To implement a wideband bandpass filter with improved skirt-selectivity and out-band characteristics, a new parallel-coupled three-line unit with two short-circuited stubs symmetrically-loaded at the center line is proposed. Unlike most traditional ones, the passband of the proposed parallel-coupled three-line structure is based on the cross-coupling between non-adjacent lines rather than the direct-coupling between adjacent ones, whereas a pair of attenuation poles is found in the stopbands. After revealing its work mechanism, an efficient filter-design-scheme is correspondingly proposed for the presented structure. Firstly, based on a chebyshev-filter synthesis theory, a wideband passband filter consisting of a parallel-coupled two-line and two short-circuited stubs loaded at the input- and output- ports is designed. Furthermore, by putting a properly-designed 3/4-wavelength stepped-impedance resonator (SIR) in between the parallel-coupled two lines, two attenuation poles are then realized at the frequencies very close to the cutoff ones. Accordingly, the roll-off characteristics of the filter are significantly-improved to greater than 100,dB/GHz. Furthermore, two-section open-ended stubs are used to replace the short-circuited ones to realize a pair of extra attenuation poles in stopbands. To validate the proposed techniques, a wideband filter with a bandwidth of 3--5,GHz (Fractional bandwidth (FBW) $= (5,GHz-3,GHz)/4,GHz =50%)$ was designed, simulated, fabricated and measured. The measured responses of the filter agree well with the simulation and theoretical ones, which validates the effectiveness of the newly-proposed three-line unit and the corresponding design scheme.

In this paper, an accurate player tracking method in far-view soccer videos based on a composite energy function is presented. In far-view soccer videos, player tracking methods that perform processing based only on visual features cannot accurately track players since each player region becomes small, and video coding causes color bleeding between player regions and the soccer field. In order to solve this problem, the proposed method performs player tracking on the basis of the following three elements. First, we utilize visual features based on uniform colors and player shapes. Second, since soccer players play in such a way as to maintain a formation, which is a positional pattern of players, we use this characteristic for player tracking. Third, since the movement direction of each player tends to change smoothly in successive frames of soccer videos, we also focus on this characteristic. Then we adopt three energies: a potential energy based on visual features, an elastic energy based on formations and a movement direction-based energy. Finally, we define a composite energy function that consists of the above three energies and track players by minimizing this energy function. Consequently, the proposed method achieves accurate player tracking in far-view soccer videos.

In this paper, we propose a representation method based on local spatial strokes for scene character recognition. High-level semantic information, namely co-occurrence of several strokes is incorporated by learning a sparse dictionary, which can further restrain noise brought by single stroke detectors. The encouraging results outperform state-of-the-art algorithms.

In this letter, a new sparse locality preserving projection (SLPP) algorithm is developed and applied to facial expression recognition. In comparison with the original locality preserving projection (LPP) algorithm, the presented SLPP algorithm is able to simultaneously find the intrinsic manifold of facial feature vectors and deal with facial feature selection. This is realized by the use of l1-norm regularization in the LPP objective function, which is directly formulated as a least squares regression pattern. We use two real facial expression databases (JAFFE and Ekman's POFA) to testify the proposed SLPP method and certain experiments show that the proposed SLPP approach respectively gains 77.60% and 82.29% on JAFFE and POFA database.

The recently suggested regular-type triangular quadrature amplitude modulation (TQAM) provides considerable power gain over square quadrature amplitude modulation (SQAM) at the expense of a slight increase in detection complexity. However, the power gain of the TQAM is limited due to the constraint that signal points should be regularly located at the vertexes of contiguous equilateral triangles. In this paper, we investigate two irregular (optimum and suboptimum) TQAMs where signal points are irregularly distributed while preserving the equilateral triangular lattice, and calculate achievable power gains of the proposed constellations. We also address optimum and suboptimum bit stream mapping methods and suggest a simple and optimum detection method for the constellations to be meaningful in practical implementation, and present analytical and simulation results. The proposed constellations can provide the asymptotic power gains of 0.825dB and 0.245dB over SQAM and regular TQAM, respectively.

