and direct manipulation widgets <=SEP=> Most direct volume renderings produced today employ one-dimensional transfer functions, which assign color and opacity to the volume based solely on the single scalar quantity which comprises the dataset. Though they have not received widespread attention, multi-dimensional transfer functions are a very effective way to extract specific material boundaries and convey subtle surface properties. However, identifying good transfer functions is difficult enough in one dimension, let alone two or three dimensions. This paper demonstrates an important class of three-dimensional transfer functions for scalar data (based on data value, gradient magnitude, and a second directional derivative), and describes a set of direct manipulation widgets which make specifying such transfer functions intuitive and convenient. We also describe how to use modern graphics hardware to interactively render with multi-dimensional transfer functions. The transfer functions, widgets, and hardware combine to form a powerful system for interactive volume exploration.

ORTHONORMAL BASES OF COMPACTLY SUPPORTED WAVELETS <=SEP=> We construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity. The order of regularity increases linearly with the support width. We start by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction. The construction then follows from a synthesis of these different approaches.

Wavelets for computer graphics - theory and applications <=SEP=> For a complete copy of the book, please visit the MorganKaufmann website or Amazon.com.

Utilizing Anaerobic Fungi for Two-stage Sugar Extraction and Biofuel Production from Lignocellulosic Biomass <=SEP=> Lignocellulosic biomass is a vast and underutilized resource for the production of sugars and biofuels. However, the structural complexity of lignocellulosic biomass and the need for multiple pretreatment and enzymatic steps for sugar release renders this process economically challenging. Here, we report a novel approach for direct, single container, exogenous enzyme-free conversion of lignocellulosic biomass to sugars and biofuels using the anaerobic fungal isolate strain C1A. This approach utilizes simple physiological manipulations for timely inhibition and uncoupling of saccharolytic and fermentative capabilities of strain C1A, leading to the accumulation of sugar monomers (glucose and xylose) in the culture medium. The produced sugars, in addition to fungal hyphal lysate, are subsequently converted by Escherichia coli strain K011 to ethanol. Using this approach, we successfully recovered 17.0% (w/w) of alkali-pretreated corn stover (20.0% of its glucan and xylan content) as sugar monomers in the culture media. More importantly, 14.1% of pretreated corn stover (17.1% of glucan and xylan content) was recovered as ethanol at a final concentration of 28.16 mM after the addition of the ethanologenic strain K011. The high ethanol yield obtained is due to its accumulation as a minor fermentation end product by strain C1A during its initial growth phase, the complete conversion of sugars to ethanol by strain K011, and the possible conversion of unspecified substrates in the hyphal lysate of strain C1A to ethanol by strain K011. This study presents a novel, versatile, and exogenous enzyme-free strategy that utilizes a relatively unexplored group of organisms (anaerobic fungi) for direct biofuel production from lignocellulosic biomass.

The human phenotype ontology. <=SEP=> A standardized, controlled vocabulary allows phenotypic information to be described in an unambiguous fashion in medical publications and databases. The Human Phenotype Ontology (HPO) is being developed in an effort to provide such a vocabulary. The use of an ontology to capture phenotypic information allows the use of computational algorithms that exploit semantic similarity between related phenotypic abnormalities to define phenotypic similarity metrics, which can be used to perform database searches for clinical diagnostics or as a basis for incorporating the human phenome into large-scale computational analysis of gene expression patterns and other cellular phenomena associated with human disease. The HPO is freely available at http://www.human-phenotype-ontology.org.

Detailed 3D Representations for Object Recognition and Modeling <=SEP=> Geometric 3D reasoning at the level of objects has received renewed attention recently in the context of visual scene understanding. The level of geometric detail, however, is typically limited to qualitative representations or coarse boxes. This is linked to the fact that today's object class detectors are tuned toward robust 2D matching rather than accurate 3D geometry, encouraged by bounding-box-based benchmarks such as Pascal VOC. In this paper, we revisit ideas from the early days of computer vision, namely, detailed, 3D geometric object class representations for recognition. These representations can recover geometrically far more accurate object hypotheses than just bounding boxes, including continuous estimates of object pose and 3D wireframes with relative 3D positions of object parts. In combination with robust techniques for shape description and inference, we outperform state-of-the-art results in monocular 3D pose estimation. In a series of experiments, we analyze our approach in detail and demonstrate novel applications enabled by such an object class representation, such as fine-grained categorization of cars and bicycles, according to their 3D geometry, and ultrawide baseline matching.

