A queue layout of a graph G consists of a linear order of its vertices, and a partition of its edges into queues, such that no two edges in the same queue are nested. The queuenumber qn(G) is the minimum number of queues required in a queue layout of G. The Cartesian product of two graphs G1 = (V1,E1) and G2 = (V2,E2), denoted by G1 × G2, is the graph with {:v1 ∈ V1 and v2 ∈ V2} as its vertex set and an edge (,) belongs to G1×G2 if and only if either (u1,v1) ∈ E1 and u2 = v2 or (u2,v2) ∈ E2 and u1 = v1. Let Tk1,k2,...,kn denote the n-dimensional toroidal grid defined by the Cartesian product of n cycles with varied lengths, i.e., Tk1,k2,...,kn = Ck1 × Ck2 × … × Ckn, where Cki is a cycle of length ki ≥ 3. If k1 = k2 = … = kn = k, the graph is also called the k-ary n-cube and is denoted by Qnk. In this paper, we deal with queue layouts of toroidal grids and show the following bound: qn(Tk1,k2,...,kn) ≤ 2n-2 if n ≥ 2 and ki ≥ 3 for all i = 1,2,...,n. In particular, for n = 2 and k1,k2 ≥ 3, we acquire qn(Tk1,k2) = 2. Recently, Pai et al. (Inform. Process. Lett. 110 (2009) pp.50-56) showed that qn(Qnk) ≤ 2n-1 if n ≥1 and k ≥9. Thus, our result improves the bound of qn(Qnk) when n ≥2 and k ≥9.

Permutation polynomials have important applications in cryptography, coding theory and combinatorial designs. In this letter, we construct four classes of permutation polynomials over 𝔽2n × 𝔽2n, where 𝔽2n is the finite field with 2n elements.

Permutation polynomials have been studied for a long time and have important applications in cryptography, coding theory and combinatorial designs. In this paper, by means of the multivariate method and the resultant, we propose four new classes of permutation quadrinomials over 𝔽q3, where q is a prime power. We also show that they are not quasi-multiplicative equivalent to known ones. Moreover, we compare their differential uniformity with that of some known classes of permutation trinomials for some small q.

At Crypto 2019, Gohr first adopted the neural distinguisher for differential cryptanalysis, and since then, this work received increasing attention. However, most of the existing work focuses on improving and applying the neural distinguisher, the studies delving into the intrinsic principles of neural distinguishers are finite. At Eurocrypt 2021, Benamira et al. conducted a study on Gohr’s neural distinguisher. But for the neural distinguishers proposed later, such as the r-round neural distinguishers trained with k ciphertext pairs or ciphertext differences, denoted as NDcpk_r (Gohr’s neural distinguisher is the special NDcpk_r with K = 1) and NDcdk_r , such research is lacking. In this work, we devote ourselves to study the intrinsic principles and relationship between NDcdk_r and NDcpk_r. Firstly, we explore the working principle of NDcd1_r through a series of experiments and find that it strongly relies on the probability distribution of ciphertext differences. Its operational mechanism bears a strong resemblance to that of NDcp1_r given by Benamira et al.. Therefore, we further compare them from the perspective of differential cryptanalysis and sample features, demonstrating the superior performance of NDcp1_r can be attributed to the relationships between certain ciphertext bits, especially the significant bits. We then extend our investigation to NDcpk_r, and show that its ability to recognize samples heavily relies on the average differential probability of k ciphertext pairs and some relationships in the ciphertext itself, but the reliance between k ciphertext pairs is very weak. Finally, in light of the findings of our research, we introduce a strategy to enhance the accuracy of the neural distinguisher by using a fixed difference to generate the negative samples instead of the random one. Through the implementation of this approach, we manage to improve the accuracy of the neural distinguishers by approximately 2% to 8% for 7-round Speck32/64 and 9-round Simon32/64.

Parameter estimation theorems for LFM signals have been developed due to the advantages of fractional Fourier transform (FrFT). The traditional estimation methods in the fractional Fourier domain (FrFD) are almost based on two-dimensional search which have the contradiction between estimation performance and complexity. In order to solve this problem, we introduce the orthogonal matching pursuit (OMP) into the FrFD, propose a modified optimization method to estimate initial frequency and final frequency of fractional bandlimited LFM signals. In this algorithm, the differentiation fractional spectrum which is used to form observation matrix in OMP is derived from the spectrum analytical formulations of the LFM signal, and then, based on that the LFM signal has approximate rectangular spectrum in the FrFD and the correlation between the LFM signal and observation matrix yields a maximal value at the edge of the spectrum (see Sect.3.3 for details), the edge spectrum information can be extracted by OMP. Finally, the estimations of initial frequency and final frequency are obtained through multiplying the edge information by the sampling frequency resolution. The proposed method avoids reconstruction and the traditional peak-searching procedure, and the iterations are needed only twice. Thus, the computational complexity is much lower than that of the existing methods. Meanwhile, Since the vectors at the initial frequency and final frequency points both have larger modulus, so that the estimations are closer to the actual values, better normalized root mean squared error (NRMSE) performance can be achieved. Both theoretical analysis and simulation results demonstrate that the proposed algorithm bears a relatively low complexity and its estimation precision is higher than search-based and reconstruction-based algorithms.

