Stability and synchronization for Markovian jump neural networks with partly unknown transition probabilities

This paper addresses the problems of stability and synchronization for a class of Markovian jump neural networks with partly unknown transition probabilities. We first study the stability analysis problem for a single neural network and present a sufficient condition guaranteeing the mean square asymptotic stability. Then based on the Lyapunov functional method and the Kronecker product technique, the chaos synchronization problem of an array of coupled networks is considered. Both the stability and the synchronization conditions are delay-dependent, which are expressed in terms of linear matrix inequalities. The effectiveness of the developed methods is shown by simulation examples.     

Robust parametric CMAC with self-generating design for uncertain nonlinear systems

In this paper, a robust parametric cerebellar model articulation controller (RP-CMAC) with self-generating design, called RPCSGD, is proposed for uncertain nonlinear systems. The proposed controller consists of two parts: one is the parametric CMAC with self-generating design (PCSGD), which is utilized to approximate the ideal controller and the other is the robust controller, which is designed to achieve a specified H^∞ robust tracking performance of the system. The corresponding memory size of the proposed controller can be suitably constructed via the self-generating design. Thus, the useless or untrained memories will not take possession of the space. Besides, the concept of sliding-mode control (SMC) is adopted so that the proposed controller has more robustness against the approximated error and uncertainties. The stability of the system can be guaranteed surely due to the derivations of the adaptive laws of the proposed RPCSGD based on the Lyapunov function. Finally, the proposed controller is applied to the second-order chaotic system and the one-link rigid robotic manipulator. The tracking performance and effectiveness of the proposed controller are verified by simulations of the computer.     

Growing fuzzy topology adaptive resonance theory models with a push-pull learning algorithm

A new incrementally growing neural network model, called the growing fuzzy topology ART (GFTART) model, is proposed based on integrating the conventional fuzzy ART model with the incremental topology-preserving mechanism of the growing cell structure (GCS) model. This is in addition, to a new training algorithm, called the push-pull learning algorithm. The proposed GFTART model has two purposes: First, to reduce the proliferation of incrementally generated nodes in the F2 layer by the conventional fuzzy ART model based on replacing each F2 node with a GCS. Second, to enhance the class-dependent clustering representation ability of the GCS model by including the categorization property of the conventional fuzzy ART model. In addition, the proposed push-pull training algorithm enhances the cluster discriminating property and partially improves the forgetting problem of the training algorithm in the GCS model.     

Development of high throughput microfluidic cell culture chip for perfusion 3-dimensional cell culture-based chemosensitivity assay

This study reports a microfluidic cell culture chip encompassing 36 microbioreactors for high throughput perfusion 3-dimensional (3D) cell culture-based chemosensitivity assays. Its advantages include the capability for multiplexed medium delivery, and the function for both efficient and high throughput micro-scale 3D culture construct preparation and loading. The results showed that the proposed medium pumping mechanism was able to provide a uniform pumping rates ranging from 1.2 to 3.9 μl h⁻¹. In addition, the simple cell/hydrogel loading scheme has been proven to be able to carry out 3D cell culture construct preparation and loading precisely and efficiently. Furthermore, a chemosensitivity assay was successfully demonstrated using the proposed cell culture chip. The results obtained were also compared with the same evaluation based on a conventional 2D monolayer cell culture. It can be concluded that the choice of cell culture format can result in different chemosensitivity evaluation results. Overall, because of the nature of miniaturized perfusion 3D cell culture, the cell culture chip not only can provide stable, well-defined and more biologically relevant culture environments, but it also features low consumption of research resources. All these traits are found particularly useful for high-precision and high-throughput 3D cell culture-based assays.     

Pumping with electroosmosis of the second kind in mesoporous skeletons

This paper presents the use of mesoporous silica skeletons as substrates for electroosmotic (EO) micropumps. Mesoporous silica skeletons have bimodal pore size distributions consisting of macropores and cation-permselective mesopores. These materials have the potential for high flow rate per power because the cation-permselective mesopores can generate an induced charge layer (ICL) and electroosmosis of the second kind (EO-2) under high applied electric fields. The diffuse charge layers induced by the electric field result in an EO-2 flow rate that increases quadratically with increasing electric field. In contrast, the flow rate of the more common electroosmosis of the first kind (EO-1) is linearly proportional to electric field. Here, we investigate the impact of finite pressure loads on the EO-2 flow rate with experiments and an engineering model to evaluate the potential of mesoporous skeletons for micropumping applications. Our results include analyses of maximum flow rate, maximum pressure, and flow rate with intermediate pressure loads. The results indicate the existence of a critical pressure load at which reverse pressure-driven flow significantly diminishes the EO-2 flow. We also investigate the scaling of flow rate per power with respect to substrate thickness and area, demonstrating significant increases in flow rate per power with thinner substrates and favorable scaling for miniaturization of EO-2 pumps.     

