A unidirectional scheme for high-fidelity optical-carrier dissemination using phase-modulation, homodyne coherent-detection, and frequency entrainment

We report for the first time an ultra-stable optical-carrier dissemination technique for transmission over a 20km unidirectional fibre link. The optical-linewidth of the recovered carrier matches closely that of the original carrier. © 2014 OSA.

Detection and direction-discrimination of diotic and dichotic ramp modulations in amplitude and phase

When the source of a tone moves with respect to a listener's ears, dichotic (or interaural) phase and amplitude modulations (PM and AM) are produced. Two experiments investigated the psychophysical characteristics of dichotic linear ramp modulations in phase and amplitude, and compared them with the psychophysics of diotic PM and AM. In experiment 1, subjects were substantially more sensitive to dichotic PM than diotic PM, but AM sensitivity was equivalent in the dichotic and diotic conditions. Thresholds for discriminating modulation direction were smaller than detection thresholds for dichotic AM, and both diotic AM and PM. Dichotic PM discrimination thresholds were similar to detection thresholds. In experiment 2, the effects of ramp duration were examined. Sensitivity to dichotic AM and PM, and diotic AM increased as duration was increased from 20 ms to 200 ms. The functions relating sensitivity to ramp duration differed across the stimuli; sensitivity to dichotic PM increased more rapidly than sensitivity to dichotic or diotic AM. This was also reflected in shorter time-constants and minimum integration times for dichotic PM detection. These findings support the hypothesis that the analysis of dichotic PM and AM rely on separate mechanisms. © 2003 Acoustical Society of America.

Experiments in Detection and Correction of Russian Malapropisms by Means of the WEB

Malapropism is a semantic error that is hardly detectable because it usually retains syntactical links between words in the sentence but replaces one content word by a similar word with quite different meaning. A method of automatic detection of malapropisms is described, based on Web statistics and a specially defined Semantic Compatibility Index (SCI). For correction of the detected errors, special dictionaries and heuristic rules are proposed, which retains only a few highly SCI-ranked correction candidates for the user’s selection. Experiments on Web-assisted detection and correction of Russian malapropisms are reported, demonstrating efficacy of the described method.

Detection and quantification of protein oxidation in sarcopenic models:a mass spectrometry study

Oxidised biomolecules in aged tissue could potentially be used as biomarkers for age-related diseases; however, it is still unclear whether they causatively contribute to ageing or are consequences of the ageing process. To assess the potential of using protein oxidation as markers of ageing, mass spectrometry (MS) was employed for the identification and quantification of oxidative modifications in obese (ob/ob) mice. Lean muscle mass and strength is reduced in obesity, representing a sarcopenic model in which the levels of oxidation can be evaluated for different muscular systems including calcium homeostasis, metabolism and contractility. Several oxidised residues were identified by tandem MS (MS/MS) in both muscle homogenate and isolated sarcoplasmic reticulum (SR), an organelle that regulates intracellular calcium levels in muscle. These modifications include oxidation of methionine, cysteine, tyrosine, and tryptophan in several proteins such as sarcoplasmic reticulum calcium ATPase (SERCA), glycogen phosphorylase, and myosin. Once modifications had been identified, multiple reaction monitoring MS (MRM) was used to quantify the percentage modification of oxidised residues within the samples. Preliminary data suggests proteins in ob/ob mice are more oxidised than the controls. For example SERCA, which constitutes 60-70% of the SR, had approximately a 2-fold increase in cysteine trioxidation of Cys561 in the obese model when compared to the control. Other obese muscle proteins have also shown a similar increase in oxidation for various residues. Further analysis with complex protein mixtures will determine the potential diagnostic use of MRM experiments for analysing protein oxidation in small biological samples such as muscle needle biopsies.

Perception of global image contrast is predicted by the same spatial integration model of gain control as detection and discrimination

How are the image statistics of global image contrast computed? We answered this by using a contrast-matching task for checkerboard configurations of ‘battenberg’ micro-patterns where the contrasts and spatial spreads of interdigitated pairs of micro-patterns were adjusted independently. Test stimuli were 20 × 20 arrays with various sized cluster widths, matched to standard patterns of uniform contrast. When one of the test patterns contained a pattern with much higher contrast than the other, that determined global pattern contrast, as in a max() operation. Crucially, however, the full matching functions had a curious intermediate region where low contrast additions for one pattern to intermediate contrasts of the other caused a paradoxical reduction in perceived global contrast. None of the following models predicted this: RMS, energy, linear sum, max, Legge and Foley. However, a gain control model incorporating wide-field integration and suppression of nonlinear contrast responses predicted the results with no free parameters. This model was derived from experiments on summation of contrast at threshold, and masking and summation effects in dipper functions. Those experiments were also inconsistent with the failed models above. Thus, we conclude that our contrast gain control model (Meese & Summers, 2007) describes a fundamental operation in human contrast vision.

