Bit-error probability for direct detection of optical RZ signal degraded by ASE noise and timing jitter

We analyze theoretically the interplay between optical return-to-zero signal degradation due to timing jitter and additive amplified-spontaneous-emission noise. The impact of these two factors on the performance of a square-law direct detection receiver is also investigated. We derive an analytical expression for the bit-error probability and quantitatively determine the conditions when the contributions of the effects of timing jitter and additive noise to the bit error rate can be treated separately. The analysis of patterning effects is also presented. © 2007 IEEE.

GTM: the generative topographic mapping

This thesis describes the Generative Topographic Mapping (GTM) --- a non-linear latent variable model, intended for modelling continuous, intrinsically low-dimensional probability distributions, embedded in high-dimensional spaces. It can be seen as a non-linear form of principal component analysis or factor analysis. It also provides a principled alternative to the self-organizing map --- a widely established neural network model for unsupervised learning --- resolving many of its associated theoretical problems. An important, potential application of the GTM is visualization of high-dimensional data. Since the GTM is non-linear, the relationship between data and its visual representation may be far from trivial, but a better understanding of this relationship can be gained by computing the so-called magnification factor. In essence, the magnification factor relates the distances between data points, as they appear when visualized, to the actual distances between those data points. There are two principal limitations of the basic GTM model. The computational effort required will grow exponentially with the intrinsic dimensionality of the density model. However, if the intended application is visualization, this will typically not be a problem. The other limitation is the inherent structure of the GTM, which makes it most suitable for modelling moderately curved probability distributions of approximately rectangular shape. When the target distribution is very different to that, theaim of maintaining an `interpretable' structure, suitable for visualizing data, may come in conflict with the aim of providing a good density model. The fact that the GTM is a probabilistic model means that results from probability theory and statistics can be used to address problems such as model complexity. Furthermore, this framework provides solid ground for extending the GTM to wider contexts than that of this thesis.

Risk-based maintenance model for offshore oil and gas pipelines:a case study

Offshore oil and gas pipelines are vulnerable to environment as any leak and burst in pipelines cause oil/gas spill resulting in huge negative Impacts on marine lives. Breakdown maintenance of these pipelines is also cost-intensive and time-consuming resulting in huge tangible and intangible loss to the pipeline operators. Pipelines health monitoring and integrity analysis have been researched a lot for successful pipeline operations and risk-based maintenance model is one of the outcomes of those researches. This study develops a risk-based maintenance model using a combined multiple-criteria decision-making and weight method for offshore oil and gas pipelines in Thailand with the active participation of experienced executives. The model's effectiveness has been demonstrated through real life application on oil and gas pipelines in the Gulf of Thailand. Practical implications. Risk-based inspection and maintenance methodology is particularly important for oil pipelines system, as any failure in the system will not only affect productivity negatively but also has tremendous negative environmental impact. The proposed model helps the pipelines operators to analyze the health of pipelines dynamically, to select specific inspection and maintenance method for specific section in line with its probability and severity of failure.

Regional foreign direct investment and wage spillovers:plant level evidence from the UK electronics industry

This paper examines the extent to which foreign investment in the UK generates wage spillovers in the domestic sector of the economy using a simultaneous dynamic panel data model and focusing on the electronics sector, possibly the most ‘globalized’ sector of UK manufacturing. It finds evidence that the higher wages paid by foreign firms cause wages in the domestic sector to be bid up. This phenomenon is, however, largely confined to the region where foreign direct investment takes place.

Innovation and export performance:evidence from UK and German manufacturing plants

Using comparable plant-level surveys we demonstrate significant differences between the determinants of export performance among the UK and German manufacturing plants. Product innovation, however measured, has a strong effect on the probability and propensity to export in both countries. Being innovative is positively related to export probability in both countries. In the UK the scale of plants’ innovation activity is also related positively to export propensity. In Germany, however, where levels of innovation intensity are higher but the proportion of sales attributable to new products is lower, there is some evidence of a negative relationship between the scale of innovation activity and export performance. Significant differences are identified between innovative and non-innovative plants, especially in their absorption of spill-over effects. Innovative UK plants are more effective in their ability to exploit spill-overs from the innovation activities of companies in the same sector. In Germany, by contrast, non-innovators are more likely to absorb regional and supply-chain spill-over effects. Co-location to other innovative firms is generally found to discourage exporting.