Prolonged sensitivity of immobilized thylakoid membranes in cross-linked matrix to atrazine

Freshly prepared pea thylakoid membranes were immobilized in bovine serum albumin-glutaraldehyde cross-linked matrix (BSA-GA matrix) and their stability under long term storage was analyzed by Pulse-Amplitude-Modulated (PAM) chlorophyll fluorescence and photosynthetic oxygen evolution measured by oxygen rate electrode. The thylakoid membranes stored at 4 °C showed prolonged stability in BSA-GA matrix and additional adsorption on nitrocellulose membrane filters gave them more stability. The sensitivity of the parameters of the oxygen evolution of thylakoid membranes to atrazine increased with immobilization. The half-inhibition time for oxygen evolution and quantum efficiency of photosynthesis could be prolonged to more than 15 days. These results suggest that the immobilized thylakoid membranes in BSA-GA matrix can be used as biological receptor in biosensors for a long period of time (up to 25 days) applying the proposed new method for atrazine detection by using polarographic oxygen rate electrode. This method is more sensitive, faster and easier to use than other methods for detection of herbicides based on determination of the photochemical activity of photosystem II.     

Evanescent wave sensor based on permanently bent single mode optical fiber

A novel refractive index sensing scheme based on evanescent wave interaction through locally and permanently bent single mode optical fibers is proposed. Local and permanent bends in single mode optical fibers enable significant power coupling between core and cladding modes. Order and number of excited cladding modes depend on bend features and determine the field profile at the output of the bent region. This in turn constitutes a simple mechanism to tailor the field distribution in single mode optical fibers useful for spatial light modulation. Moreover, since guided cladding modes are strongly influenced by the surrounding refractive index (SRI), the power transmitted at the output of the bent region as well as its dependence on the optical wavelength are strongly sensitive to the SRI opening new scenarios in sensing applications.     

Ascorbic acid imprinted polymer-modified graphite electrode: A diagnostic sensor for hypovitaminosis C at ultra trace ascorbic acid level

A new kind of molecularly imprinted polymer-modified graphite electrode was fabricated by “grafting-to” approach, incorporating sol–gel technique, for the detection of acute deficiency in serum ascorbic acid level (SAAL), manifesting hypovitaminosis C. The modified electrode exhibited ascorbic acid (AA) oxidation at less positive potential (0.0 V) than the earlier reported methods, resulting in a limit of detection as low as 6.13 ng mL⁻¹ (RSD = 1.2%, S/N = 3). The diffusion coefficient (1.096 × 10⁻⁵ cm² s⁻¹), rate constant (7.308 s⁻¹), and Gibb's free energy change (−12.59 kJ mol⁻¹) due to analyte adsorption, were also calculated to explore the kinetics of AA oxidation. The proposed sensor was found to enhance sensitivity substantially so as to detect ultra trace level of AA in the presence of other biologically important compounds (dopamine, uric acid, etc.), without any cross interference and matrix complications from biological fluids and pharmaceutical samples.     

Stochastic approximation learning for mixtures of multivariate elliptical distributions

Most of the current approaches to mixture modeling consider mixture components from a few families of probability distributions, in particular from the Gaussian family. The reasons of these preferences can be traced to their training algorithms, typically versions of the Expectation-Maximization (EM) method. The re-estimation equations needed by this method become very complex as the mixture components depart from the simplest cases. Here we propose to use a stochastic approximation method for probabilistic mixture learning. Under this method it is straightforward to train mixtures composed by a wide range of mixture components from different families. Hence, it is a flexible alternative for mixture learning. Experimental results are presented to show the probability density and missing value estimation capabilities of our proposal.     

Fault tolerant machine learning for nanoscale cognitive radio

We introduce a machine learning-based classifier that identifies free radio channels for cognitive radio. The architecture is designed for nanoscale implementation, under nanoscale implementation constraints; we do not describe all physical details but believe future physical implementation to be feasible. The system uses analog computation and consists of cyclostationary feature extraction and a radial basis function network for classification. We describe a model for nanoscale faults in the system, and simulate experimental performance and fault tolerance in recognizing WLAN signals, under different levels of noise and computational errors. The system performs well under expected non-ideal manufacturing and operating conditions.