Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Software and Hardware System for Collision Detection

Collision detection is a fundamental problem inmyriad applications, spanning a wide variety of elds. Although the core concept is relatively straightforward, signi cant complexities result from the demand for interactive or real-time performance for ever-increasing workloads. In this thesis, a di erent approach is applied to the problem of collision detection. is approach consists of a hybrid so ware and hardware system that utilises the properties of both so ware and hardware to achieve an acceleration over the current state of the art. At its core, the system consists of a custom microarchitecture performing broad phase collision detection, with the narrow phase executing in so ware. e microarchitecture consists of a plurality of memories internal to the microarchitecture, which parallelise the input data. ese data are then formed into pairs using a custom data access algorithm, before being processed using a parallel version of the all-pairs algorithm. Two equivalent microarchitectures are presented: the rst is designed for microarchitecture platforms with highly constrained resources, while the second supports many more objects. Both designs are prototyped using a recon gurable hardware platform known as a eld-programmable gate array (FPGA). Despite the major advantages that can be achieved using this system, a question remains over how to deal with limitations on the size of internal memories in microarchitecture platforms. ese limitations would signi cantly curtail the number of objects that could be processed by the microarchitectures. To answer this question, spatial partitioning is proposed. is partitions the environment into cells of appropriate size for processing by the microarchitecture. A er the so ware reads a potentially colliding set (PCS) from the microarchitecture, narrow phase processing of the objects begins and this processing is overlapped with the microarchitecture’s processing of the subsequent cell. A framework for benchmarking collision detection algorithms is also presented. is framework is based on a physics engine, in order to accurately re ect scenarios found in many interactive applications, and includes a set of parameters that allow the creation of myriad practical benchmarks from a single generic benchmark. Collision detection algorithm developers can use the benchmark to rapidly determine which scenarios perform ine ciently with their algorithm. In addition, developers of applications utilising collision detection can recreate their scenarios using the benchmark to rapidly nd the most e cient algorithm for those scenarios.
MPI: The Complete Reference

From the Publisher: MPI, the Message Passing Interface, is a standard and portable library of communications subroutines for parallel programming designed to function on a wide variety of parallel computers. It is useful on both parallel computers, such as IBM's SP2, the Cray ResearchT3D, and the Connection Machine, as well as networks of workstations. Written by five of the principal creators of the latest MPI standard MPI: The Complete Reference is an annotated manual for the latest 1.1 version of the standard that illuminates the more advanced and subtle features of MPI. It can be read in conjunction with the companion tutorial volume, Using MPI: Portable Parallel Programming with the Message-Passing Interface, by William Gropp, Ewing Lusk, and Anthony Skjellum. MPI: The Complete Reference is the only source that covers such advanced issues in parallel computing and programming as true portability, deadlock, high-performance message passing, and libraries for distributed and parallel computing. The annotations provide numerous illustrative programming examples and delve into even the most esoteric features or consequences of the standard. They explain why certain design choices were made, how users should use the interface, and how implementors should construct their own version of MPI. Scientific and Engineering Computation series
Architectural improvements and FPGA implementation of a multimodel neuroprocessor

Since neural networks (NNs) require an enormous amount of learning time, various kinds of dedicated parallel computers have been developed. In the paper a 2-D systolic array (SA) of dedicated processing elements (PEs) also called systolic cells (SCs) is presented as the heart of a multimodel neural-network accelerator. The instruction set of the SA allows the implementation of several neural algorithms, including error back propagation and a self organizing feature map algorithm. Several special architectural facilities are presented in the paper in order to improve the 2-D SA performance. A swapping mechanism of the weight matrix allows the implementation of NNs larger than 2-D SA. A systolically propagated instruction word accompanying each input vector inside the 2-D SA allows the operating mode to be changed progressively, avoiding intermediate inactive cycles inside the 2-D SA. An FPGA implementation of the proposed 2-D SA is presented.
MICE for multiple imputation of missing values

The publication of Royston (2004)'s Stata implementation of the MICE method for multiple imputation of missing values has stimulated much interest, comment and further development of the software. In this talk I will describe enhancements of what used to be called mvis.ado and is now known as mice.ado. The main changes are greatly increased flexibility in the specification of the prediction equations for individual variables, better handling of ordered and nominal categoric variables, and support for so-called passive imputation in which derived variables are updated from primary variables. All of these features reflect van Buuren's implementation of MICE on a different statistical platform. I will demonstrate their use by an example with real data. An article on the topic is in preparation (Royston 2005).
Emulatoren-Testing für die digitale Langzeitarchivierung

