Facile preparation of agarose-chitosan hybrid materials and nanocomposite ionogels using an ionic liquid via dissolution, regeneration and sol-gel transition

We report simultaneous dissolution of agarose (AG) and chitosan (CH) in varying proportions in an ionic liquid (IL), 1-butyl-3-methylimidazolium chloride [C4mim][Cl]. Composite materials were constructed from AG-CH-IL solutions using the antisolvent methanol, and IL was recovered from the solutions. Composite materials could be uniformly decorated with silver oxide (Ag2O) nanoparticles (Ag NPs) to form nanocomposites in a single step by in situ synthesis of Ag NPs in AG-CH-IL sols, wherein the biopolymer moiety acted as both reducing and stabilizing agent. Cooling of Ag NPs-AG-CH-IL sols to room temperature resulted in high conductivity and high mechanical strength nanocomposite ionogels. The structure, stability and physiochemical properties of composite materials and nanocomposites were characterized by several analytical techniques, such as Fourier transform infrared (FTIR), CD spectroscopy, differential scanning colorimetric (DSC), thermogravimetric analysis (TGA), gel permeation chromatography (GPC), and scanning electron micrography (SEM). The result shows that composite materials have good thermal and conformational stability, compatibility and strong hydrogen bonding interactions between AG-CH complexes. Decoration of Ag NPs in composites and ionogels was confirmed by UV-Vis spectroscopy, SEM, TEM, EDAX and XRD. The mechanical and conducting properties of composite ionogels have been characterized by rheology and current-voltage measurements. Since Ag NPs show good antimicrobial activity, Ag NPs -AG-CH composite materials have the potential to be used in biotechnology and biomedical applications whereas nanocomposite ionogels will be suitable as precursors for applications such as quasi-solid dye sensitized solar cells, actuators, sensors or electrochromic displays

DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structure. In this paper, we propose an automatic feature discovery framework for extracting discriminative class-specific features and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific features which are suitable for representing samples from the same class while are poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian lung images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, show the significance of DFDL model in a variety problems over state-of-the-art methodsComment: Accepted to IEEE International Symposium on Biomedical Imaging (ISBI), 201

ADAPTIVE PREDICTIVE FUNCTIONAL CONTROLLER

A controller device and a method for controlling a system that utilizes an adaptive mechanism to self-learn the system char acteristics and incorporates this adaptive self-learning ability to predict a control parameter correctly to provide precise control of a system component

A TMS320C54 system for effective online Signature Verification using Hidden Markov Models

In this paper we present a scheme for real time implementation of a Hidden Markov Model based Signature Verification System on a TMS320C54 processor. Here we explain in detail our overall methodology and the subsequent DSP implementation. We also propose two new algorithms which would further facilitate real-time operation. We use the Baum-We1ch Algorithm for training the HMM and the Viterbi Algorithm for the testing of our proposed system. It may be noted that the technique of HMMs have hitherto been applied for speech modelling and only recently has its application to the field of Signature Verification been considered. Our proposed system has an overall accuracy of 11.64% FAR and 0.64% FRR