Dissociation of detection and evaluation of facial expressions in adolescence

Holistic face perception, i.e. the mandatory integration of featural information across the face, hasbeen considered to play a key role when recognizing emotional face expressions (e.g., Tanaka et al.,2002). However, despite their early onset holistic processing skills continue to improvethroughout adolescence (e.g., Schwarzer et al., 2010) and therefore might modulate theevaluation of facial expressions. We tested this hypothesis using an attentional blink (AB)paradigm to compare the impact of happy, fearful and neutral faces in adolescents (10–13 years)and adults on subsequently presented neutral target stimuli (animals, plants and objects) in a rapidserial visual presentation stream. Adolescents and adults were found to be equally reliable whenreporting the emotional expression of the face stimuli. However, the detection of emotional butnot neutral faces imposed a significantly stronger AB effect on the detection of the neutral targetsin adults compared to adolescents. In a control experiment we confirmed that adolescents ratedemotional faces lower in terms of valence and arousal than adults. The results suggest a protracteddevelopment of the ability to evaluate facial expressions that might be attributed to the latematuration of holistic processing skills.

Graled (Grapevine leaf digitalization) Software for the Detection and Graphic Reconstruction of Ampelometric Differences Between Vitis Leaves

Raster graphic ampelometric software was not exclusively developed for the estimation of leaf area, but also for the characterization of grapevine (Viti vinifera L.) leaves. The software was written in C-Hprogramming language, using the C-1-1- Builder 2007 for Windows 95-XP and Linux operation systems. It handles desktop-scanned images. On the image analysed with the GRA.LE.D., the user has to determine 11 points. These points are then connected and the distances between them calculated. The GRA.LE.D. software supports standard ampelometric measurements such as leaf area, angles between the veins and lengths of the veins. These measurements are recorded by the software and exported into plain ASCII text files for single or multiple samples. Twenty-two biometric data points of each leaf are identified by the GRA.LE.D. It presents the opportunity to statistically analyse experimental data, allows comparison of cultivars and enables graphic reconstruction of leaves using the Microsoft Excel Chart Wizard. The GRA. LE.D. was thoroughly calibrated and compared to other widely used instruments and methods such as photo-gravimetry, LiCor L0100, WinDIAS2.0 and ImageTool. By comparison, the GRA.LE.D. presented the most accurate measurements of leaf area, but the LiCor L0100 and the WinDIAS2.0 were faster, while the photo-gravimetric method proved to be the most time-consuming. The WinDIAS2.0 instrument was the least reliable. The GRA.LE.D. is uncomplicated, user-friendly, accurate, consistent, reliable and has wide practical application.

A framework for automatic feature extraction from airborne light detection and ranging data

Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points. ^ In this dissertation, a framework is proposed to filter out information about different kinds of geometrical objects, such as terrain and buildings from LIDAR automatically. They are essential to numerous applications such as flood modeling, landslide prediction and hurricane animation. The framework consists of several intuitive algorithms. Firstly, a progressive morphological filter was developed to detect non-ground LIDAR measurements. By gradually increasing the window size and elevation difference threshold of the filter, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Then, building measurements are identified from no-ground measurements using a region growing algorithm based on the plane-fitting technique. Raw footprints for segmented building measurements are derived by connecting boundary points and are further simplified and adjusted by several proposed operations to remove noise, which is caused by irregularly spaced LIDAR measurements. To reconstruct 3D building models, the raw 2D topology of each building is first extracted and then further adjusted. Since the adjusting operations for simple building models do not work well on 2D topology, 2D snake algorithm is proposed to adjust 2D topology. The 2D snake algorithm consists of newly defined energy functions for topology adjusting and a linear algorithm to find the minimal energy value of 2D snake problems. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. The results demonstrated that the proposed framework achieves a very good performance. ^

Geophysical Analysis of a Central Florida Karst Terrain using Light Detection and Ranging (LIDAR) and Ground Penetrating Radar (GPR) Derived Surfaces

Airborne LIDAR (Light Detecting and Ranging) is a relatively new technique that rapidly and accurately measures micro-topographic features. This study compares topography derived from LIDAR with subsurface karst structures mapped in 3-dimensions with ground penetrating radar (GPR). Over 500 km of LIDAR data were collected in 1995 by the NASA ATM instrument. The LIDAR data was processed and analyzed to identify closed depressions. A GPR survey was then conducted at a 200 by 600 m site to determine if the target features are associated with buried karst structures. The GPR survey resolved two major depressions in the top of a clay rich layer at ~10m depth. These features are interpreted as buried dolines and are associated spatially with subtle (< 1m) trough-like depressions in the topography resolved from the LIDAR data. This suggests that airborne LIDAR may be a useful tool for indirectly detecting subsurface features associated with sinkhole hazard.

