Developing a robust appearance model is a challenging task due to appearance variations of objects such as partial occlusion, illumination variation, rotation and background clutter. Existing tracking algorithms employ linear combinations of target templates to represent target appearances, which are not accurate enough to deal with appearance variations. The underlying relationship between target candidates and the target templates is highly nonlinear because of complicated appearance variations. To address this, this paper presents a regularized kernel representation for visual tracking. Namely, the feature vectors of target appearances are mapped into higher dimensional features, in which a target candidate is approximately represented by a nonlinear combination of target templates in a dimensional space. The kernel based appearance model takes advantage of considering the non-linear relationship and capturing the nonlinear similarity between target candidates and target templates. l₂-regularization on coding coefficients makes the approximate solution of target representations more stable. Comprehensive experiments demonstrate the superior performances in comparison with state-of-the-art trackers.

This study proposes a maximum-likelihood-estimation method for a quadrotor UAV given the existence of sensor delays. The state equation of the UAV is nonlinear, and thus, we propose an approximated method that consists of two steps. The first step estimates the past state based on the delayed output through an extended Kalman filter. The second step involves calculating an estimate of the present state by simulating the original system from the past to the present. It is proven that the proposed method provides an approximated maximum-likelihood-estimation. The effectiveness of the estimator is verified by performing experiments.

In the process of VLSI design, ECO (Engineering Change Order) may occur at any design phase. When ECO happens after the netlist is generated and optimized, designers may like to modify the netlist directly. This is because if ECO is performed in the high-level description, the netlist should be resynthesized and the result may be significantly different from the original one, even if the modification in the high-level description is small. As the result, the efforts spent on optimization so far may become useless. When the netlist is modified directly, the C description should be revised accordingly. This paper proposes a method to reconstruct a C description from the revised netlist. In the proposed method, designers need to provide a template represented in C, which has some vacant (blanked) places and is created from the original C description. The vacant places are automatically synthesized using a CEGIS-based method (Counter Example Guided Inductive Synthesis). Using a set of use-cases, our method tries to find the correct expressions for the vacant places so that the entire description becomes functionally equivalent to the given modified netlist, by only simulating the netlist. Experimental results show that the proposed method can reconstruct C descriptions successfully within practical time for several examples including the one having around 9,000 lines of executable statements. Moreover, the proposed method can be applied to equivalence checking between a netlist and a C description, as shown by our experimental results.

In encryption schemes, the sender may not generate randomness properly if generating randomness is costly, and the sender is not concerned about the security of a message. The problem was studied by the first author (2016), and was formalized in a game-theoretic framework. In this work, we construct an encryption scheme with an optimal round complexity on the basis of the mechanism of repeated games.

In this paper, a new generalized cyclotomy over Z_pq is presented based on cyclotomy and Chinese remainder theorem, where p and q are different odd primes. Several new construction methods for binary sequence pairs of period pq with ideal two-level correlation are given by utilizing these generalized cyclotomic classes. All the binary sequence pairs from our constructions have both ideal out-of-phase correlation values -1 and optimum balance property.

In this paper, we propose a new enhancement method for color images. In color image processing, hue preserving is required. The proposed method is performed into HSI color space whose gamut is same as RGB color space. The differential gray-level histogram equalization (DHE) is effective for gray scale images. The proposed method is an extension version of the DHE for color images, and furthermore, the enhancement degree is variable by introducing two parameters. Since our processing method is applied to not only intensity but also saturation, the contrast and the colorfulness of the output image can be varied. It is an important issue how to determine the two parameters. Thus, we give the guideline for how to decide the two parameters. By using the guideline, users can easily obtain their own enhancement images.

In this paper, an optimal method is proposed to design sparse-coefficient notch filters with principal basic vectors in the column space of a matrix constituted with frequency samples. The proposed scheme can perform in two stages. At the first stage, the principal vectors can be determined in the least-squares sense. At the second stage, with some components of the principal vectors, the notch filter design is formulated as a linear optimization problem according to the desired specifications. Optimal results can form sparse coefficients of the notch filter by solving the linear optimization problem. The simulation results show that the proposed scheme can achieve better performance in designing a sparse-coefficient notch filter of small order compared with other methods such as the equiripple method, the orthogonal matching pursuit based scheme and the L₁-norm based method.

A dynamic neural network has ternary connection parameters and can generate various binary periodic orbits. In order to analyze the dynamics, we present two feature quantities which characterize stability and transient phenomenon of a periodic orbit. Calculating the feature quantities, we investigate influence of connection sparsity on stability of a target periodic orbit corresponding to a circuit control signal. As the sparsity increases, at first, stability of a target periodic orbit tends to be stronger. In the next, the stability tends to be weakened and various transient phenomena exist. In the most sparse case, the network has many periodic orbits without transient phenomenon.

SPARX-128/256 is one of the two versions of the SPARX-128 block cipher family. It has 128-bit block size and 256-bit key size. SPARX has been developed using ARX-based S-boxes with the aim of achieving provable security against single-trail differential and linear cryptanalysis. In this letter, we propose 20-round impossible differential distinguishers for SPARX-128. Then, we utilize these distinguishers to attack 24 rounds (out of 40 rounds) of SPARX-128/256. Our attack has time complexity of 2²³² memory accesses, memory complexity of 2^160.81 128-bit blocks, and data complexity of 2¹⁰⁴ chosen plaintexts.

In this letter, we consider the power allocation scheme with rate proportional fairness to maximize energy efficiency in the downlink the non-orthogonal multiple access (NOMA) systems. The optimization problem of energy efficiency is a non-convex optimization problem, and the fractional programming is used to transform the original problem into a series of optimization sub-problems. A two-layer iterative algorithm is proposed to solve these sub-problems, in which power allocation with the fixed energy efficiency is achieved in the inner layer, and the optimal energy efficiency of the system is obtained by the bisection method in the outer layer. Simulation results show the effectiveness of the proposed algorithm.