In this letter, a new scheme for multirate coprime sampling and reconstructing of sparse multiband signals with very high carrier frequencies is proposed, where the locations of the signal bands are not known a priori. Simulation results show that the new scheme can simultaneously reduce both the number of sampling channels and the sampling rate for perfect reconstruction, compared to the existing schemes requiring high number of sampling channels or high sampling rate.

We present a rebound attack on the 4-branch type-2 generalized Feistel structure with an SPS round function, which is called the type-2 GFN-SPS in this paper. Applying a non-full-active-match technique, we construct a 6-round known-key truncated differential distinguisher, and it can deduce a near-collision attack on compression functions of this structure embedding the MMO or MP modes. Extending the 6-round attack, we build a 7-round truncated differential path to get a known-key differential distinguisher with seven rounds. The results give some evidences that this structure is not stronger than the type-2 GFN with an SP round function and not weaker than that with an SPSP round function against the rebound attack.

In this paper, we propose a compressed sensing scheme using sparse-graph codes and peeling decoder (SGPD). By using a mix method for construction of sensing matrices proposed by Pawar and Ramchandran, it generates local sensing matrices and implements sensing and signal recovery in an adaptive manner. Then, we show how to optimize the construction of local sensing matrices using the theory of sparse-graph codes. Like the existing compressed sensing schemes based on sparse-graph codes with “good” degree profile, SGPD requires only O(k) measurements to recover a k-sparse signal of dimension n in the noiseless setting. In the presence of noise, SGPD performs better than the existing compressed sensing schemes based on sparse-graph codes, still with a similar implementation cost. Furthermore, the average variable node degree for sensing matrices is empirically minimized for SGPD among various existing CS schemes, which can reduce the sensing computational complexity.

Traditional pipeline methods for task-oriented dialogue systems are designed individually and expensively. Existing memory augmented end-to-end methods directly map the inputs to outputs and achieve promising results. However, the most existing end-to-end solutions store the dialogue history and knowledge base (KB) information in the same memory and represent KB information in the form of KB triples, making the memory reader's reasoning on the memory more difficult, which makes the system difficult to retrieve the correct information from the memory to generate a response. Some methods introduce many manual annotations to strengthen reasoning. To reduce the use of manual annotations, while strengthening reasoning, we propose a hierarchical memory model (HM2Seq) for task-oriented systems. HM2Seq uses a hierarchical memory to separate the dialogue history and KB information into two memories and stores KB in KB rows, then we use memory rows pointer combined with an entity decoder to perform hierarchical reasoning over memory. The experimental results on two publicly available task-oriented dialogue datasets confirm our hypothesis and show the outstanding performance of our HM2Seq by outperforming the baselines.

An improved multivariate wavelet denoising algorithm combined with subspace and principal component analysis is presented in this paper. The key element is deriving an optimal orthogonal matrix that can project the multivariate observation signal to a signal subspace from observation space. Univariate wavelet shrinkage operator is then applied to the projected signals channel-wise resulting in the improvement of the output SNR. Finally, principal component analysis is performed on the denoised signal in the observation space to further improve the denoising performance. Experimental results based on synthesized and real world ECG data verify the effectiveness of the proposed algorithm.

With the continuous refinement of Deep Neural Networks (DNNs), a series of deep and complex networks such as Residual Networks (ResNets) show impressive prediction accuracy in image classification tasks. Unfortunately, the structural complexity and computational cost of residual networks make hardware implementation difficult. In this paper, we present the quantized and reconstructed deep neural network (QR-DNN) technique, which first inserts batch normalization (BN) layers in the network during training, and later removes them to facilitate efficient hardware implementation. Moreover, an accurate and efficient residual network accelerator (RNA) is presented based on QR-DNN with batch-normalization-free structures and weights represented in a logarithmic number system. RNA employs a systolic array architecture to perform shift-and-accumulate operations instead of multiplication operations. QR-DNN is shown to achieve a 1∼2% improvement in accuracy over existing techniques, and RNA over previous best fixed-point accelerators. An FPGA implementation on a Xilinx Zynq XC7Z045 device achieves 804.03 GOPS, 104.15 FPS and 91.41% top-5 accuracy for the ResNet-50 benchmark, and state-of-the-art results are also reported for AlexNet and VGG.

