Feature extractor plays an important role in visual tracking, but most state-of-the-art methods employ the same feature representation in all scenes. Taking into account the diverseness, a tracker should choose different features according to the videos. In this work, we propose a novel feature adaptive correlation tracker, which decomposes the tracking task into translation and scale estimation. According to the luminance of the target, our approach automatically selects either hierarchical convolutional features or histogram of oriented gradient features in translation for varied scenarios. Furthermore, we employ a discriminative correlation filter to handle scale variations. Extensive experiments are performed on a large-scale benchmark challenging dataset. And the results show that the proposed algorithm outperforms state-of-the-art trackers in accuracy and robustness.

In this paper we propose a method for estimating time-to-contact in scattering media. Images taken in scattering media are often unclear and blurry, making it difficult to detect appropriate geometric information from these images for computing the 3 dimensional properties of the scene. Therefore, instead of searching for geometric information, we attempt to use photometric information instead. In our approach, we use the observed image intensity. The method proposed in this paper is able to utilize the effect of scattering media on the resultant image and estimate the time-to-contact toward objects without any prior knowledge of the scene, cameras, and the scattering media. This method is then evaluated using simulated and real images.

The compressive sensing (CS) theory has been widely used in synthetic aperture radar (SAR) imaging for its ability to reconstruct image from an extremely small set of measurements than what is generally considered necessary. Because block-based CS approaches in SAR imaging always cause block boundaries between two adjacent blocks, resulting in namely the block artefacts. In this paper, we propose a weighted overlapped block-based compressive sensing (WOBCS) method to reduce the block artefacts and accomplish SAR imaging. It has two main characteristics: 1) the strategy of sensing small and recovering big and 2) adaptive weighting technique among overlapped blocks. This proposed method is implemented by the well-known CS recovery schemes like orthogonal matching pursuit (OMP) and BCS-SPL. Promising results are demonstrated through several experiments.

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.

The Android pattern unlock is a widely adopted graphical password system that requires a user to draw a secret pattern connecting points arranged in a grid. The theoretical security of pattern unlock can be defined by the number of possible patterns. However, only upper bounds of the number of patterns have been known except for 3×3 and 4×4 grids for which the exact number of patterns was found by brute-force enumeration. In this letter, we present the first lower bound by computing the minimum number of visible points from each point in various subgrids.

This paper presents a hybrid push/pull streaming scheme to take advantage of both the interval caching-based push method and the mesh-based pull method. When a new peer joins, a mesh-based pull method is adopted to avoid the overhead to reorganize the structure only if all of its potential preceding peers are likely to leave before the end of its playback. Otherwise, an interval caching-based push method is adopted so that the better performance of the push method can be maintained until it completes the playback. We demonstrate that our proposed scheme outperforms compared with when either the interval caching-based push method or mesh-based pull method is employed alone.

A text-dependent i-vector extraction scheme and a lexicon-based binary vector (L-vector) representation are proposed to improve the performance of text-dependent speaker verification. I-vector and L-vector are used to represent the utterances for enrollment and test. An improved cosine distance kernel is constructed by combining i-vector and L-vector together and is used to distinguish both speaker identity and lexical (or text) diversity with back-end support vector machine (SVM). Experiments are conducted on RSR 2015 Corpus part 1 and part 2, the results indicate that at most 30% improvement can be obtained compared with traditional i-vector baseline.

A comprehensive model is presented for estimating the bit error rate (BER) of write disturbance in a resistive memory composed of a cross-point array. While writing a datum into the selected address, the non-selected addresses are biased by word-line (WL) and bit-line (BL). The stored datum in the non-selected addresses will be disturbed if the bias is large enough. It is necessary for the current flowing through the non-selected address to be calculated in order to estimate the BER of the write disturbance. Since it takes a long time to calculate the current flowing in a large-scale cross-point array, several simplified circuits have been utilized to decrease the calculating time. However, these simplified circuits are available to the selected address, not to the non-selected one. In this paper, new simplified circuits are proposed for calculating the current flowing through the non-selected address. The proposed and the conventional simplified circuits are used, and on that basis the trade-off between the write disturbance and the write error is discussed. Furthermore, the error correcting code (ECC) is introduced to improve the trade-off and to provide the low-cost memory chip matching current production lines.

