Visualizing uncertainty in multi-spectral remotely sensed imagery

Error and uncertainty in remotely sensed data come from several sources, and can be increased or mitigated by the processing to which that data is subjected (e.g. resampling, atmospheric correction). Historically the effects of such uncertainty have only been considered overall and evaluated in a confusion matrix which becomes high-level meta-data, and so is commonly ignored. However, some of the sources of uncertainty can be explicity identified and modelled, and their effects (which often vary across space and time) visualized. Others can be considered overall, but their spatial effects can still be visualized. This process of visualization is of particular value for users who need to assess the importance of data uncertainty for their own practical applications. This paper describes a Java-based toolkit, which uses interactive and linked views to enable visualization of data uncertainty by a variety of means. This allows users to consider error and uncertainty as integral elements of image data, to be viewed and explored, rather than as labels or indices attached to the data. © 2002 Elsevier Science Ltd. All rights reserved.

The New Software Package for Dynamic Hierarchical Clustering for Circles Types of Shapes

In data mining, efforts have focused on finding methods for efficient and effective cluster analysis in large databases. Active themes of research focus on the scalability of clustering methods, the effectiveness of methods for clustering complex shapes and types of data, high-dimensional clustering techniques, and methods for clustering mixed numerical and categorical data in large databases. One of the most accuracy approach based on dynamic modeling of cluster similarity is called Chameleon. In this paper we present a modified hierarchical clustering algorithm that used the main idea of Chameleon and the effectiveness of suggested approach will be demonstrated by the experimental results.

NLSE-based model of a random distributed feedback fiber laser

In this work we propose a NLSE-based model of power and spectral properties of the random distributed feedback (DFB) fiber laser. The model is based on coupled set of non-linear Schrödinger equations for pump and Stokes waves with the distributed feedback due to Rayleigh scattering. The model considers random backscattering via its average strength, i.e. we assume that the feedback is incoherent. In addition, this allows us to speed up simulations sufficiently (up to several orders of magnitude). We found that the model of the incoherent feedback predicts the smooth and narrow (comparing with the gain spectral profile) generation spectrum in the random DFB fiber laser. The model allows one to optimize the random laser generation spectrum width varying the dispersion and nonlinearity values: we found, that the high dispersion and low nonlinearity results in narrower spectrum that could be interpreted as four-wave mixing between different spectral components in the quasi-mode-less spectrum of the random laser under study could play an important role in the spectrum formation. Note that the physical mechanism of the random DFB fiber laser formation and broadening is not identified yet. We investigate temporal and statistical properties of the random DFB fiber laser dynamics. Interestingly, we found that the intensity statistics is not Gaussian. The intensity auto-correlation function also reveals that correlations do exist. The possibility to optimize the system parameters to enhance the observed intrinsic spectral correlations to further potentially achieved pulsed (mode-locked) operation of the mode-less random distributed feedback fiber laser is discussed.

Evolutionary Clustering of Complex Systems and Processes

In a paper the method of complex systems and processes clustering based use of genetic algorithm is offered. The aspects of its realization and shaping of fitness-function are considered. The solution of clustering task of Ukraine areas on socio-economic indexes is represented and comparative analysis with outcomes of classical methods is realized.

Probabilistic multiple model neural network based leak detection system:experimental study

This paper presents an effective decision making system for leak detection based on multiple generalized linear models and clustering techniques. The training data for the proposed decision system is obtained by setting up an experimental pipeline fully operational distribution system. The system is also equipped with data logging for three variables; namely, inlet pressure, outlet pressure, and outlet flow. The experimental setup is designed such that multi-operational conditions of the distribution system, including multi pressure and multi flow can be obtained. We then statistically tested and showed that pressure and flow variables can be used as signature of leak under the designed multi-operational conditions. It is then shown that the detection of leakages based on the training and testing of the proposed multi model decision system with pre data clustering, under multi operational conditions produces better recognition rates in comparison to the training based on the single model approach. This decision system is then equipped with the estimation of confidence limits and a method is proposed for using these confidence limits for obtaining more robust leakage recognition results.

Linear dispersive pre-defined peak amplitude modulation of spectrally modulated airy-based pulses

Spectrally modulated Airy-based pulses peak amplitude modulation (PAM) in linear dispersive media is investigated, designed, and numerically simulated. As it is shown here, it is possible to design the spectral modulation of the initial Airy-based pulses to obtain a pre-defined PAM profile as the pulse propagates. Although optical pulses self-amplitude modulation is a well-known effect under non-linear propagation, the designed Airy-based pulses exhibit PAM under linear dispersive propagation. This extraordinary linear propagation property can be applied in many kinds of dispersive media, enabling its use in a broad range of experiments and applications. © 2013 Optical Society of America.

