In this paper, a passive multiple-input multiple-output (MIMO) radar system with widely separated antennas that estimates the positions and velocities of multiple moving targets by utilizing time delay (TD) and doppler shift (DS) measurements is proposed. Passive radar systems can detect targets by using multiple uncoordinated and un-synchronized illuminators and we assume that all the measurements including TD and DS have been known by a preprocessing method. In this study, the algorithm can be divided into three stages. First, based on location information within a certain range and utilizing the DBSCAN cluster algorithm we can obtain the initial position of each target. In the second stage according to the correlation between the TD measurements of each target in a specific receiver and the DSs, we can find the set of DS measurements for each target. Therefore, the initial speed estimated values can be obtained employing the least squares (LS) method. Finally, maximum likelihood (ML) estimation of a first-order Taylor expansion joint TD and DS is applied for a better solution. Extensive simulations show that the proposed algorithm has a good estimation performance and can achieve the Cramér-Rao lower bound (CRLB) under the condition of moderate measurement errors.

The dyadic filter bank property of multivariate empirical mode decomposition (MEMD) for white Gaussian noise (WGN) is well established. In order to investigate the way MEMD behaves in the presence of fractional Gaussian noise (fGn), we conduct thorough numerical experiments for MEMD for fGn inputs. It turns out that similar to WGN, MEMD follows dyadic filter bank structure for fGn inputs, which is more stable than empirical mode decomposition (EMD) regardless of the Hurst exponent. Moreover, the estimation of the Hurst exponent of fGn contaminated with different kinds of signals is also presented via MEMD in this work.

In this letter, a method of wideband direction of arrival (DOA) estimation based on co-prime arrays with sub-Nyquist sampling is proposed. Previous works have employed co-prime arrays for wideband DOA estimation, which can increase the degrees of freedom (DOFs) in the spatial domain. However, they are all based on Nyquist sampling. Different from existing methods, we incorporate a sub-Nyquist sampling scheme called multicoset sampling for DOA estimation to relax hardware condition. Simulation results show the correctness and effectiveness.

Maneuvering target tracking under mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions has received considerable interest in the last decades. In this paper, a hierarchical interacting multiple model (HIMM) method is proposed for estimating target position under mixed LOS/NLOS conditions. The proposed HIMM is composed of two layers with Markov switching model. The purpose of the upper layer, which is composed of two interacting multiple model (IMM) filters in parallel, is to handle the switching between the LOS and the NLOS environments. To estimate the target kinetic variables (position, speed and acceleration), the unscented Kalman filter (UKF) with the current statistical (CS) model is used in the lower-layer. Simulation results demonstrate the effectiveness and superiority of the proposed method, which obtains better tracking accuracy than the traditional IMM.

IEEE 802.15.6 provides PHY and MAC layer profiles for wearable and implanted Wireless Body Area Networks (WBANs). The critical requirements of QoS guarantee and ultra-low-power are severe challenges when implementing IEEE 802.15.6. In this paper, the key problem in IEEE 802.15.6 standard that “How to allocate EAP (Exclusive Access Phase)?” is solved for the first time: An analysis of network performance indicates that too much EAP allocation can not promote traffic performance obviously and effectually. However, since EAP allocation plays an important role in guaranteeing quality of service, a customized and quantitative EAP allocation solution is proposed. Simulation results show that the solution can obtain the optimal network performance. Furthermore, the estimated models of delay and energy are developed, which help to design the WBAN according to application requirements and analyze the network performance according to the traffic characteristics. The models are simple, effective, and relatively accurate. Results show that the models have approximated mean and the correlation coefficient is greater than 0.95 compared with the simulations of IEEE 802.15.6 using NS2 platform. The work of this paper can solve crucial practical problems in using IEEE 802.15.6, and will propel WBANs applications widely.

Memory-efficient Internet Protocol (IP) lookup with high speed is essential to achieve link-speed packet forwarding in IP routers. The rapid growth of Internet traffic and the development of optical link technologies have made IP lookup a major performance bottleneck in core routers. In this paper, we propose a new IP route lookup architecture based on hardware called Prefix-Route Trie (PR-Trie), which supports both IPv4 and IPv6 addresses. In PR-Trie, we develop a novel structure called Overlapping Hybrid Trie (OHT) to perform fast longest-prefix-matching (LPM) based on Multibit-Trie (MT), and a hash-based level matching query used to achieve only one off-chip memory access per lookup. In addition, the proposed PR-Trie also supports fast incremental updates. Since the memory complexity in MT-based IP lookup schemes depends on the level-partitioning solution and the data structure used, we develop an optimization algorithm called Bitmap-based Prefix Partitioning Optimization (BP2O). The proposed BP2O is based on a heuristic search using Ant Colony Optimization (ACO) algorithms to optimize memory efficiency. Experimental results using real-life routing tables prove that our proposal has superior memory efficiency. Theoretical performance analyses show that PR-Trie outperforms the classical Trie-based IP lookup algorithms.

