An indirect bridge inspection method incorporating a wavelet-based damage indicator and pattern recognition

In recent years, there has been a move towards the development of indirect structural health monitoring (SHM)techniques for bridges; the low-cost vibration-based method presented in this paper is such an approach. It consists of the use of a moving vehicle fitted with accelerometers on its axles and incorporates wavelet analysis and statistical pattern recognition. The aim of the approach is to both detect and locate damage in bridges while reducing the need for direct instrumentation of the bridge. In theoretical simulations, a simplified vehicle-bridge interaction model is used to investigate the effectiveness of the approach in detecting damage in a bridge from vehicle accelerations. For this purpose, the accelerations are processed using a continuous wavelet transform as when the axle passes over a damaged section, any discontinuity in the signal would affect the wavelet coefficients. Based on these coefficients, a damage indicator is formulated which can distinguish between different damage levels. However, it is found to be difficult to quantify damage of varying levels when the vehicle’s transverse position is varied between bridge crossings. In a real bridge field experiment, damage was applied artificially to a steel truss bridge to test the effectiveness of the indirect approach in practice; for this purpose a two-axle van was driven across the bridge at constant speed. Both bridge and vehicle acceleration measurements were recorded. The dynamic properties of the test vehicle were identified initially via free vibration tests. It was found that the resulting damage indicators for the bridge and vehicle showed similar patterns, however, it was difficult to distinguish between different artificial damage scenarios.

Directional Modulation Far-field Pattern Separation Synthesis Approach

In this study, a far-field power pattern separation approach is proposed for the synthesis of directional modulation (DM) transmitter arrays. Separation into information patterns and interference patterns is enabled by far-field pattern null steering. Compared with other DM synthesis methods, for example, bit error rate-driven DM optimisation and orthogonal vector injection, the approach developed in this study facilitates manipulation of artificial interference spatial distributions. With such capability more interference power can be projected into those spatial directions most vulnerable to eavesdropping, that is, the information side lobes. In such a fashion, information leaked through radiation side lobes can be effectively mitigated in a transmitter power efficient manner. Furthermore, for the first time, the authors demonstrate how multi-beam DM transmitters can be synthesised via this approach.

Pattern Recognition Approach for Road Collision Hotspots Analysis: Case Study of Northern Ireland

In order to address road safety effectively, it is essential to understand all the factors, which
attribute to the occurrence of a road collision. This is achieved through road safety
assessment measures, which are primarily based on historical crash data. Recent advances
in uncertain reasoning technology have led to the development of robust machine learning
techniques, which are suitable for investigating road traffic collision data. These techniques
include supervised learning (e.g. SVM) and unsupervised learning (e.g. Cluster Analysis).
This study extends upon previous research work, carried out in Coll et al. [3], which
proposed a non-linear aggregation framework for identifying temporal and spatial hotspots.
The results from Coll et al. [3] identified Lisburn area as the hotspot, in terms of road safety,
in Northern Ireland. This study aims to use Cluster Analysis, to investigate and highlight any
hidden patterns associated with collisions that occurred in Lisburn area, which in turn, will
provide more clarity in the causation factors so that appropriate countermeasures can be put
in place.

Pool Evolution: A Parallel Pattern for Evolutionary and Symbolic Computing

We introduce a new parallel pattern derived from a specific application domain and show how it turns out to have application beyond its domain of origin. The pool evolution pattern models the parallel evolution of a population subject to mutations and evolving in such a way that a given fitness function is optimized. The pattern has been demonstrated to be suitable for capturing and modeling the parallel patterns underpinning various evolutionary algorithms, as well as other parallel patterns typical of symbolic computation. In this paper we introduce the pattern, we discuss its implementation on modern multi/many core architectures and finally present experimental results obtained with FastFlow and Erlang implementations to assess its feasibility and scalability.

Ecological dynamics of extinct species in empty habitat networks. 1. The role of habitat pattern and quantity, stochasticity and dispersal

We examined a remnant host plant (Primula veris L.) habitat network that was last inhabited by the rare butterfly Hamearis lucina L. in north Wales in 1943, to assess the relative contribution of several spatial parameters to its regional extinction. We first examined relationships between P. veris characteristics and H. lucina eggs in surviving H. lucina populations, and used these to predict the suitability and potential carrying capacity of the habitat network in north Wales. This resulted in an estimate of roughly 4500 eggs (ca 227 adults). We developed a discrete space, discrete time metapopulation model to evaluate the relative contribution of dispersal distance, habitat and environmental stochasticity as possible causes of extinction. We simulated the potential persistence of the butterfly in the current network as well as in three artificial (historical and present) habitat networks that differed in the quantity (current and X3) and fragmentation of the habitat (current and aggregated). We identified that reduced habitat quantity and increased isolation would have increased the probability of regional extinction, in conjunction with environmental stochasticity and H. lucina's dispersal distance. This general trend did not change in a qualitative manner when we modified the ability of dispersing females to stay in, and find suitable habitats (by changing the size of the grid cells used in the model). Contrary to most metapopulation model predictions, system persistence declined with increasing migration rate, suggesting that the mortality of migrating individuals in fragmented landscapes may pose significant risks to system-wide persistence. Based on model predictions for the present landscape we argue that a major programme of habitat restoration would be required for a re-established metapopulation to persist for > 100 years.

Conical Horn Antenna with Spiral Phase Plate for Difference Pattern Generation

This paper describes the performance characteristics and experimental validation of a compact conical horn antenna with a dielectric cylinder spiral phase plate attached at its aperture. This performs the function of a spatial phase imprinting device creating a helical wave-front which results in a null in the far field radiation pattern of the antenna assembly.

