Acoustic emission source location and damage detection in a metallic structure using a graph-theory-based geodesic pproach

A geodesic-based approach using Lamb waves is proposed to locate the acoustic emission (AE) source and damage in an isotropic metallic structure. In the case of the AE (passive) technique, the elastic waves take the shortest path from the source to the sensor array distributed in the structure. The geodesics are computed on the meshed surface of the structure using graph theory based on Dijkstra's algorithm. By propagating the waves in reverse virtually from these sensors along the geodesic path and by locating the first intersection point of these waves, one can get the AE source location. The same approach is extended for detection of damage in a structure. The wave response matrix of the given sensor configuration for the healthy and the damaged structure is obtained experimentally. The healthy and damage response matrix is compared and their difference gives the information about the reflection of waves from the damage. These waves are backpropagated from the sensors and the above method is used to locate the damage by finding the point where intersection of geodesics occurs. In this work, the geodesic approach is shown to be suitable to obtain a practicable source location solution in a more general set-up on any arbitrary surface containing finite discontinuities. Experiments were conducted on aluminum specimens of simple and complex geometry to validate this new method.

A Proof of Convergence of the B-RED and P-RED Algorithms for Random Early Detection

In [8], we recently presented two computationally efficient algorithms named B-RED and P-RED for random early detection. In this letter, we present the mathematical proof of convergence of these algorithms under general conditions to local minima.

Microbial diversity in the municipal composting process and development of detection methods

Composting refers to aerobic degradation of organic material and is one of the main waste treatment methods used in Finland for treating separated organic waste. The composting process allows converting organic waste to a humus-like end product which can be used to increase the organic matter in agricultural soils, in gardening, or in landscaping. Microbes play a key role as degraders during the composting-process, and the microbiology of composting has been studied for decades, but there are still open questions regarding the microbiota in industrial composting processes. It is known that with the traditional, culturing-based methods only a small fraction, below 1%, of the species in a sample is normally detected. In recent years an immense diversity of bacteria, fungi and archaea has been found to occupy many different environments. Therefore the methods of characterising microbes constantly need to be developed further. In this thesis the presence of fungi and bacteria in full-scale and pilot-scale composting processes was characterised with cloning and sequencing. Several clone libraries were constructed and altogether nearly 6000 clones were sequenced. The microbial communities detected in this study were found to differ from the compost microbes observed in previous research with cultivation based methods or with molecular methods from processes of smaller scale, although there were similarities as well. The bacterial diversity was high. Based on the non-parametric coverage estimations, the number of bacterial operational taxonomic units (OTU) in certain stages of composting was over 500. Sequences similar to Lactobacillus and Acetobacteria were frequently detected in the early stages of drum composting. In tunnel stages of composting the bacterial community comprised of Bacillus, Thermoactinomyces, Actinobacteria and Lactobacillus. The fungal diversity was found to be high and phylotypes similar to yeasts were abundantly found in the full-scale drum and tunnel processes. In addition to phylotypes similar to Candida, Pichia and Geotrichum moulds from genus Thermomyces and Penicillium were observed in tunnel stages of composting. Zygomycetes were detected in the pilot-scale composting processes and in the compost piles. In some of the samples there were a few abundant phylotypes present in the clone libraries that masked the rare ones. The rare phylotypes were of interest and a method for collecting them from clone libraries for sequencing was developed. With negative selection of the abundant phylotyps the rare ones were picked from the clone libraries. Thus 41% of the clones in the studied clone libraries were sequenced. Since microbes play a central role in composting and in many other biotechnological processes, rapid methods for characterization of microbial diversity would be of value, both scientifically and commercially. Current methods, however, lack sensitivity and specificity and are therefore under development. Microarrays have been used in microbial ecology for a decade to study the presence or absence of certain microbes of interest in a multiplex manner. The sequence database collected in this thesis was used as basis for probe design and microarray development. The enzyme assisted detection method, ligation-detection-reaction (LDR) based microarray, was adapted for species-level detection of microbes characteristic of each stage of the composting process. With the use of a specially designed control probe it was established that a species specific probe can detect target DNA representing as little as 0.04% of total DNA in a sample. The developed microarray can be used to monitor composting processes or the hygienisation of the compost end product. A large compost microbe sequence dataset was collected and analysed in this thesis. The results provide valuable information on microbial community composition during industrial scale composting processes. The microarray method was developed based on the sequence database collected in this study. The method can be utilised in following the fate of interesting microbes during composting process in an extremely sensitive and specific manner. The platform for the microarray is universal and the method can easily be adapted for studying microbes from environments other than compost.

