This paper addresses the problems of fingerprint identification and verification when a query fingerprint is taken under conditions that differ from those under which the fingerprint of the same person stored in a database was constructed. This occurs when using a different fingerprint scanner with a different pressure, resulting in a fingerprint impression that is smeared and distorted in accordance with a geometric transformation (e.g., affine or even non-linear). Minutiae points on a query fingerprint are matched and aligned to those on one of the fingerprints in the database, using a set of absolute invariants constructed from the shape and/or size of minutiae triangles depending on the assumed map. Once the best candidate match is declared and the corresponding minutiae points are flagged, the query fingerprint image is warped against the candidate fingerprint image in accordance with the estimated warping map. An identification/verification cost function using a combination of distance map and global directional filterbank (DFB) features is then utilized to verify and identify a query fingerprint against candidate fingerprint(s). Performance of the algorithm yields an area of 0.99967 (perfect classification is a value of 1) under the receiver operating characteristic (ROC) curve based on a database consisting of a total of 1680 fingerprint images captured from 240 fingers. The average probability of error was found to be 0.713%. Our algorithm also yields the smallest false non-match rate (FNMR) for a comparable false match rate (FMR) when compared to the well-known technique of DFB features and triangulation-based matching integrated with modeling non-linear deformation. This work represents an advance in resolving the fingerprint identification problem beyond the state-of-the-art approaches in both performance and robustness.

A third-order low-distortion delta-sigma modulator (DSM), whose third-order noise-shaping ability is achieved by just a single opamp, is proposed. Since only one amplifier is required in the whole circuit, the designed DSM is very power efficient. To realize the adder in front of quantizer without employing the huge-power opamp, a capacitive passive adder, which is the digital-to-analog converter (DAC) array of a successive-approximation-type quantizer, is used. In addition, the feedback path timing is extended from a nonoverlapping interval for the conventional low-distortion structure to half of the clock period, so that the strict operation timing issue with regard to quantization and the dynamic element matching (DEM) logic operation can be solved. In the proposed DSM structure, the features of the unity-gain signal transfer function (STF) and finite-impulse-response (FIR) noise transfer function (NTF) are still preserved, and thus advantages such as a relaxed opamp slew rate and reduced output swing are also maintained, as with the conventional low-distortion DSM. Moreover, the memory effect in the proposed DSM is analyzed when employing the opamp sharing for integrators. The proposed third-order DSM with a 4-bit SAR ADC as the quantizer is implemented in a 90-nm CMOS process. The post-layout simulations show a 79.8-dB signal-to-noise and distortion ratio (SNDR) in the 1.875-MHz signal bandwidth (OSR=16). The active area of the circuit is 0.35mm2 and total power consumption is 2.85mW, resulting in a figure of merit (FOM) of 95 fJ/conversion-step.

Artificial blurring is a typical operation in image forging. Most existing image forgery detection methods consider only one single feature of artificial blurring operation. In this manuscript, we propose to adopt feature fusion, with multifeatures for artificial blurring operation in image tampering, to improve the accuracy of forgery detection. First, three feature vectors that address the singular values of the gray image matrix, correlation coefficients for double blurring operation, and image quality metrics (IQM) are extracted and fused using principal component analysis (PCA), and then a support vector machine (SVM) classifier is trained using the fused feature extracted from training images or image patches containing artificial blurring operations. Finally, the same procedures of feature extraction and feature fusion are carried out on the suspected image or suspected image patch which is then classified, using the trained SVM, into forged or non-forged classes. Experimental results show the feasibility of the proposed method for image tampering feature fusion and forgery detection.

In phrase-based statistical machine translation, long distance reordering problem is one of the most challenging issues when translating syntactically distant language pairs. In this paper, we propose a novel reordering model to solve this problem. In our model, reordering is affected by the overall structures of sentences such as listings, reduplications, and modifications as well as the relationships of adjacent phrases. To this end, we reflect global syntactic contexts including the parts that are not yet translated during the decoding process.

