Inverse optimality in the class of Hopfield neural networks with input nonlinearity

This paper presents the chaos suppression problem in the class of Hopfield neural networks (HNNs) with input nonlinearity using inverse optimality approach. Using the inverse optimality technique and based on Lyapunov stability theory, a stabilizing control law, which is optimal with respect to meaningful cost functional, is determined to achieve global asymptotically stability in the closed-loop system. Numerical simulation is performed on a four-dimensional hyper-chaotic HNN to demonstrate the effectiveness of the proposed method.     

Intelligent facial emotion recognition and semantic-based topic detection for a humanoid robot

Automatic perception of human affective behaviour from facial expressions and recognition of intentions and social goals from dialogue contexts would greatly enhance natural human robot interaction. This research concentrates on intelligent neural network based facial emotion recognition and Latent Semantic Analysis based topic detection for a humanoid robot. The work has first of all incorporated Facial Action Coding System describing physical cues and anatomical knowledge of facial behaviour for the detection of neutral and six basic emotions from real-time posed facial expressions. Feedforward neural networks (NN) are used to respectively implement both upper and lower facial Action Units (AU) analysers to recognise six upper and 11 lower facial actions including Inner and Outer Brow Raiser, Lid Tightener, Lip Corner Puller, Upper Lip Raiser, Nose Wrinkler, Mouth Stretch etc. An artificial neural network based facial emotion recogniser is subsequently used to accept the derived 17 Action Units as inputs to decode neutral and six basic emotions from facial expressions. Moreover, in order to advise the robot to make appropriate responses based on the detected affective facial behaviours, Latent Semantic Analysis is used to focus on underlying semantic structures of the data and go beyond linguistic restrictions to identify topics embedded in the users’ conversations. The overall development is integrated with a modern humanoid robot platform under its Linux C++ SDKs. The work presented here shows great potential in developing personalised intelligent agents/robots with emotion and social intelligence.     

Quality assessment of 3D synthesized images based on structural and textural distortion

Depth image based rendering (DIBR) is a popular technique for rendering virtual 3D views in stereoscopic and autostereoscopic displays. The quality of DIBR-synthesized images may decrease due to various factors, e.g., imprecise depth maps, poor rendering techniques, inaccurate camera parameters. The quality of synthesized images is important as it directly affects the overall user experience. Therefore, the need arises for designing algorithms to estimate the quality of the DIBR-synthesized images. The existing 2D image quality assessment metrics are found to be insufficient for 3D view quality estimation because the 3D views not only contain color information but also make use of disparity to achieve the real depth sensation. In this paper, we present a new algorithm for evaluating the quality of DIBR generated images in the absence of the original references. The human visual system is sensitive to structural information; any deg radation in structure or edges affects the visual quality of the image and is easily noticeable for humans. In the proposed metric, we estimate the quality of the synthesized view by capturing the structural and textural distortion in the warped view. The structural and textural information from the input and the synthesized images is estimated and used to calculate the image quality. The performance of the proposed quality metric is evaluated on the IRCCyN IVC DIBR images dataset. Experimental evaluations show that the proposed metric outperforms the existing 2D and 3D image quality metrics by achieving a high correlation with the subjective ratings.

     

Lyophilized insulin nanoparticles prepared from quaternized N-aryl derivatives of chitosan as a new strategy for oral delivery of insulin: in vitro,ex vivo and in vivo characterizations

Objective: The purpose of this research was the development, in vitro, ex vivo and in vivo characterization of lyophilized insulin nanoparticles prepared from quaternized N-aryl derivatives of chitosan.

Methods: Insulin nanoparticles were prepared from methylated N-(4-N,N-dimethylaminobenzyl), methylated N-(4 pyridinyl) and methylated N-(benzyl). Insulin nanoparticles containing non-modified chitosan and also trimethyl chiotsan (TMC) were also prepared as control. The effects of the freeze-drying process on physico-chemical properties of nanoparticles were investigated. The release of insulin from the nanoparticles was studied in vitro. The mechanism of the release of insulin from different types of nanoparticles was determined using curve fitting. The secondary structure of the insulin released from the nanoparticles was analyzed using circular dichroism and the cell cytotoxicity of nanoparticles on a Caco-2 cell line was determined. Ex vivo studies were performed on excised rat jejunum using Frantz diffusion cells. In vivo studies were performed on diabetic male Wistar rats and blood glucose level and insulin serum concentration were determined.

Results: Optimized nanoparticles with proper physico-chemical properties were obtained. The lyophilization process was found to cause a decrease in zeta potential and an increase in PdI as well as and a decrease in entrapment efficiency (EE%) and loading efficiency (LE%) but conservation in size of nanoparticles. Atomic force microscopy (AFM) images showed non-aggregated, stable and spherical to sub-spherical nanoparticles. The in vitro release study revealed higher release rates for lyophilized compared to non-lyophilized nanoparticles. Cytotoxicity studies on Caco-2 cells revealed no significant cytotoxicity for prepared nanoparticles after 3-h post-incubation but did show the concentration-dependent cytotoxicity after 24?h. The percentage of cumulative insulin determined from ex vivo studies was significantly higher in nanoparticles prepared from quaternized aromatic derivatives of chitosan. In vivo data showed significantly higher insulin intestinal absorption in nanoparticles prepared from methylated N-(4-N, N-dimethylaminobenzyl) chitosan nanoparticles compared to trimethyl chitosan.

