Focusing on the defects of famous defogging algorithms for fog images based on the atmosphere scattering model, we find that it is necessary to obtain accurate transmission map that can reflect the real depths both in large depth and close range. And it is hard to tackle this with just one prior because of the differences between the large depth and close range in foggy images. Hence, we propose a novel prior that simplifies the solution of transmission map by transferring coefficient, called saturation prior. Then, under the Random Walk model, we constrain the transferring coefficient with the color attenuation prior that can obtain good transmission map in large depth regions. More importantly, we design a regularization weight to balance the influences of saturation prior and color attenuation prior to the transferring coefficient. Experimental results demonstrate that the proposed defogging method outperforms the state-of-art image defogging methods based on single prior in terms of details restoring and color preserving.

In social networks, predicting user behavior under social hotspots can aid in understanding the development trend of a topic. In this paper, we propose a retweeting prediction method for social hotspots based on tensor decomposition, using user information, relationship and behavioral data. The method can be used to predict the behavior of users and analyze the evolvement of topics. Firstly, we propose a tensor-based mechanism for mining user interaction, and then we propose that the tensor be used to solve the problem of inaccuracy that arises when interactively calculating intensity for sparse user interaction data. At the same time, we can analyze the influence of the following relationship on the interaction between users based on characteristics of the tensor in data space conversion and projection. Secondly, time decay function is introduced for the tensor to quantify further the evolution of user behavior in current social hotspots. That function can be fit to the behavior of a user dynamically, and can also solve the problem of interaction between users with time decay. Finally, we invoke time slices and discretization of the topic life cycle and construct a user retweeting prediction model based on logistic regression. In this way, we can both explore the temporal characteristics of user behavior in social hotspots and also solve the problem of uneven interaction behavior between users. Experiments show that the proposed method can improve the accuracy of user behavior prediction effectively and aid in understanding the development trend of a topic.

Bel-T is the national block cipher encryption standard of the Republic of Belarus. It operates on 128-bit blocks and uses either 128, 192 or 256-bit keys. Bel-T combines a Feistel network with a Lai-Massey scheme and it has a complex round function with 7 S-box layers. In this work, we use a Mixed Integer Linear Programming (MILP) approach to find a a related-key differential characteristic that extends for 4 rounds and 5 S-box layers ($4 rac{5}{7}$ rounds) with probability higher than 2-128. To build an MILP model of Bel-T that a solver can practically handle, we use a partial Difference Distribution Table (DDT) based on the Hamming weight of the input and output differences. The identified differential characteristic is used to mount a key recovery attack on 5 rounds and 6 S-box layers ($5 rac{6}{7}$ out of 8 rounds) of Bel-T-256 with 2123.28 chosen plaintexts and 2228.4 encryptions. According to the best of our knowledge, this is the first public cryptanalysis of Bel-T in the black-box attack model.

The isomorphism of polynomials with two secret (IP2S) problem is one candidate of computational assumptions for post-quantum cryptography. The idea of identification scheme based on IP2S is firstly introduced in 1996 by Patarin. However, the scheme was not described concretely enough and no more details are provided on how to transcribe the idea into a real-world implementation. Moreover, the security of the scheme has not been formally proven and the originally proposed security parameters are no longer secure based on the most recent research. In this paper, we propose a concrete identification scheme based on IP2S with the idea of Patarin as the starting point. We provide formal security proof of the proposed scheme against impersonation under passive attack, sequential active attack, and concurrent active attack. We also propose techniques to reduce the implementation cost such that we are able to cut the storage cost and average communication cost to an extent that under parameters for the standard 80-bit security, the scheme is implementable even on the lightweight devices in the current market.