Massive digital elevation models require a large number of geometric primitives that exceed the throughput of the existing graphics hardware. For the interactive visualization of these datasets, several adaptive reconstruction methods that reduce the number of primitives have been introduced over the decades. Quadtree triangulation, based on subdivision of the terrain into rectangular patches at different resolutions, is the most frequently used terrain reconstruction method. This usually accomplishes the triangulation using LOD (level-of-detail) selection and crack removal based on geometric errors. In this paper, we present bimodal vertex splitting, which performs LOD selection and crack removal concurrently on a GPU. The first mode splits each vertex for LOD selection and the second splits each vertex for crack removal. By performing these two operations concurrently on a GPU, we can efficiently accelerate the rendering speed by reducing the computation time and amount of transmission data in comparison with existing quadtree-based rendering methods.

A 10-bit digital-to-analog converter (DAC) with a small area is proposed for data-driver integrated circuits of active-matrix liquid crystal display systems. The 10-bit DAC consists of a 7-bit resistor string, a 7-bit two-step decoder, a 2-bit logarithmic time interpolator, and a buffer amplifier. The proposed logarithmic time interpolation is achieved by controlling the charging time of a first-order low-pass filter composed of a resistor and a capacitor. The 7-bit two-step decoder that follows the 7-bit resistor string outputs an analog signal of the stepped wave with two voltage levels using the additional 1-bit digital code for the logarithmic time interpolation. The proposed 10-bit DAC is implemented using a 0.35-µm CMOS process and its supply voltage is scalable from 3.3V to 5.0V. The area of the proposed 10-bit logarithmic time interpolation DAC occupies 57% of that of the conventional 10-bit resistor-string DAC. The DNL and INL of the implemented 10-bit DAC are +0.29/-0.30 and +0.47/-0.36 LSB, respectively.

This letter proposes an adaptive base plane filtering algorithm for the inter-plane estimation of RGB images in HEVC RExt. Because most high-frequency components of RGB images have low inter-plane correlation, our proposed scheme adaptively removes the high-frequency components of the base plane in order to enhance the inter-plane estimation accuracy. The experimental results show that the proposed scheme provides average BD rate gains of 0.6%, 1.0%, and 1.2% in the G, B, and R planes, respectively, with slightly decreased complexity, as compared to the previous inter-plane filtering method.

To improve metal oxide semiconductor field effect transistors (MOSFET) performance, flat interface between gate insulator and silicon (Si) should be realized. In this paper, the influence of Si surface roughness on electrical characteristics of MOSFET with hafnium oxynitride (HfON) gate insulator formed by electron cyclotron resonance (ECR) plasma sputtering was investigated for the first time. The surface roughness of Si substrate was reduced by Ar/4.9%H2 annealing utilizing conventional rapid thermal annealing (RTA) system. The obtained root-mean-square (RMS) roughness was 0.07nm (without annealed: 0.18nm). The HfON was formed by 2nm-thick HfN deposition followed by the Ar/O2 plasma oxidation. The electrical properties of HfON gate insulator were improved by reducing Si surface roughness. It was found that the current drivability of fabricated nMOSFETs was remarkably increased by reducing Si surface roughness. Furthermore, the reduction of Si surface roughness also leads to decrease of the 1/f noise.

Although code clones (i.e. code fragments that have similar or identical code fragments in the source code) are regarded as a factor that increases the complexity of software maintenance, tools for supporting clone refactoring (i.e. merging a set of code clones into a single method or function) are not commonly used. To promote the development of refactoring tools that can be more widely utilized, we present an investigation of clone refactoring carried out in the development of open source software systems. In the investigation, we identified the most frequently used refactoring patterns and discovered how merged code clone token sequences and differences in token sequence lengths vary for each refactoring pattern.

For the past couple of decades, the usage of the Web as a platform for deploying software products has become incredibly popular. Web applications became more prevalent, as well as more complex. Countless Web applications have already been designed, developed, tested, and deployed on the Internet. However, it is noticeable that many common functionalities are present among these vast number of applications. This paper proposes an approach based on a database containing information from previous test artifacts. The information is used to generate test scenarios for Web applications under test. We have developed a tool based on our proposed approach, with the aim of reducing the effort required from software test engineers and professionals during the test planning and creation stage of software engineering. We evaluated our approach from three viewpoints: comparison between our approach and manual generation, qualitative evaluation by professional software engineers, and comparison between our approach and two open-source tools.