Compared with classical objects (books, clay/tone boards, paper) the long-term preservation of digital objects places the scientists before new challenges. Since the runtime environment needed by digital objects can be obsolet, this fact leaves the question open over the future view or treatment of digital objects. Emulators are used in order to make the obsolote runtime einvironment available virtually. As software the emulators are exposed also to the old process. Therefore they are to be updated, in order to be adapted on the today's hardware architecture. T he update of a software requires execution certain tests, in order to guarantee that it fulfills its requirements. For the use in a archives system it is to be examined on the basis suitable tests whether the updated emulators received their past functionalities. In the current work a system was developed, which the admission of a emulator in a archives management system. The developed Framework have to give the user the possibility of create emulator tests and run it automatically. to support and thus the efficiency of the system.
Big Data Implementation of Natural Disaster Monitoring and Alerting System in Real Time Social Network using Hadoop Technology

Background/Objectives: The information generated by the social networks is exponentially higher and demand effective systems to yield effective results. In conventional techniques stay unqualified because they ignore the social related data. Methods/Statistical Analysis: The existing system doesn’t provide any proper methodology to report people about any natural disasters rapidly or to notify the rescue agencies for taking immediate actions to carry out the rescue process. The only existing methodology of reporting people is the media, i.e. news, radio, etc. The use of a particle filter fetches the necessary keywords from tweets by the use of Stemming along with the location and time. When the system encounters keywords related to natural disasters, an auto alert is sent to the people in the nearby locations and the rescue teams based on a proper verification algorithm. Findings: The method finding the two data set representations: one is considering the two directional social relations, and the other considering the one directional social relation. It is seen that the performance of the recommender system can be greatly boosted by the mentioned contextual factors. Application/ Improvements: On validation of the authenticity user, the user is allowed to enter the application. The user is allowed to tweet using the same application.
Three dimensional structure estimation and planning with vision and range

A 3D structure estimation approach is proposed. The uniqueness of the approach lies in the fusion of vision and 2D range, which is a common sensor combination for mobile robots. 2D range information can be used for hypothesizing 3D structures, feature association, and improving the estimate accuracy. These problems are difficult to solve if using only vision. The proposed approach will be beneficial to 3D map building in mobile robots. Experimental results validate the effectiveness of the approach.
Evaluating the developer's interface to a kbs shell: some reflections

In this paper, the evaluation of the knowledge-based system shell, NEXPERT, is reported. It has been argued that AI tools of this kind have not had the success anticipated because of problems with the developer's interface. It would appear that software of this type does not help users develop a good conceptual model. An evaluation of NEXPERT has been conducted using various approaches. A group of students who had experience with the tool were asked to fill in the Software Usability Measurement Inventory (SUMI) 50 item questionnaire. The data was analysed and various aspects of usability measured: learnability, helpfulness, affect, control and efficiency. An expert user and a group of students were also interviewed to find out what they believed were the best and worst aspects of the software. The overall results are discussed and suggestions made for improving the interface of NEXPERT.
View Transformation Based on a Single Outdoor Image

Abstract An automatic approach for view transformation based on a single outdoor image is proposed in this paper. First, the hierarchical segmentation method is conducted to segment an outdoor image into several meaningful regions and each region is labelled as sky, ground or standing object. Then, different methods are used to estimate each region's depth according to its label. After that, the obtained depth information is utilized to create a new view image after any rotation, translation and pitch. Finally, the image inpainting work for the missing colour region is accomplished using its neighbour's colour. Extensive experiments show the proposed approach not only improves the accuracy of view transformation, but also performs well even for images with occlusion phenomena.
Neural, Novel & Hybrid Algorithms for Time Series Prediction

From the Publisher: Expert Timothy Masters provides you with carefully paced, step-by-step advice and guidance plus the proven tools and techniques you need to develop successful applications for business forecasting, stock market prediction, engineering process control, economic cycle tracking, marketing analysis, and more. CD-ROM includes NPREDICT - both DOS and Windows NT versions - a powerful time series program that can be customized to make accurate predictions in any application area, and much useful source code, including the complex-general multivariate fast Fourier transform in both C++ and Pentium-optimized assembler.
Inducing Expertise in History Doctoral Students via Information Retrieval Design

