Hybrid deep convolutional neural models for iris image recognition

Multimedia Tools and Applications - This paper briefly explains about the application of deep learning-based methods for biometric applications. This work attempts to solve the problem of limited...     

Multi Criteria Decision Making of Abrasive Flow Oriented Process Parameters in Abrasive Water Jet Machining Using Taguchi–DEAR Methodology

Silicon - Owing to its ability of machining higher strength materials such as titanium alloy with less heat affected zone and higher material removal rate, abrasive water jet machining process is...     

Direct Heating Amino Acids with Silica: A Universal Solvent‐Free Assembly Approach to Highly Nitrogen‐Doped Mesoporous Carbon Materials

A general solvent‐free assembly approach via directly heating amino acid and mesoporous silica mixtures is developed for the synthesis of a family of highly nitrogen‐doped mesoporous carbons. Amino acids have been used as the sole precursors for templating synthesis of a series of ordered mesoporous carbons. During heating, amino acids are melted and strongly interact with silica, leading to effective loading and improved carbon yields (up to ≈25 wt%), thus to successful structure replication and nitrogen‐doping. Unique solvent‐free structure assembly mechanisms are proposed and elucidated semi‐quantitatively by using two affinity scales. Significantly high nitrogen‐doping levels are achieved, up to 9.4 (16.0) wt% via carbonization at 900 (700) °C. The diverse types of amino acids, their variable interactions with silica and different pyrolytic behaviors lead to nitrogen‐doped mesoporous carbons with tunable surface areas (700–1400 m² g^?1), pore volumes (0.9–2.5 cm³ g^?1), pore sizes (4.3–10 nm), and particle sizes from a single template. As demonstrations, the typical nitrogen‐doped carbons show good performance in CO₂ capture with high CO₂/N₂ selectivities up to ≈48. Moreover, they show attractive performance for oxygen reduction reaction, with an onset and a half‐wave potential of ≈?0.06 and ?0.14 V (vs Ag/AgCl).     

Overconfidence effects in category learning: A comparison of connectionist and exemplar memory models.

Exemplar and connectionist models were compared on their ability to predict overconfidence effects in category learning data. In the standard task, participants learned to classify hypothetical patients with particular symptom patterns into disease categories and reported confidence judgments in the form of probabilities. The connectionist model asserts that classifications and confidence are based on the strength of learned associations between symptoms and diseases. The exemplar retrieval model (ERM) proposes that people learn by storing examples and that their judgments are often based on the first example they happen to retrieve. Experiments 1 and 2 established that overconfidence increases when the classification step of the process is bypassed. Experiments 2 and 3 showed that a direct instruction to retrieve many exemplars reduces overconfidence. Only the ERM predicted the major qualitative phenomena exhibited in these experiments. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Image restoration of compressed image using classified vector quantization

To reduce communication bandwidth or storage space, image compression is needed. However, the subjective quality of compressed images may be unacceptable and the improvement of quality for compressed images may be desirable. This paper extends and modifies classified vector quantization (CVQ) to improve the quality of compressed images. The process consists of two phases: the encoding phase and the decoding phase. The encoding procedure needs a codebook for the encoder, which transforms a compressed image to a set of codeword-indices. The decoding phase also requires a different codebook for the decoder, which enhances a compressed image from a set of codeword-indices. Using CVQ to improve a compressed image's quality is different from the existing algorithm, which cannot reconstruct the high frequency components for compressed images. The experimental results show that the image quality is improved dramatically. For images in the training set, the improvement of PSNR is about 3 dB. For images, which are outside the training set, the improvement of PSNR is about 0.57 dB, which is comparable to the existing method.     

Some long time delay sliding mode control approaches

This paper presents a combined approach of predictive structures with sliding mode control (SMC). Control schemes have been proposed looking for performance and robustness improvement. These structures were designed for processes that can be approximated either by a first order plus time delay or an integral first order plus time delay model broadly used on chemical processes. The proposed schemes were tested for performance and robustness against set point changes and disturbances as compared with classical approaches.     

Modeling the point-spread function in helium-ion lithography

We present here a hybrid approach to modeling helium-ion lithography that combines the power and ease-of-use of the Stopping and Range of Ions in Matter (SRIM) software with the results of recent work simulating secondary electron (SE) yield in helium-ion microscopy. This approach traces along SRIM-produced helium-ion trajectories, generating and simulating trajectories for SEs using a Monte Carlo method. We found, both through simulation and experiment, that the spatial distribution of energy deposition in a resist as a function of radial distance from beam incidence, i.e. the point spread function, is not simply a sum of Gauss functions.     

An e-patient’s End-user community (EUCY): The value added of social network applications

Decreasing revenues and increasing expenses has led many healthcare organizations to adopt newer technological applications in order to address the informational needs of their patients. One such adoption technique is to develop a more robust e-patient environment. Health care organizations may increase their effectiveness in meeting the needs of a growing e-patient population through the implementation of high-quality social networking applications such as Twitter. These applications may help to support and maintain a valuable and informed community. A literature review identifies three characteristics that have an impact on information exchange inherent to social networks: number of members, contact frequency, and type of knowledge. Data from a case study of a juvenile diabetic using Twitter helps to demonstrate these aforementioned characteristics. A framework is developed that may be used by health care organizations to better align social network objectives with expectations of an End user community (EUCY). Managerial implications of this study are discussed that can help information technology professionals as well as health administrators when implementing social networks.     

The grafting of methyl methacrylate onto ultrahigh molecular weight polyethylene fiber by plasma and UV treatment

In this study, ultrahigh molecular weight polyethylene (UHMWPE) fibers were treated with a gas plasma of argon for 5 min. The plasma-treated UHMWPE fibers were put into a methyl methacrylate solution with n-hexane or chloroform as the solvent and irradiated with a UV lamp. The UV irradiation time was changed. The surface topography that had a significant change on the fiber was seen by scanning electron microscopy. Infrared spectra (IR) and electron spectroscopy for chemical analysis (ESCA) were also studied to probe the surface atomic chemistry and to identify the functional groups and their relative changes with treatment conditions. The grafting content was estimated by the titration of esterfication method. It was found that the grafting amount for the system with chloroform as the solvent is larger than that for the system with n-hexane as the solvent. © 1997 John Wiley & Sons, Inc. J Appl Polym Sci 65:365–371, 1997