Wireless sensor networks are being used in many disaster-related applications. Certain types of disasters are studied and modeled with different and dynamic risk estimations in different areas, hence requiring different levels of monitoring. Such nonuniform and dynamic coverage requirements pose a challenge to a sensor coverage problem. This work proposes the Mobile sensor Relocation using Delaunay triangulation And Shifting on Hill climbing (MR-DASH) approach, which calculates an appropriate location for each mobile sensor as an attempt to maximize coverage ratio. Based on a probabilistic sensing model, it constructs a Delaunay triangulation from static sensors' locations and vertices of interesting regions. The resulting triangles are then prioritized based on their sizes and corresponding levels of requirement so that mobile sensors can be relocated accordingly. The proposed method was both compared with an existing previous work and demonstrated with real-world disaster scenarios by simulation. The result showed that MR-DASH gives appropriate target locations that significantly improve the coverage ratio with relatively low total sensors' moving distance, while properly adapting to variations in coverage requirements.

When debugging bugs, programmers often prepare test cases to reproduce buggy behaviours. However, for concurrent programs, test cases alone are typically insufficient to reproduce buggy behaviours, due to the nondeterminism of multi-threaded executions. In literature, various approaches have been proposed to reproduce buggy behaviours for concurrency bugs deterministically, but to the best of our knowledge, they are still limited. In particular, we have recognized three debugging scenarios from programming practice, but existing approaches can handle only one of the scenarios. In this paper, we propose a novel approach, called SPDebugger, that provides finer-grained thread controlling over test cases, programs under test, and even third party library code, to reproduce the predesigned thread execution schedule. The evaluation shows that SPDebugger handles more debugging scenarios than the state-of-the-art tool, called IMUnit, with similar human effort.

3GPP (3rd Generation Partnership Project) has started to discuss D2D (Device-to-Device)-aided OTDOA (Observed Time Difference Of Arrival) as one of the mobile positioning enhancement techniques for LTE (Long Term Evolution) systems. It is a kind of multi-node based OTDOA which directly receives D2D signals from adjacent multiple UEs (User Equipment) to measure RSTD (Reference Signal's Time Difference). D2D signals provide valuable advantages in terms of OTDOA positioning because it can guarantee more reference nodes and high SNR (Signal-to-Noise Ratio) of PRS (Positioning Reference Signal). Two typical methods for multi-node based OTDOA can be applied to D2D-aided OTDOA. Multiple OTDOA positioning is one of the multi-node based methods that averages multiple results from OTDOA; however, it cannot always guarantee high accuracy due to the non-uniform geometry of UEs. OTDOA positioning based on TSE (Taylor Series Expansion) algorithm may be one of the solutions; however, it has the initial value problem and high computational complexity due to its iterative procedure. Therefore, in this paper, we propose a novel D2D-aided OTDOA positioning method which utilizes UEs not as reference node of OTDOA but as assisting node for RSTD error reduction. The proposed method can reduce RSTD error of eNB based hyperbola by using multiple hyperbola bands. The hyperbola band indicates the possible range in which a hyperbola can occur due to RSTD error. Then, by using principal axes of hyperbolas, we estimate a modified hyperbola from the overlap area of hyperbola bands, which has less RSTD error. We verify that the proposed method can effectively reduce RSTD error and improve positioning performance with lower computational complexity.

In this review, we present recent advances relating to superconducting nanowire single-photon detectors (SSPDs or SNSPDs) and their broad range of applications. During a period exceeding ten years, the system performance of SSPDs has been drastically improved, and lately excellent detection efficiencies have been realized in practical systems for a wide range of target photon wavelengths. Owing to their advantages such as high system detection efficiency, low dark count rate, and excellent timing jitter, SSPDs have found application in various research fields such as quantum information, quantum optics, optical communication, and also in the life sciences. We summarize the photon detection principle and the current performance status of practical SSPD systems. In addition, we introduce application examples in which SSPDs have been applied.

Antenna-coupled kinetic inductance detectors (KIDs) have recently shown great promise as microwave detection systems with a large number of channels. However, this technique, still has difficulties in eliminating the radiation loss of the resonator signals. To solve this problem, we propose a design in which the absorption area connected to an antenna is located on the ground-side of a coplanar waveguide. Thereby, radiation loss due to leakage from the resonator to the antenna can be considerably reduced. This simple design also enables the use of a contact aligner for fabrication. We have developed KIDs with this design, named as the ground-side absorption (GSA)-KIDs and demonstrated that they have higher quality factors than those of the existing KIDs, while maintaining a good total sensitivity.

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.