Improving distributed representation of word sense via WordNet Gloss composition and context clustering

In recent years, there has been an increas-ing interest in learning a distributed rep-resentation of word sense. Traditional context clustering based models usually require careful tuning of model parame-ters, and typically perform worse on infre-quent word senses. This paper presents a novel approach which addresses these lim-itations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned represen-tations outperform the publicly available embeddings on 2 out of 4 metrics in the word similarity task, and 6 out of 13 sub tasks in the analogical reasoning task.

All-fibre twist sensor system based on 45° and 8° tilted fibre gratings

We experimentally demonstrated a highly sensitive twist sensor system based on a 45° and an 81° tilted fibre grating (TFG). The 81°-TFG has a set of dual-peaks that are due to the birefringence induced by its extremely tilted structure. When the 81°-TFG subjected to twist, the coupling to the two peaks would interchange from each other, providing a mechanism to measure and monitor the twist. We have investigated the performance of the sensor system by three interrogation methods (spectral, power-measurement and voltage-measurement). The experimental results clearly show that the 81°-TFG and the 45°-TFG could be combined forming a full fibre twist sensor system capable of not just measuring the magnitude but also recognising the direction of the applied twist.

Self-Learning Fuzzy Spiking Neural Network as a Nonlinear Pulse-Position Threshold Detection Dynamic System Based on Second-Order Critically Damped Response Units

Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered. It is proposed to treat a spiking neural network in terms of classical automatic control theory apparatus based on the Laplace transform. It is shown that synapse functioning can be easily modeled by a second order damped response unit. Spiking neuron soma is presented as a threshold detection unit. Thus, the proposed fuzzy spiking neural network is an analog-digital nonlinear pulse-position dynamic system. It is demonstrated how fuzzy probabilistic and possibilistic clustering approaches can be implemented on the base of the presented spiking neural network.

Multi-wavelength regeneration of phase encoded signals based on phase sensitive amplifiers

Future high capacity optical links will have to make use of frequent signal regeneration to enable long distance transmission. In this respect, the role of all-optical signal processing becomes increasingly important because of its potential to mitigate signal impairments at low cost and power consumption. More substantial benefits are expected if regeneration is achieved simultaneously on a multiple signal band. Until recently, this had been achieved only for on-off keying modulation formats. However, as in future transmission links the information will be encoded also in the phase for enhancing the spectral efficiency, novel subsystem concepts will be needed for multichannel processing of such advanced signal formats. In this paper we show that phase sensitive amplifiers can be an ideal technology platform for developing such regenerators and we discuss our recent demonstration of the first multi-channel regenerator for phase encoded signals.

Polarization-independent high-resolution spectral interrogation of FBGs using a BFBG-CCD array for optical sensing applications

Optical fibre strain sensors using Fibre Bragg Gratings (FBGs) are poised to play a major role in structural health monitoring in a variety of application from aerospace to civil engineering. At the heart of technology is the optoelectronic instrumentation required to convert optical signals into measurands. Users are demanding compact, lightweight, rugged and low cost solutions. This paper describes development of a new device based on a blazed FBG and CCD array that can potentially meet the above demands. We have shown that this very low cost technique may be used to interrogate a WDM array of sensor gratings with highly accurate and highly repeatable results unaffected by the polarisation state of the radiation. In this paper, we present results showing that sensors may be interrogated with an RMS error of 1.7pm, drift below 0.12pm and dynamic range of up to 65nm.

