Three-dimensional frame fields computed on CAD models often contain singular curves that are not compatible with hexahedral meshing. In this paper, we show how CAD feature curves can induce non meshable 3-5 singular curves and we study four different approaches that aims at correcting the frame field topology. All approaches consist in modifying the frame field computation, the two first ones consisting in applying internal constraints and the two last ones consisting in modifying the boundary conditions. Approaches based on internal constraints are shown not to be very reliable because of their interactions with other singularities. On the other hand, boundary condition modifications are more promising as their impact is very localized. We eventually recommend the 3-5 singular curve boundary snapping strategy, which is simple to implement and allows to generate topologically correct frame fields.

This tutorial discusses the static calibration of a deformable mirror using a Twyman–Green interferometer setup, combined with single-frame fringe analysis for phase extraction. We provide a reference implementation for this method in the form of a toolbox written in Python. In addition, we include detailed instructions to build a compact, slot-in interferometer. This protocol, accompanying software, and hardware design facilitate calibration of deformable mirrors for a range of applications.

This paper proposes a robust and effective approach to overcome a major difficulty associated to surface finite element mesh generation: the handling surfaces with irregular (singular) parametrizations such as spheres, cones or other surfaces of revolution produced by common Computer Aided Design tools. The main idea is to represent triangles incident to irregular points as trapezoids with one degenerated edge. This new approach has been implemented in Gmsh and examples containing thousands of surfaces with irregular points are presented at the end of the paper.

The huge amounts of data and information that need to be analyzed for possible malicious intent are one of the big and significant challenges that the Web faces today. Malicious software, also referred to as malware developed by attackers, is polymorphic and metamorphic in nature which can modify the code as it spreads. In addition, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses that typically use signature-based techniques and are unable to detect malicious executables previously unknown. Malware family variants share typical patterns of behavior that indicate their origin and purpose. The behavioral trends observed either statically or dynamically can be manipulated by using machine learning techniques to identify and classify unknown malware into their established families. This survey paper gives an overview of the malware detection and analysis techniques and tools. KEYWORDS Malware, Detection, Analysis, Tools, Machine Learning.

The important and key concept of the clouds is sharing their resources on many different nodes that are making beneficial for any application. Among these according to the user requirements scheduling the resource is very challenging in cloud environment indeed for the low latency over high velocity data streams. As the resources vary in their performance while allocating to the streaming application on infrastructure , which leads to the distraction of QoS of the application meanwhile the resource cost also should be concise to balance the deployment cost. To formalize the optimization with respect to balance the application cost (domain value) QoS, and resource cost by representing the deployment and runtime (dynamic) resource allocation .So we are proposing a new concept alternate path for the dataflow which dynamic in nature to the infrastructure. We propose a Ranking with deadline based algorithm that will implement in the alternative path to provide the end users with more sophisticated control and resource mapping heuristics for communal of dataflow to give near optimal solution. The effect of both variable dynamic data rate and reactive resource performance should be maintained to balance the throughput constraint and application value.

The visual representations of the inner parts of body along with functions of the organs or tissues are called as Medical Imaging. This is of critical importance for early diagnosis and treatment. The images obtained by various techniques such as Magnetic Resonant Imaging (MRI), Computed Tomography (CT) are processed for medical assistance and treatment. First step is image preprocessing. After preprocessing, morphological operations are done. This transformed image undergoes watershed segmentation and Active contour process. The result thus will be fed to a classifier for detecting the presence of hemorrhage.

This paper presents a technique for employing high-performance computing for accelerating the exact evaluation of geometric predicates. Arithmetic filters are implemented using interval arithmetic to reduce the necessity of exact arithmetic while ensuring the results of the predicates are still exact. Furthermore, the computation with interval arithmetic is offloaded to a CUDA-enabled GPU. If the GPU detects that some results cannot be trusted, the corresponding predicates are re-evaluated in parallel on the CPU using arbitrary-precision rational numbers. As a case study, a red-blue segment intersection algorithm has been implemented. Since the intervals are implemented using floating-point numbers, the parallel computing power of GPUs for processing these numbers led to a speedup of up to 289 times (when compared against a similar sequential implementation) in the evaluation of these predicates (and up to 40 times if the entire runnning-time of the algorithm is considered). The excellent performance associated to the exactness makes this technique suitable for accelerating geometric operations in fields such as CAD, GIS and VLSI design.

We describe new machine-learning-based methods to defeature CAD models for tetrahedral meshing. Using machine learning predictions of mesh quality for geometric features of a CAD model prior to meshing we can identify potential problem areas and improve meshing outcomes by presenting a prioritized list of suggested geometric operations to users. Our machine learning models are trained using a combination of geometric and topological features from the CAD model and local quality metrics for ground truth. We demonstrate a proof-of-concept implementation of the resulting workflow using Sandia’s Cubit Geometry and Meshing Toolkit.