Hydrothermal synthesis of CdTe QDs: Their luminescence quenching in the presence of bio-molecules and observation of bistable memory effect in CdTe QD/PEDOT:PSS heterostructure

We report one-pot hydrothermal synthesis of nearly mono-disperse 3-mercaptopropionic acid capped water-soluble cadmium telluride (CdTe) quantum dots (QDs) using an air stable Te source. The optical and electrical characteristics were also studied here. It was shown that the hydrothermal synthesis could be tuned to synthesize nano structures of uniform size close to nanometers. The emissions of the CdTe QDs thus synthesized were in the range of 500-700 nm by varying the duration of synthesis. The full width at half maximum (FWHM) of the emission peaks is relatively narrow (40-90 nm), which indicates a nearly uniform distribution of QD size. The structural and optical properties of the QDs were characterized by transmission electron microscopy (TEM), photoluminescence (PL) and Ultraviolet-visible (UV-Vis) spectroscopy. The photoluminescence quenching of CdTe QDs in the presence of L-cysteine and DNA confirms its biocompatibility and its utility for biosensing applications. The room temperature current-voltage characteristics of QD film on ITO coated glass substrate show an electrically induced switching between states with high and low conductivities. The phenomenon is explained on the basis of charge confinement in quantum dots. (C) 2011 Elsevier B.V. All rights reserved.

High-temperature deformation processing maps for a NiTiCu shape memory alloy

The properties of widely used Ni-Ti-based shape memory alloys (SMAs) are highly sensitive to the underlying microstructure. Hence, controlling the evolution of microstructure during high-temperature deformation becomes important. In this article, the ``processing maps'' approach is utilized to identify the combination of temperature and strain rate for thermomechanical processing of a Ni(42)Ti(50)Cu(8) SMA. Uniaxial compression experiments were conducted in the temperature range of 800-1050 degrees C and at strain rate range of 10(-3) and 10(2) s(-1). Two-dimensional power dissipation efficiency and instability maps have been generated and various deformation mechanisms, which operate in different temperature and strain rate regimes, were identified with the aid of the maps and complementary microstructural analysis of the deformed specimens. Results show that the safe window for industrial processing of this alloy is in the range of 800-850 degrees C and at 0.1 s(-1), which leads to grain refinement and strain-free grains. Regions of the instability were identified, which result in strained microstructure, which in turn can affect the performance of the SMA.

A Profilometric Approach to 3D Face Reconstruction and Its Application to Face Recognition

3D Face Recognition is an active area of research for past several years. For a 3D face recognition system one would like to have an accurate as well as low cost setup for constructing 3D face model. In this paper, we use Profilometry approach to obtain a 3D face model.This method gives a low cost solution to the problem of acquiring 3D data and the 3D face models generated by this method are sufficiently accurate. We also develop an algorithm that can use the 3D face model generated by the above method for the recognition purpose.

Scalable processes for fabricating non-volatile memory devices using self-assembled 2D arrays of gold nanoparticles as charge storage nodes

We propose robust and scalable processes for the fabrication of floating gate devices using ordered arrays of 7 nm size gold nanoparticles as charge storage nodes. The proposed strategy can be readily adapted for fabricating next generation (sub-20 nm node) non-volatile memory devices.

Interference in Multiprocessor Systems with Localized Memory Access Probabilities

Past studies of memory interference in multiprocessor systems have generally assumed that the references of each processor are uniformly distributed among the memory modules. In this paper we develop a model with local referencing, which reflects more closely the behavior of real-life programs. This model is analyzed using Markov chain techniques and expressions are derived for the multiprocessor performance. New expressions are also obtained for the performance in the traditional uniform reference model and are compared with other expressions-available in the literature. Results of a simulation study are given to show the accuracy of the expressions for both models.

Unrestricted Kannada online handwritten akshara recognition using SDTW

In this paper, we present an unrestricted Kannada online handwritten character recognizer which is viable for real time applications. It handles Kannada and Indo-Arabic numerals, punctuation marks and special symbols like $, &, # etc, apart from all the aksharas of the Kannada script. The dataset used has handwriting of 69 people from four different locations, making the recognition writer independent. It was found that for the DTW classifier, using smoothed first derivatives as features, enhanced the performance to 89% as compared to preprocessed co-ordinates which gave 85%, but was too inefficient in terms of time. To overcome this, we used Statistical Dynamic Time Warping (SDTW) and achieved 46 times faster classification with comparable accuracy i.e. 88%, making it fast enough for practical applications. The accuracies reported are raw symbol recognition results from the classifier. Thus, there is good scope of improvement in actual applications. Where domain constraints such as fixed vocabulary, language models and post processing can be employed. A working demo is also available on tablet PC for recognition of Kannada words.

Comparison of HMM and SDTW for Tamil handwritten character recognition

In this paper, we compare the experimental results for Tamil online handwritten character recognition using HMM and Statistical Dynamic Time Warping (SDTW) as classifiers. HMM was used for a 156-class problem. Different feature sets and values for the HMM states & mixtures were tried and the best combination was found to be 16 states & 14 mixtures, giving an accuracy of 85%. The features used in this combination were retained and a SDTW model with 20 states and single Gaussian was used as classifier. Also, the symbol set was increased to include numerals, punctuation marks and special symbols like $, & and #, taking the number of classes to 188. It was found that, with a small addition to the feature set, this simple SDTW classifier performed on par with the more complicated HMM model, giving an accuracy of 84%. Mixture density estimation computations was reduced by 11 times. The recognition is writer independent, as the dataset used is quite large, with a variety of handwriting styles.

