Construction of multiple output functions is one of the most important problems in the design and analysis of stream ciphers. Generally, such a function has to be satisfied with several criteria, such as high nonlinearity, resiliency and high algebraic degree. But there are mutual restraints among the cryptographic parameters. Finding a way to achieve the optimization is always regarded as a hard task. In this paper, by using the disjoint linear codes and disjoint spectral functions, two classes of resilient multiple output functions are obtained. It has been proved that the obtained functions have high nonlinearity and high algebraic degree.

In this paper, by using the properties of the cyclic Hadamard matrices of order 4t, an infinite class of (4t-1)-variable 2-resilient rotation symmetric Boolean functions is constructed, and the nonlinearity of the constructed functions are also studied. To the best of our knowledge, this is the first class of direct constructions of 2-resilient rotation symmetric Boolean functions. The spirit of this method is different from the known methods depending on the solutions of an equation system proposed by Du Jiao, et al. Several situations are examined, as the direct corollaries, three classes of (4t-1)-variable 2-resilient rotation symmetric Boolean functions are proposed based on the corresponding sequences, such as m sequences, Legendre sequences, and twin primes sequences respectively.

Convolutional approximate message-passing (CAMP) is an efficient algorithm to solve linear inverse problems. CAMP aims to realize advantages of both approximate message-passing (AMP) and orthogonal/vector AMP. CAMP uses the same low-complexity matched-filter as AMP. To realize the asymptotic Gaussianity of estimation errors for all right-orthogonally invariant matrices, as guaranteed in orthogonal/vector AMP, the Onsager correction in AMP is replaced with a convolution of all preceding messages. CAMP was proved to be asymptotically Bayes-optimal if a state-evolution (SE) recursion converges to a fixed-point (FP) and if the FP is unique. However, no proofs for the convergence were provided. This paper presents a theoretical analysis for the convergence of the SE recursion. Gaussian signaling is assumed to linearize the SE recursion. A condition for the convergence is derived via a necessary and sufficient condition for which the linearized SE recursion has a unique stationary solution. The SE recursion is numerically verified to converge toward the Bayes-optimal solution if and only if the condition is satisfied. CAMP is compared to conjugate gradient (CG) for Gaussian signaling in terms of the convergence properties. CAMP is inferior to CG for matrices with a large condition number while they are comparable to each other for a small condition number. These results imply that CAMP has room for improvement in terms of the convergence properties.

In this letter, firstly, a novel adaptive beamformer using independent component analysis (ICA) algorithm is proposed. By this algorithm, the ambiguity of amplitude and phase resulted from blind source separation is removed utilizing the special structure of array manifolds matrix. However, there might exist great calibration error when the powers of interferences are far larger than that of desired signal at many applications such as sonar, radio astronomy, biomedical engineering and earthquake detection. As a result, this will lead to a significant reduction in separation performance. Then, a new method based on the combination of ICA and primary component analysis (PCA) is proposed to recover the desired signal's amplitude under strong interference. Finally, computer simulation is carried out to indicate the effectiveness of our methods. The simulation results show that the proposed methods can obtain higher SNR and more accurate power estimation of desired signal than diagonal loading sample matrix inversion (LSMI) and worst-case performance optimization (WCPO) method.

Exponential growth in data volumes has promoted widespread interest in data-selective adaptive algorithms. In a pioneering work, Diniz developed the data-selective least mean square (DS-LMS) algorithm, which is able to reduce specific quantities of computation data without compromising performance. Note however that the existing framework fails to consider the issue of impulse noise (IN), which can greatly undermine the benefits of reduced computation. In this letter, we present an error-based IN detection algorithm for implementation in conjunction with the DS-LMS algorithm. Numerical evaluations confirm the effectiveness of our proposed IN-tolerant DS-LMS algorithm.

In this paper, the random numbers generated by a true random number generator, using the oscillator sampling method, are formulated using a renewal process, and this formulation is used to demonstrate the uniformity of the random numbers and the independence between different bits. Using our results, a lower bound for the speed of random number generation could easily be identified, according to the required statistical quality.

Frequency-hopping sequence (FHS) sets with low-hit-zone (LZH) can be well applied in quasi-synchronous (QS) frequency-hopping multiple-access (FHMA) systems to reduce the mutual interference among different users. On the other hand, LHZ-FHS sets with wide-gap (WG) property can effectively resist the broadband blocking interference, the single frequency narrowband interference, the multipath fading and the tracking interference. In this letter, a new family of WG-LHZ-FHS sets is constructed. Besides, these new WG-LHZ-FHS sets possess optimal average periodic Hamming correlation (APHC) properties.

In this letter, we derive a novel and accurate closed-form bit error rate (BER) approximation of the optical wireless communications (OWC) systems for the sub-carrier intensity modulation (SIM) employing binary phase-shift keying (BPSK) with multiple transmit and single receive apertures over strong atmospheric turbulence channels, which makes it possible to effectively investigate and predict the BER performance for various system configurations. Furthermore, we also derive a concise asymptotic BER formula to quantitatively evaluate the asymptotically achievable error performance (i.e., asymptotic diversity and combining gains) in the high signal-to-noise (SNR) regimes. Some numerical results are provided to corroborate the accuracy and effectiveness of our theoretical expressions.