The k-error linear complexity of periodic sequences is an important security index of stream cipher systems. By using an interesting decomposing approach, we investigate the intrinsic structure for the set of 2n-periodic binary sequences with fixed complexity measures. For k ≤ 4, we construct the complete set of error vectors that give the k-error linear complexity. As auxiliary results we obtain the counting functions of the k-error linear complexity of 2n-periodic binary sequences for k ≤ 4, as well as the expectations of the k-error linear complexity of a random sequence for k ≤ 3. Moreover, we study the 2t-error linear complexity of the set of 2n-periodic binary sequences with some fixed linear complexity L, where t < n-1 and the Hamming weight of the binary representation of 2n-L is t. Also, we extend some results to pn-periodic sequences over Fp. Finally, we discuss some potential applications.

Landslides during heavy rainfall cause a great amount of damage in terms of both property and human life. To predict landslide disasters, we designed and implemented a wireless sensor network using our existing highly fault tolerant ad-hoc network. Since many sensors must be used, we propose a new MAC protocol that allows the network to support more sensor terminals. Our protocol is a hybrid CSMA/Psuedo-TDMA scheme which allows the terminals to decide their transmission timing independently in a random fashion. A timing beacon is not required, so power consumption can be reduced. Simulation results show that the number of terminals supported by the network can be greatly increased.

This paper proposes a generic construction of hierarchical identity-based identification (HIBI) protocols secure against impersonation under active and concurrent attacks in the standard model. The proposed construction converts a digital signature scheme existentially unforgeable against chosen message attacks, where the scheme has a protocol for showing possession of a signing key, not a signature. Our construction is based on the so-called certificate-based construction of hierarchical identity-based cryptosystems, and utilizes a variant of the well-known OR-proof technique to ensure the security against impersonation under active and concurrent attacks. We also present several concrete examples of our construction employing the Waters signature (EUROCRYPT 2005), and other signatures. As results, its concurrent security of each instantiation is proved under the computational Diffie-Hellman (CDH) assumption, the RSA assumption, or their variants in the standard model. Chin, Heng, and Goi proposed an HIBI protocol passively and concurrently secure under the CDH and one-more CDH assumption, respectively (FGIT-SecTech 2009). However, its security is proved in the random oracle model.

This letter proposes a pipelined architecture with prediction mode scheduling for high efficiency video coding (HEVC). An increased number of intra prediction modes in HEVC have introduced a new technique, named rough mode decision (RMD). This development, however, means that pipeline architectures for H.264 cannot be used in HEVC. The proposed scheme executes the RMD and the rate-distortion optimization (RDO) process simultaneously by grouping the intra prediction modes and changing the candidate selection method of the RMD algorithm. The proposed scheme reduces execution cycle by up to 26% with negligible coding loss.

We prove NP-completeness of deciding whether a given loop of colored right isosceles triangles, hinged together at edges, can be folded into a specified rectangular three-color pattern. By contrast, the same problem becomes polynomially solvable with one color or when the target shape is a tree-shaped polyomino.

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.

The label estimation technique provides a new way to design semi-supervised learning algorithms. If the labels of the unlabeled data can be estimated correctly, the semi-supervised methods can be replaced by the corresponding supervised versions. In this paper, we propose a novel semi-supervised learning algorithm, called Geodesic Weighted Sparse Representation (GWSR), to estimate the labels of the unlabeled data. First, the geodesic distance and geodesic weight are calculated. The geodesic weight is utilized to reconstruct the labeled samples. The Euclidean distance between the reconstructed labeled sample and the unlabeled sample equals the geodesic distance between the original labeled sample and the unlabeled sample. Then, the unlabeled samples are sparsely reconstructed and the sparse reconstruction weight is obtained by minimizing the L1-norm. Finally, the sparse reconstruction weight is utilized to estimate the labels of the unlabeled samples. Experiments on synthetic data and USPS hand-written digit database demonstrate the effectiveness of our method.

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.

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.

This paper introduces an unsupervised method for motion pattern learning and abnormality detection from video surveillance. In the preprocessing steps, trajectories are segmented based on their locations, and the sub-trajectories are represented as codebooks. Under our framework, Hidden Markov Models (HMMs) are used to characterize the motion pattern feature of the trajectory groups. The state of trajectory is represented by a HMM and has a probability distribution over the possible output sub-trajectories. Bayesian Information Criterion (BIC) is introduced to measure the similarity between groups. Based on the pairwise similarity scores, an affinity matrix is constructed which indicates the distance between different trajectory groups. An Adaptable Dynamic Hierarchical Clustering (ADHC) tree is proposed to gradually merge the most similar groups and form the trajectory motion patterns, which implements a simpler and more tractable dynamical clustering procedure in updating the clustering results with lower time complexity and avoids the traditional overfitting problem. By using the HMM models generated for the obtained trajectory motion patterns, we may recognize motion patterns and detect anomalies by computing the likelihood of the given trajectory, where a maximum likelihood for HMM indicates a pattern, and a small one below a threshold suggests an anomaly. Experiments are performed on EIFPD trajectory datasets from a structureless scene, where pedestrians choose their walking paths randomly. The experimental results show that our method can accurately learn motion patterns and detect anomalies with better performance.