This letter presents a new entropy measure for electroencephalograms (EEGs), which reflects the underlying dynamics of EEG over multiple time scales. The motivation behind this study is that neurological signals such as EEG possess distinct dynamics over different spectral modes. To deal with the nonlinear and nonstationary nature of EEG, the recently developed empirical mode decomposition (EMD) is incorporated, allowing an EEG to be decomposed into its inherent spectral components, referred to as intrinsic mode functions (IMFs). By calculating Shannon entropy of IMFs in a time-dependent manner and summing them over adaptive multiple scales, the result is an adaptive subscale entropy measure of EEG. Simulation and experimental results show that the proposed entropy properly reveals the dynamical changes over multiple scales.

Due to the recent technical advances, GPUs are used for general applications as well as screen display. Many research results have been proposed to the performance of previous CPU-based algorithms by a few hundred times using the GPUs. In this paper, we propose a density-based clustering algorithm called GSCAN, which reduces the number of unnecessary distance computations using a grid structure. As a result of our experiments, GSCAN outperformed CUDA-DClust [2] and DBSCAN [3] by up to 13.9 and 32.6 times, respectively.

This paper proposes a visualization system for supporting the task of monitoring bug update information. Recent growth of the Web has brought us various kinds of text stream data, such as bulletin board systems (BBS), blogs, and social networking services (SNS). Bug update information managed by bug tracking systems (BTS) is also a kind of text stream data. As such text stream data continuously generates new data, it is difficult for users to watch it all the time. Therefore, the task of monitoring text stream data inevitably involves breaks of monitoring, which would cause users to lose the context of monitoring. In order to support such a monitoring task involving breaks, the proposed system employs several visualization techniques. The dynamic relationship between bugs is visualized with animation, and a function of highlighting updated bugs as well as that of replaying a part of the last monitoring time is also proposed in order to help a user grasping the context of monitoring. The result of experiment with test participants shows that highlighting and replay functions can reduce frequency of checking data and monitoring time.

Manifold learning based image clustering models are usually employed at local level to deal with images sampled from nonlinear manifold. Multimode patterns in image data matrices can vary from nominal to significant due to images with different expressions, pose, illumination, or occlusion variations. We show that manifold learning based image clustering models are unable to achieve well separated images at local level for image datasets with significant multimode data patterns. Because gray level image features used in these clustering models are not able to capture the local neighborhood structure effectively for multimode image datasets. In this study, we use nearest neighborhood quality (NNQ) measure based criterion to improve local neighborhood structure in terms of correct nearest neighbors of images locally. We found Gist as the optimal image descriptor among HOG, Gist, SUN, SURF, and TED image descriptors based on an overall maximum NNQ measure on 10 benchmark image datasets. We observed significant performance improvement for recently reported clustering models such as Spectral Embedded Clustering (SEC) and Nonnegative Spectral Clustering with Discriminative Regularization (NSDR) using proposed approach. Experimentally, significant overall performance improvement of 10.5% (clustering accuracy) and 9.2% (normalized mutual information) on 13 benchmark image datasets is observed for SEC and NSDR clustering models. Further, overall computational cost of SEC model is reduced to 19% and clustering performance for challenging outdoor natural image databases is significantly improved by using proposed NNQ measure based optimal image representations.

