Accurate stereo matching remains a challenging problem in case of weakly-textured areas, discontinuities and occlusions. In this letter, a novel stereo matching method, consisting of leveraging feature ensemble network to compute matching cost, error detection network to predict outliers and priority-based occlusion disambiguation for refinement, is presented. Experiments on the Middlebury benchmark demonstrate that the proposed method yields competitive results against the state-of-the-art algorithms.

This paper aims to improve the ability of low voltage ride through (LVRT) of doubly-fed induction generation (DFIG) under the asymmetric grid fault. The traditional rotor of the Crowbar device requires a large reactive support during the period of protection, which causes large fluctuations to the reactive power of the output grid while cut in and off for Crowbar. This case would influence the quality and efficiency of entire power system. In order to solve the fluctuation of reactive power and the stability of the wind power system, this paper proposes the coordinated control of the fuzzy-neural D-STATCOM and the rotor of the Crowbar. The simulation results show that the system has the performance of the rotor current with faster decay and faster dynamic response, high steady-state characteristic during the grid fault, which improve the ability of LVRT of DFIG.

The ignored side effect reflecting in the introduction of mismatching brought by contrast enhancement in representative SIFT based vein recognition model is investigated. To take advantage of contrast enhancement in increasing keypoints generation, hierarchical keypoints selection and mismatching removal strategy is designed to obtain state-of-the-art recognition result.

In this paper, the concept of gradient order is introduced and a novel gradient order curve descriptor (GOCD) for curve matching is proposed. The GOCD is constructed in the following main steps: firstly, curve support region independent of the dominant orientation is determined and then divided into several sub-regions based on gradient magnitude order; then gradient order feature (GOF) of each feature point is generated by encoding the local gradient information of the sample points; the descriptor is finally achieved by turning to the description matrix of GOF. Since both the local and the global gradient information are captured by GOCD, it is more distinctive and robust compared with the existing curve matching methods. Experiments under various changes, such as illumination, viewpoint, image rotation, JPEG compression and noise, show the great performance of GOCD. Furthermore, the application of image mosaic proves GOCD can be used successfully in actual field.

Establishing local visual correspondences between images taken under different conditions is an important and challenging task in computer vision. A common solution for this task is detecting keypoints in images and then matching the keypoints with a feature descriptor. This paper proposes a robust and low-dimensional local feature descriptor named Adaptively Integrated Gradient and Intensity Feature (AIGIF). The proposed AIGIF descriptor partitions the support region surrounding each keypoint into sub-regions, and classifies the sub-regions into two categories: edge-dominated ones and smoothness-dominated ones. For edge-dominated sub-regions, gradient magnitude and orientation features are extracted; for smoothness-dominated sub-regions, intensity feature is extracted. The gradient and intensity features are integrated to generate the descriptor. Experiments on image matching were conducted to evaluate performances of the proposed AIGIF. Compared with SIFT, the proposed AIGIF achieves 75% reduction of feature dimension (from 128 bytes to 32 bytes); compared with SURF, the proposed AIGIF achieves 87.5% reduction of feature dimension (from 256 bytes to 32 bytes); compared with the state-of-the-art ORB descriptor which has the same feature dimension with AIGIF, AIGIF achieves higher accuracy and robustness. In summary, the AIGIF combines the advantages of gradient feature and intensity feature, and achieves relatively high accuracy and robustness with low feature dimension.

We previously proposed a lossless image coding scheme using example-based probability modeling, wherein the probability density function of image signals was dynamically modeled pel-by-pel. To appropriately estimate the peak positions of the probability model, several examples, i.e., sets of pels whose neighborhoods are similar to the local texture of the target pel to be encoded, were collected from the already encoded causal area via template matching. This scheme primarily makes use of non-local information in image signals. In this study, we introduce a prediction technique into the probability modeling to offer a better trade-off between the local and non-local information in the image signals.

In this study, expressions were compared with reference material using the coaxial feed-type open-ended cut-off circular waveguide reflection method to support simple and instantaneous evaluation of dielectric constants in small amounts of scarce liquids over a broad frequency range. S11 values were determined via electromagnetic analysis for individual jig structure conditions and dielectric property values without actual S11 measurement under the condition that the tip of the measurement jig with open and short-ended conditions and with the test material inserted. Next, information on the relationships linking jig structure, dielectric properties and S11 properties was stored on a database to simplify the procedure and improve accuracy in reference material evaluation. The accuracy of the estimation formula was first theoretically verified for cases in which values indicating the dielectric properties of the reference material and the actual material differed significantly to verify the effectiveness of the proposed method. The results indicated that dielectric property values for various liquids measured at 0.5 and 1.0GHz using the proposed method corresponded closely to those obtained using the method previously proposed by the authors. The effectiveness of the proposed method was evaluated by determining the dielectric properties of certain liquids at octave-range continuous frequencies between 0.5 and 1.0GHz based on interpolation from limited data of several frequencies. The results indicated that the approach enables quicker and easier measurement to establish the complex permittivity of liquids over a broad frequency range than the previous method.

