An inexact hybrid projection-proximal point algorithm for solving generalized mixed variational inequalities

In this paper, we investigate an inexact hybrid projection-proximal method for solving a class of generalized mixed variational inequalities in Hilbert spaces. We construct a general inexact hybrid projection-proximal point algorithm, in which an inexact relaxed proximal point step is followed by a suitable orthogonal projection onto a hyperplane. Under some suitable conditions concerned with the pseudomonotone set-valued mapping T, the nonsmooth convex function f and the step size λ_k, we prove the convergence of the inexact hybrid projection-proximal point algorithm for solving generalized mixed variational inequalities in Hilbert spaces.     

Existence and uniqueness of strictly nondecreasing and positive solution for a fractional three-point boundary value problem

In this paper, we consider the following nonlinear fractional three-point boundary value problem

     

Two augmentation preconditioners for nonsymmetric and indefinite saddle point linear systems with singular (1, 1) blocks

In this paper, two preconditioners based on augmentation are introduced for the solution of large saddle point-type systems with singular (1, 1) blocks. We study the spectral characteristics of the preconditioners, show that all eigenvalues of preconditioned matrices are strongly clustered. Finally, numerical experiments are also reported for illustrating the efficiency of the presented preconditioners.     

PIRF-Nav 2.0: Fast and online incremental appearance-based loop-closure detection in an indoor environment

This paper presents a fast and online incremental solution for an appearance-based loop-closure detection problem in a dynamic indoor environment. Closing the loop in a dynamic environment has been an important topic in robotics for decades. Recently, PIRF-Nav has been reported as being successful in achieving high recall rate at precision 1. However, PIRF-Nav has three main disadvantages: (i) the computational expense of PIRF-Nav is beyond real-time, (ii) it utilizes a large amount of memory in the redundant process of keeping signatures of places, and (iii) it is ill-suited to an indoor environment. These factors hinder the use of PIRF-Nav in a general environment for long-term, high-speed mobile robotic applications. Therefore, this paper proposes two techniques: (i) new modified PIRF extraction that makes the system more suitable for an indoor environment and (ii) new dictionary management that can eliminate redundant searching and conserve memory consumption. The results show that our proposed method can complete tasks up to 12 times faster than PIRF-Nav with only a slight percentage decline in recall. In addition, we collected additional data from a university canteen crowded during lunch time. Even in this crowded indoor environment, our proposed method has better real-time processing performance compared with other methods.     

Visual quality recognition of nonwovens using generalized Gaussian density model and robust Bayesian neural network

This work is dedicated to develop an algorithm for the visual quality recognition of nonwoven materials, in which image analysis and neural network are involved in feature extraction and pattern recognition stage, respectively. During the feature extraction stage, each image is decomposed into four levels using the 9-7 bi-orthogonal wavelet base. Then the wavelet coefficients in each subband are independently modeled by the generalized Gaussian density (GGD) model to calculate the scale and shape parameters with maximum likelihood (ML) estimator as texture features. While for the recognition stage, the robust Bayesian neural network is employed to classify the 625 nonwoven samples into five visual quality grades, i.e., 125 samples for each grade. Finally, we carry out the outlier detection of the training set using the outlier probability and select the most suitable model structure and parameters from 40 Bayesian neural networks using the Occam's razor. When 18 relevant textural features are extracted for each sample based on the GGD model, the average recognition accuracy of the test set arranges from 88% to 98.4% according to the different number of the hidden neurons in the Bayesian neural network.     