For reliable communication, this letter proposes cooperative transmission scheme with spatial phase coding (SPC) in the edge area among base stations. The diversity method has the a difficulty in terms of the price and complexity in a base station with multiple antennas. Thus, this problem may be resolved by using the cooperative scheme among the base stations and the proposed scheme increases that uses economically resource by using less feedback bits. Especially, if the coverage of many base stations is overlapped, the performance of the proposed scheme is improved. From the simulation results, the proposed scheme has the better performance compared to the conventional scheme in heterogeneous network.

Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly in inferring a new user's consumption preference. Our model can also learn meaningful consumption-specific topics automatically.

We consider the problem of two-dimensional (2-D) angles of arrival estimation using a newly proposed structure of nonuniform linear array, referred to as nested coprime array with compressed inter-element spacing (CACIS). By constructing a cross-correlation matrix of the received signals, the nested CACIS exhibits a larger number of degrees of freedom. A two-step weighted l1-norm penalty strategy is proposed to fully utilize these degrees of freedom, where the weight matrices are constructed by MUSIC spectrum function and the threshold function, respectively. The proposed method has several salient advantages over the compared method, including increased resolution and accuracy, estimating many more number of sources and suppressing spurious peaks efficiently. Simulation results validate the superiority of the proposed method.

Doppler lidar (LIght Detection And Ranging) can provide accurate wind velocity vector estimates by processing the time delay and Doppler spectrum of received signals. This system is essential for real-time wind monitoring to assist aircraft taking off and landing. Considering the difficulty of calibration and cost, a single Doppler lidar model is more attractive and practical than a multiple lidar model. In general, it is impossible to estimate two or three dimensional wind vectors from a single lidar model without any prior information, because lidar directly observes only a 1-dimensional (radial direction) velocity component of wind. Although the conventional VAD (Velocity Azimuth Display) and VVP (Velocity Volume Processing) methods have been developed for single lidar model, both of them are inaccurate in the presence of local air turbulence. This paper proposes an accurate wind velocity estimation method based on a parametric approach using typical turbulence models such as tornado, micro-burst and gust front. The results from numerical simulation demonstrate that the proposed method remarkably enhances the accuracy for wind velocity estimation in the assumed modeled turbulence cases, compared with that obtained by the VAD or other conventional method.

Timing fault detection techniques address the problems caused by increased variations on a chip, especially with dynamic voltage and frequency scaling (DVFS). The Razor flip-flop (FF) is a timing fault detection technique that employs double sampling by the main and shadow FFs. In order for the Razor FF to correctly detect a timing fault, not the main FF but the shadow FF must sample the correct value. The application of Razor FFs to static logic relaxes the timing constraints; however, the naive application of Razor FFs to dynamic precharged logic such as SRAM read circuits is not effective. This is because the SRAM precharge cannot start before the shadow FF samples the value; otherwise, the transition of the bitline of the SRAM stops and the value sampled by the shadow FF will be incorrect. Therefore, the detect period cannot overlap the precharge period. This paper proposes a novel application of Razor FFs to SRAM read circuits. Our proposal employs a conditional precharge according to the value of a bitline sampled by the main FF. This enables the detect period to overlap the precharge period, thereby relaxing the timing constraints. The additional circuit required by this method is simple and only needed around the sense amplifier, and there is no need for a clock delayed from the system clock. Consequently, the area overhead of the proposed circuit is negligible. This paper presents SPICE simulations of the proposed circuit. Our proposal reduces the minimum cycle time by 51.5% at a supply voltage of 1.1 V and the minimum voltage by 31.8% at cycle time of 412.5 ps.

RC4 is a well-known stream cipher designed by Rivest. Due to considerable cryptanalysis efforts over past 20 years, several kinds of statistic biases in a key stream of RC4 have been observed so far. Finally, practical full plaintext recovery attacks on RC4 in SSL/TLS were independently proposed by AlFardan et al. and Isobe et al. in 2013. Responded to these attacks, usage of RC4 has drastically decreased in SSL/TLS. However, according to the research by Trustworthy Internet Movement, RC4 is still used by some websites for the encryption on SSL/TLS. In this paper, we shows a new plaintext recovery attack for RC4 under the assumption of HTTPS. We develop a method for exploiting single-byte and double-byte biases together to efficiently guess the target bytes, while previous attacks use either single-byte biases or double-byte biases. As a result, target plaintext bytes can be extracted with higher probability than previous best attacks given 229 ciphertexts encrypted by randomly-chosen keys. In the most efficient case, the success probability of our attack are more than twice compared to previous best attacks.