Grant support and exporting activity

This paper investigates whether government support can act to increase exporting activity. We use a uniquely rich data set on Irish manufacturing plants and employ an empirical strategy that combines a nonparametric matching procedure with a difference-in-differences estimator in order to deal with the potential selection problem inherent in the analysis. Our results suggest that if grants are large enough, they can encourage already exporting firms to compete more effectively on the international market. However, there is little evidence that grants encourage nonexporters to start exporting.

Mach bands and multiscale models of spatial vision:the role of first, second, and third derivative operators in encoding bars and edges

Ernst Mach observed that light or dark bands could be seen at abrupt changes of luminance gradient in the absence of peaks or troughs in luminance. Many models of feature detection share the idea that bars, lines, and Mach bands are found at peaks and troughs in the output of even-symmetric spatial filters. Our experiments assessed the appearance of Mach bands (position and width) and the probability of seeing them on a novel set of generalized Gaussian edges. Mach band probability was mainly determined by the shape of the luminance profile and increased with the sharpness of its corners, controlled by a single parameter (n). Doubling or halving the size of the images had no significant effect. Variations in contrast (20%-80%) and duration (50-300 ms) had relatively minor effects. These results rule out the idea that Mach bands depend simply on the amplitude of the second derivative, but a multiscale model, based on Gaussian-smoothed first- and second-derivative filtering, can account accurately for the probability and perceived spatial layout of the bands. A key idea is that Mach band visibility depends on the ratio of second- to first-derivative responses at peaks in the second-derivative scale-space map. This ratio is approximately scale-invariant and increases with the sharpness of the corners of the luminance ramp, as observed. The edges of Mach bands pose a surprisingly difficult challenge for models of edge detection, but a nonlinear third-derivative operation is shown to predict the locations of Mach band edges strikingly well. Mach bands thus shed new light on the role of multiscale filtering systems in feature coding. © 2012 ARVO.

Temporal and statistical properties of the ytterbium doped fiber laser

Recently, temporal and statistical properties of quasi-CW fiber lasers have attracted a great attention. In particular, properties of Raman fiber laser (RFLs) have been studied both numerically and experimentally [1,2]. Experimental investigation is more challengeable, as the full generation optical bandwidth (typically hundreds of GHz for RFLs) is much bigger than real-time bandwidth of oscilloscopes (up to 60GHz for the newest models). So experimentally measured time dynamics is highly bandwidth averaged and do not provide precise information about overall statistical properties. To overpass this, one can use the spectral filtering technique to study temporal and statistical properties within optical bandwidth comparable with measurement bandwidth [3] or indirect measurements [4]. Ytterbium-doped fiber lasers (YDFL) are more suitable for experimental investigation, as their generation spectrum usually 10 times narrower. Moreover, recently ultra-narrow-band generation has been demonstrated in YDFL [5] which provides in principle possibility to measure time dynamics and statistics in real time using conventional oscilloscopes. © 2013 IEEE.

Portfolio size, non-trading frequency and portfolio return autocorrelation

In this paper we re-examine the relationship between non-trading frequency and portfolio return autocorrelation. We show that in portfolios where security specific effects have not been completely diversified, portfolio autocorrelation will not increase monotonically with increasing non-trading, as indicated in Lo and MacKinlay (1990). We show that at high levels of non-trading, portfolio autocorrelation will become a decreasing function of non-trading probability and may take negative values. We find that heterogeneity among the means, variances and betas of the component securities in a portfolio can act to increase the induced autocorrelation, particularly in portfolios containing fewer stocks. Security specific effects remain even when the number of securities in the portfolio is far in excess of that considered necessary to diversify security risk. © 2014 Elsevier B.V.

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support Vector Machines (SVMs) are widely used classifiers for detecting physiological patterns in Human-Computer Interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the application of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables, and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.