Privacy-preserving biometric verification with outsourced correlation filter computation

Multimedia Tools and Applications - In traditional biometric verification systems, personal computer stores biometric database and performs verification process. Because of limited storage,...     

Enhancing level set brain tumor segmentation using fuzzy shape prior information and deep learning

Magnetic resonance imaging (MRI) brain tumor segmentation is a crucial task for clinical treatment. However, it is challenging owing to variations in type, size, and location of tumors. In addition, anatomical variation in individuals, intensity non-uniformity, and noises adversely affect brain tumor segmentation. To address these challenges, an automatic region-based brain tumor segmentation approach is presented in this paper which combines fuzzy shape prior term and deep learning. We define a new energy function in which an Adaptively Regularized Kernel-Based Fuzzy C-Means (ARKFCM) Clustering algorithm is utilized for inferring the shape of the tumor to be embedded into the level set method. In this way, some shortcomings of traditional level set methods such as contour leakage and shrinkage have been eliminated. Moreover, a fully automated method is achieved by using U-Net to obtain the initial contour, reducing sensitivity to initial contour selection. The proposed method is validated on the BraTS 2017 benchmark dataset for brain tumor segmentation. Average values of Dice, Jaccard, Sensitivity and specificity are 0.93 ± 0.03, 0.86 ± 0.06, 0.95 ± 0.04, and 0.99 ± 0.003, respectively. Experimental results indicate that the proposed method outperforms the other state-of-the-art methods in brain tumor segmentation.     

High-Frequency Shear Modulus and Relaxation Time of Soft-Sphere and Lennard–Jones Fluids

The high-frequency shear modulus, G, and shear relaxation time, _shear, are obtained using the Zwanzig–Mountain equation for soft-sphere and Lennard-Jones potentials. The Hansen and Weis soft-sphere radial distribution function and the Matteoli–Mansoori Lennard-Jones radial distribution function are used in the equation. The shear relaxation times of different isotherms for both of these fluids pass through a minimum at a reduced density of about 0.7, which indicates a change from fluid-like behavior to viscoelastic behavior. The origins of this common density point are discussed. It is also shown that for the Lennard-Jones fluid, if the ratio of the reduced relaxation time to a power of the reduced temperature is plotted as a function of the reduced density, all isotherms become superimposed on a single curve.     

Highly efficient dye-sensitized solar cell prepared by electrophoretic deposition method: the effect of TiO<Subscript>2</Subscript> films thickness on the performance of cells

In this work, nanocrystalline P25 TiO₂ films with different thicknesses were deposited on FTO coated glass substrates by an electrophoretic deposition technique (EPD) and applied as the work electrode for dye-sensitized solar cells (DSSC) using cis-bis(isothiocyanato)(2,2'-bipyridyl-4,4'-dicarboxylato)(4,4'di-nonyl-2'-bipyridyl) ruthenium(II) (Z907, Dyesol) as sensitizing dye.The results showed that the increasing the thickness of TiO₂ films lead to increase the adsorption of the dye on the TiO₂ layers which in turns improved the short-circuit photocurrent (J_sc) and open-circuit voltage (V_oc), respectively. Furthermore, it was found that the effects of the surface states on the recombination of the photo-injected electrons (electron–hole pairs) in the TiO₂ films strongly depend on theTiO₂ electrode annealing temperature. Finally, a DSSC with a 32.82 μm thickness for TiO₂ film annealed at 600°C produced the highest conversion efficiency with an incident solar energy of 100 mW/cm² (η = 8.23%, J_sc = 15.98, V_oc = 0.73, FF = 0.7).     