With the ever-growing prevalence of web 2.0, users can access information and resources easily and ubiquitously. It becomes increasingly important to understand the characteristics of user's complex behavior for efficient network management and security monitoring. In this paper, we develop a novel method to visualize and measure user's web-communication-behavior character in large-scale networks. First, we employ the active and passive monitoring methods to collect more than 20,000 IP addresses providing web services, which are divided into 12 types according to the content they provide, e.g. News, music, movie and etc, and then the IP address library is established with elements as (servicetype, IPaddress). User's behaviors are complex as they stay in multiple service types during any specific time period, we propose the behavior spectrum to model this kind of behavior characteristics in an easily understandable way. Secondly, two kinds of user's behavior characters are analyzed: the character at particular time instants and the dynamic changing characters among continuous time points. We then employ Renyi cross entropy to classify the users into different groups with the expectation that users in the same groups have similar behavior profiles. Finally, we demonstrated the application of behavior spectrum in profiling network traffic patterns and finding illegal users. The efficiency and correctness of the proposed methods are verified by the experimental results using the actual traffic traces collected from the Northwest Regional Center of China Education and Research Network (CERNET).

We propose a new Bloom filter that efficiently filters out non-members. With extra bits assigned and asymmetrically distributed, the new filter reduces hash computations and memory accesses. For an error rate of 10-6, the new filter reduces cost by 31.31% with 4.33% additional space, while the standard method saves offers a 20.42% reduction.

Cognitive radio (CR) systems aim for more efficient spectrum utilization by having so called secondary users (SUs) transmit on a frequency band reserved for licensed primary users (PUs). The secondary transmissions are allowed provided that no harmful interference will be caused to the PUs. SU terminals with multiple antennas can employ transmit power control with transmit precoding in order to control the interference levels. In most of the existing works, perfect channel state information (CSI) is assumed to be available for the SUs. However, in practical systems where perfect CSI is not available, the SUs are not able to guarantee that the interference constraints are sufficiently satisfied. In this paper, we investigate the problem of spectrum sharing for multiantenna CR systems using estimated CSI. Due to the random nature of the estimation error, we set a probabilistic interference constraint and, in order to satisfy it, provide a density function for the interference power. In addition, we present a power control framework for the SU to meet the probabilistic interference constraint.

This paper presents a new technique to enhance the bandwidth of a printed dipole antenna for ultra-wideband applications. The basic idea is to exploit mutual coupling between the feeding line, which is designed closed and paralleled to dipole arms, the dipole arms and other elements of the antenna. Dipole arms, feeding lines as well as other parts are investigated in order to expand antenna bandwidth while still retaining antenna compactness. Based on the proposed technique, we develop two sample printed dipole antennas for advanced wireless communications. One is an ultra-wideband antenna which is suitable for multi-band-mode ultra-wideband applications or being a sensing antenna in cognitive radio. The other is a reconfigurable antenna which would be applicable for wideband cognitive radios. Antenna characteristics such as radiation patterns, current distributions, and gains at different frequencies are also investigated for both sample antennas.

This paper derives a maximum lock range of an injection locked ring oscillator in a direct injection method and presents an injection locked charge-pump phase-locked loop (CPPLL) with a replica of a ring oscillator. The proposed injection-locked PLL separates the injection-locked VCO from the continuous phase-tracking loop of the PLL such that can provide stable lock-state maintenance and tolerance to temperature and supply voltage variation. The measurement results show that the proposed injection-locked PLL can be tolerable to voltage variation of 11.2% in supply voltage of 1.2V. In-band noises of the injection-locked oscillator at offset frequencies of 10kHz and 100kHz are -108.2dBc/Hz and -114.6dBc/Hz, respectively.

A high-speed and low-power 8-bit subranging analog-to-digital converter (ADC) based on 65-nm CMOS technology was fabricated. Rather than using digital foreground calibration, an analog-centric approach was adopted to reduce power dissipation. An offset cancelling charge-steering amplifier and capacitive-averaging technique effectively reduce the offset, noise, and power dissipation of the ADC. Moreover, the circuit used to compensate the kickback noise current from the comparator can also reduce the power dissipation. The reference-voltage generator for the fine ADC is composed of a fine ladder and a capacitor providing an AC signal path. This configuration reduces the power dissipation of the selection signal drivers for the analog multiplexer. A test chip fabricated using 65-nm digital CMOS technology achieved a high sampling rate of 1GHz, a low power dissipation of 17.5mW, and a figure of merit of 118fJ/conv.-step.