R1SVM: A randomised nonlinear approach to large-scale anomaly detection

The problem of unsupervised anomaly detection arises in a wide variety of practical applications. While one-class support vector machines have demonstrated their effectiveness as an anomaly detection technique, their ability to model large datasets is limited due to their memory and time complexity for training. To address this issue for supervised learning of kernel machines, there has been growing interest in random projection methods as an alternative to the computationally expensive problems of kernel matrix construction and sup-port vector optimisation. In this paper we leverage the theory of nonlinear random projections and propose the Randomised One-class SVM (R1SVM), which is an efficient and scalable anomaly detection technique that can be trained on large-scale datasets. Our empirical analysis on several real-life and synthetic datasets shows that our randomised 1SVM algorithm achieves comparable or better accuracy to deep auto encoder and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

Visual detection of occluded crop: For automated harvesting

This paper presents a novel crop detection system applied to the challenging task of field sweet pepper (capsicum) detection. The field-grown sweet pepper crop presents several challenges for robotic systems such as the high degree of occlusion and the fact that the crop can have a similar colour to the background (green on green). To overcome these issues, we propose a two-stage system that performs per-pixel segmentation followed by region detection. The output of the segmentation is used to search for highly probable regions and declares these to be sweet pepper. We propose the novel use of the local binary pattern (LBP) to perform crop segmentation. This feature improves the accuracy of crop segmentation from an AUC of 0.10, for previously proposed features, to 0.56. Using the LBP feature as the basis for our two-stage algorithm, we are able to detect 69.2% of field grown sweet peppers in three sites. This is an impressive result given that the average detection accuracy of people viewing the same colour imagery is 66.8%.

Automated feature engineering for HTTP tunnel detection

Generating discriminative input features is a key requirement for achieving highly accurate classifiers. The process of generating features from raw data is known as feature engineering and it can take significant manual effort. In this paper we propose automated feature engineering to derive a suite of additional features from a given set of basic features with the aim of both improving classifier accuracy through discriminative features, and to assist data scientists through automation. Our implementation is specific to HTTP computer network traffic. To measure the effectiveness of our proposal, we compare the performance of a supervised machine learning classifier built with automated feature engineering versus one using human-guided features. The classifier addresses a problem in computer network security, namely the detection of HTTP tunnels. We use Bro to process network traffic into base features and then apply automated feature engineering to calculate a larger set of derived features. The derived features are calculated without favour to any base feature and include entropy, length and N-grams for all string features, and counts and averages over time for all numeric features. Feature selection is then used to find the most relevant subset of these features. Testing showed that both classifiers achieved a detection rate above 99.93% at a false positive rate below 0.01%. For our datasets, we conclude that automated feature engineering can provide the advantages of increasing classifier development speed and reducing development technical difficulties through the removal of manual feature engineering. These are achieved while also maintaining classification accuracy.

