Cancellable biometrics and user-dependent multi-state discretization in BioHash

Although the use of biometrics for security access is convenient and easy to be implemented, it also introduced privacy and other security concerns when the original biometric templates are compromised. BioHash was introduced as a form of cancellable or replaceable biometrics through the integration of a set of user-specific random numbers with biometric features to address these concerns. However, the main drawback of the original form of BioHash is its inferior performance when an imposter obtains a legitimate token and uses it to claim as a genuine user (also known as the stolen-token scenario). In this paper, the problem is circumvented by a user-dependent multi-state discretization method. The experimental results on fingerprint database FVC2002 demonstrated a promising performance improvement on the stolen-token scenario when this discretization method was incorporated in the BioHash scheme. Moreover, the discretization method can render a long bit string, which is a useful feature to resist brute-force attacks. Some desired properties such as one-way transformation and diversity are also analyzed.     

An efficient dynamic reliability-dependent bit allocation for biometric discretization

A biometric discretization scheme converts biometric features into a binary string via segmenting every one-dimensional feature space into multiple labelled intervals, assigning each interval-captured feature element with a short binary string and concatenating the binary output of all feature elements into a bit string. This paper proposes a bit allocation algorithm for biometric discretization to allocate bits dynamically to every feature element based on a Binary Reflected Gray code. Unlike existing bit allocation schemes, our scheme bases upon a combination of bit statistics (reliability measure) and signal to noise ratio (discriminability measure) in performing feature selection and bit allocation procedures. Several empirical comparative studies are conducted extensively on two popular face datasets to justify the efficiency and feasibility of our proposed approach.     

A direct boosting algorithm for the k-nearest neighbor classifier via local warping of the distance metric

Though the k-nearest neighbor (k-NN) pattern classifier is an effective learning algorithm, it can result in large model sizes. To compensate, a number of variant algorithms have been developed that condense the model size of the k-NN classifier at the expense of accuracy. To increase the accuracy of these condensed models, we present a direct boosting algorithm for the k-NN classifier that creates an ensemble of models with locally modified distance weighting. An empirical study conducted on 10 standard databases from the UCI repository shows that this new Boosted k-NN algorithm has increased generalization accuracy in the majority of the datasets and never performs worse than standard k-NN.     

Study of factors affecting the hardness of ball bonds in copper wire bonding

Copper wire bonding has gained popularity due to its economic advantage and superior electrical performance. However, copper is harder than gold, and replacing gold wire with copper wire introduces hardness related issues. This article reports investigations of the properties including microhardness of the copper balls bonded using ?25.4-μm copper wire and different combinations of electronic-flame-off (EFO) current and firing time settings with forming gas (5%H₂ and 95%N₂) as the inert cover gas. FABs with an identical diameter, obtained under different EFO firing conditions, were ball bonded with the same wire bonding parameters established using design of experiments. Microhardness tests were then performed on the cross-section of the bonded balls. The study revealed that ultrasonic generator current is the most significant factor to increase the bonded mashed ball diameter, ball shear and shear per unit area and to decrease the ball height. The microhardness of bonded copper balls is related to the EFO parameters, with FABs obtained by higher EFO current being softer. The lower hardness is attributed to the higher maximum temperature during the FAB melting state.     

Some examples of nondestructive flaw detection by shearography

This paper presents some examples of nondestructive flaw detection using an optical method based on speckle shearing interferometry called shearography. In the method, a structure under study is illuminated by laser and imaged by a special image-shearing camera. After suitable processing, a fringe pattern which represents loci of surface displacement derivatives, i.e., strains, is observed in the image. Since defects in structures usually induce strain concentrations around them and since strain concentrations usually cause perturbations on the surface of structures, shearography reveals defects from anomalies in the recorded fringe pattern. In this work, the technique has been applied to the nondestructive detection of various flaws in plain and welded pipes, composite plates, and other engineering components. A simple model was also developed for the estimation of the depth of disbonds in glassfiber reinforced plastic sheets. Results obtained are good and demonstrate the usefulness of the method as a complement to other conventional NDT techniques.     

