In this paper, we propose a floorplan aware high-level synthesis algorithm with body biasing for delay variation compensation, which minimizes the average leakage energy of manufactured chips. In order to realize floorplan-aware high-level synthesis, we utilize huddle-based distributed register architecture (HDR architecture). HDR architecture divides the chip area into small partitions called a huddle and we can control a body bias voltage for every huddle. During high-level synthesis, we iteratively obtain expected leakage energy for every huddle when applying a body bias voltage. A huddle with smaller expected leakage energy contributes to reducing expected leakage energy of the entire circuit more but can increase the latency. We assign control-data flow graph (CDFG) nodes in non-critical paths to the huddles with larger expected leakage energy and those in critical paths to the huddles with smaller expected leakage energy. We expect to minimize the entire leakage energy in a manufactured chip without increasing its latency. Experimental results show that our algorithm reduces the average leakage energy by up to 39.7% without latency and yield degradation compared with typical-case design with body biasing.

In this paper, an algorithm is proposed for license plate recognition (LPR) in video traffic surveillance applications. In an LPR system, the primary steps are license plate detection and character segmentation. However, in practice, false alarms often occur due to images of vehicle parts that are similar in appearance to a license plate or detection rate degradation due to local illumination changes. To alleviate these difficulties, the proposed license plate segmentation employs an adaptive binarization using a superpixel-based local contrast measurement. From the binarization, we apply a set of rules to a sequence of characters in a sub-image region to determine whether it is part of a license plate. This process is effective in reducing false alarms and improving detection rates. Our experimental results demonstrate a significant improvement over conventional methods.

Narrow swipe sensor has been widely used in embedded systems such as smart-phone. However, the size of captured image is much smaller than that obtained by the traditional area sensor. Therefore, the limited template coverage is the performance bottleneck of such kind of systems. Aiming to increase the geometry coverage of templates, a novel fingerprint template feature synthesis scheme is proposed in the present study. This method could synthesis multiple input fingerprints into a wider template by clustering the minutiae descriptors. The proposed method consists of two modules. Firstly, a user behavior-based Registration Pattern Inspection (RPI) algorithm is proposed to select the qualified candidates. Secondly, an iterative clustering algorithm Modified Fuzzy C-Means (MFCM) is proposed to process the large amount of minutiae descriptors and then generate the final template. Experiments conducted over swipe fingerprint database validate that this innovative method gives rise to significant improvements in reducing FRR (False Reject Rate) and EER (Equal Error Rate).

Nowadays, vector map content is widely used in the areas of life, science and the military. Due to the fact that vector maps bring great value and that their production process is expensive, a large volume of vector map data is attacked, stolen and illegally distributed by pirates. Thus, vector map data must be encrypted before being stored and transmitted in order to ensure the access and to prevent illegal copying. This paper presents a novel perceptual encryption algorithm for ensuring the secured storage and transmission of vector map data. Polyline data of vector maps are extracted to interpolate a spline curve, which is represented by an interpolating vector, the curvature degree coefficients, and control points. The proposed algorithm is based on encrypting the control points of the spline curve in the frequency domain of discrete cosine transform. Control points are transformed and selectively encrypted in the frequency domain of discrete cosine transform. They are then used in an inverse interpolation to generate the encrypted vector map. Experimental results show that the entire vector map is altered after the encryption process, and the proposed algorithm is very effective for a large dataset of vector maps.

In this paper, a miniaturized absorptive/transmissive radome with switchable passband and wide absorbing band is designed. Pin diodes are loaded on the radome in order to obtain switchable passband and miniaturized unit cells, while the resistor loaded double square loops are used to absorb the incident wave. The total thickness of the radome is only 4.5mm. Its transmission and absorbing properties are verified by both synthetic experiments and measurements in the anechoic chamber. Furthermore, the switchable passband of the radome is also evaluated using a waveguide simulator.

In this paper, the virtual antenna technique is applied to a single input multiple output (SIMO) radar system to enhance the performance of the conventional beamforming direction of arrival (DOA) estimation method. Combining the virtual array generated by the interpolated array technique and the real array, the angular resolution of the DOA estimation algorithm is improved owing to the extended number of antennas and aperture size. Based on the proposed interpolation technique, we transform the position of the antenna elements in a uniform linear array (ULA) to the arbitrary positions to suppress the grating lobe and side lobe levels. In simulations, the pseudo spectrum of the Bartlett algorithm and the root mean square error (RMSE) of the DOA estimation with the signal-to-noise ratio (SNR) are analyzed for the real array and the proposed virtually extended array. Simulation results show that the angular resolution of the proposed array is better than that of the real array using the same aperture size of array and the number of antennas. The proposed technique is verified with the practical data from commercialized radar system.

