This letter studies secure transmission design with finite alphabet input for cooperative jamming network under individual power constraint. By adopting the zero-force scheme, where the jamming signal is fully laid in the null space of the relay-destination channel, the problem of enhancing the achievable secrecy rate is decomposed into two independent subproblems: relay weights design and power control. We reveal that the problem of relay weights design is identical to the problem of minimizing the maximal equivalent source-eavesdropper channel gain, which can be transformed into a semi-definite programming (SDP) problem and thus is tackled using interior point method. Besides, the problem of power control is solved with the fundamental relation between mutual information and minimum mean square error (MMSE). Numerical results show that the proposed scheme achieves significant performance gains compared to the conventional Gaussian design.

This letter proposes performance evaluation of phase-only correlation (POC) functions using signal-to-noise ratio (SNR) and peak-to-correlation energy (PCE). We derive the general expressions of SNR and PCE of the POC functions as correlation performance measures. SNR is expressed by simple fractional function of circular variance. PCE is simply given by squared peak value of the POC functions, and its expectation can be expressed in terms of circular variance.

This paper proposes an image denoising method using singular value decomposition (SVD) with block-rotation-based operations in wavelet domain. First, we decompose a noisy image to some sub-blocks, and use the single-level discrete 2-D wavelet transform to decompose each sub-block into the low-frequency image part and the high-frequency parts. Then, we use SVD and rotation-based SVD with the rank-1 approximation to filter the noise of the different high-frequency parts, and get the denoised sub-blocks. Finally, we reconstruct the sub-block from the low-frequency part and the filtered the high-frequency parts by the inverse wavelet transform, and reorganize each denoised sub-blocks to obtain the final denoised image. Experiments show the effectiveness of this method, compared with relevant methods.

Reinforcement learning has been used to adaptive service composition. However, traditional algorithms are not suitable for large-scale service composition. Based on Q-Learning algorithm, a multi-task oriented algorithm named multi-Q learning is proposed to realize subtask-assistance strategy for large-scale and adaptive service composition. Differ from previous studies that focus on one task, we take the relationship between multiple service composition tasks into account. We decompose complex service composition task into multiple subtasks according to the graph theory. Different tasks with the same subtasks can assist each other to improve their learning speed. The results of experiments show that our algorithm could obtain faster learning speed obviously than traditional Q-learning algorithm. Compared with multi-agent Q-learning, our algorithm also has faster convergence speed. Moreover, for all involved service composition tasks that have the same subtasks between each other, our algorithm can improve their speed of learning optimal policy simultaneously in real-time.

Radar is expected in advanced driver-assistance systems for environmentally robust measurements. In this paper, we propose a novel radar signal segmentation method by using a complex-valued fully convolutional network (CvFCN) that comprises complex-valued layers, real-valued layers, and a bidirectional conversion layer between them. We also propose an efficient automatic annotation system for dataset generation. We apply the CvFCN to two-dimensional (2D) complex-valued radar signal maps (r-maps) that comprise angle and distance axes. An r-maps is a 2D complex-valued matrix that is generated from raw radar signals by 2D Fourier transformation. We annotate the r-maps automatically using LiDAR measurements. In our experiment, we semantically segment r-map signals into pedestrian and background regions, achieving accuracy of 99.7% for the background and 96.2% for pedestrians.

In this study, the effect of atomically flat Si(100) surface on Hf-based Metal-Oxide-Nitride-Oxide-Silicon (MONOS) structure was investigated. After the atomically flat Si(100) surface formation by annealing at 1050/60min in Ar/4%H2 ambient, HfO2(O)/HfN1.0(N)/HfO2(O) structure with thickness of 10/3/2nm, respectively, was in-situ deposited by electron cyclotron resonance (ECR) plasma sputtering. The memory window (MW) of Al/HfO2/HfN1.0/HfO2/p-Si(100) diodes was increased from 1.0V to 2.5V by flattening of Si(100) surface. The program and erase (P/E) voltage/time were set as 10V/5s and -8V/5s, respectively. Furthermore, it was found that the gate current density after the 103P/E cycles was decreased one order of magnitude by flattening of Si(100) surface in Ar/4.0%H2 ambient.

