A review of ADP processing software

Data processing is an essential part of Acoustic Doppler Profiler (ADP) surveys, which have become the standard tool in assessing flow characteristics at tidal power development sites. In most cases, further processing beyond the capabilities of the manufacturer provided software tools is required. These additional tasks are often implemented by every user in mathematical toolboxes like MATLAB, Octave or Python. This requires the transfer of the data from one system to another and thus increases the possibility of errors. The application of dedicated tools for visualisation of flow or geographic data is also often beneficial and a wide range of tools are freely available, though again problems arise from the necessity of transferring the data. Furthermore, almost exclusively PCs are supported directly by the ADP manufacturers, whereas small computing solutions like tablet computers, often running Android or Linux operating systems, seem better suited for online monitoring or data acquisition in field conditions. While many manufacturers offer support for developers, any solution is limited to a single device of a single manufacturer. A common data format for all ADP data would allow development of applications and quicker distribution of new post processing methodologies across the industry.

Streaming Elements for FPGA Signal and Image Processing Accelerators

Field programmable gate array devices boast abundant resources with which custom accelerator components for signal, image and data processing may be realised; however, realising high performance, low cost accelerators currently demands manual register transfer level design. Software-programmable ’soft’ processors have been proposed as a way to reduce this design burden but they are unable to support performance and cost comparable to custom circuits. This paper proposes a new soft processing approach for FPGA which promises to overcome this barrier. A high performance, fine-grained streaming processor, known as a Streaming Accelerator Element, is proposed which realises accelerators as large scale custom multicore networks. By adopting a streaming execution approach with advanced program control and memory addressing capabilities, typical program inefficiencies can be almost completely eliminated to enable performance and cost which are unprecedented amongst software-programmable solutions. When used to realise accelerators for fast fourier transform, motion estimation, matrix multiplication and sobel edge detection it is shown how the proposed architecture enables real-time performance and with performance and cost comparable with hand-crafted custom circuit accelerators and up to two orders of magnitude beyond existing soft processors.

A distributed contention-vector division multiple access (D-CVDMA) protocol for wireless networks

Traditional Time Division Multiple Access (TDMA) protocol provides deterministic periodic collision free data transmissions. However, TDMA lacks flexibility and exhibits low efficiency in dynamic environments such as wireless LANs. On the other hand contention-based MAC protocols such as the IEEE 802.11 DCF are adaptive to network dynamics but are generally inefficient in heavily loaded or large networks. To take advantage of the both types of protocols, a D-CVDMA protocol is proposed. It is based on the k-round elimination contention (k-EC) scheme, which provides fast contention resolution for Wireless LANs. D-CVDMA uses a contention mechanism to achieve TDMA-like collision-free data transmissions, which does not need to reserve time slots for forthcoming transmissions. These features make the D-CVDMA robust and adaptive to network dynamics such as node leaving and joining, changes in packet size and arrival rate, which in turn make it suitable for the delivery of hybrid traffic including multimedia and data content. Analyses and simulations demonstrate that D-CVDMA outperforms the IEEE 802.11 DCF and k-EC in terms of network throughput, delay, jitter, and fairness.

Time of flight mass spectrometry for quantitative data analysis in fast transient studies using a Temporal Analysis of Products (TAP) reactor

A Time of flight (ToF) mass spectrometer suitable in terms of sensitivity, detector response and time resolution, for application in fast transient Temporal Analysis of Products (TAP) kinetic catalyst characterization is reported. Technical difficulties associated with such application as well as the solutions implemented in terms of adaptations of the ToF apparatus are discussed. The performance of the ToF was validated and the full linearity of the specific detector over the full dynamic range was explored in order to ensure its applicability for the TAP application. The reported TAP-ToF setup is the first system that achieves the high level of sensitivity allowing monitoring of the full 0-200 AMU range simultaneously with sub-millisecond time resolution. In this new setup, the high sensitivity allows the use of low intensity pulses ensuring that transport through the reactor occurs in the Knudsen diffusion regime and that the data can, therefore, be fully analysed using the reported theoretical TAP models and data processing.