Quantum dot-based terahertz photoconductive antennas

We present novel Terahertz (THz) emitting optically pumped Quantum Dot (QD) photoconductive (PC) materials and antenna structures on their basis both for pulsed and CW pumping regimes. Full text Quantum dot and microantenna design - Presented here are design considerations for the semiconductor materials in our novel QD-based photoconductive antenna (PCA) structures, metallic microantenna designs, and their implementation as part of a complete THz source or transceiver system. Layers of implanted QDs can be used for the photocarrier lifetime shortening mechanism[1,2]. In our research we use InAs:GaAs QD structures of varying dot layer number and distributed Bragg reflector(DBR)reflectivity range. According to the observed dependence of carrier lifetimes on QD layer periodicity [3], it is reasonable to assume that electron lifetimes can be potentially reduced down to 0.45ps in such structures. Both of these features; long excitation wavelength and short carriers lifetime predict possible feasibility of QD antennas for THz generation and detection. In general, relatively simple antenna configurations were used here, including: coplanar stripline (CPS); Hertzian-type dipoles; bow-ties for broadband and log-spiral(LS)or log-periodic(LP)‘toothed’ geometriesfor a CW operation regime. Experimental results - Several lasers are used for antenna pumping: Ti:Sapphire femtosecond laser, as well as single-[4], double-[5] wavelength, and pulsed [6] QD lasers. For detection of the THz signal different schemes and devices were used, e.g. helium-cooled bolometer, Golay cell and a second PCA for coherent THz detection in a traditional time-domain measurement scheme.Fig.1shows the typical THz output power trend from a 5 um-gap LPQD PCA pumped using a tunable QD LD with optical pump spectrum shown in (b). Summary - QD-based THz systems have been demonstrated as a feasible and highly versatile solution. The implementation of QD LDs as pump sources could be a major step towards ultra-compact, electrically controllable transceiver system that would increase the scope of data analysis due to the high pulse repetition rates of such LDs [3], allowing real-time THz TDS and data acquisition. Future steps in development of such systems now lie in the further investigation of QD-based THz PCA structures and devices, particularly with regards to their compatibilitywith QD LDs as pump sources. [1]E. U. Rafailov et al., “Fast quantum-dot saturable absorber for passive mode-locking of solid-State lasers,”Photon.Tech.Lett., IEEE, vol. 16 pp. 2439-2441(2004) [2]E. Estacio, “Strong enhancement of terahertz emission from GaAs in InAs/GaAs quantum dot structures. Appl.Phys.Lett., vol. 94 pp. 232104 (2009) [3]C. Kadow et al., “Self-assembled ErAs islands in GaAs: Growth and subpicosecond carrier dynamics,” Appl. Phys. Lett., vol. 75 pp. 3548-3550 (1999) [4]T. Kruczek, R. Leyman, D. Carnegie, N. Bazieva, G. Erbert, S. Schulz, C. Reardon, and E. U. Rafailov, “Continuous wave terahertz radiation from an InAs/GaAs quantum-dot photomixer device,” Appl. Phys. Lett., vol. 101(2012) [5]R. Leyman, D. I. Nikitichev, N. Bazieva, and E. U. Rafailov, “Multimodal spectral control of a quantum-dot diode laser for THz difference frequency generation,” Appl. Phys. Lett., vol. 99 (2011) [6]K.G. Wilcox, M. Butkus, I. Farrer, D.A. Ritchie, A. Tropper, E.U. Rafailov, “Subpicosecond quantum dot saturable absorber mode-locked semiconductor disk laser, ” Appl. Phys. Lett. Vol 94, 2511 © 2014 IEEE.

InP-based active and passive components for communication systems at 2 μm

Progress on advanced active and passive photonic components that are required for high-speed optical communications over hollow-core photonic bandgap fiber at wavelengths around 2 μm is described in this paper. Single-frequency lasers capable of operating at 10 Gb/s and covering a wide spectral range are realized. A comparison is made between waveguide and surface normal photodiodes with the latter showing good sensitivity up to 15 Gb/s. Passive waveguides, 90° optical hybrids, and arrayed waveguide grating with 100-GHz channel spacing are demonstrated on a large spot-size waveguide platform. Finally, a strong electro-optic effect using the quantum confined Stark effect in strain-balanced multiple quantum wells is demonstrated and used in a Mach-Zehnder modulator capable of operating at 10 Gb/s.

A laser induced breakdown spectroscopy quantitative analysis method based on the robust least squares support vector machine regression model

Data fluctuation in multiple measurements of Laser Induced Breakdown Spectroscopy (LIBS) greatly affects the accuracy of quantitative analysis. A new LIBS quantitative analysis method based on the Robust Least Squares Support Vector Machine (RLS-SVM) regression model is proposed. The usual way to enhance the analysis accuracy is to improve the quality and consistency of the emission signal, such as by averaging the spectral signals or spectrum standardization over a number of laser shots. The proposed method focuses more on how to enhance the robustness of the quantitative analysis regression model. The proposed RLS-SVM regression model originates from the Weighted Least Squares Support Vector Machine (WLS-SVM) but has an improved segmented weighting function and residual error calculation according to the statistical distribution of measured spectral data. Through the improved segmented weighting function, the information on the spectral data in the normal distribution will be retained in the regression model while the information on the outliers will be restrained or removed. Copper elemental concentration analysis experiments of 16 certified standard brass samples were carried out. The average value of relative standard deviation obtained from the RLS-SVM model was 3.06% and the root mean square error was 1.537%. The experimental results showed that the proposed method achieved better prediction accuracy and better modeling robustness compared with the quantitative analysis methods based on Partial Least Squares (PLS) regression, standard Support Vector Machine (SVM) and WLS-SVM. It was also demonstrated that the improved weighting function had better comprehensive performance in model robustness and convergence speed, compared with the four known weighting functions.

Radio-frequency spectrum coding in ultra-long fibre laser based cryptography

We propose a new approach for secret key exchange involving the variation of the cavity length of an ultra-long fibre laser. The scheme is based on the realisation that the free spectral range of the laser cavity can be used as an information carrier. We present a proof-of-principle demonstration of this new concept using a 50-km-long fibre laser to link two users, both of whom can randomly add an extra 1-km-long fibre segment.