The development of an extremely efficient packet inspection algorithm for lookup engines is important in order to realize high throughput and to lower energy dissipation. In this paper, we propose a new lookup engine based on a combination of a mismatch detection circuit and a linked-list hash table. The engine has an automatic rule registration and deletion function; the results are that it is only necessary to input rules, and the various tables included in the circuits, such as the Mismatch Table, Index Table, and Rule Table, will be automatically configured using the embedded hardware. This function utilizes a match/mismatch assessment for normal packet inspection operations. An experimental chip was fabricated using 40-nm 8-metal CMOS process technology. The chip operates at a frequency of 100MHz under a power supply voltage of VDD =1.1V. A throughput of 100Mpacket/s (=51.2Gb/s) is obtained at an operating frequency of 100MHz, which is three times greater than the throughput of 33Mpacket/s obtained with a conventional lookup engine without a mismatch detection circuit. The measured energy dissipation was a 1.58pJ/b·Search.

In the present paper, we propose a novel high-resolution analog-to-digital converter (ADC) for low-power biomedical analog front-ends, which we call the successive stochastic approximation ADC. The proposed ADC uses a stochastic flash ADC (SF-ADC) to realize a digitally controlled variable-threshold comparator in a successive-approximation-register ADC (SAR-ADC), which can correct errors originating from the internal digital-to-analog converter in the SAR-ADC. For the residual error after SAR-ADC operation, which can be smaller than thermal noise, the SF-ADC uses the statistical characteristics of noise to achieve high resolution. The SF-ADC output for the residual signal is combined with the SAR-ADC output to obtain high-precision output data using the supervised machine learning method.

A non-deterministic finite automaton (NFA)-based parallel string matching scheme is proposed. To parallelize the operations of NFAs, a graphic processing unit (GPU) is adopted. Considering the resource occupancy of threads and size of the shared memory, the optimized resource allocation is performed in the proposed string matching scheme. Therefore, the performance is enhanced significantly in all evaluations.

In this paper, we propose a method for removing block noise which appears in JPEG (Joint Photographic Experts Group) encoded images. We iteratively perform the 3D wiener filtering and correction of the coefficients. In the wiener filtering, we perform the block matching for each patch in order to get the patches which have high similarities to the reference patch. After wiener filtering, the collected patches are returned to the places where they were and aggregated. We compare the performance of the proposed method to some conventional methods, and show that the proposed method has an excellent performance.

This paper deals with a problem to decide whether a given graph structure appears as a pattern in the structure of a given graph. A graph pattern is a triple p=(V,E,H), where (V,E) is a graph and H is a set of variables, which are ordered lists of vertices in V. A variable can be replaced with an arbitrary connected graph by a kind of hyperedge replacements. A substitution is a collection of such replacements. The graph pattern matching problem (GPMP) is the computational problem to decide whether or not a given graph G is obtained from a given graph pattern p by a substitution. In this paper, we show that GPMP for a graph pattern p and a graph G is solvable in polynomial time if the length of every variable in p is 2, p is of bounded treewidth, and G is connected.

We present a new data structure called the packed compact trie (packed c-trie) which stores a set S of k strings of total length n in nlog σ+O(klog n) bits of space and supports fast pattern matching queries and updates, where σ is the alphabet size. Assume that α=logσn letters are packed in a single machine word on the standard word RAM model, and let f(k,n) denote the query and update times of the dynamic predecessor/successor data structure of our choice which stores k integers from universe [1,n] in O(klog n) bits of space. Then, given a string of length m, our packed c-tries support pattern matching queries and insert/delete operations in $O(rac{m}{alpha} f(k,n))$ worst-case time and in $O(rac{m}{alpha} + f(k,n))$ expected time. Our experiments show that our packed c-tries are faster than the standard compact tries (a.k.a. Patricia trees) on real data sets. We also discuss applications of our packed c-tries.