Variable Granularity Access Tracking Scheme for Improving the Performance of Software Transactional Memory

Software transactional memory (STM) has been proposed as a promising programming paradigm for shared memory multi-threaded programs as an alternative to conventional lock based synchronization primitives. Typical STM implementations employ a conflict detection scheme, which works with uniform access granularity, tracking shared data accesses either at word/cache line or at object level. It is well known that a single fixed access tracking granularity cannot meet the conflicting goals of reducing false conflicts without impacting concurrency adversely. A fine grained granularity while improving concurrency can have an adverse impact on performance due to lock aliasing, lock validation overheads, and additional cache pressure. On the other hand, a coarse grained granularity can impact performance due to reduced concurrency. Thus, in general, a fixed or uniform granularity access tracking (UGAT) scheme is application-unaware and rarely matches the access patterns of individual application or parts of an application, leading to sub-optimal performance for different parts of the application(s). In order to mitigate the disadvantages associated with UGAT scheme, we propose a Variable Granularity Access Tracking (VGAT) scheme in this paper. We propose a compiler based approach wherein the compiler uses inter-procedural whole program static analysis to select the access tracking granularity for different shared data structures of the application based on the application's data access pattern. We describe our prototype VGAT scheme, using TL2 as our STM implementation. Our experimental results reveal that VGAT-STM scheme can improve the application performance of STAMP benchmarks from 1.87% to up to 21.2%.

Stress Evolution With Temperature in Titanium-Rich NiTi Shape Memory Alloy Films

The effect of deposition temperature on residual stress evolution with temperature in Ti-rich NiTi films deposited on silicon substrates was studied. Ti-rich NiTi films were deposited on 3? Si (100) substrates by DC magnetron sputtering at three deposition temperatures (300, 350 and 400 degrees C) with subsequent annealing in vacuum at their respective deposition temperatures for 4 h. The initial value of residual stress was found to be the highest for the film deposited and annealed at 400 degrees C and the lowest for the film deposited and annealed at 300 degrees C. All the three films were found to be amorphous in the as-deposited and annealed conditions. The nature of the stress response with temperature on heating in the first cycle (room temperature to 450 degrees C) was similar for all three films although the spike in tensile stress, which occurs at similar to 330 degrees C, was significantly higher in the film deposited and annealed at 300 degrees C. All the films were also found to undergo partial crystallisation on heating up to 450 degrees C and this resulted in decrease in the stress values around 5560 degrees C in the cooling cycle. The stress response with temperature in the second thermal cycle (room temperature to 450 degrees C and back), which is reflective of the intrinsic film behaviour, was found to be similar in all cases and the elastic modulus determined from the stress response was also more or less identical. The three deposition temperatures were also not found to have a significant effect on the transformation characteristics of these films such as transformation start and finish temperatures, recovery stress and hysteresis.

Self assembly of bivalent glycolipids on single walled carbon nanotubes and their specific molecular recognition properties

Solubilization of single walled carbon nanotubes (SWNTs) in aqueous milieu by self assembly of bivalent glycolipids is described. Thorough analysis of the resulting composites involving Vis/near-IR spectroscopy, surface plasmon resonance, confocal Raman and atomic force microscopy reveals that glycolipid-coated SWNTs possess specific molecular recognition properties towards lectins.

Prototype learning methods for online handwriting recognition

In this paper, we study different methods for prototype selection for recognizing handwritten characters of Tamil script. In the first method, cumulative pairwise- distances of the training samples of a given class are used to select prototypes. In the second method, cumulative distance to allographs of different orientation is used as a criterion to decide if the sample is representative of the group. The latter method is presumed to offset the possible orientation effect. This method still uses fixed number of prototypes for each of the classes. Finally, a prototype set growing algorithm is proposed, with a view to better model the differences in complexity of different character classes. The proposed algorithms are tested and compared for both writer independent and writer adaptation scenarios.

Automatic Recognition of Facial Features and Landmarking of Digital Human Head

Ergonomic design of products demands accurate human dimensions-anthropometric data. Manual measurement over live subjects, has several limitations like long time, required presence of subjects for every new measurement, physical contact etc. Hence the data currently available is limited and anthropometric data related to facial features is difficult to obtain. In this paper, we discuss a methodology to automatically detect facial features and landmarks from scanned human head models. Segmentation of face into meaningful patches corresponding to facial features is achieved by Watershed algorithms and Mathematical Morphology tools. Many Important physiognomical landmarks are identified heuristically.

Time Series Classification for Online Tamil Handwritten Character Recognition – A Kernel Based Approach

In this paper, we consider the problem of time series classification. Using piecewise linear interpolation various novel kernels are obtained which can be used with Support vector machines for designing classifiers capable of deciding the class of a given time series. The approach is general and is applicable in many scenarios. We apply the method to the task of Online Tamil handwritten character recognition with promising results.