A new Steiner patch based file format for Additive Manufacturing processes

Additive Manufacturing (AM) processes adopt a layering approach for building parts in continuous slices and use the Standard Tessellation Language (STL) file format as an input to generate the slices during part manufacturing. However, the current STL format uses planar triangular facets to approximate the surfaces of the parts. This approximation introduces errors in the part representation which leads to additional errors downstream in the parts produced by AM processes. Recently, another file format called Additive Manufacturing File (AMF) was introduced by ASTM which seeks to use curved triangles based on second degree Hermite curves. However, while generating the slices for manufacturing the part, the curved triangles are recursively sub-divided back to planar triangles which may lead to the same approximation error present in the STL file. This paper introduces a new file format which uses curved Steiner patches instead of planar triangles for not only approximating the part surfaces but also for generating the slices. Steiner patches are bounded Roman surfaces and can be parametrically represented by rational Bezier equations. Since Steiner surfaces are of higher order, this new Steiner file format will have a better accuracy than the traditional STL and AMF formats and will lead to lower Geometric Dimensioning and Tolerancing (GD&T) errors in parts manufactured by AM processes. Since the intersection of a plane and the Steiner patch is a closed form mathematical solution, the slicing of the Steiner format can be accomplished with very little computational complexity. The Steiner representation has been used to approximate the surfaces of two test parts and the chordal errors in the surfaces are calculated. The chordal errors in the Steiner format are compared with the STL and AMF formats of the test surfaces and the results have been presented. Further, an error based adaptive tessellation algorithm is developed for generating the Steiner representation which reduces the number of curved facets while still improving the accuracy of the Steiner format. The test parts are virtually manufactured using the adaptive Steiner, STL and AMF format representations and the GD&T errors of the manufactured parts are calculated and compared. The results demonstrate that the modified Steiner format is able to significantly reduce the chordal and profile errors as compared to the STL and AMF formats.     

An energy-conserving approach for data formatting and trusted document exchange in resource-constrained networks

Lightweight data formatting and document exchanging schemes are of interest in conserving energy while exchanging documents, particularly in the resource constrained networks (RCNs). This paper presents an energy-conserving, lightweight method for data representation and trusted document exchange. It is based on a format derived from the YAML Ain’t Markup Language (YAML), a lightweight data serialization language and includes a schema extraction process to separate data from its structure. The resultant schema is useful for thinning the data format and also for checking the rewriting attacks. The rewriting attack is checked using a two-tiered approach. It is observed that the proposed document format is less verbose and more energy conserving than XML and other popular non-binary formats. The format can be used to represent non-document data also.     

Displaying uncertainty: investigating the effects of display format and specificity

We conducted four studies regarding the representation of probabilistic information. Experiments 1 through 3 compared performance on a simulated stock purchase task, in which information regarding stock profitability was probabilistic. Two variables were manipulated: display format for probabilistic information (blurred and colored icons, linguistic phrases, numeric expressions, and combinations) and specificity level (in which the number and size of discrete steps into which the probabilistic information was mapped differed). Results indicated few performance differences attributable to display format; however, performance did improve with greater specificity. Experiment 4, in which participants generated membership functions corresponding to three display formats, found a high degree of similarity in functions across formats and participants and a strong relationship between the shape of the membership function and the intended meaning of the representation. These results indicate that participants can successfully interpret nonnumeric representations of uncertainty and can use such representations in a manner similar to the way numeric expressions are used in a decision-making task. Actual or potential applications of this research include the use of graphical representations of uncertainty in systems such as command and control and situation displays.     

An effective format for representing graphic information

An intelligent format of the compressed graphic data representation, based on correlation-extremal recognition methods is described in this work. Corresponding fields of a new data representation structure are formed by compression, recognition, masking, and coding methods for describing data in the format of the linear-contour model obtained by vectoring initial images of graphic documents. The theoretical reasoning for the efficiency of the graphic data representation in the proposed format is given, which is demonstrated by examples in comparison with the most known formats.     

Linear algebra for tensor problems

By a tensor problem in general, we mean one where all the data on input and output are given (exactly or approximately) in tensor formats, the number of data representation parameters being much smaller than the total amount of data. For such problems, it is natural to seek for algorithms working with data only in tensor formats maintaining the same small number of representation parameters—by the price of all results of computation to be contaminated by approximation (recompression) to occur in each operation. Since approximation time is crucial and depends on tensor formats in use, in this paper we discuss which are best suitable to make recompression inexpensive and reliable. We present fast recompression procedures with sublinear complexity with respect to the size of data and propose methods for basic linear algebra operations with all matrix operands in the Tucker format, mostly through calls to highly optimized level-3 BLAS/LAPACK routines. We show that for three-dimensional tensors the canonical format can be avoided without any loss of efficiency. Numerical illustrations are given for approximate matrix inversion via proposed recompression techniques.      