Many basic tasks in Wireless Sensor Networks (WSNs) rely heavily on the availability and accuracy of target locations. Since the number of targets is usually limited, localization benefits from Compressed Sensing (CS) in the sense that measurements can be greatly reduced. Though some CS-based localization schemes have been proposed, all of these solutions make an assumption that all targets are located on a pre-sampled and fixed grid, and perform poorly when some targets are located off the grid. To address this problem, we develop an adaptive dictionary algorithm where the grid is adaptively adjusted. To achieve this, we formulate localization as a joint parameter estimation and sparse signal recovery problem. Additionally, we transform the problem into a tractable convex optimization problem by using Taylor approximation. Finally, the block coordinate descent method is leveraged to iteratively optimize over the parameters and sparse signal. After iterations, the measurements can be linearly represented by a sparse signal which indicates the number and locations of targets. Extensive simulation results show that the proposed adaptive dictionary algorithm provides better performance than state-of-the-art fixed dictionary algorithms.

Side match vector quantization (SMVQ) has been originally developed for image compression and is also useful for steganography. SMVQ requires to create its own state codebook for each block in both encoding and decoding phases. Since the conventional method for the state codebook generation is extremely time-consuming, this letter proposes a fast generation method. The proposed method is tens times faster than the conventional one without loss of perceptual visual quality.

Effective indoor localization is the essential part of VR (Virtual Reality) and AR (Augmented Reality) technologies. Tracking the RGB-D camera becomes more popular since it can capture the relatively accurate color and depth information at the same time. With the recovered colorful point cloud, the traditional ICP (Iterative Closest Point) algorithm can be used to estimate the camera poses and reconstruct the scene. However, many works focus on improving ICP for processing the general scene and ignore the practical significance of effective initialization under the specific conditions, such as the indoor scene for VR or AR. In this work, a novel indoor prior based initialization method has been proposed to estimate the initial motion for ICP algorithm. We introduce the generation process of colorful point cloud at first, and then introduce the camera rotation initialization method for ICP in detail. A fast region growing based method is used to detect planes in an indoor frame. After we merge those small planes and pick up the two biggest unparallel ones in each frame, a novel rotation estimation method can be employed for the adjacent frames. We evaluate the effectiveness of our method by means of qualitative observation of reconstruction result because of the lack of the ground truth. Experimental results show that our method can not only fix the failure cases, but also can reduce the ICP iteration steps significantly.

In this paper, the posterior matching scheme proposed by Shayevits and Feder is extended to the Gaussian broadcast channel with feedback, and the error probabilities and achievable rate region are derived for this coding strategy by using the iterated random function theory. A variant of the Ozarow-Leung code for the general two-user broadcast channel with feedback can be realized as a special case of our coding scheme. Furthermore, for the symmetric Gaussian broadcast channel with feedback, our coding scheme achieves the linear-feedback sum-capacity like the LQG code and outperforms the Kramer code.

With the scaling of technology, nanoscale CMOS integrated circuits are becoming more sensitive to single event double node upsets induced by charge sharing. A novel highly robust hardened latch design is presented that is fully resilient to single event double node upsets and single node upsets. The proposed latch employs multiple redundant C-elements to form a dual interlocked structure in which the redundant C-elements can bring the affected nodes back to the correct states regardless of the energy of the striking particle. Detailed HSPICE results confirm that the proposed latch features complete resilience to double node upsets and achieves an improved trade-off in terms of robustness, area, delay and power in comparison with previous latches. Extensive Monte Carlo simulations validate the proposed latch features as less sensitive to process, supply voltage and temperature variations.

A 1.9GHz film bulk acoustic resonator (FBAR)-based low-phase-noise complementary cross-coupled voltage-controlled oscillator (VCO) is presented. The FBAR-VCO is designed and fabricated in 0.18µm CMOS process. The DC latch and the low frequency instability are resolved by employing the NMOS source coupling capacitor and the DC blocked cross-coupled pairs. Since no additional voltage headroom is required, the proposed FBAR-VCO can be operated at a low power supply voltage of 1.1V with a wide voltage swing of 0.9V. An effective phase noise optimization is realized by a reasonable trade-off between the output resistance and the trans-conductance of the cross-coupled pairs. The measured performance shows the proposed FBAR-VCO achieves a phase noise of -148dBc/Hz at 1MHz offset with a figure of merit (FoM) of -211.6dB.

A high-image-rejection wireless receiver with an N-phase active RC complex filter is proposed and analyzed. Signal analysis shows that the double-conversion receiver with (N+N2) mixers corrects the gain and phase mismatches of the adjacent image. Monte Carlo simulations evaluate the relation between image-rejection performances and the dispersions of device parameters for the double-conversion wireless receiver. The Monte Carlo simulations show that the image rejection ratio of the adjacent image depends almost only on R and C mismatches in the complex filter.