The forgiving tree: a self-healing distributed data structure

We consider the problem of self-healing in peer-to-peer networks that are under repeated attack by an omniscient adversary. We assume that the following process continues for up to n rounds where n is the total number of nodes initially in the network: the adversary deletesan arbitrary node from the network, then the network responds by quickly adding a small number of new edges.

We present a distributed data structure that ensures two key properties. First, the diameter of the network is never more than O(log Delta) times its original diameter, where Delta is the maximum degree of the network initially. We note that for many peer-to-peer systems, Delta is polylogarithmic, so the diameter increase would be a O(loglog n) multiplicative factor. Second, the degree of any node never increases by more than 3 over its original degree. Our data structure is fully distributed, has O(1) latency per round and requires each node to send and receive O(1) messages per round. The data structure requires an initial setup phase that has latency equal to the diameter of the original network, and requires, with high probability, each node v to send O(log n) messages along every edge incident to v. Our approach is orthogonal and complementary to traditional topology-based approaches to defending against attack.

Hybrid address spaces: A methodology for implementing scalable high-level programming models on non-coherent many-core architectures

This paper introduces hybrid address spaces as a fundamental design methodology for implementing scalable runtime systems on many-core architectures without hardware support for cache coherence. We use hybrid address spaces for an implementation of MapReduce, a programming model for large-scale data processing, and the implementation of a remote memory access (RMA) model. Both implementations are available on the Intel SCC and are portable to similar architectures. We present the design and implementation of HyMR, a MapReduce runtime system whereby different stages and the synchronization operations between them alternate between a distributed memory address space and a shared memory address space, to improve performance and scalability. We compare HyMR to a reference implementation and we find that HyMR improves performance by a factor of 1.71× over a set of representative MapReduce benchmarks. We also compare HyMR with Phoenix++, a state-of-art implementation for systems with hardware-managed cache coherence in terms of scalability and sustained to peak data processing bandwidth, where HyMR demon- strates improvements of a factor of 3.1× and 3.2× respectively. We further evaluate our hybrid remote memory access (HyRMA) programming model and assess its performance to be superior of that of message passing.

Mobile Wearable Communications

This special issue provides the latest research and development on wireless mobile wearable communications. According to a report by Juniper Research, the market value of connected wearable devices is expected to reach $1.5 billion by 2014, and the shipment of wearable devices may reach 70 million by 2017. Good examples of wearable devices are the prominent Google Glass and Microsoft HoloLens. As wearable technology is rapidly penetrating our daily life, mobile wearable communication is becoming a new communication paradigm. Mobile wearable device communications create new challenges compared to ordinary sensor networks and short-range communication. In mobile wearable communications, devices communicate with each other in a peer-to-peer fashion or client-server fashion and also communicate with aggregation points (e.g., smartphones, tablets, and gateway nodes). Wearable devices are expected to integrate multiple radio technologies for various applications' needs with small power consumption and low transmission delays. These devices can hence collect, interpret, transmit, and exchange data among supporting components, other wearable devices, and the Internet. Such data are not limited to people's personal biomedical information but also include human-centric social and contextual data. The success of mobile wearable technology depends on communication and networking architectures that support efficient and secure end-to-end information flows. A key design consideration of future wearable devices is the ability to ubiquitously connect to smartphones or the Internet with very low energy consumption. Radio propagation and, accordingly, channel models are also different from those in other existing wireless technologies. A huge number of connected wearable devices require novel big data processing algorithms, efficient storage solutions, cloud-assisted infrastructures, and spectrum-efficient communications technologies.

RMA with quantile normalization mixes biological signals between different sample groups in microarray data analysis

Quantile normalization (QN) is a technique for microarray data processing and is the default normalization method in the Robust Multi-array Average (RMA) procedure, which was primarily designed for analysing gene expression data from Affymetrix arrays. Given the abundance of Affymetrix microarrays and the popularity of the RMA method, it is crucially important that the normalization procedure is applied appropriately. In this study we carried out simulation experiments and also analysed real microarray data to investigate the suitability of RMA when it is applied to dataset with different groups of biological samples. From our experiments, we showed that RMA with QN does not preserve the biological signal included in each group, but rather it would mix the signals between the groups. We also showed that the Median Polish method in the summarization step of RMA has similar mixing effect. RMA is one of the most widely used methods in microarray data processing and has been applied to a vast volume of data in biomedical research. The problematic behaviour of this method suggests that previous studies employing RMA could have been misadvised or adversely affected. Therefore we think it is crucially important that the research community recognizes the issue and starts to address it. The two core elements of the RMA method, quantile normalization and Median Polish, both have the undesirable effects of mixing biological signals between different sample groups, which can be detrimental to drawing valid biological conclusions and to any subsequent analyses. Based on the evidence presented here and that in the literature, we recommend exercising caution when using RMA as a method of processing microarray gene expression data, particularly in situations where there are likely to be unknown subgroups of samples.

