A detection method for grass carp hemorrhagic virus (GCHV) based on a reverse transcription polymerase chain reaction

A rapid, sensitive and highly specific detection method for grass carp hemorrhagic virus (GCHV) based on a reverse transcription-polymerase chain reaction (RT-PCR) has been developed. Two pairs of PCR primers were synthesized according to the cloned cDNA sequences of the GCHV-861 strain. For each primer combination, only one specific major product was obtained when amplification was performed by using the genomic dsRNA of GCHV-861 strain. The lengths of their expected products were 320 and 223 bp, respectively. No products were obtained when nucleic acids other than GCHV-861 genomic RNA were used as RT-PCR templates. To assess the sensitivity of the method, dilutions of purified GCHV-861 dsRNA total genome (0.01 pg up to 1000 pg) were amplified and quantities of as little as 0.1 pg of purified dsRNA were detectable when the amplification product was analyzed by 1.5% agarose gel electrophoresis. This technique could detect GCHV-861 not only in infected cell culture fluids, but also in infected grass carp Ctenopharyngodon idellus and rare minnow Gobiocypris rarus with or without hemorrhagic symptoms. The results show that the RT-PCR amplification method is useful for the direct detection of GCHV.

Feature Extraction Without Edge Detection

Information representation is a critical issue in machine vision. The representation strategy in the primitive stages of a vision system has enormous implications for the performance in subsequent stages. Existing feature extraction paradigms, like edge detection, provide sparse and unreliable representations of the image information. In this thesis, we propose a novel feature extraction paradigm. The features consist of salient, simple parts of regions bounded by zero-crossings. The features are dense, stable, and robust. The primary advantage of the features is that they have abstract geometric attributes pertaining to their size and shape. To demonstrate the utility of the feature extraction paradigm, we apply it to passive navigation. We argue that the paradigm is applicable to other early vision problems.

Implementation of a Theory of Edge Detection

This report describes the implementation of a theory of edge detection, proposed by Marr and Hildreth (1979). According to this theory, the image is first processed independently through a set of different size filters, whose shape is the Laplacian of a Gaussian, ***. Zero-crossings in the output of these filters mark the positions of intensity changes at different resolutions. Information about these zero-crossings is then used for deriving a full symbolic description of changes in intensity in the image, called the raw primal sketch. The theory is closely tied with early processing in the human visual systems. In this report, we first examine the critical properties of the initial filters used in the edge detection process, both from a theoretical and practical standpoint. The implementation is then used as a test bed for exploring aspects of the human visual system; in particular, acuity and hyperacuity. Finally, we present some preliminary results concerning the relationship between zero-crossings detected at different resolutions, and some observations relevant to the process by which the human visual system integrates descriptions of intensity changes obtained at different resolutions.

Rapid detection method for microbial spoilage using FT-IR and machine learning

Ellis, D.I., Broadhurst, D., Rowland, J.J. and Goodacre, R. (2005) Rapid detection method for microbial spoilage using FT-IR and machine learning. In: Rapid Methods for Food and Feed Quality Determination, (Eds) van Amerongen, A., Barug, D and Lauwaars, M., Wageningen Academic Publishers, Wageningen, Netherlands, in press.

An ESPRIT-SAA-Based Detection Method for Broken Rotor Bar Fault in Induction Motors

This paper presents a novel detection method for broken rotor bar fault (BRB) in induction motors based on Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) and Simulated Annealing Algorithm (SAA). The performance of ESPRIT is tested with simulated stator current signal of an induction motor with BRB. It shows that even with a short-time measurement data, the technique is capable of correctly identifying the frequencies of the BRB characteristic components but with a low accuracy on the amplitudes and initial phases of those components. SAA is then used to determine their amplitudes and initial phases and shows satisfactory results. Finally, experiments on a 3kW, 380V, 50Hz induction motor are conducted to demonstrate the effectiveness of the ESPRIT-SAA-based method in detecting BRB with short-time measurement data. It proves that the proposed method is a promising choice for BRB detection in induction motors operating with small slip and fluctuant load.

