Authenticated-Encryption with Associated-Data (AEAD) plays an important role in guaranteeing confidentiality, integrity, and authenticity in network communications. To meet the requirements of high-performance applications, several AEADs make use of AES New Instructions (AES-NI), which can conduct operations of AES encryption and decryption dramatically fast by hardware accelerations. At SAC 2013, Wu and Preneel proposed an AES-based AEAD scheme called AEGIS-128/128L/256, to achieve high-speed software implementation. At FSE 2016, Jean and Nikolić generalized the construction of AEGIS and proposed more efficient round functions. At ToSC 2021, Sakamoto et al. further improved the constructions of Jean and Nikolić, and proposed an AEAD scheme called Rocca for beyond 5G. In this study, we first evaluate the security of the initialization phases of Rocca and AEGIS family against differential and integral attacks using MILP (Mixed Integer Linear Programming) tools. Specifically, according to the evaluation based on the lower bounds for the number of active S-boxes, the initialization phases of AEGIS-128/128L/256 are secure against differential attacks after 4/3/6 rounds, respectively. Regarding integral attacks, we present the integral distinguisher on 6 rounds and 6/5/7 rounds in the initialization phases of Rocca and AEGIS-128/128L/256, respectively. Besides, we evaluate the round function of Rocca and those of Jean and Nikolić as cryptographic permutations against differential, impossible differential, and integral attacks. Our results indicate that, for differential attacks, the growth rate of increasing the number of active S-boxes in Rocca is faster than those of Jean and Nikolić. For impossible differential and integral attacks, we show that the round function of Rocca achieves the sufficient level of the security against these attacks in smaller number of rounds than those of Jean and Nikolić.

This paper concentrates on a class of pseudorandom sequences generated by combining q-ary m-sequences and quadratic characters over a finite field of odd order, called binary generalized NTU sequences. It is shown that the relationship among the sub-sequences of binary generalized NTU sequences can be formulated as combinatorial structures called Hadamard designs. As a consequence, the combinatorial structures generalize the group structure discovered by Kodera et al. (IEICE Trans. Fundamentals, vol.E102-A, no.12, pp.1659-1667, 2019) and lead to a finite-geometric explanation for the investigated group structure.

Histogram equalization (HE) is the one of the simplest and most effective methods for contrast enhancement. It can automatically define the gray-level mapping function based on the distribution of gray-level included in the image. However, since HE does not use a spatial feature included in the input image, HE fails to produce satisfactory results for broad range of low-contrast images. The differential gray-level histogram (DH), which is contained edge information of the input image, was defined and the differential gray-level histogram equalization (DHE) has been proposed. The DHE shows better enhancement results compared to HE for many kinds of images. In this paper, we propose a generalized histogram equalization (GHE) including HE and DHE. In GHE, the histogram is created using the power of the differential gray-level, which includes the spatial features of the image. In HE, the mean brightness of the enhancement image cannot be controlled. On the other hand, GHE can control the mean brightness of the enhancement image by changing the power, thus, the mean brightness of the input image can be perfectly preserved while maintaining good contrast enhancement.

In order to improve the frequency band utilization and avoid mutual interference between signals, the BD3 satellite signals adopt Binary Offset Carrier (BOC) modulation. On one hand, BOC modulation has a narrow main peak width and strong anti-interference ability; on the other hand, the phenomenon of false acquisition locking caused by the multi-peak characteristic of BOC modulation itself needs to be resolved. In this context, this paper proposes a new BOC(n,n) unambiguous acquisition algorithm based on segmentation reconstruction. The algorithm is based on splitting the local BOC signal into four parts in each subcarrier period. The branch signal and the received signal are correlated with the received signal to generate four branch correlation signals. After a series of combined reconstructions, the final signal detection function completely eliminates secondary peaks. A simulation shows that the algorithm can completely eliminate the sub-peak interference for the BOC signals modulated by subcarriers with different phase. The characteristics of narrow correlation peak are retained. Experiments show that the proposed algorithm has superior performance in detection probability and peak-to-average ratio.

