Effect of signal intensity normalization on the multivariate analysis of spectral data in complex 'real-world' datasets

Spectral signal intensities, especially in 'real-world' applications with nonstandardized sample presentation due to uncontrolled variables/factors, commonly require additional spectral processing to normalize signal intensity in an effective way. In this study, we have demonstrated the complexity of choosing a normalization routine in the presence of multiple spectrally distinct constituents by probing a dataset of Raman spectra. Variation in absolute signal intensity (90.1% of total variance) of the Raman spectra of these complex biological samples swamps the variation in useful signals (9.4% of total variance), degrading its diagnostic and evaluative potential.

On Entropy-Based Molecular Descriptors: Statistical Analysis of Real and Synthetic Chemical Structures

This paper presents an analysis of entropy-based molecular descriptors. Specifically, we use real chemical structures, as well as synthetic isomeric structures, and investigate properties of and among descriptors with respect to the used data set by a statistical analysis. Our numerical results provide evidence that synthetic chemical structures are notably different to real chemical structures and, hence, should not be used to investigate molecular descriptors. Instead, an analysis based on real chemical structures is favorable. Further, we find strong hints that molecular descriptors can be partitioned into distinct classes capturing complementary information.

Development of a minor groove binder assay for real-time one-step RT-PCR detection of swine vesicular disease virus

The design and development of a 5' conjugated minor groove binder (MGB) probe real-time RT-PCR assay are described for rapid, sensitive and specific detection of swine vesicular disease virus (SVDV) RNA. The assay is designed to target the 2C gene of the SVDV genome and is capable of detecting 2 x 10(2) copies of an RNA standard per reaction. It does not detect any of the other RNA viruses that cause vesicular disease in pigs, or the human enterovirus, Coxsackie B5 virus (CVB5) which is closely related antigenically to SVDV. The linear range of this test was from 2 x 10(2) to 2 x 10(8) copies/mu l. The assay is rapid and can detect SVDV RNA in just over 3.5 h including the time required for nucleic acid extraction. The development of this assay provides a useful tool for the differential diagnosis of SVD or for the detection of SVDV in research applications. This study demonstrates the suitability of MGB probes as a real-time PCR chemistry for the diagnosis of swine vesicular disease. (C) 2010 Elsevier B.V. All rights reserved.

Sensitive detection of African swine fever virus using real-time PCR with a 5 ' conjugated minor groove binder probe

The design of a 5' conjugated minor groove binder (MGB) probe real-time PCR assay is described for the rapid, sensitive and specific detection of African swine fever virus (ASFV) DNA. The assay is designed against the 9GL region and is capable of detecting 20 copies of a DNA standard. It does not detect any of the other common swine DNA viruses tested in this study. The assay can detect ASFV DNA in a range of clinical samples. Sensitivity was equivalent to the Office International des Epizooties (OIE) recommended TaqMan assay. In addition the assay was found to have a detection limit 10-fold more sensitive than the conventional PCR recommended by the OIE. Linear range was ten logs from 2 x 10(1) to 2 x 10(10). The assay is rapid with an amplification time just over 2 h. The development of this assay provides a useful tool for the specific diagnosis of ASF in statutory or emergency testing programs or for the detection of ASFV DNA in research applications. (C) 2010 Elsevier B.V. All rights reserved.

A Kernel Interleaved Scheduling Method for Streaming Applications on Soft-core Vector Processors

Massively parallel networks of highly efficient, high performance Single Instruction Multiple Data (SIMD) processors have been shown to enable FPGA-based implementation of real-time signal processing applications with performance and
cost comparable to dedicated hardware architectures. This is achieved by exploiting simple datapath units with deep processing pipelines. However, these architectures are highly susceptible to pipeline bubbles resulting from data and control hazards; the only way to mitigate against these is manual interleaving of
application tasks on each datapath, since no suitable automated interleaving approach exists. In this paper we describe a new automated integrated mapping/scheduling approach to map algorithm tasks to processors and a new low-complexity list scheduling technique to generate the interleaved schedules. When applied to a spatial Fixed-Complexity Sphere Decoding (FSD) detector
for next-generation Multiple-Input Multiple-Output (MIMO) systems, the resulting schedules achieve real-time performance for IEEE 802.11n systems on a network of 16-way SIMD processors on FPGA, enable better performance/complexity balance than current approaches and produce results comparable to handcrafted implementations.