The Effect of Sample and Sample Matrix on DNA Processing: Mechanisms for the Detection and Management of Inhibition in Forensic Samples

The presence of inhibitory substances in biological forensic samples has, and continues to affect the quality of the data generated following DNA typing processes. Although the chemistries used during the procedures have been enhanced to mitigate the effects of these deleterious compounds, some challenges remain. Inhibitors can be components of the samples, the substrate where samples were deposited or chemical(s) associated to the DNA purification step. Therefore, a thorough understanding of the extraction processes and their ability to handle the various types of inhibitory substances can help define the best analytical processing for any given sample. A series of experiments were conducted to establish the inhibition tolerance of quantification and amplification kits using common inhibitory substances in order to determine if current laboratory practices are optimal for identifying potential problems associated with inhibition. DART mass spectrometry was used to determine the amount of inhibitor carryover after sample purification, its correlation to the initial inhibitor input in the sample and the overall effect in the results. Finally, a novel alternative at gathering investigative leads from samples that would otherwise be ineffective for DNA typing due to the large amounts of inhibitory substances and/or environmental degradation was tested. This included generating data associated with microbial peak signatures to identify locations of clandestine human graves. Results demonstrate that the current methods for assessing inhibition are not necessarily accurate, as samples that appear inhibited in the quantification process can yield full DNA profiles, while those that do not indicate inhibition may suffer from lowered amplification efficiency or PCR artifacts. The extraction methods tested were able to remove >90% of the inhibitors from all samples with the exception of phenol, which was present in variable amounts whenever the organic extraction approach was utilized. Although the results attained suggested that most inhibitors produce minimal effect on downstream applications, analysts should practice caution when selecting the best extraction method for particular samples, as casework DNA samples are often present in small quantities and can contain an overwhelming amount of inhibitory substances.

Text analytics of social media: Sentiment analysis, event detection and summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. ^ Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. ^ In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data. ^

Design and development of an enzyme-linked biosensor for detection and quantification of phosphate species

The objective of this study is to design and development of an enzyme-linked biosensor for detection and quantification of phosphate species. Various concentrations of phosphate species were tested and completed for this study. Phosphate is one of the vital nutrients for all living organisms. Phosphate compounds can be found in nature (e.g., water sediments), and they often exist in aninorganic form. The amount of phosphates in the environment strongly influences the operations of living organisms. Excess amount of phosphate in the environment causes eutrophication which in turn causes oxygen deficit for the other living organisms. Fish die and degradation of habitat in the water occurs as a result of eutrophication. In contrast, low phosphate concentration causes death of vegetation since plants utilize the inorganic phosphate for photosynthesis, respiration, and regulation of enzymes. Therefore, the phosphate quantity in lakes and rivers must be monitored. Result demonstrated that phosphate species could be detected in various organisms via enzyme-linked biosensor in this research.

Text Analytics of Social Media: Sentiment Analysis, Event Detection and Summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.

Land-cover detection and landscape structure analysis in the Pachitea Basin, Peruvian Amazon

Classification procedures, including atmospheric correction satellite images as well as classification performance utilizing calibration and validation at different levels, have been investigated in the context of a coarse land-cover classification scheme for the Pachitea Basin. Two different correction methods were tested against no correction in terms of reflectance correction towards a common response for pseudo-invariant features (PIF). The accuracy of classifications derived from each of the three methods was then assessed in a discriminant analysis using crossvalidation at pixel, polygon, region, and image levels. Results indicate that only regression adjusted images using PIFs show no significant difference between images in any of the bands. A comparison of classifications at different levels suggests though that at pixel, polygon, and region levels the accuracy of the classifications do not significantly differ between corrected and uncorrected images. Spatial patterns of land-cover were analyzed in terms of colonization history, infrastructure, suitability of the land, and landownership. The actual use of the land is driven mainly by the ability to access the land and markets as is obvious in the distribution of land cover as a function of distance to rivers and roads. When considering all rivers and roads a threshold distance at which disproportional agro-pastoral land cover switches from over represented to under represented is at about 1km. Best land use suggestions seem not to affect the choice of land use. Differences in abundance of land cover between watersheds are more prevailing than differences between colonist and indigenous groups.

Industrial fault detection and diagnosis using Bayesian belief network

Rapid development in industry have contributed to more complex systems that are prone to failure. In applications where the presence of faults may lead to premature failure, fault detection and diagnostics tools are often implemented. The goal of this research is to improve the diagnostic ability of existing FDD methods. Kernel Principal Component Analysis has good fault detection capability, however it can only detect the fault and identify few variables that have contribution on occurrence of fault and thus not precise in diagnosing. Hence, KPCA was used to detect abnormal events and the most contributed variables were taken out for more analysis in diagnosis phase. The diagnosis phase was done in both qualitative and quantitative manner. In qualitative mode, a networked-base causality analysis method was developed to show the causal effect between the most contributing variables in occurrence of the fault. In order to have more quantitative diagnosis, a Bayesian network was constructed to analyze the problem in probabilistic perspective.