Complex extreme learning machine applications in terahertz pulsed signals feature sets

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets.

Towards pain-free diagnosis of skin diseases through multiplexed microneedles: biomarker extraction and detection using a highly sensitive blotting method

Immunodiagnostic microneedles provide a novel way to extract protein biomarkers from the skin in a minimally invasive manner for analysis in vitro. The technology could overcome challenges in biomarker analysis specifically in solid tissue, which currently often involves invasive biopsies. This study describes the development of a multiplex immunodiagnostic device incorporating mechanisms to detect multiple antigens simultaneously, as well as internal assay controls for result validation. A novel detection method is also proposed. It enables signal detection specifically at microneedle tips and therefore may aid the construction of depth profiles of skin biomarkers. The detection method can be coupled with computerised densitometry for signal quantitation. The antigen specificity, sensitivity and functional stability of the device were assessed against a number of model biomarkers. Detection and analysis of endogenous antigens (interleukins 1α and 6) from the skin using the device was demonstrated. The results were verified using conventional enzyme-linked immunosorbent assays. The detection limit of the microneedle device, at ≤10 pg/mL, was at least comparable to conventional plate-based solid-phase enzyme immunoassays.

Extraction of unexpected rules from Twitter hashtags and its application to sport events

Twitter has become a dependable microblogging tool for real time information dissemination and newsworthy events broadcast. Its users sometimes break news on the network faster than traditional newsagents due to their presence at ongoing real life events at most times. Different topic detection methods are currently used to match Twitter posts to real life news of mainstream media. In this paper, we analyse tweets relating to the English FA Cup finals 2012 by applying our novel method named TRCM to extract association rules present in hash tag keywords of tweets in different time-slots. Our system identify evolving hash tag keywords with strong association rules in each time-slot. We then map the identified hash tag keywords to event highlights of the game as reported in the ground truth of the main stream media. The performance effectiveness measure of our experiments show that our method perform well as a Topic Detection and Tracking approach.

Enzyme assisted extraction of chitin from shrimp shells (Litopenaeus vannamei)

BACKGROUND: Chemical chitin extraction generates large amounts of wastes and increases partial deacetylation of the product. Therefore, the use of biological methods for chitin extraction is an interesting alternative. The effects of process conditions on enzyme assisted extraction of chitin from the shrimp shells in a systematic way were the focal points of this study. RESULTS: Demineralisation conditions of 25C, 20 min, shells-lactic acid ratio of 1:1.1 w/w; and shells-acetic acid ratio of 1:1.2 w/w, the maximum demineralisation values were 98.64 and 97.57% for lactic and acetic acids, respectively. A total protein removal efficiency of 91.10% by protease from Streptomyces griseus with enzyme-substrate ratio 55 U/g, pH 7.0 and incubation time 3 h is obtained when the particle size range is 50-25 μm, which was identified as the most critical factor. The X-ray diffraction and 13C NMR spectroscopy analysis showed that the lower percent crystallinity and higher degree of acetylation of chitin from enzyme assisted extraction may exhibit better solubility properties and less depolymerisation in comparison with chitin from the chemical extraction. CONCLUSION: The present work investigates the effects of individual factors on process yields, and it has shown that, if the particle size is properly controlled a reaction time of 3 h is more than enough for deproteination by protease. Physicochemical analysis indicated that the enzyme assisted production of chitin seems appropriate to extract chitin, possibly retaining its native structure.

Aqueous enzyme assisted oil extraction from oilseeds and emulsion de-emulsifying methods: a review

Regulatory, safety, and environmental issues have prompted the development of aqueousenzymatic extraction (AEE) for extracting components from oil-bearing materials. The emulsion resulting from AEE requires de-emulsification to separate the oil; when enzymes are used for this purpose, the method is known as aqueous enzymatic emulsion de-emulsification (AEED). In general, enzyme assisted oil extraction is known to yield oil having highly favourable characteristics. This review covers technological aspects of enzyme assisted oil extraction, and explores the quality characteristics of the oils obtained,focusing particularly on recent efforts undertaken to improve process economics by recovering and reusing enzymes.

Temporally stable feature clusters for maritime object tracking in visible and thermal imagery

This paper describes a new approach to detect and track maritime objects in real time. The approach particularly addresses the highly dynamic maritime environment, panning cameras, target scale changes, and operates on both visible and thermal imagery. Object detection is based on agglomerative clustering of temporally stable features. Object extents are first determined based on persistence of detected features and their relative separation and motion attributes. An explicit cluster merging and splitting process handles object creation and separation. Stable object clus- ters are tracked frame-to-frame. The effectiveness of the approach is demonstrated on four challenging real-world public datasets.