A Far-field Pattern Separation Approach for the Synthesis of Directional Modulation Transmitter Arrays (Invited)

In this paper a far-field power pattern separation approach is proposed for the synthesis of directional modulation (DM) transmitter arrays. Separation into information pattern and interference patterns is enabled by far-field pattern null steering. Compared with other DM synthesis methods, e.g., BER-driven DM optimization and orthogonal vector injection, this approach facilitates manipulation of artificial interference spatial distributions. With such capability more interference power can be projected into those most vulnerable to eavesdropping spatial directions in free space, i.e., the information sidelobes. In such a fashion information leaked through radiation sidelobes can be effectively mitigated in a transmitter power efficient manner. The proposed synthesis approach is further validated via bit error rate (BER) simulations.

CD38 expression level and pattern of expression remains a reliable and robust marker of progressive disease in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) follows a variable clinical course which is difficult to predict at diagnosis. We assessed somatic mutation (SHM) status, CD38 and ZAP-70 expression in 87 patients (49 male, 38 female) with stage A CLL and known cytogenetic profile to compare their role in predicting disease progression, which was assessed by the treatment free interval (TFI) from diagnosis. Sixty (69%) patients were SHM+, 24 (28%) were CD38+ and ten (12%) were ZAP-70+. The median TFI for: (i) SHM + versus SHM- patients was 124 versus 26 months; hazard ratio (HR) = 3.6 [95% confidence interval (CI) = 1.8 - 7.3; P = 0.001]: (ii) CD38- versus CD38+ patients was 120 versus 34 months; HR = 2.4 (95% CI = 1.4 - 5.3; P = 0.02); and (iii) ZAP70- versus ZAP70+ was 120 versus 16 months; HR = 3.4 (95% CI = 1.4 - 8.7; P = 0.01). SHM status and CD38 retained prognostic significance on multivariate analysis whereas ZAP-70 did not. We conclude that ZAP-70 analysis does not provide additional prognostic information in this group of patients.

Privacy-Protected Facial Biometric Verification via Fuzzy Forest Learning

Although visual surveillance has emerged as an effective technolody for public security, privacy has become an issue of great concern in the transmission and distribution of surveillance videos. For example, personal facial images should not be browsed without permission. To cope with this issue, face image scrambling has emerged as a simple solution for privacyrelated applications. Consequently, online facial biometric verification needs to be carried out in the scrambled domain thus bringing a new challenge to face classification. In this paper, we investigate face verification issues in the scrambled domain and propose a novel scheme to handle this challenge. In our proposed method, to make feature extraction from scrambled face images robust, a biased random subspace sampling scheme is applied to construct fuzzy decision trees from randomly selected features, and fuzzy forest decision using fuzzy memberships is then obtained from combining all fuzzy tree decisions. In our experiment, we first estimated the optimal parameters for the construction of the random forest, and then applied the optimized model to the benchmark tests using three publically available face datasets. The experimental results validated that our proposed scheme can robustly cope with the challenging tests in the scrambled domain, and achieved an improved accuracy over all tests, making our method a promising candidate for the emerging privacy-related facial biometric applications.

Flow Pattern in Compound Channel with Floodplain Double Groyne

The results of an experimental study and velocity analysis of the flow characteristics in the vicinityof a floodplain with two rows of permeable/impermeable groynes in compound channels with oneand two floodplains are presented. A 60% permeable groyne model with three different lengthsrelative to the floodplain width was used. The results showed that double groyne could beconsidered as one groyne (one block) for aspect ratio Sr < 2 (Sr = distance between twosuccessive groynes/groyne length). When Sr > 2, each groyne started to act independently.The velocity reduction was more than 45-52% of the floodplain’s approach velocity compared with30-35% in the case of a single groyne. The significant velocity reduction was located at a distance1.5-2 times the groyne length downstream of the single or the double groynes. Generally, themaximum velocities in the main channel ranged from 1.1 to 1.35 times the original approachingvelocity. The effective groyne relative length and aspect ratio should not to be more than 0.5 and 2,respectively.

Facial cleanliness and risk of trachoma in families

Trachoma is the leading infectious cause of blindness worldwide, and epidemiologic studies of factors that may increase the transmission of ocular Chlamydia trachomatis are needed. In two villages in a hyperendemic area of Central Tanzania, 472 (90%) of 527 preschool-aged children were examined for specific signs of unclean faces and presence of trachoma. The odds of trachoma were 70% higher in children with flies and nasal discharge on their faces. Other facial signs were not important. In large families, the odds of trachoma increased 4.8-fold if a sibling had trachoma and 6.8-fold if a sibling had trachoma and an unclean face. Health education strategies aimed at improving face washing need to target cleaning nasal discharge and keeping flies off children's faces.

Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels

With the rapid development of internet-of-things (IoT), face scrambling has been proposed for privacy protection during IoT-targeted image/video distribution. Consequently in these IoT applications, biometric verification needs to be carried out in the scrambled domain, presenting significant challenges in face recognition. Since face models become chaotic signals after scrambling/encryption, a typical solution is to utilize traditional data-driven face recognition algorithms. While chaotic pattern recognition is still a challenging task, in this paper we propose a new ensemble approach – Many-Kernel Random Discriminant Analysis (MK-RDA) to discover discriminative patterns from chaotic signals. We also incorporate a salience-aware strategy into the proposed ensemble method to handle chaotic facial patterns in the scrambled domain, where random selections of features are made on semantic components via salience modelling. In our experiments, the proposed MK-RDA was tested rigorously on three human face datasets: the ORL face dataset, the PIE face dataset and the PUBFIG wild face dataset. The experimental results successfully demonstrate that the proposed scheme can effectively handle chaotic signals and significantly improve the recognition accuracy, making our method a promising candidate for secure biometric verification in emerging IoT applications.