Engineering Time-dependent One-To-All Computation

Abstract Very recently a new algorithm to the nonnegative single-source shortest path problem on road networks has been discovered. It is very cacheefficient, but only on static road networks. We show how to augment it to the time-dependent scenario. The advantage if the new approach is that it settles nodes, even for a profile query, by scanning all downward edges. We improve the scanning of the downward edges with techniques developed for time-dependent many-to-many computations

Monotonic solution of heterogeneous anisotropic diffusion problems

Anisotropic problems arise in various areas of science and engineering, for example groundwater transport and petroleum reservoir simulations. The pure diffusive anisotropic time-dependent transport problem is solved on a finite number of nodes, that are selected inside and on the boundary of the given domain, along with possible internal boundaries connecting some of the nodes. An unstructured triangular mesh, that attains the Generalized Anisotropic Delaunay condition for all the triangle sides, is automatically generated by properly connecting all the nodes, starting from an arbitrary initial one. The control volume of each node is the closed polygon given by the union of the midpoint of each side with the "anisotropic" circumcentre of each final triangle. A structure of the flux across the control volume sides similar to the standard Galerkin Finite Element scheme is derived. A special treatment of the flux computation, mainly based on edge swaps of the initial mesh triangles, is proposed in order to obtain a stiffness M-matrix system that guarantees the monotonicity of the solution. The proposed scheme is tested using several literature tests and the results are compared with analytical solutions, as well as with the results of other algorithms, in terms of convergence order. Computational costs are also investigate

Dynamic Matrix-Fracture Transfer Behaviour in Dual-Porosity Models

Imperial Users onl

Abandoned project restoration model (APRM) for residential construction projects

Incompletion of construction projects is a common phenomenon in Malaysia. Project abandonment has given an adverse consequences to the economy, society and environment. In the best interest of the end users and other parties involved in the contract, the best resolution for this abandoned projects is to successfully revive them, which has its’ stages and barriers along the way as well. The main aim of this research is to develop an effective model as a guide towards project restoration which could be used to mitigate the issue of abandoned residential construction projects in Malaysia. Identifying the factors contributing towards the restoration of the abandoned projects are important to have a successful completed project. This research was conducted in the purpose of identifying those significant factors in order to obtain the restoration process for abandoned projects where lastly the Abandoned Project Restoration Model (APRM) was developed. The research focuses on residential construction projects. This research comprises of both quantitative and qualitative approaches and process, where a pilot survey and full survey, and as well as interview analysis were conducted. Factor model was developed using AMOS and lastly the developed model was validated and tested by related officials. The outcome of this research showed that the most significant factor for abandoned project restoration is Management Aspects. A complete restoration process based on the significant factors identified were also obtained. This model is seen as useful in contributing and as well as assisting the restoration of the abandoned projects in Malaysia and could be used as a guideline for that purpose

Route Planning in Transportation Networks

We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4, previously published by Microsoft Research. This work was mostly done while the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at Microsoft Research Silicon Valle

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system