Valorisation of side streams from wheat milling and confectionery industries for consolidated production and extraction of microbial lipids

Crude enzymes produced via solid state fermentation (SSF) using wheat milling by-products have been employed for both fermentation media production using flour-rich waste (FRW) streams and lysis of Rhodosporidium toruloides yeast cells. Filter sterilization of crude hydrolysates was more beneficial than heat sterilization regarding yeast growth and microbial oil production. The initial carbon to free amino nitrogen ratio of crude hydrolysates was optimized (80.2 g/g) in fed-batch cultures of R. toruloides leading to a total dry weight of 61.2 g/L with microbial oil content of 61.8 % (w/w). Employing a feeding strategy where the glucose concentration was maintained in the range of 12.2 – 17.6 g/L led to the highest productivity (0.32 g/L∙h). The crude enzymes produced by SSF were utilised for yeast cell treatment leading to simultaneous release of around 80% of total lipids in the broth and production of a hydrolysate suitable as yeast extract replacement.

Edible oil from Tiger nut (Cyperus esculentus L.): mechanical pressing and aqueous enzymatic extraction methods

The tiger nut tuber of the Cyperus esculentus L. plant is an unusual storage system with similar amounts of starch and lipid. The extraction of its oil employing both mechanical pressing and aqueous enzymatic extraction (AEE) methods was investigated and an examination of the resulting products was carried out. The effects of particle size and moisture content of the tuber on the yield of tiger nut oil with pressing were initially studied. Smaller particles were found to enhance oil yields while a range of moisture content was observed to favour higher oil yields. When samples were first subjected to high pressures up to 700 MPa before pressing at 38 MPa there was no increase in the oil yields. Ground samples incubated with a mixture of α- Amylase, Alcalase, and Viscozyme (a mixture of cell wall degrading enzyme) as a pre-treatment, increased oil yield by pressing and 90% of oil was recovered as a result. When aqueous enzymatic extraction was carried out on ground samples, the use of α- Amylase, Alcalase, and Celluclast independently improved extraction oil yields compared to oil extraction without enzymes by 34.5, 23.4 and 14.7% respectively. A mixture of the three enzymes further augmented the oil yield and different operational factors were individually studied for their effects on the process. These include time, total mixed enzyme concentration, linear agitation speed, and solid-liquid ratio. The largest oil yields were obtained with a solid-liquid ratio of 1:6, mixed enzyme concentration of 1% (w/w) and 6 h incubation time although the longer time allowed for the formation of an emulsion. Using stationary samples during incubation surprisingly gave the highest oil yields, and this was observed to be as a result of gravity separation occurring during agitation. Furthermore, the use of high pressure processing up to 300 MPa as a pre-treatment enhanced oil yields but additional pressure increments had a detrimental effect. The quality of oils recovered from both mechanical and aqueous enzymatic extraction based on the percentage free fatty acid (% FFA) and peroxide values (PV) all reflected the good stabilities of the oils with the highest % FFA of 1.8 and PV of 1.7. The fatty acid profiles of all oils also remained unchanged. The level of tocopherols in oils were enhanced with both enzyme aided pressing (EAP) and high pressure processing before AEE. Analysis on the residual meals revealed DP 3 and DP 4 oligosaccharides present in EAP samples but these would require further assessment on their identity and quality.

A complex network approach to text summarization

Automatic summarization of texts is now crucial for several information retrieval tasks owing to the huge amount of information available in digital media, which has increased the demand for simple, language-independent extractive summarization strategies. In this paper, we employ concepts and metrics of complex networks to select sentences for an extractive summary. The graph or network representing one piece of text consists of nodes corresponding to sentences, while edges connect sentences that share common meaningful nouns. Because various metrics could be used, we developed a set of 14 summarizers, generically referred to as CN-Summ, employing network concepts such as node degree, length of shortest paths, d-rings and k-cores. An additional summarizer was created which selects the highest ranked sentences in the 14 systems, as in a voting system. When applied to a corpus of Brazilian Portuguese texts, some CN-Summ versions performed better than summarizers that do not employ deep linguistic knowledge, with results comparable to state-of-the-art summarizers based on expensive linguistic resources. The use of complex networks to represent texts appears therefore as suitable for automatic summarization, consistent with the belief that the metrics of such networks may capture important text features. (c) 2008 Elsevier Inc. All rights reserved.

Characters Extraction for Traffic Sign Destination boards in video and still images

Traffic Control Signs or destination boards on roadways offer significant information for drivers. Regulation signs tell something like your speed, turns, etc; Warning signs warn drivers of conditions ahead to help them avoid accidents; Destination signs show distances and directions to various locations; Service signs display location of hospitals, gas and rest areas etc. Because the signs are so important and there is always a certain distance from them to drivers, to let the drivers get information clearly and easily even in bad weather or other situations. The idea is to develop software which can collect useful information from a special camera which is mounted in the front of a moving car to extract the important information and finally show it to the drivers. For example, when a frame contains on a destination drive sign board it will be text something like "Linkoping 50",so the software should extract every character of "Linkoping 50", compare them with the already known character data in the database. if there is extracted character match "k" in the database then output the destination name and show to the driver. In this project C++ will be used to write the code for this software.

