Error analysis of task performance with laptop in vibration environment

The present study investigates influences of vibration directions, vibration magnitudes, object sizes, object distances and angles of approach on producing errors while performing pointing and clicking activity on a laptop monitor similar to passengers working on moving trains. Attempts made outside the boundary of an object while performing the activity is registered as an error. The number of errors produced under different exposure conditions is computed. Higher vibration magnitudes, smaller size of the objects, and diagonal angular movements are found to create more errors and affected accuracy of the activity and hence showed difficulties to perform the activity.     

Existence results for impulsive differential inclusions with nonlocal conditions

In this paper, we shall establish sufficient conditions for the existence of mild solutions for nonlocal impulsive differential inclusions. On the basis of the fixed point theorems for multivalued maps and the technique of approximate solutions, new results are obtained. Examples are also provided to illustrate our results.     

Image segmentation based on histogram analysis utilizing the cloud model

Both the cloud model and type-2 fuzzy sets deal with the uncertainty of membership which traditional type-1 fuzzy sets do not consider. Type-2 fuzzy sets consider the fuzziness of the membership degrees. The cloud model considers fuzziness, randomness, and the association between them. Based on the cloud model, the paper proposes an image segmentation approach which considers the fuzziness and randomness in histogram analysis. For the proposed method, first, the image histogram is generated. Second, the histogram is transformed into discrete concepts expressed by cloud models. Finally, the image is segmented into corresponding regions based on these cloud models. Segmentation experiments by images with bimodal and multimodal histograms are used to compare the proposed method with some related segmentation methods, including Otsu threshold, type-2 fuzzy threshold, fuzzy C-means clustering, and Gaussian mixture models. The comparison experiments validate the proposed method.     

Stability analysis and time-varying walking control for an under-actuated planar biped robot

This paper presents two walking controllers for a planar biped robot with unactuated point feet. The control is based on the tracking of reference motions expressed as a function of time. First, the reference motions are adapted at each step in order to create a hybrid zero dynamic (HZD) system. Next, the stability of the walking gait under closed-loop control is evaluated with the linearization of the restricted Poincaré map of the HZD. When the controlled outputs are selected to be the actuated coordinates, most periodic walking gaits for this robot are unstable, that is, the eigenvalues of the linearized Poincaré map (ELPM) is larger than one. Therefore, two control strategies are explored to produce stable walking. The first strategy uses an event-based feedback controller to modify the ELPM and the second one is based on the choice of controlled outputs. The stability analysis show that, for the same robot and for the same reference trajectory, the stability of the walking (or ELPM) can be modified by some pertinent choices of controlled outputs. Moreover, by studying some walking characteristics of many stable cases, a necessary condition for stable walking is proposed. It is that the height of swing foot is nearly zero at the desired moment of impact. Based on this condition, the duration of the step is almost constant in presence of initial error, so a method for choosing controlled outputs for the second controller is given. By using this method, two stable domains for the controlled outputs selection are obtained.     

Hebbian-based neural networks for bottom-up visual attention and its applications to ship detection in SAR images

This paper proposes a bottom-up attention model based on pulsed Hebbian neural networks. The salience of the visual input can be generated through the networks using a simple normalization process, which can be calculated rapidly. Moreover, visual salience in this model can be represented as binary codes that mimic neuronal pulses in the human brain. Experimental results on psychophysical patterns and eye fixation prediction for natural images prove the effectiveness and efficiency of the model. In an arduous task of detecting ships in synthetic aperture radar (SAR) images, there are large amounts of data to be processed in real time. As a fast and effective technique for saliency detection, the proposed model is applied to ship detection in SAR images and its robustness against speckles is further proved.     

An adaptive threshold in joint approximate diagonalization by assuming exponentially distributed errors

Joint approximate diagonalization (JAD) is one of well-known methods for solving blind source separation. JAD diagonalizes many cumulant matrices of given observed signals as accurately as possible, where the optimization for each pair of signals is repeated until the convergence. In each pair optimization, JAD should decide whether the pair is actually optimized by a convergence decision condition, where a fixed threshold has been employed in many cases. Though a sufficiently small threshold is desirable for the accuracy of results, the speed of convergence is quite slow if the threshold is too small. In this paper, we propose a new decision condition with an adaptive threshold for JAD under a probabilistic framework. First, it is assumed that the errors in JAD (non-diagonal elements in cumulant matrices) are given by the exponential distribution. Next, it is shown that the maximum likelihood estimation of the probabilistic model is equivalent to JAD. Then, an adaptive threshold is theoretically derived by utilizing the model selection theory. Numerical experiments verify the efficiency of the proposed method for blind source separation of artificial sources and natural images. It is also shown that the proposed method is especially effective when the number of samples is limited.     