One of the technological innovations that has enabled the VLSI semiconductor industry to reduce the transistor size, increase the number of transistors per die, and also follow Moore's law year after year is the fact that an equivalent yield and equivalent testing quality have been ensured for the same die size. This has contributed to reducing the economically optimum production cost (production cost per component) as advocated by Moore. In this paper, we will verify Moore's law using actual values from VLSI manufacturing sites while introducing some of the technical progress that occurred from 1970 to 2010.

Consider a simple undirected graph G = (V,E) with vertex set V and edge set E. Let G-u be a subgraph induced by the vertex set V-{u}. The distance δG(x,y) is defined as the length of the shortest path between vertices x and y in G. The vertex u ∈ V is a hinge vertex if there exist two vertices x,y ∈ V-{u} such that δG-u(x,y)>δG(x,y). Let U be a set consisting of all hinge vertices of G. The neighborhood of u is the set of all vertices adjacent to u and is denoted by N(u). We define d(u) = max{δG-u(x,y) | δG-u(x,y)>δG(x,y),x,y ∈ N(u)} for u ∈ U as detour degree of u. A maximum detour hinge vertex problem is to find a hinge vertex u with maximum d(u) in G. In this paper, we proposed an algorithm to find the maximum detour hinge vertex on an interval graph that runs in O(n2) time, where n is the number of vertices in the graph.

This paper presents a 120-GHz-band amplifier module with a hermetic sealing structure for a broadband wireless system. The sealing structure for F-band waveguides is a laminate composed of two sealing plates and a spacer. Each sealing plate has a fused glass window and separates inside air from the ambient atmosphere. The design process of the sealing structure is simple and has good simulation fidelity. The hermetic sealing structure designed for an amplifier in a 120-GHz-band wireless link has an insertion loss of less than 1dB and a return loss of more than 15dB in the operating band. We made three kinds of sealed modules to evaluate the sealing function. The modules sealed with this technique meet the hermetic-seal standard in MIL-STD-883F. We then verified that the sealing structure on the sealed modules has a small enough effect for the transmittance of the intrinsic characteristics. In addition, we performed 10-Gbit/s data transmission using a sealed amplifier module with the bit error rate of less than 10-10.

This paper proposes an ultra-low-power 5.5-GHz PLL which employs the new divide-by-4 injection-locked frequency divider (ILFD) and a class-C VCO with linearity-compensated varactor for low supply voltage operation. A forward-body-biasing (FBB) technique can decrease threshold voltage of MOS transistors, which can improve operation frequency and can widen the lock range of the ILFD. The FBB is also employed for linear-frequency-tuning of VCO under low supply voltage of 0.5V. The double-switch injection technique is also proposed to widen the lock range of the ILFD. The digital calibration circuit is introduced to control the lock-range of ILFD automatically. The proposed PLL was fabricated in a 65nm CMOS process. With a 34.3-MHz reference, it shows a 1-MHz-offset phase noise of -106dBc/Hz at 5.5GHz output. The supply voltage is 0.54V for divider and 0.5V for other components. Total power consumption is 0.95mW.

The composite binary offset carrier (CBOC) modulated signal contains multi-peaks in its auto-correlation function, which brings ambiguity to the signal acquisition process of a GNSS receiver. Currently, most traditional ambiguity-removing schemes for CBOC signal acquisition approximate CBOC signal as a BOC signal, which may incur performance degradation. Based on Galileo E1 CBOC signal, this paper proposes a novel adaptive ambiguity-removing acquisition scheme which doesn't adopt the approximation used in traditional schemes. According to the energy ratio of each sub-code of CBOC signal, the proposed scheme can self-adjust its local reference code to achieve unambiguous and precise signal synchronization. Monte Carlo simulation is conducted in this paper to analyze the performance of the proposed scheme and three traditional schemes. Simulation results show that the proposed scheme has higher detection probability and less mean acquisition time than the other three schemes, which verify the superiority of the proposed scheme.