A novel representation of bioacoustic events for content-based search in field audio data

Bioacoustic data can provide an important base for environmental monitoring. To explore a large amount of field recordings collected, an automated similarity search algorithm is presented in this paper. A region of an audio defined by frequency and time bounds is provided by a user; the content of the region is used to construct a query. In the retrieving process, our algorithm will automatically scan through recordings to search for similar regions. In detail, we present a feature extraction approach based on the visual content of vocalisations – in this case ridges, and develop a generic regional representation of vocalisations for indexing. Our feature extraction method works best for bird vocalisations showing ridge characteristics. The regional representation method allows the content of an arbitrary region of a continuous recording to be described in a compressed format.

A basis for instrusion detection in distributed systems using kernel-level data tainting

This project was a step forward in developing intrusion detection systems in distributed environments such as web services. It investigates a new approach of detection based on so-called "taint-marking" techniques and introduces a theoretical framework along with its implementation in the Linux kernel.

ADMRG @ MediaEval 2013 Social Event Detection

This paper elaborates the approach used by the Applied Data Mining Research Group (ADMRG) for the Social Event Detection (SED) Tasks of the 2013 MediaEval Benchmark. We extended the constrained clustering algorithm to apply to the first semi-supervised clustering task, and we compared several classifiers with Latent Dirichlet Allocation as feature selector in the second event classification task. The proposed approach focuses on scalability and efficient memory allocation when applied to a high dimensional data with large clusters. Results of the first task show the effectiveness of the proposed method. Results from task 2 indicate that attention on the imbalance categories distributions is needed.

Automatic segmentation of choroidal thickness in optical coherence tomography

The assessment of choroidal thickness from optical coherence tomography (OCT) images of the human choroid is an important clinical and research task, since it provides valuable information regarding the eye’s normal anatomy and physiology, and changes associated with various eye diseases and the development of refractive error. Due to the time consuming and subjective nature of manual image analysis, there is a need for the development of reliable objective automated methods of image segmentation to derive choroidal thickness measures. However, the detection of the two boundaries which delineate the choroid is a complicated and challenging task, in particular the detection of the outer choroidal boundary, due to a number of issues including: (i) the vascular ocular tissue is non-uniform and rich in non-homogeneous features, and (ii) the boundary can have a low contrast. In this paper, an automatic segmentation technique based on graph-search theory is presented to segment the inner choroidal boundary (ICB) and the outer choroidal boundary (OCB) to obtain the choroid thickness profile from OCT images. Before the segmentation, the B-scan is pre-processed to enhance the two boundaries of interest and to minimize the artifacts produced by surrounding features. The algorithm to detect the ICB is based on a simple edge filter and a directional weighted map penalty, while the algorithm to detect the OCB is based on OCT image enhancement and a dual brightness probability gradient. The method was tested on a large data set of images from a pediatric (1083 B-scans) and an adult (90 B-scans) population, which were previously manually segmented by an experienced observer. The results demonstrate the proposed method provides robust detection of the boundaries of interest and is a useful tool to extract clinical data.

Electrochemical detection of dopamine and cytochrome c at a nanostructured gold electrode

A nanostructured gold surface consisting of closely packed outwardly growing spikes is investigated for the electrochemical detection of dopamine and cytochrome c. A significant electrocatalytic effect for the electrooxidation of both dopamine and ascorbic acid at the nanostructured electrode was found due to the presence of surface active sites which allowed the detection of dopamine in the presence of excess ascorbic acid to be achieved by differential pulse voltammetry. By simple modification with a layer of Nafion, the enhanced electrocatalytic properties of the nanostructured surface was maintained while increasing the selectivity of dopamine detection in the presence of interfering species such as excess ascorbic and uric acids. Also, upon modification of the nanostructured surface with a monolayer of cysteine, the electrochemical response of immobilised cytochrome c in two distinct conformations was observed. This opens up the possibility of using such a nanostructured surface for the characterisation of other biomolecules and in bio-electroanalytical applications.