Molecular genetics of X-linked cone-rod dystrophy and Åland Island eye disease

Inherited retinal diseases are the most common cause of vision loss among the working population in Western countries. It is estimated that ~1 of the people worldwide suffer from vision loss due to inherited retinal diseases. The severity of these diseases varies from partial vision loss to total blindness, and at the moment no effective cure exists. To date, nearly 200 mapped loci, including 140 cloned genes for inherited retinal diseases have been identified. By a rough estimation 50% of the retinal dystrophy genes still await discovery. In this thesis we aimed to study the genetic background of two inherited retinal diseases, X-linked cone-rod dystrophy and Åland Island eye disease. X-linked cone-rod dystrophy (CORDX) is characterized by progressive loss of visual function in school age or early adulthood. Affected males show reduced visual acuity, photophobia, myopia, color vision defects, central scotomas, and variable changes in fundus. The disease is genetically heterogeneous and two disease loci, CORDX1 and CORDX2, were known prior to the present thesis work. CORDX1, located on Xp21.1-11.4, is caused by mutations in the RPGR gene. CORDX2 is located on Xq27-28 but the causative gene is still unknown. Åland Island eye disease (AIED), originally described in a family living in Åland Islands, is a congenital retinal disease characterized by decreased visual acuity, fundus hypopigmentation, nystagmus, astigmatism, red color vision defect, myopia, and defective night vision. AIED shares similarities with another retinal disease, congenital stationary night blindness (CSNB2). Mutations in the L-type calcium channel α1F-subunit gene, CACNA1F, are known to cause CSNB2, as well as AIED-like disease. The disease locus of the original AIED family maps to the same genetic interval as the CACNA1F gene, but efforts to reveal CACNA1F mutations in patients of the original AIED family have been unsuccessful. The specific aims of this study were to map the disease gene in a large Finnish family with X-linked cone-rod dystrophy and to identify the disease-causing genes in the patients of the Finnish cone-rod dystrophy family and the original AIED family. With the help of linkage and haplotype analyses, we could localize the disease gene of the Finnish cone-rod dystrophy family to the Xp11.4-Xq13.1 region, and thus establish a new genetic X-linked cone-rod dystrophy locus, CORDX3. Mutation analyses of candidate genes revealed three novel CACNA1F gene mutations: IVS28-1 GCGTC>TGG in CORDX3 patients, a 425 bp deletion, comprising exon 30 and flanking intronic regions in AIED patients, and IVS16+2T>C in an additional Finnish patient with a CSNB2-like phenotype. All three novel mutations altered splice sites of the CACNA1F gene, and resulted in defective pre-mRNA splicing suggesting altered or absent channel function as a disease mechanism. The analyses of CACNA1F mRNA also revealed novel alternative wt splice variants, which may enhance channel diversity or regulate the overall expression level of the channel. The results of our studies may be utilized in genetic counseling of the families, and they provide a basis for studies on the pathogenesis of these diseases. In the future, the knowledge of the genetic defects may be used in the identification of specific therapies for the patients.

Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.

Selective detection of single-enantiomer spectrum of chiral molecules aligned in the polypeptide liquid crystalline solvent: Transition selective one-dimensional H-1-H-1 COSY

One-dimensional (1D) proton NMR spectra of enantiomers are generally undecipherable in chiral orienting poly-gamma-benzyl-L-glutamate (PBLG)/CDCl3 solvent. This arises due to large number of couplings, in addition to superposition of spectra from both the enantiomers, severely hindering the H-1 detection. On the other hand in the present study the benefit is derived front the presence of several couplings among the entire network of interacting protons. Transition selective 1D H-1-H-1 correlation experiment (1D-COSY) which utilizes the Coupling assisted transfer of magnetization not only for unraveling the overlap but also for the selective detection of enantiopure spectrum is reported. The experiment is simple, easy to implement and provides accurate eanantiomeric excess in addition to the determination of the proton-proton couplings of an enantiomer within a short experimental time (few minutes). (C) 2009 Elsevier Inc. All rights reserved.

High-Rate Space-Time Coded Large-MIMO Systems: Low-Complexity Detection and Channel Estimation

In this paper, we present a low-complexity algorithm for detection in high-rate, non-orthogonal space-time block coded (STBC) large-multiple-input multiple-output (MIMO) systems that achieve high spectral efficiencies of the order of tens of bps/Hz. We also present a training-based iterative detection/channel estimation scheme for such large STBC MIMO systems. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed multistage likelihood ascent search (M-LAS) detector in conjunction with the proposed iterative detection/channel estimation scheme at low complexities. The fact that we could show such good results for large STBCs like 16 X 16 and 32 X 32 STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot based training for channel estimation and turbo coding) establishes the effectiveness of the proposed detector and channel estimator. We decode perfect codes of large dimensions using the proposed detector. With the feasibility of such a low-complexity detection/channel estimation scheme, large-MIMO systems with tens of antennas operating at several tens of bps/Hz spectral efficiencies can become practical, enabling interesting high data rate wireless applications.

Business busts and U.S. auditors' interest in fraud detection: Evidence from the 20th century

We find evidence that U.S. auditors increased their attention to fraud detection during or immediately after the economic contractions of the 20th century, based on a content analysis of the 12 volumes of the 20th-century auditing reference series Montgomery’s Auditing. Contractions, however, do not seem to have affected auditors’ attention to the formal goal of fraud detection. The study suggests that auditors’ aversion to the heightened risks of fraud during economic downturns leads them to focus more on fraud detection at those times regardless of the particular guidance in formal audit standards. This study is the first to find some evidence of a recession-influenced difference between fraud detection practices and formal fraud detection goals.

Detection of microorganisms using biosensors-a smarter way towards detection techniques.

Along with useful microorganisms, there are some that cause potential damage to the animals and plants. Detection and identification of these harmful organisms in a cost and time effective way is a challenge for the researchers. The future of detection methods for microorganisms shall be guided by biosensor, which has already contributed enormously in sensing and detection technology. Here, we aim to review the use of various biosensors, developed by integrating the biological and physicochemical/mechanical properties (of tranducers), which can have enormous implication in healthcare, food, agriculture and biodefence. We have also highlighted the ways to improve the functioning of the biosensor.

