WaveRange: wavelet-based data compression for three-dimensional numerical simulations on regular grids

A wavelet-based method for compression of three-dimensional simulation data is presented and its software framework is described. It uses wavelet decomposition and subsequent range coding with quantization suitable for floating-point data. The effectiveness of this method is demonstrated by applying it to example numerical tests, ranging from idealized configurations to realistic global-scale simulations. The novelty of this study is in its focus on assessing the impact of compression on post-processing and restart of numerical simulations. Graphical abstract

Urge for Novel and Secure Software Framework for Extraction and Decoding of Mobile Artifacts

Mobile Forensics is now days, increasingly becoming more challenging as it is the field of science that is continuously evolving with respect to the rapidly developing technologies and techniques for the extraction of the mobile data and its decoding. Majority of the crimes are getting committed digitally and especially the criminals are preferring mobile handsets than a laptop or desktop machines, leaving the footprints behind which could be evidence against them. The mobile handsets along with their software applications are getting more advanced and sophisticated mainly due to advances in Cloud computing where clouds are used to store data, Anti-forensics where efforts are made to defeat forensic procedures and Encryption which is used to secure the data during transit. But when compared with the pace of development in mobile hardware and software, the forensic tools and techniques are growing very slowly. Hence the contemporary forensic tools and methodologies are becoming increasingly obsolete and hence urges for the advanced forensic tools, methods which could comply with the need of today’s mobile forensics. Hence, this work presents a detailed survey of the contemporary challenges faced by the forensic experts with the current forensic tools and its methodologies and also the need, scope and opportunities associated with the novel and secure software framework that can address the majority of issues occurring while extraction and decoding of mobile artifacts.

The GAMBIT Universal Model Machine: from Lagrangians to likelihoods

We introduce the Universal Model Machine (), a tool for automatically generating code for the global fitting software framework , based on Lagrangian-level inputs. accepts models written symbolically in and formats, and can use either tool along with and to generate model, collider, dark matter, decay and spectrum code, as well as interfaces to corresponding versions of , , and (C "Image missing"). In this paper we describe the features, methods, usage, pathways, assumptions and current limitations of . We also give a fully worked example, consisting of the addition of a Majorana fermion simplified dark matter model with a scalar mediator to via , and carry out a corresponding fit.

RAP: A Software Framework of Developing Convolutional Neural Networks for Resource-constrained Devices Using Environmental Monitoring as a Case Study

Monitoring environmental conditions is an important application of cyber-physical systems. Typically, the monitoring is to perceive surrounding environments with battery-powered, tiny devices deployed in the field. While deep learning-based methods, especially the convolutional neural networks (CNNs), are promising approaches to enriching the functionalities offered by the tiny devices, they demand more computation and memory resources, which makes these methods difficult to be adopted on such devices. In this article, we develop a software framework, RAP , that permits the construction of the CNN designs by aggregating the existing, lightweight CNN layers, which are able to fit in the limited memory (e.g., several KBs of SRAM) on the resource-constrained devices satisfying application-specific timing constrains. RAP leverages the Python-based neural network framework Chainer to build the CNNs by mounting the C/C++ implementations of the lightweight layers, trains the built CNN models as the ordinary model-training procedure in Chainer, and generates the C version codes of the trained models. The generated programs are compiled into target machine executables for the on-device inferences. With the vigorous development of lightweight CNNs, such as binarized neural networks with binary weights and activations, RAP facilitates the model building process for the resource-constrained devices by allowing them to alter, debug, and evaluate the CNN designs over the C/C++ implementation of the lightweight CNN layers. We have prototyped the RAP framework and built two environmental monitoring applications for protecting endangered species using image- and acoustic-based monitoring methods. Our results show that the built model consumes less than 0.5 KB of SRAM for buffering the runtime data required by the model inference while achieving up to 93% of accuracy for the acoustic monitoring with less than one second of inference time on the TI 16-bit microcontroller platform.

Consistent Tangent Stiffness of a Three-Dimensional Wheel–Rail Interaction Element

Consistent tangent stiffness plays a crucial role in delivering a quadratic rate of convergence when using Newton’s method in solving nonlinear equations of motion. In this paper, consistent tangent stiffness is derived for a three-dimensional (3D) wheel–rail interaction element (WRI element for short) originally developed by the authors and co-workers. The algorithm has been implemented in finite element (FE) software framework (OpenSees in this paper) and proven to be effective. Application examples of wheelset and light rail vehicle are provided to validate the consistent tangent stiffness. The quadratic convergence rate is verified. The speeds of calculation are compared between the use of consistent tangent stiffness and the tangent by perturbation method. The results demonstrate the improved computational efficiency of WRI element when consistent tangent stiffness is used.