Comparing the effects of representational tools in collaborative and individual inquiry learning

Constructing a representation in which students express their domain understanding can help them improve their knowledge. Many different representational formats can be used to express one’s domain understanding (e.g., concept maps, textual summaries, mathematical equations). The format can direct students’ attention to specific aspects of the subject matter. For example, creating a concept map can emphasize domain concepts and forming equations can stress arithmetical aspects. The focus of the current study was to examine the role of tools for constructing domain representations in collaborative inquiry learning. The study was driven by three questions. First, what are the effects of collaborative inquiry learning with representational tools on learning outcomes? Second, does format have differential effects on domain understanding? And third, does format have differential effects on students’ inclination to construct a representation? A pre-test post-test design was applied with 61 dyads in a (face-to-face) collaborative learning setting and 95 students in an individual setting. The participants worked on a learning task in a simulation-based learning environment equipped with a representational tool. The format of the tool was either conceptual or arithmetical or textual. Our results show that collaborative learners outperform individuals, in particular with regard to intuitive knowledge and situational knowledge. In the case of individuals a positive relation was observed between constructing a representation and learning outcomes, in particular situational knowledge. In general, the effects of format could not be linked directly to learning outcomes, but marked differences were found regarding students’ inclination to use or not use specific formats.     

CUDA-enabled Sparse Matrix–Vector Multiplication on GPUs using atomic operations

Existing formats for Sparse Matrix–Vector Multiplication (SpMV) on the GPU are outperforming their corresponding implementations on multi-core CPUs. In this paper, we present a new format called Sliced COO (SCOO) and an efficient CUDA implementation to perform SpMV on the GPU using atomic operations. We compare SCOO performance to existing formats of the NVIDIA Cusp library using large sparse matrices. Our results for single-precision floating-point matrices show that SCOO outperforms the COO and CSR format for all tested matrices and the HYB format for all tested unstructured matrices on a single GPU. Furthermore, our dual-GPU implementation achieves an efficiency of 94% on average. Due to the lower performance of existing CUDA-enabled GPUs for atomic operations on double-precision floating-point numbers the SCOO implementation for double-precision does not consistently outperform the other formats for every unstructured matrix. Overall, the average speedup of SCOO for the tested benchmark dataset is 3.33 (1.56) compared to CSR, 5.25 (2.42) compared to COO, 2.39 (1.37) compared to HYB for single (double) precision on a Tesla C2075. Furthermore, comparison to a Sandy-Bridge CPU shows that SCOO on a Fermi GPU outperforms the multi-threaded CSR implementation of the Intel MKL Library on an i7-2700 K by a factor between 5.5 (2.3) and 18 (12.7) for single (double) precision.     

Manipulating algebraic specifications with term-based and graph-based representations

In an environment of continuous and rapid evolution, software design methodologies must incorporate techniques and tools that support changes in software artifacts. In the project, we are developing a tool targeted at software designers that integrates a collection of operations on algebraic specifications written in the language. The scope of includes not only modification of existing specifications, but also creation or derivation of new specifications, as well as their proof and execution, which are realized through inter-operability with existing tools. As involves the manipulation of software specification and inter-operability with other tools, the question of choosing appropriate representation formats is important. In this paper, we discuss the advantages and limitations of as a manipulation and exchange format in the setting of . We also present a new, graph-like format, which offers complementary features to a term-based format. Moreover, we present visualization utilities for these formats.     

Structural Encoding of Static Single Assignment Form

Static Single Assignment (SSA) form is often used as an intermediate representation during code optimization in Java Virtual Machines. Recently, SSA has successfully been used for bytecode verification. However, constructing SSA at the code consumer is costly. SSA-based mobile code transport formats have been shown to eliminate this cost by shifting SSA creation to the code producer. These new formats, however, are not backward compatible with the established Java class-file format. We propose a novel approach to transport SSA information implicitly through structural code properties of standard Java bytecode. While the resulting bytecode sequence can still be directly executed by traditional Virtual Machines, our novel VM can infer SSA form and confirm its safety with virtually no overhead.     

Neural network classification of homomorphic segmented heart sounds

A novel method for segmentation of heart sounds (HSs) into single cardiac cycle (S₁-Systole-S₂-Diastole) using homomorphic filtering and K-means clustering is presented. Feature vectors were formed after segmentation by using Daubechies-2 wavelet detail coefficients at the second decomposition level. These feature vectors were then used as input to the neural networks. Grow and Learn (GAL) and Multilayer perceptron-Backpropagation (MLP-BP) neural networks were used for classification of three different HSs (Normal, Systolic murmur and Diastolic murmur). It was observed that the classification performance of GAL was similar to MLP-BP. However, the training and testing times of GAL were lower as compared to MLP-BP. The proposed framework could be a potential solution for automatic analysis of HSs that may be implemented in real time for classification of HSs.     

