Subspace tracking with adaptive threshold rank estimation

In frequency and direction of arrival (DOA) tracking problems, singular value decomposition (SVD) can be used to track the signal subspace. Typically, for a problem sizen, only a few, sayr dominant eigencomponents need to be tracked, wherern. In this paper we show how to modify the Jacobi-type SVD to track only ther-dimensional signal subspace by forcing the (n-r)-dimensional noise subspace to be spherical. Therby, the computational complexity is brought down fromO(n²) toO(nr) per update. In addition to tracking the subspace itself, we demonstrate how to exploit the structure of the Jacobi-type SVD to estimate the signal subspace dimension via a simple adptive threshold comparison technique. Most available computationally efficient subspace tracking algorithms rely on off-line estimation of the signal subspace dimension, which acts as a bottleneck in real-time parallel implementations. The noise averaged Jacobi-type SVD updating algorithm presented in this paper is capable of simultaneously tracking the signal subspace and its dimension, while preserving both the low computational cost ofO(nr) and the parallel structure of the method, as demonstrated in a systolic implementation. Furthermore, the algorithm tracks all signal singular values. Their squares are estimates of the powers in the orthogonal modes of the signal. Thus, applications of the algorithm are not limited to only DOA and frequency tracking where information about the powers of signal components is not exploited.     

Bamboo-Membrane Inspired Multilevel Ultrafast Interlayer Ion Transport for Superior Volumetric Energy Storage

Interlayer transport of charges and carriers of 2D nanomaterials is a critical parameter that governs the material and device performance in energy storage applications. Inspired by multilevel natural bamboo-membrane with ultrafast water and electrolyte transport properties to support its super-rapid growth rate, 2D–2D multilevel heterostructured graphene-based membranes with tailored gradient interlayer channels are rationally designed for achieving ultrafast interlayer ion transport. The bioinspired heterostructured membranes possess multilevel interlayer spacing distributions, where the closely packed layers with sub-nanosized interlayer space provide ultrafast confined interlayer ion transport, while the loosely stacked outer layers consisting of open channels with large distances up to few micrometres are favorable for rapid wetting and penetration of liquid electrolytes. The combination of advantages of large-size open channels and nanosized confined channels offers ultrafast electrolyte wetting and permeation and interlayer ion transport and provide the devices with superior volumetric capacity as free-standing electrodes for rechargeable batteries.     

The number of spanning trees of a graph with given matching number

The number of spanning trees of a graph G is the total number of distinct spanning subgraphs of G that are trees. In this paper, we present sharp upper bounds for the number of spanning trees of a graph with given matching number.     

Annotating patient clinical records with syntactic chunks and named entities: the Harvey Corpus

The free text notes typed by physicians during patient consultations contain valuable information for the study of disease and treatment. These notes are difficult to process by existing natural language analysis tools since they are highly telegraphic (omitting many words), and contain many spelling mistakes, inconsistencies in punctuation, and non-standard word order. To support information extraction and classification tasks over such text, we describe a de-identified corpus of free text notes, a shallow syntactic and named entity annotation scheme for this kind of text, and an approach to training domain specialists with no linguistic background to annotate the text. Finally, we present a statistical chunking system for such clinical text with a stable learning rate and good accuracy, indicating that the manual annotation is consistent and that the annotation scheme is tractable for machine learning.     

A novel neural network based approach to latent overlapped fingerprints separation

Multimedia Tools and Applications - Overlapped fingerprints are often found in latent fingerprints lifted from crime scenes and in live-scan fingerprint images when the surface of fingerprint...     