Face Detection and Facial Feature Localization for multi-pose faces and complex backgroundimages

The objective of this thesis work, is to propose an algorithm to detect the faces in a digital image with complex background. A lot of work has already been done in the area of face detection, but drawback of some face detection algorithms is the lack of ability to detect faces with closed eyes and open mouth. Thus facial features form an important basis for detection. The current thesis work focuses on detection of faces based on facial objects. The procedure is composed of three different phases: segmentation phase, filtering phase and localization phase. In segmentation phase, the algorithm utilizes color segmentation to isolate human skin color based on its chrominance properties. In filtering phase, Minkowski addition based object removal (Morphological operations) has been used to remove the non-skin regions. In the last phase, Image Processing and Computer Vision methods have been used to find the existence of facial components in the skin regions.This method is effective on detecting a face region with closed eyes, open mouth and a half profile face. The experiment’s results demonstrated that the detection accuracy is around 85.4% and the detection speed is faster when compared to neural network method and other techniques.

First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

Dichlorvos exposure impedes extraction and amplification of DNA from insects in museum collections

Background: The insecticides dichlorvos, paradichlorobenzene and naphthalene have been commonly used to eradicate pest insects from natural history collections. However, it is not known how these chemicals affect the DNA of the specimens in the collections. We thus tested the effect of dichlorvos, paradichlorobenzene and naphthalene on DNA of insects (Musca domestica) by extracting and amplifying DNA from specimens exposed to insecticides in two different concentrations over increasing time intervals. Results: The results clearly show that dichlorvos impedes both extraction and amplification of mitochondrial and nuclear DNA after relatively short time, whereas paradichlorobenzene and naphthalene do not. Conclusion: Collections treated with paradichlorobenzene and naphthalene, are better preserved concerning DNA, than those treated with dichlorvos. Non toxic pest control methods should, however, be preferred due to physical damage of specimens and putative health risks by chemicals.

SnO2, ZnO and related polycrystalline compound semiconductors: An overview and review on the voltage-dependent resistance (non-ohmic) feature

The present review describes mainly the history of SnO2-based voltage-dependent resistors, discusses the main characteristics of these polycrystalline semiconductor systems and includes a direct comparison with traditional ZnO-based voltage-dependent resistor systems to establish the differences and similarities, giving details of the basic physical principles involved with the non-ohmic properties in both polycrystalline systems. As an overview, the text also undertakes the main difficulties involved in processing SnO2- and ZnO-based non-ohmic systems, with an evaluation of the contribution of the dopants to the electronic properties and to the final microstructure and consequently to the system's non-ohmic behavior. However, since there are at least two review texts regarding ZnO-based systems [Levinson, L. M., and Philipp, H. R. Ceramic Bulletin 1985;64:639; Clarke, D. R. Journal of American Ceramic Society 1999;82:485], the main focus of the present text is dedicated to the SnO2-based varistor systems, although the basic physical principles described in the text are universally useful in the context of dense polycrystalline devices. However, the readers must be careful of how the microstructure heterogeneity and grain-boundary chemistry are capable to interfere in the global electrical response for particular systems. New perspectives for applications, commercialization and degradation studies involving SnO2-based polycrystalline non-ohmic systems are also outlined, including recent technological developments. Finally, at the end of this review a brief section is particularly dedicated to the presentation and discussions about others emerging non-ohmic polycrystalline ceramic devices (particularly based on perovskite ceramics) which must be deeply studied in the years to come, specially because some of these systems present combined high dielectric and non-ohmic properties. From both scientific and technological point of view these perovskite systems are quite interesting. (c) 2007 Elsevier Ltd. All rights reserved.

DEVELOPMENT OF AN ALGORITHM TO ANALYZE CARTOGRAPHIC FEATURES EXTRACTION METHODS IN DIGITAL IMAGES

The algorithm creates a buffer area around the cartographic features of interest in one of the images and compare it with the other one. During the comparison, the algorithm calculates the number of equals and different points and uses it to calculate the statistical values of the analysis. One calculated statistical value is the correctness, which shows the user the percentage of points that were correctly extracted. Another one is the completeness that shows the percentage of points that really belong to the interest feature. And the third value shows the idea of quality obtained by the extraction method, since that in order to calculate the quality the algorithm uses the correctness and completeness previously calculated. All the performed tests using this algorithm were possible to use the statistical values calculated to represent quantitatively the quality obtained by the extraction method executed. So, it is possible to say that the developed algorithm can be used to analyze extraction methods of cartographic features of interest, since that the results obtained were promising.