An approach for visual attention based on biquaternion and its application for ship detection in multispectral imagery

This paper proposes an approach for visual attention based on biquaternion, and investigates its application for ship detection in multispectral imagery. The proposed approach describes high-dimensional data in the form of biquaternion and utilizes the phase spectrum of biquaternion Fourier transform to generate a required saliency map that can be used for salient target detection. In our method, the multidimensional data is processed as a whole, and the features contained in each spectral band can be extracted effectively. Compared with traditional visual attention approaches, our method has very low computational complexity. Experimental results on simulated and real multispectral remote sensing data have shown that the proposed method has excellent performance in ship detection. Furthermore, our method is robust against white noise and almost meets real-time requirements, which has great potentials in engineering applications.     

Optimization of single mode fibre sensors to detect organic vapours

The construction of single mode optical fibre (SMF) sensors to handle with volatile organic compounds (VOCs), has been optimized to operate at the third telecommunication window (1550 nm). The main motivation is to take advantage of the photonic devices used in telecommunication systems that makes easier sensors multiplexing. Moreover, the low transmission attenuation at that wavelength offers the possibility of remote sensing. The sensing materials used suffer reversible structural alterations in the presence of VOC, such as colour change, which are detectable with a photonic system. Following the Electrostatic Self Assembly method (ESAm), a nanostructure is constructed onto cleaved ended SMF, which is doped with a sensing material. The fabrication of this type of sensors was focused on multimode fibres (MMF) and in the visible spectral range (VIS) so far. The implementation has been adapted to SMF and to operate around 1550 nm, specifically, by easing the adsorption of the VOCs molecules. It has been observed that the sensing material affects the morphology of the nanostructures as well and so, to the sensors response. The devices implemented show a potential use in the identification of single and complex mixtures of VOCs.     

Two-stage image denoising by principal component analysis with local pixel grouping

This paper presents an efficient image denoising scheme by using principal component analysis (PCA) with local pixel grouping (LPG). For a better preservation of image local structures, a pixel and its nearest neighbors are modeled as a vector variable, whose training samples are selected from the local window by using block matching based LPG. Such an LPG procedure guarantees that only the sample blocks with similar contents are used in the local statistics calculation for PCA transform estimation, so that the image local features can be well preserved after coefficient shrinkage in the PCA domain to remove the noise. The LPG-PCA denoising procedure is iterated one more time to further improve the denoising performance, and the noise level is adaptively adjusted in the second stage. Experimental results on benchmark test images demonstrate that the LPG-PCA method achieves very competitive denoising performance, especially in image fine structure preservation, compared with state-of-the-art denoising algorithms.     

Controller design for Markov jumping systems subject to actuator saturation

In this paper, the stochastic stabilization problem for a class of Markov jumping linear systems (MJLS) subject to actuator saturation is considered. The concept of domain of attraction in mean square sense is used to analyze the closed-loop stability. When the jumping mode is available, a mode-dependent state feedback controller is developed. Otherwise, we give a less conservative approach to design the mode-independent state feedback controller. Both design procedures can be converted into a set of linear matrix inequalities (LMIs). Finally, a numerical example is provided to show the effectiveness of the techniques.     

On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage

In this note we consider the following problem. Suppose a set of sensors is jointly trying to estimate a process. One sensor takes a measurement at every time step and the measurements are then exchanged among all the sensors. What is the sensor schedule that results in the minimum error covariance? We describe a stochastic sensor selection strategy that is easy to implement and is computationally tractable. The problem described above comes up in many domains out of which we discuss two. In the sensor selection problem, there are multiple sensors that cannot operate simultaneously (e.g., sonars in the same frequency band). Thus measurements need to be scheduled. In the sensor coverage problem, a geographical area needs to be covered by mobile sensors each with limited range. Thus from every position, the sensors obtain a different view-point of the area and the sensors need to optimize their trajectories. The algorithm is applied to these problems and illustrated through simple examples.     