Deep neural networks show good performance in image recognition, speech recognition, and pattern analysis. However, deep neural networks also have weaknesses, one of which is vulnerability to poisoning attacks. A poisoning attack reduces the accuracy of a model by training the model on malicious data. A number of studies have been conducted on such poisoning attacks. The existing type of poisoning attack causes misrecognition by one classifier. In certain situations, however, it is necessary for multiple models to misrecognize certain data as different specific classes. For example, if there are enemy autonomous vehicles A, B, and C, a poisoning attack could mislead A to turn to the left, B to stop, and C to turn to the right simply by using a traffic sign. In this paper, we propose a multi-targeted poisoning attack method that causes each of several models to misrecognize certain data as a different target class. This study used MNIST and CIFAR10 as datasets and Tensorflow as a machine learning library. The experimental results show that the proposed scheme has a 100% average attack success rate on MNIST and CIFAR10 when malicious data accounting for 5% of the training dataset have been used for training.

In this study, a computational method is proposed for acoustic field analysis tasks that require lengthy observation times. The acoustic fields at a given observation time are obtained using a fast inverse Laplace transform with a finite-difference complex-frequency-domain. The transient acoustic field can be evaluated at arbitrary sampling intervals by obtaining the instantaneous acoustic field at the desired observation time using the proposed method.

S-box is one of the core components of symmetric cryptographic algorithms, but differential distribution table (DDT) is an important tool to research some properties of S-boxes to resist differential attacks. In this paper, we give a relationship between the sum-of-squares of DDT and the sum-of-squares indicator of (n, m)-functions based on the autocorrelation coefficients. We also get some upper and lower bounds on the sum-of-squares of DDT of balanced (n, m)-functions, and prove that the sum-of-squares of DDT of (n, m)-functions is affine invariant under affine affine equivalent. Furthermore, we obtain a relationship between the sum-of-squares of DDT and the signal-to-noise ratio of (n, m)-functions. In addition, we calculate the distributions of the sum-of-squares of DDT for all 3-bit S-boxes, the 4-bit optimal S-boxes and all 302 balanced S-boxes (up to affine equivalence), data experiments verify our results.

The differential uniformity, the boomerang uniformity, and the extended Walsh spectrum etc are important parameters to evaluate the security of S (substitution)-box. In this paper, we introduce efficient formulas to compute these cryptographic parameters of permutation polynomials of the form xrh(x(2n-1)/d) over a finite field of q=2n elements, where r is a positive integer and d is a positive divisor of 2n-1. The computational cost of those formulas is proportional to d. We investigate differentially 4-uniform permutation polynomials of the form xrh(x(2n-1)/3) and compute the boomerang spectrum and the extended Walsh spectrum of them using the suggested formulas when 6≤n≤12 is even, where d=3 is the smallest nontrivial d for even n. We also investigate the differential uniformity of some permutation polynomials introduced in some recent papers for the case d=2n/2+1.

A Mach-Zehnder optical modulator with the tunable multimode interference coupler was fabricated using Ti-diffused LiNbO3. The modulation extinction ratio could be voltage controlled to maximize up to 50 dB by tuning the coupler. Optical single-sideband modulation was also achieved with a sideband suppression ratio of more than 30 dB.

This paper details the design of a plate that controls the beam direction in an aperture array excited by a waveguide 2-plane hybrid coupler. The beam direction can be controlled in the range of ±15-32deg. in the quasi H-plane, and ±26-54deg. in the quasi E-plane at the design frequency of 66.425GHz. Inductive irises are introduced into tapered waveguides in the plate and the reflection is suppressed by narrow apertures. A plate that has a larger tilt angle in the quasi E-plane and another plate with conventional rectangular waveguide ports as a reference are fabricated and measured. The measured values agree well with the simulation results.