Dynamic Virtual Resource Allocation in Virtualized multi-RAT Cellular Networks

The legacy wireless systems are designed to exploit static configuration and deployment, and cannot handle the discrepancies of the spatio-temporal traffic demand. Cloud RAN (C-RAN) is a novel flexible radio technology that utilizes the virtualization concepts and can efficiently address the static deployment of conventional wireless systems. The C-RAN also leverages high radio network flexibility by introducing the network function virtualization approach to wireless networks. This paper presents a novel C-RAN platform that virtualizes and operates with full GSM and LTE systems. The presented platform is solely based on open-source and off the shelf solutions, providing easy implementation, low cost and high scalability. The paper also introduces a novel dynamic resource allocation algorithm that facilitates the C-RAN’s optimal performance in dynamic scenarios. The proposed algorithm is analyzed and validated on the presented C-RAN platform. The results of the performance analysis clearly show the advantages of the proposed dynamic resource allocation algorithm. Moreover, they prove the applicability of the C-RAN platform for variety of different scenarios.     

Predicting piezometric water level in dams via artificial neural networks

The safety control of dams is based on measurements of parameters of interest such as seepage flows, seepage water clarity, piezometric levels, water levels, pressures, deformations or movements, temperature variations, loading conditions, etc. Interpretation of these large sets of available data is very important for dam health monitoring and it is based on mathematical models. Modelling seepage through geological formations located near the dam site or dam bodies is a challenging task in dam engineering. The objective of this study is to develop a feedforward neural network (FNN) model to predict the piezometric water level in dams. An improved resilient propagation algorithm has been used to train the FNN. The measured data have been compared with the results of FNN models and multiple linear regression (MLR) models that have been widely used in analysis of the structural dam behaviour. The FNN and MLR models have been developed and tested using experimental data collected during 9 years. The results of this study show that FNN models can be a powerful and important tool which can be used to assess dams.     

Computer Aided Tolerancing (CAT) platform for the design of assemblies under external and internal forces

Due to the stochastic nature of manufacturing processes, the functionality of mechanical assemblies is subject to variation defined by tolerances and manufacturing process characteristics. In many assemblies, functionality is also dependent on external and internal forces. Numerous Computer Aided Tolerancing (CAT) tools have been proposed that address tolerance analysis problems in complex mechanical assemblies; however current tools do not accommodate a general class of problem where the functionality of a design is fundamentally dependent on the effects of external and internal forces. This research addresses the limitation of CAT tools to accommodate assemblies under loading by developing a tolerance analysis platform which integrates CAD, CAE and statistical analysis tools using Process Integration and Design Optimisation (PIDO) software capabilities. The platform extends the capabilities of traditional CAT tools by enabling tolerance analysis of assemblies in which assembly characteristics are dependent on external and internal forces. To demonstrate the capabilities of the developed platform, examples of tolerance analysis problems involving external forces (compliance) and internal forces (multi-body dynamics) are presented.     

A correlation to describe interfacial heat transfer coefficient during solidification of Al–Si alloy casting

A thin wall Al–9 wt.% Si alloy casting was made in a sand mold prepared by CO₂ process. The thermal history obtained from the experiment was used to solve an inverse heat conduction problem (IHCP). The IHTC was estimated by an iterative algorithm based on the function specification method. Acquired IHTC values are given as a function of time and as a function of the casting surface temperature at the interface. It has been found that pattern of IHTC variation with casting surface temperature can be described by an equation which has been proposed as a new correlation model. In order to verify broader applicability of the proposed correlation, its use is demonstrated on the IHTC results taken from the literature.     

Impurity removal and overall rate constant during low pressure treatment of liquid silicon

The research presented analyzes the effect of low pressure on the amount and reduction of impurity elements in upgraded metallurgical grade silicon. The achieved pressure was 5 kPa in the commercial electro-resistance furnace in the magnesia and mullite refractory material. The chemical composition was determined by ICP-MS method. Elements such as Al, Fe, Mn, Cu or Zn had the highest evaporation rates where higher evaporation was achieved at higher melt temperatures. The overall rate constant was deduced for four melt temperatures indicating high values even for low melt temperatures. The interfacial boundary between Si and mullite refractory showed no dissolution of Al into the liquid Si.