Conclusion: These data obtained demonstrated that as the result of optimized physico-chemical properties, drug release rate, cytotoxicity profile, ex vivo permeation enhancement and increased in vivo absorption, nanoparticles prepared from N-aryl derivatives of chitosan can be considered as valuable method for the oral delivery of insulin.     

Design and Implementation of a Flexible Manufacturing Control System Using Neural Network

Design and implementation of a sequential controller based on the concept of artificial neural networks for a flexible manufacturing system are presented. The recurrent neural network (RNN) type is used for such a purpose. Contrary to the programmable controller, an RNN-based sequential controller is based on a definite mathematical model rather than depending on experience and trial and error techniques. The proposed controller is also more flexible because it is not limited by the restrictions of the finite state automata theory. Adequate guidelines of how to construct an RNN-based sequential controller are presented. These guidelines are applied to different case studies. The proposed controller is tested by simulations and real-time experiments. These tests prove the successfulness of the proposed controller performances. Theoretical as well as experimental results are presented and discussed indicating that the proposed design procedure using Elman's RNN can be effective in designing a sequential controller for event-based type manufacturing systems. In addition, the simulation results assure the effectiveness of the proposed controller to outperform the effect of noisy inputs.     

Simultaneous Microwave Extraction and Separation of Volatile and Non-Volatile Organic Compounds of Boldo Leaves. From Lab to Industrial Scale

Microwave extraction and separation has been used to increase the concentration of the extract compared to the conventional method with the same solid/liquid ratio, reducing extraction time and separate at the same time Volatile Organic Compounds (VOC) from non-Volatile Organic Compounds (NVOC) of boldo leaves. As preliminary study, a response surface method has been used to optimize the extraction of soluble material and the separation of VOC from the plant in laboratory scale. The results from the statistical analysis revealed that the optimized conditions were: microwave power 200 W, extraction time 56 min and solid liquid ratio of 7.5% of plants in water. Lab scale optimized microwave method is compared to conventional distillation, and requires a power/mass ratio of 0.4 W/g of water engaged. This power/mass ratio is kept in order to upscale from lab to pilot plant.     

Distinctive arsenic(V) trapping modes by magnetite nanoparticles induced by different sorption processes

Arsenic sorption onto iron oxide spinels such as magnetite may contribute to arsenic immobilization at redox fronts in soils, sediments, and aquifers, as well as in putative remediation and water treatment technologies. We have investigated As(V) speciation resulting from different sorption processes on magnetite nanoparticles, including both adsorption and precipitation, using X-ray absorption fine structure (XAFS) spectroscopy and transmission electron microscopy (TEM). XAFS results suggest that AsO(4) tetrahedra form predominantly inner-sphere bidentate corner-sharing ((2)C) complexes and outer-sphere complexes on magnetite in the adsorption experiments. In the precipitation experiments, an increasing fraction of AsO(4) tetrahedra appears to be incorporated in clusters having a magnetite-like local structure with increasing As loading, the remaining fraction of As being adsorbed at the surface of magnetite particles. In the sample with the highest As loading (15.7 μmol/m(2)) XAFS data indicate that As(V) is fully incorporated in such clusters. Such processes help to explain the significantly higher arsenic uptake in precipitation samples compared to those generated in adsorption experiments. In addition, for the precipitation samples, TEM observations indicate the formation of amorphous coatings and small (~3 nm) nanoparticles associated with larger (~20-40 nm) magnetite nanoparticles, which are absent in the adsorption samples. These results suggest that As(V) could form complexes at the surfaces of the small nanoparticles and could be progressively incorporated in their structure with increasing As loading. These results provide some of the fundamental knowledge about As(V)-magnetite interactions that is essential for developing effective water treatment technologies for arsenic.     

Stochastic economic analysis of FACTS devices on contingent transmission networks using hybrid biogeography-based optimization

Electrical Engineering - Flexible AC transmission systems (FACTS) devices have advantages of enhancing AC system controllability and stability, increasing power transfer capability and relieving...     

Electroless nano zinc oxide–activate carbon composite supercapacitor electrode

An electroless deposition process was used to synthesize the nanostructured zinc oxide (ZnO)–activated carbon (AC) as supercapacitor. The composite oxide was studied by high resolution transmission electron microscopy (HRTEM), scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDX) and X-ray diffraction analysis (XRD). The electrochemical performance of the nanocomposite was analyzed through cyclic voltammetry (CV) and AC impedance spectroscopy (EIS) in 0.1 M Na₂SO₄ as electrolyte. A specific capacitance 187 F g^?1 at a scan rate of 5 mV s^?1 was obtained using cyclic voltammetry (CV) and a nearly rectangular shaped CV curve was observed for the composite oxide. The supercapacitor was quite stable during charge–discharge cycling and exhibited constant capacitance during the long-term cycling. It also yielded a specific capacitance 171 F g^?1 at 5 mA cm^?2 with a high energy density of 21.9 Wh kg^?1 and 4.2 kW kg^?1 of power density. Due to unique structure of prepared ZnO–AC nanocomposite, it is a promising candidate for supercapacitor.