Communities' Detection in Social Networks: State of the art and perspectives <=SEP=> Nowadays, social networks have been widely used by different people for different purposes in the world. The discovering of communities and clustering is a widespread subject in the space of social networks analysis. Many interesting solutions have been proposed in the literature, such as the algorithms of Newman and Girvan. However, most solutions have common problems: the stability and the community structures quality. In this paper, we give a state of the art, from 1970 until 2018 on the approaches as well as the algorithms of communities' detection in social networks. This study aims to gives a comparison between the different proposed architectures;

A SIMULATION AND REGRESSION TESTING FRAMEWORK FOR AUTONOMOUS VEHICLES <=SEP=> x

Iteratively re-weighted least squares for sparse signal reconstruction from noisy measurements <=SEP=> Finding sparse solutions of under-determined systems of linear equations is a problem of significance importance in signal processing and statistics. In this paper we study an iterative reweighted least squares (IRLS) approach to find sparse solutions of underdetermined system of equations based on smooth approximation of the L0 norm and the method is extended to find sparse solutions from noisy measurements. Analysis of the proposed methods show that weaker conditions on the sensing matrices are required. Simulation results demonstrate that the proposed method requires fewer samples than existing methods, while maintaining a reconstruction error of the same order and demanding less computational complexity.

Effect of horseback riding versus a dynamic and static horse riding simulator
on sitting ability of children with cerebral palsy: a randomized controlled
trial <=SEP=> [Purpose] A randomized controlled trial was conducted to investigate the immediate effects of horseback riding (HR) and a dynamic (DHS) and static (SHS) horse riding simulator (OSIM uGallop, Taiwan) on sitting ability of children with cerebral palsy. [Subjects and Methods] Thirty children with cerebral palsy were recruited and randomly assigned into three groups. Children received 30 minutes of exercise according to their assigned group. The Segmental Assessment of Trunk Control (SATCo) and Gross Motor Function Measure-66 (GMFM-66) sitting dimension were used to assess children in all groups both before and after the interventions. [Results] Sitting abilities were significantly improved after all interventions. Horseback riding showed the most improvement, followed by the dynamic and static horse riding simulator groups. Horseback riding also showed a significant improvement in the GMFM sitting dimension. [Conclusion] Horseback riding was the best intervention for promoting sitting ability of children with spastic cerebral palsy. However, a dynamic horse riding simulator can be a good surrogate for horseback riding when horseback riding is not available.

Bias Field Inconsistency Correction of Motion-Scattered Multislice MRI for Improved 3D Image Reconstruction <=SEP=> A common solution to clinical MR imaging in the presence of large anatomical motion is to use fast multislice 2D studies to reduce slice acquisition time and provide clinically usable slice data. Recently, techniques have been developed which retrospectively correct large scale 3D motion between individual slices allowing the formation of a geometrically correct 3D volume from the multiple slice stacks. One challenge, however, in the final reconstruction process is the possibility of varying intensity bias in the slice data, typically due to the motion of the anatomy relative to imaging coils. As a result, slices which cover the same region of anatomy at different times may exhibit different sensitivity. This bias field inconsistency can induce artifacts in the final 3D reconstruction that can impact both clinical interpretation of key tissue boundaries and the automated analysis of the data. Here we describe a framework to estimate and correct the bias field inconsistency in each slice collectively across all motion corrupted image slices. Experiments using synthetic and clinical data show that the proposed method reduces intensity variability in tissues and improves the distinction between key tissue types.

Boosting The Benefits of Hybrid SDN <=SEP=> The upgrade of a legacy network to a full software-defined networking (SDN) deployment is usually an incremental process, during which SDN switches and legacy switches coexist in the hybrid network. However, with inappropriate deployment of SDN switches and design of hybrid control, the advantages of SDN control could not exert, and it even results in performance degradation or inconsistency (e.g., loops, black-holes). Therefore, the hybrid SDN requires considerable coordination of the centralized control and distributed routing. In this paper, we propose a solution to handle the heterogeneity caused by distinct forwarding characteristics of SDN and legacy switches, therefore boosting the benefits of hybrid SDN. We plan SDN placement to enhance the SDN controllability over the hybrid network, and conduct traffic engineering considering both the forwarding characteristics of SDN and legacy switches. The experiments with various topologies show that the SDN placement planning and hybrid forwarding yield better network performance especially in the early 70% SDN deployment.