Equivalency of computer-assisted and paper-and-pencil administered versions of the Minnesota Multiphasic Personality Inventory-2

The literature on computer-assisted and paper-and-pencil administered Minnesota Multiphasic Personality Inventories (MMPIs) was reviewed. Both computer-assisted and paper-and-pencil administered formats for the MMPI-2 were then investigated. Results found the two formats to be quite comparable. Neither the validity nor the clinical scales differed by format in terms of means and standard deviations. Groups also showed homogeneity of variance across formats. Test-retest reliabilities between the two formats were significant and compared favorably with those reported for repeated paper-and-pencil testings. Stability of high-point codes appeared comparable with past research. Equivalence concerning the presence or absence of clinical elevations across the two formats appeared quite good, showing 92–97% agreement. Subjects expressed preference for the computer-assisted format, reporting more comfort with its pace and that they found it more interesting, less difficult, and more enjoyable. Results suggested that the absence of an overt ‘cannot say’ option may be the optimum presentation choice for this scale.     

浅谈数字音频文件格式及其转换应用

在广播领域使用的数字音频文件格式有多种,其中包括数字音频工作站的制作音频文件格式(S48),漫录系统音频文件格式(S48)以及Wav文件格式和MPEG-1音频压缩文件格式等等。该文针对广播电台常使用的两种音频文件格式进行介绍,并对这几种音频文件格式之间的转换方法和软件应用进行阐述。     

Effective display of medical laboratory report results on small screens: Evaluation of linear and hierarchical displays

Two studies evaluated linear and hierarchy+elision small‐screen display formats for clinical reasoning tasks. A controlled, quantitative study with 28 medically naive participants using a task abstracted from clinical use of laboratory results found that both display formats supported rapid and accurate decision making. Distribution of the search targets significantly affected speed, with decisions in linear format made 13% faster (4.7 sec) when all targets could be viewed on a single screen than when targets required scrolling between several screens and in hierarchical format 15% faster (5.1 sec) when all the targets were confined within one category. Performance was equivalent regardless of the relative order of the target results and data in the laboratory report. In a qualitative study, 7 physicians used the displays to perform a realistic diagnosis. Physicians were comfortable with both display formats, but preference varied with clinical experience. The 5 less experienced clinicians favored hierarchy+elision, whereas the 2 highly experienced clinicians tended to prefer the linear display.     

Fraction-free matrix factors: new forms for LU and QR factors

Gaussian elimination and LU factoring have been greatly studied from the algorithmic point of view, but much less from the point view of the best output format. In this paper, we give new output formats for fraction free LU factoring and for QR factoring. The formats and the algorithms used to obtain them are valid for any matrix system in which the entries are taken from an integral domain, not just for integer matrix systems. After discussing the new output format of LU factoring, the complexity analysis for the fraction free algorithm and fraction free output is given. Our new output format contains smaller entries than previously suggested forms, and it avoids the gcd computations required by some other partially fraction free computations. As applications of our fraction free algorithm and format, we demonstrate how to construct a fraction free QR factorization and how to solve linear systems within a given domain.     

Fraction-free matrix factors: new forms for LU and QR factors

Gaussian elimination and LU factoring have been greatly studied from the algorithmic point of view, but much less from the point view of the best output format. In this paper, we give new output formats for fraction free LU factoring and for QR factoring. The formats and the algorithms used to obtain them are valid for any matrix system in which the entries are taken from an integral domain, not just for integer matrix systems. After discussing the new output format of LU factoring, the complexity analysis for the fraction free algorithm and fraction free output is given. Our new output format contains smaller entries than previously suggested forms, and it avoids the gcd computations required by some other partially fraction free computations. As applications of our fraction free algorithm and format, we demonstrate how to construct a fraction free QR factorization and how to solve linear systems within a given domain.     

A Sparse Matrix Arithmetic Based on \Cal H-Matrices. Part I: Introduction to {\Cal H}-Matrices

A class of matrices (-matrices) is introduced which have the following properties. (i) They are sparse in the sense that only few data are needed for their representation. (ii) The matrix-vector multiplication is of almost linear complexity. (iii) In general, sums and products of these matrices are no longer in the same set, but their truncations to the -matrix format are again of almost linear complexity. (iv) The same statement holds for the inverse of an -matrix. This paper is the first of a series and is devoted to the first introduction of the -matrix concept. Two concret formats are described. The first one is the simplest possible. Nevertheless, it allows the exact inversion of tridiagonal matrices. The second one is able to approximate discrete integral operators. Received: July 30, 1998; revised December 28, 1998     

BiELL: A bisection ELLPACK-based storage format for optimizing SpMV on GPUs

Sparse matrix–vector multiplication (SpMV) is one of the most important high level operations for basic linear algebra. Nowadays, the GPU has evolved into a highly parallel coprocessor which is suited to compute-intensive, highly parallel computation. Achieving high performance of SpMV on GPUs is relatively challenging, especially when the matrix has no specific structure. For these general sparse matrices, a new data structure based on the bisection ELLPACK format, BiELL, is designed to realize the load balance better, and thus improve the performance of the SpMV. Besides, based on the same idea of JAD format, the BiJAD format can be obtained. Experimental results on various matrices show that the BiELL and BiJAD formats perform better than other similar formats, especially when the number of non-zero elements per row varies a lot.