Assessing Virtual Laboratories in a Digital-Filter Design Course: An Experimental Study

One hundred thirty-three undergraduate students in a digital filter design course participated in an experimental study. Two independent variables which occurred in a virtual laboratory environment were studied: 1) instructional treatments (online text-only materials, online texts with illustrations, and online texts with simulations); and 2) prior Internet experience (high and low). Three dependent variables were measured: 1) a knowledge achievement test; 2) intention to use instructional treatments; and 3) interaction levels with instructional treatments. The experimental research design of the study was a 3 times 2 randomized posttest design. Multivariate analysis of variance (MANOVA) was used to analyze collected data. The main effects and the potential interaction of the two independent variables were examined. Results indicate that the presentation of waveform variations and the change of parameters in the course content renders significant higher learning outcomes than online text-only materials and online texts with illustrations (Lambda (8,248) = 0.637, p < 0.05).     

Applications of Cellular Neural Networks to Noise Cancelation in Gray Images Based on Adaptive Particle-swarm Optimization

This paper develops a novel method for designing templates for discrete-time cellular neural networks (DTCNN) via an adaptive particle-swarm optimization (APSO) for gray image noise cancelation. Proper selection of the inertia weight for the APSO gives a balance between global and local searching. The research results show that a larger weight helps to increase the convergence speed while a smaller one benefits the convergence accuracy. This APSO-based method can automatically update template parameters of a discrete-time cellular neural network and optimize them to remove noise interference in polluted images. Finally, examples are given to illustrate the effectiveness of the proposed APSO-CNN methodology.     

Carbon nanotubes as photoacoustic molecular imaging agents in living mice

Photoacoustic imaging of living subjects offers higher spatial resolution and allows deeper tissues to be imaged compared with most optical imaging techniques. As many diseases do not exhibit a natural photoacoustic contrast, especially in their early stages, it is necessary to administer a photoacoustic contrast agent. A number of contrast agents for photoacoustic imaging have been suggested previously, but most were not shown to target a diseased site in living subjects. Here we show that single-walled carbon nanotubes conjugated with cyclic Arg-Gly-Asp (RGD) peptides can be used as a contrast agent for photoacoustic imaging of tumours. Intravenous administration of these targeted nanotubes to mice bearing tumours showed eight times greater photoacoustic signal in the tumour than mice injected with non-targeted nanotubes. These results were verified ex vivo using Raman microscopy. Photoacoustic imaging of targeted single-walled carbon nanotubes may contribute to non-invasive cancer imaging and monitoring of nanotherapeutics in living subjects.     

An FIR Equalizer Design for Nonlinear Channels Using Hybrid GA and LMI

In this paper, a finite impulse response (FIR) equalizer for nonlinear discrete-time channels is designed by employing a hybrid genetic algorithm (GA) and linear matrix inequality (LMI) approach from an H_∞ perspective. The GA technique is utilized to linearize the nonlinear channel model, and the approximate error can be viewed as a state uncertainty. Then, the design of the FIR equalizer is transformed into LMIs, and the coefficients of the FIR equalizer can be obtained by solving an LMI optimization problem. Finally, numerical examples are included to illustrate the effectiveness of the proposed methodology.     

Humic Acid Increases Amyloid β-Induced Cytotoxicity by Induction of ER Stress in Human SK-N-MC Neuronal Cells

Humic acid (HA) is a possible etiological factor associated with for several vascular diseases. It is known that vascular risk factors can directly increase the susceptibility to Alzheimer’s disease (AD), which is a neurodegenerative disorder due to accumulation of amyloid β (Aβ) peptide in the brain. However, the role that HA contributes to Aβ-induced cytotoxicity has not been demonstrated. In the present study, we demonstrate that HA exhibits a synergistic effect enhancing Aβ-induced cytotoxicity in cultured human SK-N-MC neuronal cells. Furthermore, this deterioration was mediated through the activation of endoplasmic reticulum (ER) stress by stimulating PERK and eIF2α phosphorylation. We also observed HA and Aβ-induced cytotoxicity is associated with mitochondrial dysfunction caused by down-regulation of the Sirt1/PGC1α pathway, while in contrast, treating the cells with the ER stress inhibitor Salubrinal, or over-expression of Sirt1 significantly reduced loss of cell viability by HA and Aβ. Our findings suggest a new mechanism by which HA can deteriorate Aβ-induced cytotoxicity through modulation of ER stress, which may provide significant insights into the pathogenesis of AD co-occurring with vascular injury.     

Mean arterial pressure control system using model predictive control and particle swarm optimization

Microsystem Technologies - Linear controllers have been designed to regulate mean arterial pressure (MAP) in treating various cardiovascular diseases. For patients with hemodynamic fluctuations,...     

How are depression and autobiographical memory retrieval related to culture?

This study investigated how culture influences the association between autobiographical memory retrieval and depression. Thirty clinically depressed patients and 30 controls, 15 each from Britain and Taiwan, completed the English and Chinese versions of the Autobiographical Memory Cueing Task (AMT). Overall, the depressed individuals from both cultural groups retrieved significantly fewer specific and more categoric autobiographical memories than their matched, nondepressed controls. Within the control groups, the British participants retrieved significantly more specific autobiographical memories and fewer categoric memories than their Taiwanese counterparts. These results suggest that difficulty in retrieving specific autobiographical memories typical of depression may be a cognitive bias that occurs across cultures. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Cellular Neural Networks for Gray Image Noise Cancellation Based on a Hybrid Linear Matrix Inequality and Particle Swarm Optimization Approach

This paper describes a technique for gray image noise cancellation. This method employs linear matrix inequality (LMI) and particle swarm optimization (PSO) based on cellular neural networks (CNN).We use two images that one is desired image and the other is corrupted to find the CNN template. The Lyapunov stability theorem is employed to derive the criterion for uniqueness and global asymptotic stability of the CNN equilibrium point. The current study characterizes the template design problem as a standard LMI problem and the optimization parameters of the templates are carried out by PSO. Finally, the examples are given to illustrate the effectiveness of the proposed method.     

Robust FIR equalization of uncertain communication channels – An LMI approach

This paper proposes a new robust FIR equalizer for discrete time systems subject to parameter uncertainties in the matrices of the system state-space model from an H perspective. The uncertain parameters are supposed to belong to a given convex bounded polyhedral domain. The discrete algebraic Riccati inequality (DARI) and linear matrix inequality (LMI) provide an extremely superior methodology in the H control fields. By the H and robust methods in conjunction with the bounded real lemma, the robust FIR equalizer design is transformed into a standard LMI optimization problem. An example is given to illustrate the proposed methodology.     

FIR fuzzy equalizer design for nonlinear channels LMI-based fuzzy approach

In this paper an LMI based fuzzy methodology for nonlinear channel equalization from an H _∞ perspective is proposed. According to Takagi-Sugeno (T-S) fuzzy modeling concept, the discrete-time nonlinear channel can be constructed by the piecewise linear subsystems. The FIR fuzzy equalizer design for nonlinear channel is transformed into standard linear matrix inequality (LMI) optimization problem, and the coefficients of the equalizer are obtained by solving LMIs. Besides, the stability of T-S fuzzy system has been investigated based on Lyapunov approach. Finally, simulation result is given to demonstrate the effectiveness of the proposed methodology.