Compositional data analysis of Holocene sediments from the West Bengal Sundarbans, India: Geochemical proxies for grain-size variability in a delta environment

This paper is part of a special issue of Applied Geochemistry focusing on reliable applications of compositional multivariate statistical methods. This study outlines the application of compositional data analysis (CoDa) to calibration of geochemical data and multivariate statistical modelling of geochemistry and grain-size data from a set of Holocene sedimentary cores from the Ganges-Brahmaputra (G-B) delta. Over the last two decades, understanding near-continuous records of sedimentary sequences has required the use of core-scanning X-ray fluorescence (XRF) spectrometry, for both terrestrial and marine sedimentary sequences. Initial XRF data are generally unusable in ‘raw-format’, requiring data processing in order to remove instrument bias, as well as informed sequence interpretation. The applicability of these conventional calibration equations to core-scanning XRF data are further limited by the constraints posed by unknown measurement geometry and specimen homogeneity, as well as matrix effects. Log-ratio based calibration schemes have been developed and applied to clastic sedimentary sequences focusing mainly on energy dispersive-XRF (ED-XRF) core-scanning. This study has applied high resolution core-scanning XRF to Holocene sedimentary sequences from the tidal-dominated Indian Sundarbans, (Ganges-Brahmaputra delta plain). The Log-Ratio Calibration Equation (LRCE) was applied to a sub-set of core-scan and conventional ED-XRF data to quantify elemental composition. This provides a robust calibration scheme using reduced major axis regression of log-ratio transformed geochemical data. Through partial least squares (PLS) modelling of geochemical and grain-size data, it is possible to derive robust proxy information for the Sundarbans depositional environment. The application of these techniques to Holocene sedimentary data offers an improved methodological framework for unravelling Holocene sedimentation patterns.

On the Virtualization of CUDA Based GPU Remoting on ARM and X86 Machines in the GVirtuS Framework

The astonishing development of diverse and different hardware platforms is twofold: on one side, the challenge for the exascale performance for big data processing and management; on the other side, the mobile and embedded devices for data collection and human machine interaction. This drove to a highly hierarchical evolution of programming models. GVirtuS is the general virtualization system developed in 2009 and firstly introduced in 2010 enabling a completely transparent layer among GPUs and VMs. This paper shows the latest achievements and developments of GVirtuS, now supporting CUDA 6.5, memory management and scheduling. Thanks to the new and improved remoting capabilities, GVirtus now enables GPU sharing among physical and virtual machines based on x86 and ARM CPUs on local workstations,computing clusters and distributed cloud appliances.

Multidimensional DSP Core Synthesis for FPGA

This paper, chosen as a best paper from the 2004 SAMOS Workshop on Computer Systems: describes a novel, efficient methodology for automatically creating embedded DSP computer systems. The novelty arises since now embedded electronic signal processing systems, such as radar or sonar, can be designed by anyone from the algorithm level, i.e. no low level system design experience is required, whilst still achieving low controllable implementation overheads and high real time performance. In the chosen design example, a bank of Normalised Lattice Filter (NLF) components is created which a four-fold reduction in the required processing resource with no performance decrease.

Microscopy of heat treated titanium alloy BT16

Metallographic characterisation is combined with statistical analysis to study the microstructure of a BT16 titanium alloy after different heat treatment processes. It was found that the length, width and aspect ratio of α plates in this alloy follow the three-parameter Weibull distribution. Increasing annealing temperature or time causes the probability distribution of the length and the width of α plates to tend toward a normal distribution. The phase transformation temperature of the BT16 titanium alloy was found to be 875±5°C.