In this paper, we propose four new general constructions of LCZ/ZCZ sequence sets based on interleaving technique and affine transformations. A larger family of LCZ/ZCZ sequence sets with longer period are generated by these constructions, which are more flexible among the selection of the alphabet size, the period of the sequences and the length of LCZ/ZCZ, compared with those generated by the known constructions. Especially, two families of the newly constructed sequences can achieve or almost achieve the theoretic bound.

In this paper, we introduce a construction of 16-QAM sequences based on known binary sequences using multiple sequences, interleaved sequences and Gray mappings. Five kinds of binary sequences of period N are put into the construction to get five kinds of new 16-QAM sequences of period 4N. These resultant sequences have 5-level autocorrelation {0, ±8, ±8N}, where ±8N happens only once each. The distributions of the periodic autocorrelation are also given. These will provide more choices for many applications.

Based on the generalized cyclotomy of order two with respect to n=p1e1+1p2e2+1…ptet+1, where p1, p2, …,pt are pairwise distinct odd primes and e1, e2,…, et are non-negative integers satisfying gcd (piei (pi-1), pjej (pj-1)) = 2 for all i ≠ j, this paper constructs a new family of generalized cyclotomic sequences of order two with length n and investigates their linear complexity. In the view of cascade theory, this paper obtains the linear complexity of a representative sequence.

In this letter, a new addressing scheme for hexagonal networks is proposed. Using the new addressing scheme, many routing algorithms designed for networks using square-based topologies such as mesh and torus can also be applied to hexagonal networks. Methods of applying the turn model to hexagonal networks are derived, with some new minimal and partial adaptive routing algorithms obtained. Simulations of the new routing algorithms under different working conditions are carried on hexagonal networks of various sizes. The results show that the proposed algorithms can offer lower packet delay and loss rate than the popular dimension order routing algorithm.

In this paper, we propose new constructions of binary sequences based on an interleaving technique. In our constructions, we make use of any binary sequences with ideal 2-level autocorrelation, a special shift sequence as well as the perfect binary sequence or sequence (0,1,1) in the interleaved structure to get the new sequences. Except for the most autocorrelation values of our new sequences, we find that the unexpected autocorrelation values only occur four or two times in each period no matter how long the period is. We state that the sequences have a good autocorrelation in this case. In particular, the autocorrelation distribution of our sequences is determined.

Topological inference is the foundation of network performance analysis and optimization. Due to the difficulty of obtaining prior topology information of wireless sensor networks, we propose routing topology inference, RTI, which reconstructs the routing topology from source nodes to sink based on marking packets and probing locally. RTI is not limited to any specific routing protocol and can adapt to a dynamic and lossy networks. We select topological distance and reconstruction time to evaluate the correctness and effectiveness of RTI and then compare it with PathZip and iPath. Simulation results indicate that RTI maintains adequate reconstruction performance in dynamic and packet loss environments and provides a global routing topology view for wireless sensor networks at a lower reconstruction cost.

Sound source localization is an essential technique in many applications, e.g., speech enhancement, speech capturing and human-robot interaction. However, the performance of traditional methods degrades in noisy or reverberant environments, and it is sensitive to the spatial location of sound source. To solve these problems, we propose a sound source localization framework based on bi-direction interaural matching filter (IMF) and decision weighting fusion. Firstly, bi-directional IMF is put forward to describe the difference between binaural signals in forward and backward directions, respectively. Then, a hybrid interaural matching filter (HIMF), which is obtained by the bi-direction IMF through decision weighting fusion, is used to alleviate the affection of sound locations on sound source localization. Finally, the cosine similarity between the HIMFs computed from the binaural audio and transfer functions is employed to measure the probability of the source location. Constructing the similarity for all the spatial directions as a matrix, we can determine the source location by Maximum A Posteriori (MAP) estimation. Compared with several state-of-the-art methods, experimental results indicate that HIMF is more robust in noisy environments.

In this letter, a property of the characteristic matrix of the Rotation Symmetric Boolean Functions (RSBFs) is characterized, and a sufficient and necessary condition for RSBFs being 1st correlation-immune (1-CI for simplicity) is obtained. This property is applied to construct resilient RSBFs of order 1 (1-resilient for simplicity) on pq variables, where p and q are both prime consistently in this letter. The results show that construction and counting of 1-resilient RSBFs on pq variables are equivalent to solving an equation system and counting the solutions. At last, the counting of all 1-resilient RSBFs on pq variables is also proposed.

Orthogonal arrays have important applications in statistics and computer science, as well as in coding theory. In this letter, a new construction method of symmetric orthogonal arrays of strength t is proposed, which is a concatenation of two orthogonal partitions according to a latin square. As far as we know, this is a new construction of symmetric orthogonal arrays of strength t, where t is a given integer. Based on the different latin squares, we also study the enumeration problem of orthogonal partitions, and a lower bound on the count of orthogonal partitions is derived.

The linear complexity of binary sequences plays a fundamental part in cryptography. In the paper, we construct more general forms of generalized cyclotomic binary sequences with period 2pm+1qn+1. Furthermore, we establish the formula of the linear complexity of proposed sequences. The results reveal that such sequences with period 2pm+1qn+1 have a good balance property and high linear complexity.