A rich and robust representation for scene characters plays a significant role in automatically understanding the text in images. In this letter, we focus on the issue of feature representation, and propose a novel encoding method named bilateral convolutional activations encoded with Fisher vectors (BCA-FV) for scene character recognition. Concretely, we first extract convolutional activation descriptors from convolutional maps and then build a bilateral convolutional activation map (BCAM) to capture the relationship between the convolutional activation response and the spatial structure information. Finally, in order to obtain the global feature representation, the BCAM is injected into FV to encode convolutional activation descriptors. Hence, the BCA-FV can effectively integrate the prominent features and spatial structure information for character representation. We verify our method on two widely used databases (ICDAR2003 and Chars74K), and the experimental results demonstrate that our method achieves better results than the state-of-the-art methods. In addition, we further validate the proposed BCA-FV on the “Pan+ChiPhoto” database for Chinese scene character recognition, and the experimental results show the good generalization ability of the proposed BCA-FV.

In this paper, we present self-interference (SI) cancellation techniques in the digital domain for in-band full-duplex systems employing orthogonal frequency division multiple access (OFDMA) in the downlink (DL) and single-carrier frequency division multiple access (SC-FDMA) in the uplink (UL), as in the long-term evolution (LTE) system. The proposed techniques use UL subcarrier nulling to accurately estimate SI channels without any UL interference. In addition, by exploiting the structures of the transmitter imperfection and the known or estimated parameters associated with the imperfection, the techniques can further improve the accuracy of SI channel estimation. We also analytically derive the lower bound of the mean square error (MSE) performance and the upper bound of the signal-to-interference-plus-noise ratio (SINR) performance for the techniques, and show that the performance of the techniques are close to the bounds. Furthermore, by utilizing the SI channel estimates and the nonlinear signal components of the SI caused by the imperfection to effectively eliminate the SI, the proposed techniques can achieve SINR performance very close to the one in perfect SI cancellation. Finally, because the SI channel estimation of the proposed techniques is performed in the time domain, the techniques do not require symbol time alignment between SI and UL symbols.

The correlation filter-based trackers with an appearance model established by single feature have poor robustness to challenging video environment which includes factors such as occlusion, fast motion and out-of-view. In this paper, a long-term tracking algorithm based on multi-feature adaptive fusion for video target is presented. We design a robust appearance model by fusing powerful features including histogram of gradient, local binary pattern and color-naming at response map level to conquer the interference in the video. In addition, a random fern classifier is trained as re-detector to detect target when tracking failure occurs, so that long-term tracking is implemented. We evaluate our algorithm on large-scale benchmark datasets and the results show that the proposed algorithm have more accurate and more robust performance in complex video environment.

In recent years, the number of traffic accidents caused by elderly drivers has increased in Japan. However, cars are an important mode of transportation for the elderly. Therefore, to ensure safe driving, a system that can assist elderly drivers is required. We propose a driver-agent system that provides support to elderly drivers during and after driving and encourages them to improve their driving. This paper describes the prototype system and the analysis conducted of the teaching records of a human instructor, the impression caused by the instructions on a subject during driving, and subjective evaluation of the driver-agent system.

We have investigated the influence of the ferromagnet magnetization on the transport properties of intrinsic Josephson junctions in Co/Au/BSCCO/Au/Co hybrid structure under applied magnetic fields. The current-voltage characteristic at 77K in a zero-field showed the multiple quasiparticle branches with hysteresis similar to that of conventional intrinsic Josephson junctions. On the other hand, it was observed that the critical current shows a clear asymmetric field dependence with respect to the direction of the field sweep, resulting in hysteretic behavior. By comparing the field dependence of critical current with magnetization curve of the sample, we found that the critical current is strongly suppressed in the antiparallel configuration of the relative magnetization orientation of two Co layers due to the accumulation of spin-polarized quasiparticles in intrinsic Josephson junctions. The observed suppression of the critical current is as large as more than 20%.