A facile chemical screening method for the detection of stress corrosion cracking in 9 carat gold alloys

Stress corrosion cracking (SCC) is a well known form of environmental attack in low carat gold jewellery. It is desirable to have a quick, easy and cost effective way to detect SCC in alloys and prevent them from being used and later failing in their application. A facile chemical method to investigate SCC of 9 carat gold alloys is demonstrated. It involves a simple application of tensile stress to a wire sample in a corrosive environment such as 1–10 % FeCl3 which induces failure in less than 5 minutes. In this study three quaternary (Au, Ag, Cu and Zn) 9 carat gold alloy compositions were investigated for their resistance to SCC and the relationship between time to failure and processing conditions is studied. It is envisaged that the use of such a rapid and facile screening procedure at the production stage may readily identify alloy treatments that produce jewellery that will be susceptible to SCC in its lifetime.

Application of texture analysis in tear film surface assessment based on videokeratoscopy

Purpose Videokeratoscopy images can be used for the non-invasive assessment of the tear film. In this work the applicability of an image processing technique, textural-analysis, for the assessment of the tear film in Placido disc images has been investigated. Methods In the presence of tear film thinning/break-up, the reflected pattern from the videokeratoscope is disturbed in the region of tear film disruption. Thus, the Placido pattern carries information about the stability of the underlying tear film. By characterizing the pattern regularity, the tear film quality can be inferred. In this paper, a textural features approach is used to process the Placido images. This method provides a set of texture features from which an estimate of the tear film quality can be obtained. The method is tested for the detection of dry eye in a retrospective dataset from 34 subjects (22-normal and 12-dry eye), with measurements taken under suppressed blinking conditions. Results To assess the capability of each texture-feature to discriminate dry eye from normal subjects, the receiver operating curve (ROC) was calculated and the area under the curve (AUC), specificity and sensitivity extracted. For the different features examined, the AUC value ranged from 0.77 to 0.82, while the sensitivity typically showed values above 0.9 and the specificity showed values around 0.6. Overall, the estimated ROCs indicate that the proposed technique provides good discrimination performance. Conclusions Texture analysis of videokeratoscopy images is applicable to study tear film anomalies in dry eye subjects. The proposed technique appears to have demonstrated its clinical relevance and utility.

Intrusion detection in distributed systems, an approach based on taint marking

This paper presents a new framework for distributed intrusion detection based on taint marking. Our system tracks information flows between applications of multiple hosts gathered in groups (i.e., sets of hosts sharing the same distributed information flow policy) by attaching taint labels to system objects such as files, sockets, Inter Process Communication (IPC) abstractions, and memory mappings. Labels are carried over the network by tainting network packets. A distributed information flow policy is defined for each group at the host level by labeling information and defining how users and applications can legally access, alter or transfer information towards other trusted or untrusted hosts. As opposed to existing approaches, where information is most often represented by two security levels (low/high, public/private, etc.), our model identifies each piece of information within a distributed system, and defines their legal interaction in a fine-grained manner. Hosts store and exchange security labels in a peer to peer fashion, and there is no central monitor. Our IDS is implemented in the Linux kernel as a Linux Security Module (LSM) and runs standard software on commodity hardware with no required modification. The only trusted code is our modified operating system kernel. We finally present a scenario of intrusion in a web service running on multiple hosts, and show how our distributed IDS is able to report security violations at each host level.

Design, construction, and analysis of cell line arrays and tissue microarrays for gene expression analysis

Cell line array (CMA) and tissue microarray (TMA) technologies are high-throughput methods for analysing both the abundance and distribution of gene expression in a panel of cell lines or multiple tissue specimens in an efficient and cost-effective manner. The process is based on Kononen's method of extracting a cylindrical core of paraffin-embedded donor tissue and inserting it into a recipient paraffin block. Donor tissue from surgically resected paraffin-embedded tissue blocks, frozen needle biopsies or cell line pellets can all be arrayed in the recipient block. The representative area of interest is identified and circled on a haematoxylin and eosin (H&E)-stained section of the donor block. Using a predesigned map showing a precise spacing pattern, a high density array of up to 1,000 cores of cell pellets and/or donor tissue can be embedded into the recipient block using a tissue arrayer from Beecher Instruments. Depending on the depth of the cell line/tissue removed from the donor block 100-300 consecutive sections can be cut from each CMA/TMA block. Sections can be stained for in situ detection of protein, DNA or RNA targets using immunohistochemistry (IHC), fluorescent in situ hybridisation (FISH) or mRNA in situ hybridisation (RNA-ISH), respectively. This chapter provides detailed methods for CMA/TMA design, construction and analysis with in-depth notes on all technical aspects including tips to deal with common pitfalls the user may encounter. © Springer Science+Business Media, LLC 2011.