BAP: A Binary Analysis Platform <=SEP=> BAP is a publicly available infrastructure for performing program verification and analysis tasks on binary (i.e., executable) code. In this paper, we describe BAP as well as lessons learned from previous incarnations of binary analysis platforms. BAP explicitly represents all side effects of instructions in an intermediate language (IL), making syntaxdirected analysis possible. We have used BAP to routinely generate and solve verification conditions that are hundreds of megabytes in size and encompass 100,000’s of assembly instructions.

EasyTracker: automatic transit tracking, mapping, and arrival time prediction using smartphones <=SEP=> In order to facilitate the introduction of transit tracking and arrival time prediction in smaller transit agencies, we investigate an automatic, smartphone-based system which we call EasyTracker. To use EasyTracker, a transit agency must obtain smartphones, install an app, and place a phone in each transit vehicle. Our goal is to require no other input.
 This level of automation is possible through a set of algorithms that use GPS traces collected from instrumented transit vehicles to determine routes served, locate stops, and infer schedules. In addition, online algorithms automatically determine the route served by a given vehicle at a given time and predict its arrival time at upcoming stops.
 We evaluate our algorithms on real datasets from two existing transit services. We demonstrate our ability to accurately reconstruct routes and schedules, and compare our system's arrival time prediction performance with the current "state of the art" for smaller transit operators: the official schedule. Finally, we discuss our current prototype implementation and the steps required to take it from a research prototype to a real system.

Topic Chronicle Forest for Topic Discovery and Tracking <=SEP=> To ease comprehension of given time-stamped corpora, we extend topic models to handle both the specificity and temporality of topics; this is a significant advance over previous models which fail to provide both views simultaneously. Our proposed model consists of the Topic Chronicle Forest(TCF) and Thematic Dirichlet Processes(TDP). TCF is a set of Topic Chronicle Trees, where each tree is a hierarchy of topics that becomes more specialized toward the leaves. Only one tree is defined in each time interval, a region, and is used for TDP to generate a document. The advantage of our approach lies in providing more compact topic organization, while preserving both the semantic of a given corpus and the thematic of each document. Experiments show that TCF is a useful extension for longitudinal topic discovery and tracking, and helps us to organize and digest data sets.

Expression of stressful experiences through writing: effects of a self-regulation manipulation for pessimists and optimists. <=SEP=> This study assessed the effectiveness of a writing task designed to foster self-regulatory coping with stressful experiences to reduce medical clinic visits and to promote adjustment. Students entering college (N = 122) who were classified as optimists or pessimists by using a dispositional optimism measure participated in a self-regulation task (expressing thoughts and feelings about entering college and then formulating coping plans), a disclosure task (expressing thoughts and feelings only), or a control task (writing about trivial topics) for 3 weekly writing sessions. Among optimists, both the self-regulation task and the disclosure task reduced illness-related clinic visits during the following month; among pessimists, only the self-regulation task reduced clinic visits. In general, the self-regulation task beneficially affected mood state and college adjustment whereas the disclosure task increased grade point averages.

A New Face Database Simultaneously Acquired in Visible, Near-Infrared and Thermal Spectrums <=SEP=> In this paper, we present a new database acquired with three different sensors (visible, near infrared and thermal) under different illumination conditions. This database consists of 41 people acquired in four different acquisition sessions, five images per session and three different illumination conditions. The total amount of pictures is 7,380 pictures. Experimental results consist of single sensor experiments as well as the combination of two and three sensors under different illumination conditions (natural, infrared and artificial illumination). We have found that the three studied spectral bands contribute in a nearly equal proportion to a combined system. Experimental results show a significant improvement combining the three spectrums, even when using a simple classifier and feature extractor. In six of the nine studied scenarios, we obtained identification rates higher or equal to 98 %, when using a trained combination rule, and two cases of nine when using a fixed rule.