Channel capacity is a useful numerical index not only for grasping the upper limit of the transmission bit rate but also for comparing the abilities of various digital transmission schemes commonly used in radio-wave propagation environments because the channel capacity does not depend on specific communication methods such as modulation/demodulation schemes or error correction schemes. In this paper, modeling of the noncoherent capacity in a highly underspread WSSUS channel is investigated using a new approach. Unlike the conventional method, namely, the information theoretic method, a very straightforward formula can be obtained in a statistical manner. Although the modeling in the present study is carried out using a somewhat less rigorous approach, the result obtained is useful for roughly understanding the channel capacity in doubly selective fading environments. We clarify that the radio wave propagation parameter of the spread factor, which is the product of the Doppler spread and the delay spread, can be related quantitatively to the effective maximum signal-to-interference ratio by a simple formula. Using this model, the physical limit of wireless digital transmission is discussed from a radio wave propagation perspective.

Our previous work proposed a semi-blind single antenna interference cancellation scheme to cope with severe inter-cell interference in heterogeneous networks. This paper extends the scheme to allow multiple-receive-antenna implementation. It does not require knowledge of the training sequences of interfering signals and can cancel multiple interfering signals irrespective of the number of receive antennas. The proposed scheme applies an enhanced version of the quantized channel approach to suboptimal joint channel estimation and signal detection (JCESD) during the training period in order to blindly estimate channels of the interfering signals, while reducing the computational complexity of optimum JCESD drastically. Different from the previous work, the proposed scheme applies the quantized channel generation and local search at each individual receive antenna so as to estimate transmitted symbol matrices during the training period. Then, joint estimation is newly introduced in order to estimate a channel matrix from the estimated symbol matrices, which operates in the same manner as the expectation maximization (EM) algorithm and considers signals received at all receive antennas. Using the estimated channels, the proposed scheme performs multiuser detection (MUD) during the data period under the maximum likelihood (ML) criterion in order to cancel the interference. Computer simulations with two receive antennas under two-interfering-stream conditions show that the proposed scheme outperforms interference rejection combining (IRC) with perfect channel state information (CSI) and MUD with channels estimated by a conventional scheme based on the generalized Viterbi algorithm, and can achieve almost the same average bit error rate (BER) performance as MUD with channels estimated from sufficiently long training sequences of both the desired stream(s) and the interfering streams, while reducing the computational complexity significantly compared with full search involving all interfering signal candidates during the training period.

We consider device-to-device (D2D) direct communication underlying cellular networks where the D2D link reuses the frequency resources of the cellular downlink. In this paper, we propose a linear precoder design scheme for a base station (BS) and D2D transmitter using the weighted sum-rate of the cellular downlink and D2D link as a cost function. Because the weighted sum-rate maximization problem is not convex on the precoding matrices of BS and D2D transmitters, an equivalent mean-squared error (MSE) minimization problem which is convex on the precoding matrices is proposed by introducing auxiliary matrices. We show that the two optimization problems have the same optimal solution for the precoding matrices. Then, an iterative algorithm for solving the equivalent MSE minimization problem is presented. Through a computer simulation, we show that the proposed scheme offers better weighted sum-rate performance that a conventional scheme.

Determining the validity of knowledge triples and filling in the missing entities or relationships in the knowledge graph are the crucial tasks for large-scale knowledge graph completion. So far, the main solutions use machine learning methods to learn the low-dimensional distributed representations of entities and relationships to complete the knowledge graph. Among them, translation models obtain excellent performance. However, the proposed translation models do not adequately consider the indirect relationships among entities, affecting the precision of the representation. Based on the long short-term memory neural network and existing translation models, we propose a multiple-module hybrid neural network model called TransP. By modeling the entity paths and their relationship paths, TransP can effectively excavate the indirect relationships among the entities, and thus, improve the quality of knowledge graph completion tasks. Experimental results show that TransP outperforms state-of-the-art models in the entity prediction task, and achieves the comparable performance with previous models in the relationship prediction task.