A computer image analysis system for microvessel density measurement in solid tumours

Microvessel density (MVD) is a widely used surrogate measure of angiogenesis in pathological specimens and tumour models. Measurement of MVD can be achieved by several methods. Automation of counting methods aims to increase the speed, reliability and reproducibility of these techniques. The image analysis system described here enables MVD measurement to be carried out with minimal expense in any reasonably equipped pathology department or laboratory. It is demonstrated that the system translates easily between tumour types which are suitably stained with minimal calibration. The aim of this paper is to offer this technique to a wider field of researchers in angiogenesis.

Real-time video event detection in crowded scenes using MPEG derived features : a multiple instance learning approach

This paper presents an investigation into event detection in crowded scenes, where the event of interest co-occurs with other activities and only binary labels at the clip level are available. The proposed approach incorporates a fast feature descriptor from the MPEG domain, and a novel multiple instance learning (MIL) algorithm using sparse approximation and random sensing. MPEG motion vectors are used to build particle trajectories that represent the motion of objects in uniform video clips, and the MPEG DCT coefficients are used to compute a foreground map to remove background particles. Trajectories are transformed into the Fourier domain, and the Fourier representations are quantized into visual words using the K-Means algorithm. The proposed MIL algorithm models the scene as a linear combination of independent events, where each event is a distribution of visual words. Experimental results show that the proposed approaches achieve promising results for event detection compared to the state-of-the-art.

Advances in skin cancer early detection and diagnosis

OBJECTIVES: To provide an overview of 1) traditional methods of skin cancer early detection, 2) current technologies for skin cancer detection, and 3) evolving practice models of early detection. DATA SOURCES: Peer-reviewed databased articles and reviews, scholarly texts, and Web-based resources. CONCLUSION: Early detection of skin cancer through established methods or newer technologies is critical for reducing both skin cancer mortality and the overall skin cancer burden. IMPLICATIONS FOR NURSING PRACTICE: A basic knowledge of recommended skin examination guidelines and risk factors for skin cancer, traditional methods to further examine lesions that are suspicious for skin cancer and evolving detection technologies can guide patient education and skin inspection decisions.

Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the a mission should be aborted due to mechanical or other failure. On-board cameras provide information that can be used in the determination of potential landing sites, which are continually updated and ranked to prevent injury and minimize damage. Pulse Coupled Neural Networks have been used for the detection of features in images that assist in the classification of vegetation and can be used to minimize damage to the aerial vehicle. However, a significant drawback in the use of PCNNs is that they are computationally expensive and have been more suited to off-line applications on conventional computing architectures. As heterogeneous computing architectures are becoming more common, an OpenCL implementation of a PCNN feature generator is presented and its performance is compared across OpenCL kernels designed for CPU, GPU and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images obtained during unmanned aerial vehicle trials to determine the plausibility for real-time feature detection.

Skin cancer detection by one click - are we any closer?

To the editor...

The detection of incipient faults in small multi-cylinder diesel engines using multiple acoustic emission sensors

This thesis investigates condition monitoring (CM) of diesel engines using acoustic emission (AE) techniques. The AE signals recorded from a small size diesel engine are mixtures of multiple sources from multiple cylinders. Thus, it is difficult to interpret the information conveyed in the signals for CM purposes. This thesis develops a series of practical signal processing techniques to overcome this problem. Various experimental studies conducted to assess the CM capabilities of AE analysis for diesel engines. A series of modified signal processing techniques were proposed. These techniques showed promising results of capability for CM of multiple cylinders diesel engine using multiple AE sensors.