Robust filtering with stochastic nonlinearities and multiple missing measurements

This paper is concerned with the filtering problem for a class of discrete-time uncertain stochastic nonlinear time-delay systems with both the probabilistic missing measurements and external stochastic disturbances. The measurement missing phenomenon is assumed to occur in a random way, and the missing probability for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution over the interval . Such a probabilistic distribution could be any commonly used discrete distribution over the interval . The multiplicative stochastic disturbances are in the form of a scalar Gaussian white noise with unit variance. The purpose of the addressed filtering problem is to design a filter such that, for the admissible random measurement missing, stochastic disturbances, norm-bounded uncertainties as well as stochastic nonlinearities, the error dynamics of the filtering process is exponentially mean-square stable. By using the linear matrix inequality (LMI) method, sufficient conditions are established that ensure the exponential mean-square stability of the filtering error, and then the filter parameters are characterized by the solution to a set of LMIs. Illustrative examples are exploited to show the effectiveness of the proposed design procedures.     

A fast segmentation method based on constraint optimization and its applications: Intensity inhomogeneity and texture segmentation

We propose a new constraint optimization energy and an iteration scheme for image segmentation which is connected to edge-weighted centroidal Voronoi tessellation (EWCVT). We show that the characteristic functions of the edge-weighted Voronoi regions are the minimizers (may not unique) of the proposed energy at each iteration. We propose a narrow banding algorithm to accelerate the implementation, which makes the proposed method very fast. We generalize the CVT segmentation to hand intensity inhomogeneous and texture segmentation by incorporating the global and local image information into the energy functional. Compared with other approaches such as level set method, the experimental results in this paper have shown that our approach greatly improves the calculation efficiency without losing segmentation accuracy.     

Computer-assisted pit-pattern classification in different wavelet domains for supporting dignity assessment of colonic polyps

In this paper, we show that zoom-endoscopy images can be well classified according to the pit-pattern classification scheme by using texture-analysis methods in different wavelet domains. We base our approach on three different variants of the wavelet transform and propose that the color channels of the RGB and LAB color model are an important source for computing image features with high discriminative power. Color-channel information is incorporated by either using simple feature vector concatenation and cross-cooccurrence matrices in the wavelet domain. Our experimental results based on k-nearest neighbor classification and forward feature selection exemplify the advantages of the different wavelet transforms and show that color-image analysis is superior to grayscale-image analysis regarding our medical image classification problem.     

Group search optimizer based optimal location and capacity of distributed generations

This paper presents a novel efficient population-based heuristic approach for optimal location and capacity of distributed generations (DGs) in distribution networks, with the objectives of minimization of fuel cost, power loss reduction, and voltage profile improvement. The approach employs an improved group search optimizer (iGSO) proposed in this paper by incorporating particle swarm optimization (PSO) into group search optimizer (GSO) for optimal setting of DGs. The proposed approach is executed on a networked distribution system—the IEEE 14-bus test system for different objectives. The results are also compared to those that executed by basic GSO algorithm and PSO algorithm on the same test system. The results show the effectiveness and promising applications of the proposed approach in optimal location and capacity of DGs.     

Electrochemical performances of B doped and undoped diamond-like carbon (DLC) films deposited by femtosecond pulsed laser ablation for heavy metal detection using square wave anodic stripping voltammetric (SWASV) technique

Pure diamond-like carbon (DLC) thin films and boron-doped DLC thin films have been deposited on silicon substrates using femtosecond pulsed laser. The amorphous carbon materials (DLC), have been deposited at room temperature by ablating graphite targets with an amplified Ti:sapphire laser of 800 nm wavelength and a pulse duration of 150 fs in high vacuum conditions. Doping with boron has been performed by ablating alternatively graphite and boron targets.In this study, the DLC films were used as working electrodes for the electrochemical detection of trace heavy metals namely, Cd²⁺, Pb²⁺, Ni²⁺ and Hg²⁺, by using square wave anodic stripping voltammetry (SWASV) technique. Four metals were detected at −1.3 V deposition potential, and 90 s deposition time. The DLC films have been characterized by multiwavelength Raman spectrometry and high resolution scanning electron microscopy. The effect of the boron doping on the electrochemical behavior has been shown. The a-C:B 8%/Si₃N₄ electrode gives the more sensitive detection. The four metals are detected simultaneously with a detection limit of 1 μg/L or 2 μg/L and a dynamic range from 1 or 2 to 25 μg/L for every metal, as presented in third table of this article. The different sensitivities obtained are 6.2, 20.0, 1.2 and 6.6 μA/ppb or μA μg⁻¹ L for Cd²⁺, Pb²⁺, Ni²⁺ and Hg²⁺, respectively.