A sparse representation based fast detection method for surface defect detection of bottle caps

A practical machine-vision-based system is developed for fast detection of defects occurring on the surface of bottle caps. This system can be used to extract the circular region as the region of interests (ROI) from the surface of a bottle cap, and then use the circular region projection histogram (CRPH) as the matching features. We establish two dictionaries for the template and possible defect, respectively. Due to the requirements of high-speed production as well as detecting quality, a fast algorithm based on a sparse representation is proposed to speed up the searching. In the sparse representation, non-zero elements in the sparse factors indicate the defect's size and position. Experimental results in industrial trials show that the proposed method outperforms the orientation code method (OCM) and is able to produce promising results for detecting defects on the surface of bottle caps.

Multi-detection method for five common microalgal toxins based on the use of microspheres coupled to a flow-cytometry system

Freshwater and brackish microalgal toxins, such as microcystins, cylindrospermopsins, paralytic toxins, anatoxins or other neurotoxins are produced during the overgrowth of certain phytoplankton and benthic cyanobacteria, which includes either prokaryotic or eukaryotic microalgae. Although, further studies are necessary to define the biological role of these toxins, at least some of them are known to be poisonous to humans and wildlife due to their occurrence in these aquatic systems. The World Health Organization (WHO) has established as provisional recommended limit 1 μg of microcystin-LR per liter of drinking water. In this work we present a microsphere-based multi-detection method for five classes of freshwater and brackish toxins: microcystin-LR (MC-LR), cylindrospermopsin (CYN), anatoxin-a (ANA-a), saxitoxin (STX) and domoic acid (DA). Five inhibition assays were developed using different binding proteins and microsphere classes coupled to a flow-cytometry Luminex system. Then, assays were combined in one method for the simultaneous detection of the toxins. The IC₅₀'s using this method were 1.9 ± 0.1 μg L⁻¹ MC-LR, 1.3 ± 0.1 μg L⁻¹ CYN, 61 ± 4 μg L⁻¹ ANA-a, 5.4 ± 0.4 μg L⁻¹ STX and 4.9 ± 0.9 μg L⁻¹ DA. Lyophilized cyanobacterial culture samples were extracted using a simple procedure and analyzed by the Luminex method and by UPLC–IT-TOF-MS. Similar quantification was obtained by both methods for all toxins except for ANA-a, whereby the estimated content was lower when using UPLC–IT-TOF-MS. Therefore, this newly developed multiplexed detection method provides a rapid, simple, semi-quantitative screening tool for the simultaneous detection of five environmentally important freshwater and brackish toxins, in buffer and cyanobacterial extracts.

A New Android Malware Detection Method Using Bayesian Classification

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.

Fluorescent Logic Systems for Sensing and Molecular Computation: Structure-Activity Relationships in Edge-Detection

Molecular logic-based computation continues to throw up new applications in sensing and switching, the newest of which is the edge detection of objects. The scope of this phenomenon is mapped out by the use of structure-activity relationships, where several structures of the molecules and of the objects are examined. The different angles and curvatures of the objects are followed with good-fidelity in the visualized edges, even when the objects are in reverse video.

Low complexity greedy detection method with generalized multicarrier index keying OFDM

Multicarrier Index Keying (MCIK) is a recently developed technique that modulates subcarriers but also indices of the subcarriers. In this paper a novel low-complexity detection scheme of subcarrier indices is proposed for an MCIK system and addresses a substantial reduction in complexity over the optimalmaximum likelihood (ML) detection. For the performance evaluation, a closed-form expression for the pairwise error probability (PEP) of an active subcarrier index, and a tight approximation of the average PEP of multiple subcarrier indices are derived in closed-form. The theoretical outcomes are validated usingsimulations, at a difference of less than 0.1dB. Compared to the optimal ML, the proposed detection achieves a substantial reduction in complexity with small loss in error performance (<= 0.6dB).