Error analysis of FFT architectures for digital video applications

This paper describes how worst-case error analysis can be applied to solve some of the practical issues in the development and implementation of a low power, high performance radix-4 FFT chip for digital video applications. The chip has been fabricated using a 0.6 µm CMOS technology and can perform a 64 point complex forward or inverse FFT on real-time video at up to 18 Megasamples per second. It comprises 0.5 million transistors in a die area of 7.8×8 mm and dissipates 1 W, leading to a cost-effective silicon solution for high quality video processing applications. The analysis focuses on the effect that different radix-4 architectural configurations and finite wordlengths has on the FFT output dynamic range. These issues are addressed using both mathematical error models and through extensive simulation.

Evaluation of a substructured soft Real-time Hybrid Test for performing Seismic analysis of complex structural systems

The hybrid test method is a relatively recently developed dynamic testing technique that uses numerical modelling combined with simultaneous physical testing. The concept of substructuring allows the critical or highly nonlinear part of the structure that is difficult to numerically model with accuracy to be physically tested whilst the remainder of the structure, that has a more predictable response, is numerically modelled. In this paper, a substructured soft-real time hybrid test is evaluated as an accurate means of performing seismic tests of complex structures. The structure analysed is a three-storey, two-by-one bay concentrically braced frame (CBF) steel structure subjected to seismic excitation. A ground storey braced frame substructure whose response is critical to the overall response of the structure is tested, whilst the remainder of the structure is numerically modelled. OpenSees is used for numerical modelling and OpenFresco is used for the communication between the test equipment and numerical model. A novel approach using OpenFresco to define the complex numerical substructure of an X-braced frame within a hybrid test is also presented. The results of the hybrid tests are compared to purely numerical models using OpenSees and a simulated test using a combination of OpenSees and OpenFresco. The comparative results indicate that the test method provides an accurate and cost effective procedure for performing
full scale seismic tests of complex structural systems.

Real-Time Face Recognition from Surveillance Video

This chapter describes an experimental system for the recognition of human faces from surveillance video. In surveillance applications, the system must be robust to changes in illumination, scale, pose and expression. The system must also be able to perform detection and recognition rapidly in real time. Our system detects faces using the Viola-Jones face detector, then extracts local features to build a shape-based feature vector. The feature vector is constructed from ratios of lengths and differences in tangents of angles, so as to be robust to changes in scale and rotations in-plane and out-of-plane. Consideration was given to improving the performance and accuracy of both the detection and recognition steps.

Tools for Finding Inconsistencies in Real-world Logic-based Systems

Currently there is extensive theoretical work on inconsistencies in logic-based systems. Recently, algorithms for identifying inconsistent clauses in a single conjunctive formula have demonstrated that practical application of this work is possible. However, these algorithms have not been extended for full knowledge base systems and have not been applied to real-world knowledge. To address these issues, we propose a new algorithm for finding the inconsistencies in a knowledge base using existing algorithms for finding inconsistent clauses in a formula. An implementation of this algorithm is then presented as an automated tool for finding inconsistencies in a knowledge base and measuring the inconsistency of formulae. Finally, we look at a case study of a network security rule set for exploit detection (QRadar) and suggest how these automated tools can be applied.

NanoStreams: Advancing the Hardware and Software Stack for Real-Time Analytics on Fast Data Streams

NanoStreams is a consortium project funded by the European Commission under its FP7 programme and is a major effort to address the challenges of processing vast amounts of data in real-time, with a markedly lower carbon footprint than the state of the art. The project addresses both the energy challenge and the high-performance required by emerging applications in real-time streaming data analytics. NanoStreams achieves this goal by designing and building disruptive micro-server solutions incorporating real-silicon prototype micro-servers based on System-on-Chip and reconfigurable hardware technologies.

An Event Driven Fusion Approach for Enjoyment Recognition in Real-time

Social signals and interpretation of carried information is of high importance in Human Computer Interaction. Often used for affect recognition, the cues within these signals are displayed in various modalities. Fusion of multi-modal signals is a natural and interesting way to improve automatic classification of emotions transported in social signals. Throughout most present studies, uni-modal affect recognition as well as multi-modal fusion, decisions are forced for fixed annotation segments across all modalities. In this paper, we investigate the less prevalent approach of event driven fusion, which indirectly accumulates asynchronous events in all modalities for final predictions. We present a fusion approach, handling short-timed events in a vector space, which is of special interest for real-time applications. We compare results of segmentation based uni-modal classification and fusion schemes to the event driven fusion approach. The evaluation is carried out via detection of enjoyment-episodes within the audiovisual Belfast Story-Telling Corpus.