Using multi-label classification for acoustic pattern detection and assisting bird species surveys

Acoustics is a rich source of environmental information that can reflect the ecological dynamics. To deal with the escalating acoustic data, a variety of automated classification techniques have been used for acoustic patterns or scene recognition, including urban soundscapes such as streets and restaurants; and natural soundscapes such as raining and thundering. It is common to classify acoustic patterns under the assumption that a single type of soundscapes present in an audio clip. This assumption is reasonable for some carefully selected audios. However, only few experiments have been focused on classifying simultaneous acoustic patterns in long-duration recordings. This paper proposes a binary relevance based multi-label classification approach to recognise simultaneous acoustic patterns in one-minute audio clips. By utilising acoustic indices as global features and multilayer perceptron as a base classifier, we achieve good classification performance on in-the-field data. Compared with single-label classification, multi-label classification approach provides more detailed information about the distributions of various acoustic patterns in long-duration recordings. These results will merit further biodiversity investigations, such as bird species surveys.

Infections in lung and heart transplant recipients : Studies on the impact of bronchoscopy in the diagnosis of respiratory infections and detection of viral infections in blood and bronchoalveolar lavage fluid

Infection is a major cause of mortality and morbidity after thoracic organ transplantation. The aim of the present study was to evaluate the infectious complications after lung and heart transplantation, with a special emphasis on the usefulness of bronchoscopy and the demonstration of cytomegalovirus (CMV), human herpes virus (HHV)-6, and HHV-7. We reviewed all the consecutive bronchoscopies performed on heart transplant recipients (HTRs) from May 1988 to December 2001 (n = 44) and lung transplant recipients (LTRs) from February 1994 to November 2002 (n = 472). To compare different assays in the detection of CMV, a total of 21 thoracic organ transplant recipients were prospectively monitored by CMV pp65-antigenemia, DNAemia (PCR), and mRNAemia (NASBA) tests. The antigenemia test was the reference assay for therapeutic intervention. In addition to CMV antigenemia, 22 LTRs were monitored for HHV-6 and HHV-7 antigenemia. The diagnostic yield of the clinically indicated bronchoscopies was 41 % in the HTRs and 61 % in the LTRs. The utility of the bronchoscopy was highest from one to six months after transplantation. In contrast, the findings from the surveillance bronchoscopies performed on LTRs led to a change in the previous treatment in only 6 % of the cases. Pneumocystis carinii and CMV were the most commonly detected pathogens. Furthermore, 15 (65 %) of the P. carinii infections in the LTRs were detected during chemoprophylaxis. None of the complications of the bronchoscopies were fatal. Antigenemia, DNAemia, and mRNAemia were present in 98 %, 72 %, and 43 % of the CMV infections, respectively. The optimal DNAemia cut-off levels (sensitivity/specificity) were 400 (75.9/92.7 %), 850 (91.3/91.3 %), and 1250 (100/91.5 %) copies/ml for the antigenemia of 2, 5, and 10 pp65-positive leukocytes/50 000 leukocytes, respectively. The sensitivities of the NASBA were 25.9, 43.5, and 56.3 % in detecting the same cut-off levels. CMV DNAemia was detected in 93 % and mRNAemia in 61 % of the CMV antigenemias requiring antiviral therapy. HHV-6, HHV-7, and CMV antigenemia was detected in 20 (91 %), 11 (50 %), and 12 (55 %) of the 22 LTRs (median 16, 31, and 165 days), respectively. HHV-6 appeared in 15 (79 %), HHV-7 in seven (37 %), and CMV in one (7 %) of these patients during ganciclovir or valganciclovir prophylaxis. One case of pneumonitis and another of encephalitis were associated with HHV-6. In conclusion, bronchoscopy is a safe and useful diagnostic tool in LTRs and HTRs with a suspected respiratory infection, but the role of surveillance bronchoscopy in LTRs remains controversial. The PCR assay acts comparably with the antigenemia test in guiding the pre-emptive therapy against CMV when threshold levels of over 5 pp65-antigen positive leukocytes are used. In contrast, the low sensitivity of NASBA limits its usefulness. HHV-6 and HHV-7 activation is common after lung transplantation despite ganciclovir or valganciclovir prophylaxis, but clinical manifestations are infrequently linked to them.