High frame rate and ultra-low delay matching system plays an increasingly important role in human-machine interactive applications which call for higher frame rate and lower delay for a better experience. The large amount of processing data and the complex computation in a local feature based matching system, make it difficult to achieve a high process speed and ultra-low delay matching with limited resource. Aiming at a matching system with the process speed of more than 1000 fps and with the delay of less than 1 ms/frame, this paper puts forward a local binary feature based matching system with field-programmable gate array (FPGA). Pixel selection based 4-1-4 parallel matching and intensity directed symmetry are proposed for the implementation of this system. To design a basic framework with the high process speed and ultra-low delay using limited resource, pixel selection based 4-1-4 parallel matching is proposed, which makes it possible to use only one-thread resource consumption to achieve a four-thread processing. Assumes that the orientation of the keypoint will bisect the patch best and will point to the region with high intensity, intensity directed symmetry is proposed to calculate the keypoint orientation in a hardware friendly way, which is an important part for a rotation-robust matching system. Software experiment result shows that the proposed keypoint orientation calculation method achieves almost the same performance with the state-of-art intensity centroid orientation calculation method in a matching system. Hardware experiment result shows that the designed image process core supports to process VGA (640×480) videos at a process speed of 1306 fps and with a delay of 0.8083 ms/frame.

In this paper, we have investigated the PdEr-silicide formation utilizing a developed PdEr-alloy target for sputtering, and evaluated the contact resistivity of PdEr-silicide layer formed on n-Si(100) by dopant segregation process for the first time. Pd2Si and ErSi2 have same hexagonal structure, while the Schottky barrier height for electron (Φbn) is different as 0.75 eV and 0.28 eV, respectively. A 20 nm-thick PdEr-alloy layer was deposited on the n-Si(100) substrates utilizing a developed PdEr-alloy target by the RF magnetron sputtering at room temperature. Then, 10 nm-thick TiN encapsulating layer was in-situ deposited at room temperature. Next, silicidation was carried out by the RTA at 500 for 5 min in N2/4.9%H2 followed by the selective etching. From the J-V characteristics of fabricated Schottky diode, qΦbn was reduced from 0.75 eV of Pd2Si to 0.43 eV of PdEr-silicide. Furthermore, 4.0x10-8Ωcm2 was extracted for the PdEr-silicide to n-Si(100) by the dopant segregation process.

Nuclear power plants require in-vessel inspections for soundness checks and preventive maintenance. One inspection procedure is visual testing (VT), which is based on video images of an underwater camera in a nuclear reactor. However, a lot of noise is superimposed on VT images due to radiation exposure. We propose a technique for improving the quality of those images by image processing that reduces radiation noise and enhances signals. Real-time video processing was achieved by applying the proposed technique with a parallel processing unit. Improving the clarity of VT images will lead to reducing the burden on inspectors.

The concept of Gaussian integer sequence pair is generalized from a single Gaussian integer sequence. In this letter, by adopting cyclic difference set pairs, a new construction method for perfect Gaussian integer sequence pairs is presented. Furthermore, the necessary and sufficient conditions for constructing perfect Gaussian integer sequence pairs are given. Through the research in this paper, a large number of perfect Gaussian integer sequence pairs can be obtained, which can greatly extend the existence of perfect sequence pairs.

Video surveillance from airborne platforms can suffer from many sources of blur, like vibration, low-end optics, uneven lighting conditions, etc. Many different algorithms have been developed in the past that aim to recover the deblurred image but often incur substantial CPU-time, which is not always available on-board. This paper shows how a “strap-on” quasi-Newton method can accelerate the convergence of existing iterative methods with little extra overhead while keeping the performance of the original algorithm, thus paving the way for (near) real-time applications using on-board processing.

A novel Doppler spread estimation scheme is proposed for an orthogonal frequency division multiplexing (OFDM) system with a Rayleigh fading channel. The proposal develops a composite power spectral density (PSD) function by averaging the multiple PSD functions computed with multiple sets of the channel frequency response (CFR) coefficients. The Doppler spread is estimated by finding the maximum location of the composite PSD quantities larger than a threshold value given by a fixed fraction of the maximum composite PSD quantity. It is shown by simulation that the proposed scheme performs better than three conventional Doppler spread estimation schemes not only in isotropic scattering environments, but also in nonisotropic scattering environments. Moreover, the proposed scheme is shown to perform well in some Rician channel environments if the Rician K-factor is small.

In this paper, we propose a method to generate a three-dimensional (3D) thermal map and RGB + thermal (RGB-T) images of a scene from thermal-infrared and RGB images. The scene images are acquired by moving both a RGB camera and an thermal-infrared camera mounted on a stereo rig. Before capturing the scene with those cameras, we estimate their respective intrinsic parameters and their relative pose. Then, we reconstruct the 3D structures of the scene by using Direct Sparse Odometry (DSO) using the RGB images. In order to superimpose thermal information onto each point generated from DSO, we propose a method for estimating the scale of the point cloud corresponding to the extrinsic parameters between both cameras by matching depth images recovered from the RGB camera and the thermal-infrared camera based on mutual information. We also generate RGB-T images using the 3D structure of the scene and Delaunay triangulation. We do not rely on depth cameras and, therefore, our technique is not limited to scenes within the measurement range of the depth cameras. To demonstrate this technique, we generate 3D thermal maps and RGB-T images for both indoor and outdoor scenes.

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 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.

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%.

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.

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.