In a previous study of doctoral history students, I observed that a common practice of these students was to collect names of people from the era and geographical area they were studying, then to place data about these names on 3 × 5-inch index cards. The names and data about the names were used as access points to the material the student was reading, but they also seemed to induce a cognitive effect that was advantageous to the student's task performance. This article investigates the cognitive effect that results from collecting names when the doctoral students recognized patterns among the data elements written down on the 3 × 5-inch index cards. The pattern recognition leads to original thesis formation and expert thinking, which is a requirement of a doctoral dissertation. This article describes this technique of name collecting, illustrates it with a case study from a larger study of doctoral history students, then uses schemata theory and theories on expert cognition to create a theory that explains how the name collecting behavior induces expert thinking in the novice student. Finally, the article describes a design concept for an information retrieval system device specifically designed to facilitate the history student's names-collecting activity and to induce expert thinking.
Automated identification of retained surgical items in radiological images

Retained surgical items (RSIs) in patients is a major operating room (OR) patient safety concern. An RSI is any surgical tool, sponge, needle or other item inadvertently left in a patients body during the course of surgery. If left undetected, RSIs may lead to serious negative health consequences such as sepsis, internal bleeding, and even death. To help physicians efficiently and effectively detect RSIs, we are developing computer-aided detection (CADe) software for X-ray (XR) image analysis, utilizing large amounts of currently available image data to produce a clinically effective RSI detection system. Physician analysis of XRs for the purpose of RSI detection is a relatively lengthy process that may take up to 45 minutes to complete. It is also error prone due to the relatively low acuity of the human eye for RSIs in XR images. The system we are developing is based on computer vision and machine learning algorithms. We address the problem of low incidence by proposing synthesis algorithms. The CADe software we are developing may be integrated into a picture archiving and communication system (PACS), be implemented as a stand-alone software application, or be integrated into portable XR machine software through application programming interfaces. Preliminary experimental results on actual XR images demonstrate the effectiveness of the proposed approach.
The applications of concurrent simulation in the power electronics laboratory

The paper presents a new concept of the concurrent simulation in the program of the power electronics laboratory and it pedagogical philosophy. In this method the transient analysis computer simulation is performed concurrently with practical exercises. Nonlinear power electronic circuits, variation in topology and parameters together with the control system, variety and nonideal nature of power semiconductor devices make power electronics one of the most difficult and demanding subjects in the electrical course. The proposed teaching method through experiments and concurrent simulation need to be continuously improved and the individual learning process must be eagerly encouraged. In order to use the method effectively the laboratory arrangements and software must fulfil certain requirements. The presented application examples demonstrate to students that in certain conditions simulation and practical experiment results are consistent. In conclusion, the author tries to determine to what degree the computer prediction can represent the true behaviour of the power electronics circuit and what needs to be done in order to improve the accuracy of the simulation.
Performance study of handoff schemes in broadband ATM mobile satellite networks

We examine the application of wireless ATM protocol in a mobile satellite communication network. We explain the concept of the wireless ATM networks and how we can utilise the advantageous characteristics of the ATM (such as QoS support) in the next generation broadband satellite networks. In particular, we consider the performance of handoff and routing algorithms in satellite systems. As the mobile satellites are in continuous movement, the communication requires frequent handoff and routing decisions. Therefore, handoff and routing can be considered as the most important issues that affect the performance in these networks. We propose new algorithms which can provide a better performance. These algorithms use some predictive characteristics in mobile satellites systems. Our performance metrics are the number of handoff and the routing delay. We show how a routing algorithm can put unnecessary burden on the network by introducing extra handoff events and how we can avoid that by intelligent routing algorithms.
Efficient specification-based testing using incremental techniques