Miniaturized ceramic differential scanning calorimeter with integrated oven and crucible in LTCC technology

A miniaturized ceramic differential scanning calorimeter (MC-DSC) with integrated oven and crucible is presented. Despite its small size of only 11 mm × 39 mm × 1.5 mm, all functions of a conventional DSC apparatus are integrated in this novel device - including the oven. The MC-DSC is fully manufactured in thick-film and green glass ceramic tape-based low temperature co-fired ceramics (LTCC) technology. Therefore, production costs are considered to be low. Initial results using indium as a sample material show a good dynamic performance of the MC-DSC. Full width at half maximum of the melting peak is 2.4 °C (sample mass approx. 11 mg, heating rate approx. 50 °C/min). Repeatability of the indium melting point is within ±0.02 °C. The melting peak area increases linearly with the sample mass up to at least 26 mg. Simulations of a strongly simplified finite element model of the MC-DSC are in a good agreement with measurement results allowing a model-based prediction of its basic characteristics.     

Mean square exponential stability in high-order stochastic impulsive BAM neural networks with time-varying delays

In this paper, a class of stochastic impulsive high-order BAM neural networks with time-varying delays is considered. By using Lyapunov functional method, LMI method and mathematics induction, some sufficient conditions are derived for the globally exponential stability of the equilibrium point of the neural networks in mean square. It is believed that these results are significant and useful for the design and applications of impulsive stochastic high-order BAM neural networks.     

Modeling the high pH sensitivity of Suspended Gate Field Effect Transistor (SGFET)

High sensitivity to chemical species of sub-micron gap Suspended-Gate FETs (more than 200 mV/pH for example) is explained from the charge distribution induced by the high field in the sub-micronic gap under the gate-bridge. Modeling of Metal-Electrolyte-Insulator-Silicon (MEIS) capacitor, which is the basic vertical structure of the transistor, is performed to highlight this effect through the response to the pH change of the solution filling the gap. The analytical model is based on the 2D-numerical resolution of Poisson's equation. The response of quasi-static C(V) plots versus pH is simulated using both electrolyte charge distribution and site-binding theory considering the influence of sites densities on silicon nitride. Device modeling and simulated/experimental electrical characteristics are presented. Effect of the gap thickness on the pH sensitivity is also discussed in this study.     

Thin-film electrode based droplet detection for microfluidic systems

We report on a droplet-producing microfluidic system with electrical impedance-based detection. The microfluidic devices are made of polydimethylsiloxane (PDMS) and glass with thin film electrodes connected to an impedance-monitoring circuit. Immiscible fluids containing the hydrophobic and hydrophilic phases are injected with syringe pumps and spontaneously break into water-in-oil droplet trains. When a droplet passes between a pair of electrodes in a medium having different electrical conductivity, the resulting impedance change signals the presence of the particle for closed-loop feedback during processing. The circuit produces a digital pulse for input into a computer control system. The droplet detector allows estimation of a droplet's arrival time at the microfluidic chip outlet for dispensing applications. Droplet detection is required in applications that count, sort, and direct microfluidic droplets. Because of their low cost and simplicity, microelectrode-based droplet detection techniques should find applications in digital microfluidics and in three-dimensional printing technology for rapid prototyping and biotechnology.     

Liquid-state motion sensing

This paper demonstrates a liquid droplet-based motion sensing system which has the advantages of simple fabrication, low power consumption and digital signal processing. The sensor consists of a dielectric substrate patterned with an array of microelectrodes, and a saline droplet as the proof mass. Once an external linear acceleration is applied, the inertial force moves the droplet on the micropatterned substrate. The acceleration is determined from the movement profile detected by the microelectrodes. In order to enhance the threshold and the sensitivity of motion sensing, two surface treatment approaches are utilized to create superhydrophobic surfaces. The result shows that the minimal sliding angle that can move a 20 μl droplet on the superhydrophobic surface is lower than 1°, corresponding to a threshold of lower than 0.017 g. A lumped-parameter model is developed to estimate the dynamic behavior of the proposed system. The result shows that the frequency response of the droplet-based sensor is more significant at low frequencies than at high frequencies, which is distinct from solid-state accelerometers. Measurement under a constant acceleration shows that the predicted value derived from the measurement has a good match with the actual applied acceleration, validating the proposed system as a viable alternative for motion sensing.