The existing literature focuses on the applications of fast filter bank due to its excellent frequency responses with low complexity. However, the topic is not addressed related to the general transfer function expressions of the corresponding subfilters for a specific channel. To do this, in this paper, general closed-form transfer function expressions for fast filter bank are derived. Firstly, the cascaded structure of fast filter bank is modelled by a binary tree, with which the index of the subfilter at each stage within the channel can be determined. Then the transfer functions for the two outputs of a subfilter are expressed in a unified form. Finally, the general closed-form transfer functions for the channel and its corresponding subfilters are obtained by variables replacement if the prototype lowpass filters for the stages are given. Analytical results and simulations verify the general expressions. With such closed-form expressions lend themselves easily to analysis and direct computation of the transfer functions and the frequency responses without the structure graph.

In software defined radio systems, a channelizer plays an important role in extracting the desired signals from a wideband signal. Compared to the conventional methods, the proposed scheme provides a solution to design a digital channelizer extracting the multiple subband signals at different center frequencies with low complexity. To do this, this paper formulates the problem as an optimization problem, which minimizes the required multiplications number subject to the constraints of the ripple in the passbands and the stopbands for single channel and combined multiple channels. In addition, a solution to solve the optimization problem is also presented and the corresponding structure is demonstrated. Simulation results show that the proposed scheme requires smaller number of the multiplications than other conventional methods. Moreover, unlike other methods, this structure can process signals with different bandwidths at different center frequencies simultaneously only by changing the status of the corresponding multiplexers without hardware reimplementation.

In the letter, two classes of optimal codebooks and asymptotically optimal codebooks in regard to the Levenshtein bound are presented, which are based on mutually unbiased bases (MUB) and approximately mutually unbiased bases (AMUB), respectively.

Low Probability of Intercept (LPI) radar waveform has complex and diverse modulation schemes, which cannot be easily identified by the traditional methods. The research on intrapulse modulation LPI radar waveform recognition has received increasing attention. In this paper, we propose an automatic LPI radar waveform recognition algorithm that uses a multi-resolution fusion convolutional neural network. First, signals embedded within the noise are processed using Choi-William Distribution (CWD) to obtain time-frequency feature images. Then, the images are resized by interpolation and sent to the proposed network for training and identification. The network takes a dual-channel CNN structure to obtain features at different resolutions and makes features fusion by using the concatenation and Inception module. Extensive simulations are carried out on twelve types of LPI radar waveforms, including BPSK, Costas, Frank, LFM, P1~P4, and T1~T4, corrupted with additive white Gaussian noise of SNR from 10dB to -8dB. The results show that the overall recognition rate of the proposed algorithm reaches 95.1% when the SNR is -6dB. We also try various sample selection methods related to the recognition task of the system. The conclusion is that reducing the samples with SNR above 2dB or below -8dB can effectively improve the training speed of the network while maintaining recognition accuracy.

In this letter, we present a novel method for reconstructing continuous data field from scattered point data, which leads to a more characteristic visualization result by volume rendering. The gradient distribution of scattered point data is analyzed for local feature investigation via singular-value decomposition. A data-adaptive ellipsoidal shaped function is constructed as the penalty function to evaluate point weight coefficient in MLS approximation. The experimental results show that the proposed method can reduce the reconstruction error and get a visualization with better feature discrimination.

Feature visualization is of great significances in volume visualization, and feature extraction has been becoming extremely popular in feature visualization. While precise definition of features is usually absent which makes the extraction difficult. This paper employs probability density function (PDF) as statistical property, and proposes a statistical property guided approach to extract features for volume data. Basing on feature matching, it combines simple liner iterative cluster (SLIC) with Gaussian mixture model (GMM), and could do extraction without accurate feature definition. Further, GMM is paired with a normality test to reduce time cost and storage requirement. We demonstrate its applicability and superiority by successfully applying it on homogeneous and non-homogeneous features.

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.