This paper proposes an effective continuous super-resolution (CSR) algorithm for the multipath channel estimation. By designing a preamble including up-chirp and down-chirp symbols, the Doppler shift and multipath delay are estimated jointly by using convex programming. Simulation results show that the proposed CSR can achieve better detection probability of the number of multipaths than the eigenvalue based methods. Moreover, compared with conventional super-resolution techniques, such as MUSIC and ESPRIT methods, the proposed CSR algorithm demonstrates its advantage in root mean square error of the Doppler shift and multipath delay, especially for the closely located paths within low SNR.

Image interpolation is one of the image upsampling technologies from a single input image. This technology obtains high resolution images by fitting functions or models. Although image interpolation methods are faster than other upsampling technologies, they tend to cause jaggies and blurs in edge and texture regions. Multi-surface Fitting is one of the image upsampling techniques from multiple input images. This algorithm utilizes multiple local functions and the weighted means of the estimations in each local function. Multi-surface Fitting obtains high quality upsampled images. However, its quality depends on the number of input images. Therefore, this method is used in only limited situations. In this paper, we propose an image interpolation method with both high quality and a low computational cost which can be used in many situations. We adapt the idea of Multi-surface Fitting for the image upsampling problems from a single input image. We also utilize local functions to reduce blurs. To improve the reliability of each local function, we introduce new weights in the estimation of the local functions. Besides, we improve the weights for weighted means to estimate a target pixel. Moreover, we utilize convolutions with small filters instead of the calculation of each local function in order to reduce the computational cost. Experimental results show our method obtains high quality output images without jaggies and blurs in short computational time.

The paper presents a small reversible language R-CORE, a structured imperative programming language with symbolic tree-structured data (S-expressions). The language is reduced to the core of a reversible language, with a single command for reversibly updating the store, a single reversible control-flow operator, a limited number of variables, and data with a single atom and a single constructor. Despite its extreme simplicity, the language is reversibly universal, which means that it is as powerful as any reversible language can be, while it is linear-time self-interpretable, and it allows reversible programming with dynamic data structures. The four-line program inverter for R-CORE is among the shortest existing program inverters, which demonstrates the conciseness of the language. The translator to R-CORE, which is used to show the formal properties of the language, is clean and modular, and it may serve as a model for related reversible translation problems. The goal is to provide a language that is sufficiently concise for theoretical investigations. Owing to its simplicity, the language may also be used for educational purposes.

A chirp spread spectrum (CSS) system uses a chirp signal which changes the instantaneous frequency according to time for spreading a transmission bandwidth. In the CSS system, the transmission performance can be simply improved by increasing the time-bandwidth product which is known as the processing gain. However, increasing the transmission bandwidth is limited because of the spectrum regulation. In this letter, we propose a correlation-based chirp rate allocation method to improve the transmission performance by analyzing the cross-correlation coefficient in the same time-bandwidth product. In order to analyze the transmission performance of the proposed method, we analytically derive the cross-correlation coefficient according to the time-bandwidth separation product and simulate the transmission performance. The simulation results show that the proposed method can analytically allocate the optimal chirp rate and improve the transmission performance.

Conventional image retargeting methods fail to avoid distortion in the case where visually important regions are distributed all over the image. To reduce distortions, this paper proposes a novel image retargeting method that incorporates letterboxing into an image warping framework. Letterboxing has the advantage of producing results without distortion or content loss although being unable to use the entire display area. Therefore, it is preferable to combine a retargeting method with a letterboxing operator when displaying images in full screen. Experimental results show that the proposed method is superior to conventional methods in terms of visual quality measured by an objective metric.

Recognition of second language (L2) speech is a challenging task even for state-of-the-art automatic speech recognition (ASR) systems, partly because pronunciation by L2 speakers is usually significantly influenced by the mother tongue of the speakers. Considering that the expressions of non-native speakers are usually simpler than those of native ones, and that second language speech usually includes mispronunciation and less fluent pronunciation, we propose a novel method that maximizes unified acoustic and linguistic objective function to derive a phoneme set for second language speech recognition. The authors verify the efficacy of the proposed method using second language speech collected with a translation game type dialogue-based computer assisted language learning (CALL) system. In this paper, the authors examine the performance based on acoustic likelihood, linguistic discrimination ability and integrated objective function for second language speech. Experiments demonstrate the validity of the phoneme set derived by the proposed method.

The quality assessment of screen content images (SCIs) has been attractive recently. Different from natural images, SCI is usually a mixture of picture and text. Traditional quality metrics are mainly designed for natural images, which do not fit well into the SCIs. Motivated by this, this letter presents a simple and effective method to naturalize SCIs, so that the traditional quality models can be applied for SCI quality prediction. Specifically, bicubic interpolation-based up-sampling is proposed to achieve this goal. Extensive experiments and comparisons demonstrate the effectiveness of the proposed method.