We investigate a problem of finding the minimum, in which each user has a real value, and we want to estimate the minimum of these values under the local differential privacy constraint. We reveal that this problem is fundamentally difficult, and we cannot construct a consistent mechanism in the worst case. Instead of considering the worst case, we aim to construct a private mechanism whose error rate is adaptive to the easiness of estimation of the minimum. As a measure of easiness, we introduce a parameter α that characterizes the fatness of the minimum-side tail of the user data distribution. As a result, we reveal that the mechanism can achieve O((ln6N/ε2N)1/2α) error without knowledge of α and the error rate is near-optimal in the sense that any mechanism incurs Ω((1/ε2N)1/2α) error. Furthermore, we demonstrate that our mechanism outperforms a naive mechanism by empirical evaluations on synthetic datasets. Also, we conducted experiments on the MovieLens dataset and a purchase history dataset and demonstrate that our algorithm achieves Õ((1/N)1/2α) error adaptively to α.

In this study, we develop a numerical method for determining transient energy deposition in biological bodies exposed to electromagnetic (EM) pulses. We use a newly developed frequency-dependent finite-difference time-domain (FD2TD) method, which is combined with the fast inverse Laplace transform (FILT) and Prony method. The FILT and Prony method are utilized to transform the Cole-Cole model of biological media into a sum of multiple Debye relaxation terms. Parameters of Debye terms are then extracted by comparison with the time-domain impulse responses. The extracted parameters are used in an FDTD formulation, which is derived using the auxiliary differential equation method, and transient energy deposition into a biological medium is calculated by the equivalent circuit method. The validity of our proposed method is demonstrated by comparing numerical results and those derived from an analytical method. Finally, transient energy deposition into human heads of TARO and HANAKO models is then calculated using the proposed method and, physical insights into pulse exposures of the human heads are provided.

A hybrid memory architecture (HMA) that consists of some distinct memory devices is expected to achieve a good balance between high performance and large capacity. Unlike conventional memory architectures, the HMA needs the metadata for data management since the data are migrated between the memory devices during the execution of an application. The memory controller caches the metadata to avoid accessing the memory devices for the metadata reference. However, as the amount of the metadata increases in proportion to the size of the HMA, the memory controller needs to handle a large amount of metadata. As a result, the memory controller cannot cache all the metadata and increases the number of metadata references. This results in an increase in the access latency to reach the target data and degrades the performance. To solve this problem, this paper proposes a metadata prefetching mechanism for HMAs. The proposed mechanism loads the metadata needed in the near future by prefetching. Moreover, to increase the effect of the metadata prefetching, the proposed mechanism predicts the metadata used in the near future based on an address difference that is the difference between two consecutive access addresses. The evaluation results show that the proposed metadata prefetching mechanism can improve the instructions per cycle by up to 44% and 9% on average.

In this study, edge diffraction of an electromagnetic plane wave by two-dimensional conducting wedges has been analyzed by the physical optics (PO) method for both E and H polarizations. Non-uniform and uniform asymptotic solutions of diffracted fields have been derived. A unified edge diffraction coefficient has also been derived with four cotangent functions from the conventional angle-dependent coefficients. Numerical calculations have been made to compare the results with those by other methods, such as the exact solution and the uniform geometrical theory of diffraction (UTD). A good agreement has been observed to confirm the validity of our method.

Various electromagnetic (EM) wave applications have become commonplace, and humans are frequently exposed to EM waves. Therefore, the effect of EM waves on the human body should be evaluated. In this study, we focused on the specific absorption rate (SAR) due to the EM waves emitted from smartphones, developed high-resolution numerical smartphone models, and studied the SAR variation by changing the position and tilt angle (the angle between the display of the smartphone model and horizontal plane) of the smartphone models vis-à-vis the human abdomen, assuming the use of the smartphone at various tilt angles in front of the abdomen. The calculations showed that the surface shape of the human model influenced the SAR variation.