This paper studies the 2-adic complexity of the self-shrinking sequence under the relationship between 2-adic integers and binary sequences. Based on the linear complexity and the number of the sequences which have the same connection integer, we conclude that the 2-adic complexity of the self-shrinking sequence constructed by a binary m-sequence of order n has a lower bound 2n-2-1. Furthermore, it is shown that its 2-adic complexity has a bigger lower bound under some circumstances.

Pseudo-random sequences with high linear complexity play important roles in many domains. We give linear complexity of generalized cyclotomic quaternary sequences with period pq over Z4 via the weights of its Fourier spectral sequence. The results show that such sequences have high linear complexity.

Quantum steganography is to send secret quantum information through a quantum channel, such that an unauthorized user will not be aware of the existence of secret data. The depolarizing channel can hide quantum information by disguising it as channel errors of a quantum error-correcting code. We improve the efficiency of quantum steganography with noisy depolarizing channels, by modifying the twirling procedure and adding quantum teleportation. The proposed scheme not only meets the requirements of quantum steganography but also has higher efficiency.

A new unsupervised classification method is proposed for polarimetric SAR images to keep the spatial coherence of pixels and edges of different kinds of targets simultaneously. We consider the label scale variability of images by combining Inhomogeneous Markov Random Field (MRF) and Bayes' theorem. After minimizing an energy function using an expansion algorithm based on Graph Cuts, we can obtain classification results that are discontinuity preserving. Using a NASA/JPL AIRSAR image, we demonstrate the effectiveness of the proposed method.

Recently, Yu, Gong and Tang found new constructions of binary sequences of period 4N with optimal autocorrelation magnitude by different interleaved structure of sequences and sequences which had special correlation property, respectively. In this paper, we derive more results on binary sequences of period 4N which also have optimal autocorrelation.

The dynamic characteristics are the key issues in the optimum design of a permanent magnetic actuator (PMA). A new approach to forecast the dynamic characteristics of the multilink PMA is proposed. By carrying out further developments of ADAMS and ANSOFT, a mathematic calculation model describing the coupling of mechanical movement, electric circuit and magnetic field considering eddy current effect, is constructed. With this model, the dynamic characteristics of the multilink PMA are calculated and compared with the experimental results. Factors that affect the opening time of the multilink PMA are analyzed with the model as well. The method is capable of providing a reference for the design of the PMA.

The finger-vein-based deep neural network authentication system has been applied widely in real scenarios, such as countries' banking and entrance guard systems. However, to ensure performance, the deep neural network should train many parameters, which needs lots of time and computing resources. This paper proposes a method that introduces artificial features with prior knowledge into the convolution layer. First, it designs a multi-direction pattern base on the traditional local binary pattern, which extracts general spatial information and also reduces the spatial dimension. Then, establishes a sample effective deep convolutional neural network via combination with convolution, with the ability to extract deeper finger vein features. Finally, trains the model with a composite loss function to increase the inter-class distance and reduce the intra-class distance. Experiments show that the proposed methods achieve a good performance of higher stability and accuracy of finger vein recognition.

In this paper, we introduce a new general construction of zero correlation zone (ZCZ) sequence set, which is based on two given ZCZ sequence sets. Compared with the two given sequence sets, the resultant sequence set not only has larger family size and longer period, but also provides more flexible choices of basic sequences, ZCZ length and family size.

Named Entity Translation Equivalents extraction plays a critical role in machine translation (MT) and cross language information retrieval (CLIR). Traditional methods are often based on large-scale parallel or comparable corpora. However, the applicability of these studies is constrained, mainly because of the scarcity of parallel corpora of the required scale, especially for language pairs of Chinese and Japanese. In this paper, we propose a method considering the characteristics of Chinese and Japanese to automatically extract the Chinese-Japanese Named Entity (NE) translation equivalents based on inductive learning (IL) from monolingual corpora. The method adopts the Chinese Hanzi and Japanese Kanji Mapping Table (HKMT) to calculate the similarity of the NE instances between Japanese and Chinese. Then, we use IL to obtain partial translation rules for NEs by extracting the different parts from high similarity NE instances in Chinese and Japanese. In the end, the feedback processing updates the Chinese and Japanese NE entity similarity and rule sets. Experimental results show that our simple, efficient method, which overcomes the insufficiency of the traditional methods, which are severely dependent on bilingual resource. Compared with other methods, our method combines the language features of Chinese and Japanese with IL for automatically extracting NE pairs. Our use of a weak correlation bilingual text sets and minimal additional knowledge to extract NE pairs effectively reduces the cost of building the corpus and the need for additional knowledge. Our method may help to build a large-scale Chinese-Japanese NE translation dictionary using monolingual corpora.

An efficient resistive random access memory (ReRAM) structure is developed for accelerating convolutional neural network (CNN) powered by the in-memory computation. A novel ReRAM cell circuit is designed with two-directional (2-D) accessibility. The entire memory system is organized as a 2-D array, in which specific memory cells can be identically accessed by both of column- and row-locality. For the in-memory computations of CNNs, only relevant cells in an identical sub-array are accessed by 2-D read-out operations, which is hardly implemented by conventional ReRAM cells. In this manner, the redundant access (column or row) of the conventional ReRAM structures is prevented to eliminated the unnecessary data movement when CNNs are processed in-memory. From the simulation results, the energy and bandwidth efficiency of the proposed memory structure are 1.4x and 5x of a state-of-the-art ReRAM architecture, respectively.