The complex local mean decomposition

The local mean decomposition (LMD) has been recently developed for the analysis of time series which have nonlinearity and nonstationarity. The smoothed local mean of the LMD surpasses the cubic spline method used by the empirical mode decomposition (EMD) to extract amplitude and frequency modulated components. To process complex-valued data, we propose complex LMD, a natural and generic extension to the complex domain of the original LMD algorithm. It is shown that complex LMD extracts the frequency modulated rotation and envelope components. Simulations on both artificial and real-world complex-valued signals support the analysis.     

Correlation between the plating regime of lead film deposition and electrode response after accumulation of organic compound. Microscopic study

The properties of the lead film electrode designed for adsorptive stripping voltammetric measurements of organic compounds were examined by electrochemical and microscopic techniques. The effect of Pb(II) concentration, the influence of deposition potential and time of the lead film formation on the determination of bleomycin at an in situ lead-coated glassy carbon electrode have been studied. Atomic force microscopy (AFM) was used to characterize the Pb crystallite growth on the glassy carbon surface. The morphology of the deposits was evaluated and its influence on the bleomycin responses was discussed.     

Electrocatalytic reduction of hydrogen peroxide and its determination in antiseptic and soft-glass cleaning solutions at phosphotungstate-doped-glutaraldehyde-cross-linked poly-l-lysine film electrodes

The present work describes the electrocatalytic behavior of phosphotungstate-doped glutaraldehyde-cross-linked poly-l-lysine (PLL-GA-PW) film electrode towards reduction of hydrogen peroxide (H₂O₂) in acidic medium. The modified electrode was prepared by means of electrostatically trapping the phosphotungstate anion into the cationic PLL-GA coating on glassy carbon electrode. The PLL-GA-PW film electrode showed excellent electrocatalytic activity towards H₂O₂ reduction in 0.1 M H₂SO₄. Under the optimized conditions, the electrochemical sensor exhibited a linear response for H₂O₂ concentration over the range 2.5 × 10⁻⁶ to 6.85 × 10⁻³ M with a sensitivity of 1.69 μA mM⁻¹. The curvature in the calibration curve at high concentration is explained in terms of Michaelis-Menten (MM) saturation kinetics, and the kinetics parameters calculated by three different methods were compared. The PLL-GA-PW film electrode did not respond to potential interferents such as dopamine, ascorbic acid and uric acid. This unique feature of PLL-GA-PW film electrode allowed selective determination of H₂O₂. Finally, the proposed electrochemical sensor was successfully applied to determine H₂O₂ in commercially available antiseptic solution and soft-contact lenses cleaning solution and the method has been validated using independent estimation by classical potassium permanganate titration method. Major advantages of the method are simple electrode fabrication, stability and high selectivity towards hydrogen peroxide.     

Novel e-nose for the discrimination of volatile organic biomarkers with an array of carbon nanotubes (CNT) conductive polymer nanocomposites (CPC) sensors

We report the original design of a new type of electronic nose (e-nose) consisting of only five sensors made of hierarchically structured conductive polymer nanocomposites (CPC). Each sensor benefits from both the exceptional electrical properties of carbon nanotubes (CNT) used to build the conductive architecture and the spray layer by layer (sLbL) assembly technique, which provides the transducers with a highly specific 3D surface structure. Excellent sensitivity and selectivity were obtained by optimizing the amount of CNT with five different polymer matrices: poly(caprolactone) (PCL), poly(lactic acid) (PLA), poly(carbonate) (PC), poly(methyl methacrylate) (PMMA) and a biobased polyester (BPR). The ability of the resulting e-nose to detect nine organic solvent vapours (isopropanol, tetrahydrofuran, dichloromethane, n-heptane, cyclohexane, methanol, ethanol, water and toluene), as well as biomarkers for lung cancer detection in breath analysis, has been demonstrated. Principal component analysis (PCA) proved to be an excellent pattern recognition tool to separate vapour clusters.