Deep neural networks show good performance in image recognition, speech recognition, and pattern analysis. However, deep neural networks show weaknesses, one of which is vulnerability to backdoor attacks. A backdoor attack performs additional training of the target model on backdoor samples that contain a specific trigger so that normal data without the trigger will be correctly classified by the model, but the backdoor samples with the specific trigger will be incorrectly classified by the model. Various studies on such backdoor attacks have been conducted. However, the existing backdoor attack causes misclassification by one classifier. In certain situations, it may be necessary to carry out a selective backdoor attack on a specific model in an environment with multiple models. In this paper, we propose a multi-model selective backdoor attack method that misleads each model to misclassify samples into a different class according to the position of the trigger. The experiment for this study used MNIST and Fashion-MNIST as datasets and TensorFlow as the machine learning library. The results show that the proposed scheme has a 100% average attack success rate for each model while maintaining 97.1% and 90.9% accuracy on the original samples for MNIST and Fashion-MNIST, respectively.

Differential Evolution (DE) algorithm is simple and effective. Since DE has been proposed, it has been widely used to solve various complex optimization problems. To further exploit the advantages of DE, we propose a new variant of DE, termed as ranking-based differential evolution (RDE), by performing ranking on the population. Progressively better individuals in the population are used for mutation operation, thus improving the algorithm's exploitation and exploration capability. Experimental results on a number of benchmark optimization functions show that RDE significantly outperforms the original DE and performs competitively in comparison with other two state-of-the-art DE variants.

Binary sequence pairs with optimal periodic correlation have important applications in many fields of communication systems. In this letter, four new families of binary sequence pairs are presented based on the generalized cyclotomy over Z5q, where q ≠ 5 is an odd prime. All these binary sequence pairs have optimal three-level correlation values {-1, 3}.

Air traffic management (ATM) systems around the world are being modernized to accommodate shifts towards performance- and trajectory-based operations. These shifts will require new indices for safety, efficiency and complexity. The authors have been developing an index for evaluating air traffic control (ATC) difficulty that utilizes the relative positions and velocity vectors of aircraft pairs as input data. Prior to practical application of the index, it is necessary to understand the effects of input data error, i.e. errors in the positions and velocities of a pair of aircraft, on the estimated difficulty value. Two sensitivity analyses were therefore performed for a pair of aircraft cruising at constant speeds on intersecting linear tracks at the same altitude. Sensitivity analysis examines how uncertainty in inputs relates to uncertainty in outputs. Firstly, an analysis of propagation error was carried out. The formula of the propagation error at a certain point was derived based on the assumed input error, and the distribution of propagation error was investigated for all possible situations and compared with the distribution of difficulty values to clarify its characteristics. Secondly, a sensitivity analysis based on variance was carried out that evaluated the effect of each input parameter using a conditional variance value called the Sobol indices. Using a Monte Carlo method, we investigated the effect of each input parameter on the calculated difficulty value for all possible situations of aircraft pairs on intersecting trajectories. As a result, it was found that the parameter that most affects the difficulty value is the intersection angle of the trajectories.

Recently the data-driven learning of dynamic systems has become a promising approach because no physical knowledge is needed. Pure machine learning approaches such as Gaussian process regression (GPR) learns a dynamic model from data, with all physical knowledge about the system discarded. This goes from one extreme, namely methods based on optimizing parametric physical models derived from physical laws, to the other. GPR has high flexibility and is able to model any dynamics as long as they are locally smooth, but can not generalize well to unexplored areas with little or no training data. The analytic physical model derived under assumptions is an abstract approximation of the true system, but has global generalization ability. Hence the optimal learning strategy is to combine GPR with the analytic physical model. This paper proposes a method to learn dynamic systems using GPR with analytic ordinary differential equations (ODEs) as prior information. The one-time-step integration of analytic ODEs is used as the mean function of the Gaussian process prior. The total parameters to be trained include physical parameters of analytic ODEs and parameters of GPR. A novel method is proposed to simultaneously learn all parameters, which is realized by the fully Bayesian GPR and more promising to learn an optimal model. The standard Gaussian process regression, the ODE method and the existing method in the literature are chosen as baselines to verify the benefit of the proposed method. The predictive performance is evaluated by both one-time-step prediction and long-term prediction. By simulation of the cart-pole system, it is demonstrated that the proposed method has better predictive performances.