Surface reconstruction is an important trend in 3D scanning. The problem is to recreate surfaces from a given point cloud within the shortest possible time and with a given quality criteria. There is a set of different approaches for solving this problem, which includes Self-Organized Maps, Bayesian reconstruction and Poisson reconstruction.
ARACHNE: A neural-neuroglial network builder with remotely controlled parallel computing
Here we put forward a newly developed simulation environment ARACHNE: it enables an investigator to build and explore cellular networks of arbitrary biophysical and architectural complexity using the logic of NEURON and a simple interface on a local computer or a mobile device.
Good (and Bad) Reasons to Outsource Software Engineering
Software outsourcing is a fairly common business practice these days. Although “outsourcing” may just mean asking somebody "outside" of the company to perform the work, it typically means that work is "shipped" off-shore.
Adaptation of Image Processing Software for Windows Azure and HPC Cluster
In parallel computing systems computations are executed simultaneously, wholly or in part. This approach is based on the partitioning of a big task into smaller subtasks that can run in parallel.
Parallel computing techniques in CAD drawing analysis
Sketches obtained as a result of import or export from different CAD packages to DXF/DWG formats often contain broken geometry relations: broken tangent, broken horizontal or vertical relations, gaps between entities, etc.
Comparison of ConcRT, OpenMP and TBB technologies for multi-core numeric computation
In order to compare ConcRT, OpenMP and TBB technologies, we implemented a few algorithms from different areas of numeric computation and compared their performance with C++ equivalents. Four functions were implemented.
Mathematical modeling of complex problems is far more mainstream than it once was, and Excel has played a major role in making that happen. From linear programming to statistical models, Excel is used to implement many different modeling techniques. Built-in functions and custom worksheets significantly enhance Excel’s flexibility and suitability as the modeling tool of choice within existing customer-based applications.
Advantages of the distributed implementation of the Monte Carlo method in neurobiology simulation
The goal of this paper is to demonstrate the performance benefits from using distributed algorithms and computational power of clusters for Monte Carlo method in simulating processes in the field of neurobiology.
Microsoft announced new technology for GPGPU called C++ Accelerated Massive Parallelism - C++ AMP. It accelerates the execution of C++ code by taking advantage of the GPUs present on video cards with DirectX11 support. The main structure of C++ AMP is influenced by its main competitors like OpenCL and CUDA. But unlike OpenCL and CUDA, that are more oriented in C code, C++ AMP looks like STL library with new C++11 support.
As a software provider, you are undoubtedly stressed. You need high quality developers but can't spend a fortune and have little time to babysit anyone. Perhaps you considered outsourcing– but you probably also have heard many horror stories in the media and from colleagues. We actually agree, typical outsourcing is not usually the answer as it’s proven to fail often. Thus, after more than 10 years providing successful outsourcing solutions, we at AMC Bridge decided to share our experiences. Here are top 5 tips we have used to ensure that outsourcing works properly.
Onshape brings cloud-native CAD and PDM to your engineering teams. AMC Bridge connects it to the rest of your
business. Since 2013, we have helped manufacturers extend Onshape beyond out-of-the-box functionality by integrating
it with PLM, ERP, and MES systems, migrating legacy data, and supporting adoption across complex engineering
environments.
AMC Bridge, a software technology company serving engineering, manufacturing, and construction organizations, announced the successful delivery of a digital thread initiative for a leading research and engineering organization supporting complex, mission-critical programs. The engagement established a connected digital engineering environment that links systems engineering, design, manufacturing, and testing data, creating a scalable foundation for improved decision-making and future AI adoption.
Engineering organizations developing complex products increasingly struggle to maintain visibility and traceability across requirements, system architecture, manufacturing, and validation activities. As programs grow in complexity, disconnected engineering tools and siloed data often create gaps in communication, slow decision-making, and limit the ability to understand the true status of products and programs.
Engineering teams today generate more data than ever before. Requirements, models, simulations, design artifacts, and test results are all critical to the development process, yet this information often remains scattered across disconnected systems and workflows. The challenge is not simply integrating data—it is enabling teams to use that information effectively to make informed decisions, identify issues earlier, reduce risk, and advance programs with greater confidence. This session explores lessons learned from implementing digital thread strategies in advanced research and engineering environments, as well as insights from a panel discussion on the realities of digital engineering and Model-Based Systems Engineering (MBSE).