Organic-inorganic hybrids derived from 2-hydroxyethylmethacrylate and (3-methacryloyloxypropyl)trimethoxysilane

Composites based on (3-methacryloyloxypropyl)trimethoxysilane and 2-hydroxyethylmethacrylate have been prepared and their properties are studied. Chemical structure of the materials made by a range of synthetic approaches was defined using nuclear magnetic resonance spectroscopy and gel permeation chromatography. Thermal properties such as glass transition temperature of the systems were determined. The inorganic phase content was measured after the samples were decomposed at high temperatures in the furnace. The small angle X-ray scattering technique was used in an attempt to describe the possible morphologies of the systems. Density was measured using a Helium Pycnometer and hardness was determined using a Vickers hardness tester. Resistance of the surface to an abrasive material was studied by following weight and transmission loss during erosion testing.     

连铸工艺中弯月面控制的发展

在生产效率和板坯质量方面,连铸工艺中的弯月面控制总是十分重要的.为了掌握弯月面控制的发展方面,本文首先概括介绍早期针对生产效率而采用的弯月面控制技术,进而介绍当前为了提高板坯质量而采用的顶部凹槽的弯月面控制技术.80年代,两项关于生产效率的弯月面控制技术发展起来.一项是结晶器状况监测系统,另一项是高速结晶器调宽技术.至于板坯质量方面,最近我们发展了在结晶器周围加上低频交流电磁场的电磁连铸技术(EMC),并且开展了中间实验和连铸生产规模的应用研究.关于EMC技术在弯月面控制的三方面作用得到证实,即结晶器和凝固坯壳之间的软接触,垂直搅拌,焦耳热使初始凝固阶段能适度冷却.     

A Low-Complexity Routing Algorithm with Power Control for Self-Organizing Short-Range Wireless Networks

Mobile computing has become very pervasive, where the number of electronic devices equipped with wireless capabilities has increased significantly in recent years. This poses serious demands on wireless, mobile and self-organizing networks. Despite the fact that devices are getting smaller and more powerful, advances in battery technology have not yet reached the stage where devices can autonomously operate for days. Therefore, devices for self-organizing networks will strongly rely on the efficient use of their batteries. We present a cluster-based low-complexity routing algorithm for self-organizing networks of mobile nodes. Our proposed algorithm, called Cluster-based Energy-saving Routing Algorithm (CERA), allows mobile nodes to autonomously create clusters to minimize the power consumption. CERA is implemented as two separate protocols: the intra-cluster data-dissemination protocol, and the inter-cluster routing protocol. We present an extensive analysis of the overall protocol architecture by varying the critical factors related to protocol behavior. As a result, the CERA implementation generally saves up to 25% of energy, while keeping the overhead, in terms of energy consumption, acceptably low.     

A New Torque and Flux Controller for Direct Torque Control of Induction Machines

This paper presents the implementation of a high-performance direct torque control (DTC) of induction machines drive. DTC has two major problems, namely, high torque ripple and variable switching frequency. In order to solve these problems, this paper proposed a pair of torque and flux controllers to replace the hysteresis-based controllers. The design of these controllers is fully discussed and a set of numerical values of the parameters for the proposed controllers is given. The simulation of the proposed controllers applied to the DTC drive is presented. The simulation results are then verified by experimental results. The hardware implementation is mainly constructed by using DSP TMS320C31 and Altera field-programmable gate array devices. The results prove that a significant torque and stator flux ripples reduction is achieved. Likewise, the switching frequency is fixed at 10.4 kHz and a more sinusoidal phase current is obtained     

Benchmarking a reduced multivariate polynomial pattern classifier

A novel method using a reduced multivariate polynomial model has been developed for biometric decision fusion where simplicity and ease of use could be a concern. However, much to our surprise, the reduced model was found to have good classification accuracy for several commonly used data sets from the Web. In this paper, we extend the single output model to a multiple outputs model to handle multiple class problems. The method is particularly suitable for problems with small number of features and large number of examples. Basic component of this polynomial model boils down to construction of new pattern features which are sums of the original features and combination of these new and original features using power and product terms. A linear regularized least-squares predictor is then built using these constructed features. The number of constructed feature terms varies linearly with the order of the polynomial, instead of having a power law in the case of full multivariate polynomials. The method is simple as it amounts to only a few lines of Matlab code. We perform extensive experiments on this reduced model using 42 data sets. Our results compared remarkably well with best reported results of several commonly used algorithms from the literature. Both the classification accuracy and efficiency aspects are reported for this reduced model.