In this paper, we propose a novel self-supervised learning of video representation which is capable to anticipate the video category by only reading its short clip. The key idea is that we employ the Siamese convolutional network to model the self-supervised feature learning as two different image matching problems. By using frame encoding, the proposed video representation could be extracted from different temporal scales. We refine the training process via a motion-based temporal segmentation strategy. The learned representations for videos can be not only applied to action anticipation, but also to action recognition. We verify the effectiveness of the proposed approach on both action anticipation and action recognition using two datasets namely UCF101 and HMDB51. The experiments show that we can achieve comparable results with the state-of-the-art self-supervised learning methods on both tasks.

Direct-binary search method has been used for converting complex holograms into binary format. However, this algorithm is optimized to reconstruct monochromatic digital holograms and is accurate only in a narrow-depth range. In this paper, we proposed an advanced direct-binary search method to increase the depth of field of 3D scenes reconstructed in RGB by binary holograms.

The physics and applications of superconducting phase shifts and their control in superconducting systems are reviewed herein. The operation principle of almost all superconducting devices is related to the superconducting phase, and an efficient control of the phase is crucial for improving the performance and scalability. Furthermore, employing new methods to shift or control the phase may lead to the development of novel superconducting device applications, such as cryogenic memory and quantum computing devices. Recently, as a result of the progress in nanofabrication techniques, superconducting phase shifts utilizing π states have been realized. In this review, following a discussion of the basic physics of phase propagation and shifts in hybrid superconducting structures, interesting phenomena and device applications in phase-shifted superconducting systems are presented. In addition, various possibilities for developing electrically and magnetically controllable 0 and π junctions are presented; these possibilities are expected to be useful for future devices.

In this paper, a dual-polarized phased array based polarization state modulation method is proposed to enhance the physical-layer security in millimeter-wave (mm-wave) communication systems. Indeed, we utilize two polarized beams to transmit the two components of the polarized signal, respectively. By randomly selecting the transmitting antennas, both the amplitude and the phase of two beams vary randomly in undesired directions, which lead to the PM constellation structure distortion in side lobes, thus the transmission security is enhanced since the symbol error rate increases at the eavesdropper side. To enhance the security performance when the eavesdropper is close to the legitimate receiver and located in main beam, the artificial noise based on the orthogonal vector approach is inserted randomly between two polarized beams, which can further distort the constellation structure in undesired directions and improve the secrecy capacity in main beam as well. Finally, theoretical analysis and simulation results demonstrate the proposed method can improve the transmission security in mm-wave communication systems.

Numerical inverse Laplace transform (NILT) methods are potential methods for time domain simulations, for instance the analysis of the transient phenomena in systems with lumped and/or distributed parameters. This paper proposes a numerical inverse Laplace transform method based originally on hyperbolic relations. The method is further enhanced by properly adapting several convergence acceleration techniques, namely, the epsilon algorithm of Wynn, the quotient-difference algorithm of Rutishauser and the Euler transform. The resulting accelerated models are compared as for their accuracy and computational efficiency. Moreover, an expansion to two dimensions is presented for the first time in the context of the accelerated hyperbolic NILT method, followed by the error analysis. The expansion is done by repeated application of one-dimensional partial numerical inverse Laplace transforms. A detailed static error analysis of the resulting 2D NILT is performed to prove the effectivness of the method. The work is followed by a practical application of the 2D NILT method to simulate voltage/current distributions along a transmission line. The method and application are programmed using the Matlab language.

We have been investigating reversible quantum-flux-parametron (RQFP), which is a reversible logic gate using adiabatic quantum-flux-parametron (AQFP), toward realizing superconductor reversible computing. In this paper, we review the recent progress of RQFP. Followed by a brief explanation on AQFP, we first review the difference between irreversible logic gates and RQFP in light of time evolution and energy dissipation, based on our previous studies. Numerical calculation results reveal that the logic state of RQFP can be changed quasi-statically and adiabatically, or thermodynamically reversibly, and that the energy dissipation required for RQFP to perform a logic operation can be arbitrarily reduced. Lastly, we show recent experimental results of an RQFP cell, which was newly designed for the latest cell library. We observed the wide operation margins of more than 4.7dB with respect to excitation currents.