Given a graph G=(V, E), where V and E are vertex and edge sets of G, and a subset VNT of vertices called a non-terminal set, the minimum spanning tree with a non-terminal set VNT, denoted by MSTNT, is a connected and acyclic spanning subgraph of G that contains all vertices of V with the minimum weight where each vertex in a non-terminal set is not a leaf. On general graphs, the problem of finding an MSTNT of G is NP-hard. We show that if G is an outerplanar graph then finding an MSTNT of G is linearly solvable with respect to the number of vertices.

Narrow swipe sensor based systems have drawn more and more attention in recent years. However, the size of captured image is significantly smaller than that obtained from the traditional area fingerprint sensor. Under this condition the available minutiae number is also limited. Therefore, only employing minutiae with the standard associated feature can hardly achieve high verification accuracy. To solve this problem, we present a novel Hybrid Minutiae Descriptor (HMD) which consists of two modules. The first one: Minutiae Ridge-Valley Orientation Descriptor captures the orientation information around minutia and also the trace points located at associated ridge and valley. The second one: Gabor Binary Code extracts and codes the image patch around minutiae. The proposed HMD enhances the representation capability of minutiae feature, and can be matched very efficiently. Experiments conducted over public databases and the database captured by the narrow swipe sensor show that this innovative method gives rise to significant improvements in reducing FRR (False Reject Rate) and EER (Equal Error Rate).

In this paper, we propose a method to find similar sentences based on language resources for building a parallel corpus between English and Korean from Wikipedia. We use a Wiki-dictionary consisted of document titles from the Wikipedia and bilingual example sentence pairs from Web dictionary instead of traditional machine readable dictionary. In this way, we perform similarity calculation between sentences using sequential matching of the language resources, and evaluate the extracted parallel sentences. In the experiments, the proposed parallel sentences extraction method finally shows 65.4% of F1-score.

In this paper, we provided a new variant of Weil pairing on a family of genus 2 curves with the efficiently computable automorphism. Our pairing can be considered as a generalization of the omega pairing given by Zhao et al. We also report the algebraic cost estimation of our pairing. We then show that our pairing is more efficient than the variant of Tate pairing with the automorphism given by Fan et al. Furthermore, we show that our pairing is slightly better than the twisted Ate pairing on Kawazoe-Takahashi curve at the 192-bit security level.

IDMs are getting more effective and secure with biometric recognition and more privacy-preserving with advanced cryptosystems. In order to meet privacy and security needs of an IDM, the cryptographic background should rely on reliable random number generation. In this study, a Biometric Random Number Generator (BRNG) is proposed which plays a crucial role in a typical cryptosystem. The proposed novel approach extracts the high-frequency information in biometric signal which is associated with uncertainty existing in nature of biometrics. This bio-uncertainty, utilized as an entropy source, may be caused by sensory noise, environmental changes, position of the biometric trait, accessories worn, etc. The filtered nondeterministic information is then utilized by a postprocessing technique to obtain a random number set fulfilling the NIST 800-22 statistical randomness criteria. The proposed technique presents random number sequences without need of an additional hardware.

Audio hashing has been successfully employed for protection, management, and indexing of digital music archives. For a reliable audio hashing system, improving hash matching accuracy is crucial. In this paper, we try to improve a binary audio hash matching performance by utilizing auxiliary information, resilience mask, which is obtained while constructing hash DB. The resilience mask contains reliability information of each hash bit. We propose a new type of resilience mask by considering spectrum scaling and additive noise distortions. Experimental results show that the proposed resilience mask is effective in improving hash matching performance.

In this paper, we present a GPU implementation of bulk multiple-length multiplications. The idea of our GPU implementation is to adopt a warp-synchronous programming technique. We assign each multiple-length multiplication to one warp that consists of 32 threads. In parallel processing using multiple threads, usually, it is costly to synchronize execution of threads and communicate within threads. In warp-synchronous programming technique, however, execution of threads in a warp can be synchronized instruction by instruction without any barrier synchronous operations. Also, inter-thread communication can be performed by warp shuffle functions without accessing shared memory. The experimental results show that our GPU implementation on NVIDIA GeForce GTX 980 attains a speed-up factor of 52 for 1024-bit multiple-length multiplication over the sequential CPU implementation. Moreover, we use this 1024-bit multiple-length multiplication for larger size of bits as a sub-routine. The GPU implementation attains a speed-up factor of 21 for 65536-bit multiple-length multiplication.