In this letter, a novel transmission scheme is proposed to eliminate the polarization dependent loss (PDL) effect in dual-polarized satellite systems. In fact, the PDL effect is the key problem that limits the performance of the systems based on the PM technique, while it is naturally eliminated in the proposed scheme since we transmit the two components of the polarized signal in turn in two symbol periods. Moreover, a simple and effective detection method based on the signal's power is proposed to distinguish the polarization characteristic of the transmit antenna. In addition, there is no requirement on the channel state information at the transmitter, which is popular in satellite systems. Finally, superiorities are validated by the theoretical analysis and simulation results in the dual-polarized satellite systems.

In this paper, we introduce a concise representation, called right-distance sequences (or RD-sequences for short), to describe all t-ary trees with n internal nodes. A result reveals that there exists a close relationship between the representation and the well-formed sequences suggested by Zaks [Lexicographic generation of ordered trees, Theoretical Computer Science 10 (1980) 63-82]. Using a coding tree and its concomitant tables, a systematical way can help us to investigate the structural representation of t-ary trees. Consequently, we develop efficient algorithms for determining the rank of a given t-ary tree in lexicographic order (i.e., a ranking algorithm), and for converting a positive integer to its corresponding RD-sequence (i.e., an unranking algorithm). Both the ranking and unranking algorithms can be run in O(tn) time and without computing all the entries of the coefficient table.

This paper presents a novel MEMD interval thresholding denoising, where relevant modes are selected by the similarity measure between the probability density functions of the input and that of each mode. Simulation and measured EEG data processing results show that the proposed scheme achieves better performance than other traditional denoisings.

Wireless body area networks (WBANs) have to work with low power and long lifetime to satisfy human biological safety requirements in e-health; therefore extremely low energy consumption is significant for WBANs. IEEE 802.15.6 standard has been published for wearable and implanted applications which provide communication technology requirements in WBANs. In this paper, the cross-layering optimization methodology is used to minimize the network energy consumption. Both the priority strategy and sleep mechanism in IEEE802.15.6 are considered. Macroscopic sleep model based on WBAN traffic priority and microscopic sleep model based on MAC structure are proposed. Then the network energy consumption optimization problem is solved by Lagrange dual method, the master problem are vertically decomposed into two sub problems in MAC and transport layers which are dealt with gradient method. Finally, a solution including self-adaption sleep mechanism and node rate controlling is proposed. The results of this paper indicate that the algorithm converges quickly and reduces the network energy consumption remarkably.

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.

This paper proposes an effective continuous super-resolution (CSR) algorithm for the multipath channel estimation. By designing a preamble including up-chirp and down-chirp symbols, the Doppler shift and multipath delay are estimated jointly by using convex programming. Simulation results show that the proposed CSR can achieve better detection probability of the number of multipaths than the eigenvalue based methods. Moreover, compared with conventional super-resolution techniques, such as MUSIC and ESPRIT methods, the proposed CSR algorithm demonstrates its advantage in root mean square error of the Doppler shift and multipath delay, especially for the closely located paths within low SNR.

This paper proposes a chirp-BOK modulation scheme for VLF (Very low frequency, 3-30kHz) communication under symmetric alpha-stable (SαS) noise. The atmospheric noise which is the main interference in VLF communication is more accurately characterized as SαS distribution in the previous literatures. Chirp-BOK, one of the chirp spread spectrum (CSS) technologies is widely used for its anti-interference performance and constant envelope properties. However, up-chirp and down-chirp are not strictly orthogonal, the bit error rate (BER) performance of chirp-BOK system is no longer improved with the increase of time-bandwidth product. So in this paper, the influence of non-orthogonal modulation waveform on the system is considered, and the model of the optimal parameters for chirp-BOK is derived from the perspective of minimum BER under gaussian noise and SαS noise respectively. Simulations for chirp-BOK scheme under gaussian noise and SαS noise with different α validate the effectiveness of the proposed method.

This paper presents key recovery attacks on Sandwich-MAC instantiating MD5, where Sandwich-MAC is an improved variant of HMAC and achieves the same provable security level and better performance especially for short messages. The increased interest in lightweight cryptography motivates us to analyze such a MAC scheme. Our attacks are based on a distinguishing-H attack on HMAC-MD5 proposed by Wang et al. We first improve its complexity from 297 to 289.04. With this improvement, we then propose key recovery attacks on Sandwich-MAC-MD5 by combining various techniques such as distinguishing-H for HMAC-MD5, IV Bridge for APOP, dBB-near-collisions for related-key NMAC-MD5, meet-in-the-middle attack etc. In particular, we generalize a previous key-recovery technique as a new tool exploiting a conditional key-dependent distribution. Surprisingly, a key which is even longer than the tag size can be recovered without the knowledge of the key size. Finally, our attack also improves the previous partial-key (K1) recovery on MD5-MAC, and extends it to recover both of K1 and K2.

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.