Trust as a facilitator in cloud computing: a survey <=SEP=> Cloud computing offers massively scalable, elastic resources (e.g., data, computing power, and services) over the internet from remote data centres to the consumers. The growing market penetration, with an evermore diverse provider and service landscape, turns Cloud computing marketplaces a highly competitive one. In this highly competitive and distributed service environment, the assurances are insufficient for the consumers to identify the dependable and trustworthy Cloud providers. This paper provides a landscape and discusses incentives and hindrances to adopt Cloud computing from Cloud consumers’ perspective. Due to these hindrances, potential consumers are not sure whether they can trust the Cloud providers in offering dependable services. Trust-aided unified evaluation framework by leveraging trust and reputation systems can be used to assess trustworthiness (or dependability) of Cloud providers. Hence, cloud-related specific parameters (QoS + ) are required for the trust and reputation systems in Cloud environments. We identify the essential properties and corresponding research challenges to integrate the QoS + parameters into trust and reputation systems. Finally, we survey and analyse the existing trust and reputation systems in various application domains, characterizing their individual strengths and weaknesses. Our work contributes to understanding 1) why trust establishment is important in the Cloud computing landscape, 2) how trust can act as a facilitator in this context and 3) what are the exact requirements for trust and reputation models (or systems) to support the consumers in establishing trust on Cloud providers.

Learning Internet-of-Things Security "Hands-On" <=SEP=> What can you glean from using inexpensive, off-the-shelf parts to create Internet of Things (IoT) use cases? As it turns out, a lot. The fast productization of IoT technologies is leaving users vulnerable to security and privacy risks.

Closing loops without places <=SEP=> This paper proposes a new topo-metric representation of the world based on co-visibility that simplifies data association and improves the performance of appearance-based recognition. We introduce the concept of dynamic bagof-words, which is a novel form of query expansion based on finding cliques in the landmark co-visibility graph. The proposed approach avoids the - often arbitrary - discretisation of space from the robot's trajectory that is common to most image-based loop closure algorithms. Instead we show that reasoning on sets of co-visible landmarks leads to a simple model that out-performs pose-based or view-based approaches. Using real and simulated imagery, we demonstrate that dynamic bag-of-words query expansion can improve precision and recall for appearance-based localisation.

Design and Analysis of DC Solid-State Circuit Breakers Using SiC JFETs <=SEP=> Protection against short-circuit faults remains a major technical challenge in increasingly popular dc power networks. This paper describes a new concept of a self-powered dc solid-state circuit breaker (SSCB) with one or more normally on SiC JFETs as the main static switch and a fast-starting isolated dc/dc converter as the protection driver. The new SSCB detects short-circuit faults by sensing its terminal voltage rise and draws power from the fault condition itself to turn and hold off the SiC switch. The new two-terminal SSCB can be directly placed in a circuit branch without requiring any external power supply or extra wiring. A low-power isolated dc/dc converter is designed and optimized to provide a fast reaction to a short-circuit fault. Unidirectional and bidirectional SSCB prototypes based on this design concept have been built. Repeated interruption of fault currents up to 180 A at a dc bus voltage of 400 V within 0.8 μs was experimentally demonstrated. DC circuit protection applications provide a unique market opportunity for wide-bandgap semiconductors, which are outside the conventional focus on power electronic converters.

Vision for Mobile Robot Navigation: A Survey <=SEP=> This paper surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment.

A model-based approach to security flaw detection of network protocol implementations <=SEP=> A lot of efforts have been devoted to the analysis of network protocol specification for reliability and security properties using formal techniques. However, faults can also be introduced during system implementation; it is indispensable to detect protocol implementation flaws, yet due to the black-box nature of protocol implementation and the unavailability of protocol specification most of the approaches resort to random or manual testing. In this paper we propose a model-based approach for security flaw detection of protocol implementation with a high fault coverage, measurability, and automation. Our approach first synthesizes an abstract behavioral model from a protocol implementation and then uses it to guide the testing process for detecting security and reliability flaws. For protocol specification synthesis we reduce the problem a trace minimization with a finite state machine model and an efficient algorithm is presented for state space reduction. Our method is implemented and applied to real network protocols. Guided by the synthesized model our testing tool reveals a number of unknown reliability and security issues by automatically crashing the implementations of the Microsoft MSN instant messaging (MSNIM) protocol. Analytical comparison between our model-based and prevalent syntax-based flaw detection schemes is also provided with the support of experimental results.

Code analysis of lightweight encryption algorithms using in RFID systems to improve cipher performance <=SEP=> This research has investigated the performance analysis of lightweight encryption algorithms, which are using in RFID applications. In the first part, the codes of the investigated ciphers are analyzed, and then, four encryption algorithms include TEA, HIGHT, KATAN, and KLEIN are implemented on AVR Microcontroller to evaluate the number of CPU clock cycle for each cipher. In the last part, the results of two previous parts are compared.