A statistical model for extracting geometric sources of transistor performance variation

This paper provides an approach to extracting geometrical variations in nominally identical devices fabricated in close proximity to each other. The method assumes correlations between factors can be neglected. With this assumption, it is shown that geometrical variations are significant, and any study of microscopic variations, such as dopant fluctuations, must first strip away these macroscopic geometrical variations. We assess the gate length L and width W dependence of threshold voltage (V/sub T/) variations. Related geometrical variations, namely corner influences where the source and drain encounter the isolation edges are examined, and incorporated in the model. Results from the model are compared to measurements at small dimensions. The differences provide lower bounds for excess variations other than these geometrical contributions. Our study shows that these other variations account for almost half the total V/sub T/ variance at the smallest device size fabricated, demonstrating the seriousness of these other variations when scaling down devices.     

Analysis of dual-VT SRAM cells with full-swingsingle-ended bit line sensing for on-chip cache

This paper compares different high-V_T and dual-V_T design choices for a large on-chip cache with single-ended sensing in a 0.13 μm technology generation. The analysis shows that the best design is the one using a dual-V_T cell, with minimum channel length pass transistors, and low-V_T peripheral circuits. This dual-V_T circuit provides 20% performance gain with only 1.3× larger active leakage power, and 2.4% larger cell area compared to the best design using high-V_T cells with nonminimum channel length pass transistors     

Efficient cluster head selection using Naïve Bayes classifier for wireless sensor networks

Data mining and approaches based on it have always been of approaches that have been considered in solving problems in the field of computer, but on some issues, this approach has been neglected. The area of wireless sensor networks and specifically the issue of optimal determining of the cluster head node are of these issues. To solve the problem of optimal determining of the cluster head node, Naïve Bayes that is the subset of data mining techniques is used in this paper. The results obtained after simulation of the presented algorithm show that the efficiency of this algorithm is significantly higher compared with other approaches that have so far been used to solve this problem, and thus it can be said that using this algorithm will lead to improved outcomes of solving this problem.     

Rural domestic water consumption behavior: A case study in Ramjerd area, Fars province, I.R. Iran

Identifying the factors that affect domestic water demand and consumption is very important in management of available regional water resources. In this study, relationships between water consumption and rural household activities are determined by comparing a snapshot of water consumption with rural household behavior of low, medium and high water consumers. In addition, the factors affecting water consumption in rural households are also determined. The data for this study were collected from a survey of 653 rural households in 33 villages of Ramjerd area, Fars Province, in southern Iran, using a simple random sampling technique. The daily water consumption data for a 5-year period (1999-2004) were used. Results of the study revealed that the daily average water consumption for the area was found to be 121.7 l per person per capita per day (Lpcd) (SD=59.2). Water consumption was also found to be significantly correlated with explanatory variables such as “household size” and “age of household's head”. Finally, the results of discriminant function analysis showed that in rural households, garden size, greenhouse size, and garden watering times per month with tap treated water are associated with water consumption.     

Assessment of MOCVD- and MBE-growth GaAs for high-efficiency solarcell applications

A critical assessment of the photovoltaic quality of epitaxial GaAs grown by metal-organic chemical vapor deposition (MOCVD) and by molecular-beam epitaxy (MBE) is reported. Epitaxial films of nominally identical structure were grown by the two techniques and were fabricated into p-n heteroface solar cells. The 0.5-cm by 0.5-cm cells were then characterized and compared. The MOCVD-grown films produced independently verified record efficiency cells of 24.8% under 1-sun AM1.5 global conditions while the MB-grown films produced similarly high efficiencies of 23.8%. The material qualities of the two films in terms of minority-carrier diffusion lengths and surface recombination velocities were quite comparable. These results demonstrate that the quality of MBE-grown films can be quite comparable to the best MOCVD-grown films and that they are suitable for high-efficiency solar cells     

Forward body bias for microprocessors in 130-nm technology generation and beyond

Device and test chip measurements show that forward body bias (FBB) can be used effectively to improve performance and reduce complexity of a 130-nm dual-V/sub T/ technology, reduce leakage power during burn-in and standby, improve circuit delay and robustness, and reduce active power. FBB allows performance advantages of low-temperature operation to be realized fully without requiring transistor redesign, and also improves V/sub T/ variations, mismatch, and saturation transconductance and output resistance product (g/sub m//spl times/r/sub o/).