As software systems grow in complexity, the need for efficient automated techniques for design, testing and verification becomes more and more critical. Specification-based testing provides an effective approach for checking the correctness of software in general. Constraint-based analysis using specifications enables checking various rich properties by automating generation of test inputs. However, as specifications get more complex, existing analyses face a scalability problem due to state explosion. This dissertation introduces a novel approach to analyze declarative specifications incrementally; presents a constraint prioritization and partitioning methodology to enable efficient incremental analyses; defines a suite of optimizations to improve the analyses further; introduces a novel approach for testing software product lines; and provides an experimental evaluation that shows the feasibility and scalability of the approach. The key insight behind the incremental technique is declarative slicing, which is a new class of optimizations. The optimizations are inspired by traditional program slicing for imperative languages but are applicable to analyzable declarative languages, in general, and Alloy, in particular. We introduce a novel algorithm for slicing declarative models. Given an Alloy model, our fully automatic tool, Kato, partitions the model into a base slice and a derived slice using constraint prioritization. As opposed to the conventional use of the Alloy Analyzer, where models are analyzed as a whole, we perform analysis incrementally, i.e., using several steps. A satisfying solution to the base slice is systematically extended to generate a solution for the entire model, while unsatisfiability of the base implies unsatisfiability of the entire model. We show how our incremental technique enables different analysis tools and solvers to be used in synergy to further optimize our approach. Compared to the conventional use of the Alloy Analyzer, this means even more overall performance enhancements for solving declarative models. Incremental analyses have a natural application in the software product line domain. A product line is a family of programs built from features that are increments in program functionality. Given properties of features as first-order logic formulas, we automatically generate test inputs for each product in a product line. We show how to map a formula that specifies a feature into a transformation that defines incremental refinement of test suites. Our experiments using different data structure product lines show that our approach can provide an order of magnitude speed-up over conventional techniques.
Learning multiple categories from sequences of examples

We propose a neural network architecture together with a new learning algorithm to learn representations of multiple categories. More specifically, the algorithm learns in a supervised manner from sequences of examples of each category. We will show that our algorithm approximates the minimum of a quadratic homogeneous program. This minimum has a natural interpretation, it separates each category maximally from the mean of all the other categories. Finally, we show some examples of how our algorithm works.
Improving EEG source analysis using prior knowledge

The research described in this thesis covers different aspects of EEG and ERP source modeling. Both basic research in volume conduction and source modeling, as well as application of inverse source modeling techniques to experimental data are described. This thesis demonstrates how prior information can be applied in physical models that are used for source analysis of the EEG. It shows that realistic volume conduction models can be used that incorporate holes in the skull. The accurate placement of electrodes for high-density EEG measurements is described. It discusses how information on symmetry of sources in the brain can be used, and what the influence of incorrectly assumed symmetry in the sources is. The application of prior information on source locations is demonstrated in a forward modeling approach. A high-resolution EEG experiment is described to which source analysis was applied, making use of the prior information available in this experiment. In general, this thesis demonstrates that the application of source modeling can provide valuable information in EEG analysis
Enhancing Peer to Peer Performance Using Sandwich Methodology

Peer-to-Peer (P2P) system has become one of the hottest research topics, its excellent characteristics of fully decentralized control and self-organizing make it attractive for some particular applications. Routing algorithm has great influence in P2P applications. Current routing algorithm concentrates on creating well organized network architecture to improve the routing performance. However, for each routing procedure, system returns location information of the requested file only and the characteristics of system workload are considered. Routing performance is critical for P2P networks to achieve high performance. Using sandwich method, the HDHTR and SDHTR are combined based on the following criteria: 1. creating a P2P ring where routing tasks are first executed in lower level ring before they go to higher level thus reducing routing overhead. 2. A super node based routing algorithm which reduce the average routing latency and cost. Hence it reduces the routing overhead and average routing latency, which thereby reduce the traffic.
A more Accurate Clustering Method by using Co-author Social Networks for Author Name Disambiguation

Digital libraries may keep millions of citation records and bibliographic attributes such as title, authors' names, and the place of publication. Since the materials and contents in digital libraries are taken from diverse and distinct sources, there are some challenges regarding the use of digital libraries. One of the most important challenges is the ambiguity of authors' names. Although many methods have been proposed for solving the problem of ambiguous authors' names, their accuracy still must be enhanced. In this paper, an accurate method for author name disambiguation is proposed. It combines heuristic hierarchical clustering method and social networks to produce clusters with high accuracy. To evaluate the proposed method, an experiment is conducted using real dataset DBLP. The experimental results show that the accuracy can be enhanced using the proposed method.
Haptic Rendering : Foundations, Algorithms, and Applications

For a long time, human beings have dreamed of a virtual world where it is possible to interact with synthetic entities as if they were real. It has been shown that the ability to touch virtual objects increases the sense of presence in virtual environments. This book provides an authoritative overview of state-of-theart haptic rendering algorithms and their applications. The authors examine various approaches and techniques for designing touch-enabled interfaces for a number of applications, including medical training, model design, and maintainability analysis for virtual prototyping, scientific visualization, and creative processes.