Learning to Sketch with Shortcut Cycle Consistency <=SEP=> To see is to sketch - free-hand sketching naturally builds ties between human and machine vision. In this paper, we present a novel approach for translating an object photo to a sketch, mimicking the human sketching process. This is an extremely challenging task because the photo and sketch domains differ significantly. Furthermore, human sketches exhibit various levels of sophistication and abstraction even when depicting the same object instance in a reference photo. This means that even if photo-sketch pairs are available, they only provide weak supervision signal to learn a translation model. Compared with existing supervised approaches that solve the problem of D(E(photo)) â†’ sketch), where E(Â·) and D(Â·) denote encoder and decoder respectively, we take advantage of the inverse problem (e.g., D(E(sketch) â†’ photo), and combine with the unsupervised learning tasks of within-domain reconstruction, all within a multi-task learning framework. Compared with existing unsupervised approaches based on cycle consistency (i.e., D(E(D(E(photo)))) â†’ photo), we introduce a shortcut consistency enforced at the encoder bottleneck (e.g., D(E(photo)) â†’ photo) to exploit the additional self-supervision. Both qualitative and quantitative results show that the proposed model is superior to a number of state-of-the-art alternatives. We also show that the synthetic sketches can be used to train a better fine-grained sketch-based image retrieval (FG-SBIR) model, effectively alleviating the problem of sketch data scarcity.

Design and Experimental Results of a 300-kHz Synthetic Aperture Sonar Optimized for Shallow-Water Operations <=SEP=> The design of a shallow-water synthetic aperture sonar (SAS) requires an understanding of key system and environmental issues. The main factors that limit SAS performance are as follows: micronavigation accuracy, where micronavigation is defined as the problem of estimating the acoustic path lengths to allow the focusing of the aperture; multipath effects; and target view angle changes. All of them can degrade shadow classification performance. Micronavigation accuracy is successfully addressed by the gyrostabilized displaced phase center antenna technique, which combines data-driven motion estimates with external attitude sensors. Multipath effects in shallow water are effectively countered by narrow vertical beams. Shadow blur induced by view angle changes is mitigated by increasing the center frequency, to reduce the SAS integration length while still maintaining the desired resolution and by designing the system with a minimum grazing angle of about 6deg to reduce shadow length. The combination of these factors led to the choice of a 300-kHz center frequency and of a multipath mitigation scheme that uses multiple vertical beams. Experimental results obtained with a sonar incorporating these features have produced SAS images with 1.6 cm times 5 cm resolution in range times cross-range and shadow contrast in excess of 5 dB, at ranges of up to 170 m in 20 m of water.

Graphical Figure Classification Using Data Fusion for Integrating Text and Image Features <=SEP=> This paper describes a multimodal (image + text) learning approach for automatically identifying three graphical figure types commonly found in biomedical literature, namely, diagrams, statistical figures and flow charts. The goal is to improve retrieval of figures from biomedical journal articles. In this article, we describe a data fusion approach to combine information from both text and image sources, believed to contain complementary information. Text information about the image is extracted from the figure caption. The data fusion process includes a hybrid of evolutionary algorithm (EA) and Binary Particle Swarm Optimization (BPSO) called method applied to find an optimal subset of extracted image features. Chi-square statistic and information gain metric are used to select the optimal subset of extracted text features, which along with image features are input to Multi-Layer Perceptron Neural Network classifiers, whose outputs are characterized as fuzzy sets to determine the final classification result. Evaluation performed on 1707 figure images extracted from a test subset of Biome Central® journals extracted from U.S. National Library of Medicine's PubMed Central ® repository yielded classification accuracy as high as 96.1%.

Air-light estimation using haze-lines <=SEP=> Outdoor images taken in bad weather conditions, such as haze and fog, look faded and have reduced contrast. Recently there has been great success in single image dehazing, i.e., improving the visibility and restoring the colors from a single image. A crucial step in these methods is the calculation of the air-light color, the color of an area of the image with no objects in line-of-sight. We propose a new method for calculating the air-light. The method relies on the haze-lines prior that was recently introduced. This prior is based on the observation that the pixel values of a hazy image can be modeled as lines in RGB space that intersect at the air-light. We use Hough transform in RGB space to vote for the location of the air-light. We evaluate the proposed method on an existing dataset of real world images, as well as some synthetic and other real images. Our method performs on-par with current state-of-the-art techniques and is more computationally efficient.