In this paper, we propose a speech enhancement algorithm by using MAP estimation with variable speech spectral amplitude probability density function (speech PDF). The variable speech PDF has two adaptive shape parameters which affect the quality of enhanced speech. Noise can be efficiently suppressed when these parameters are properly applied so that the variable speech PDF shape fits to the real-speech PDF one. We derive adaptive shape parameters from real-speech PDF in various narrow SNR intervals. The proposed speech enhancement algorithm with adaptive shape parameters is examined and compared to conventional algorithms. The simulation results show that the proposed method improved SegSNR around 6 and 9 dB when the input speech signal was corrupted by white and tunnel noises at 0 dB, respectively.

We present sampling theorems that reconstruct consistent signals from noisy underdetermined measurements. The consistency criterion requires that the reconstructed signal yields the same measurements as the original one. The main issue in underdetermined cases is a choice of a complementary subspace L in the reconstruction space of the intersection between the reconstruction space and the orthogonal complement of the sampling space because signals are reconstructed in L. Conventional theorems determine L without taking noise in measurements into account. Hence, the present paper proposes to choose L such that variance of reconstructed signals due to noise is minimized. We first arbitrarily fix L and compute the minimum variance under the condition that the average of the reconstructed signals agrees with the noiseless reconstruction. The derived expression clearly shows that the minimum variance depends on L and leads us to a condition for L to further minimize the minimum value of the variance. This condition indicates that we can choose such an L if and only if L includes a subspace determined by the noise covariance matrix. Computer simulations show that the standard deviation for the proposed sampling theorem is improved by 8.72% over that for the conventional theorem.

Ant Colony Optimization (ACO) is one of the most recent techniques for solving combinatorial optimization problems, and has been unexpectedly successful. Therefore, many improvements have been proposed to improve the performance of the ACO algorithm. In this paper an ant colony optimization with memory is proposed, which is applied to the classical traveling salesman problem (TSP). In the proposed algorithm, each ant searches the solution not only according to the pheromone and heuristic information but also based on the memory which is from the solution of the last iteration. A large number of simulation runs are performed, and simulation results illustrate that the proposed algorithm performs better than the compared algorithms.

A K-user interleave-division multiple-access (IDMA) system with symbol-synchronous and equal-power users is considered. In this IDMA system, the spreading, interleaving, and multiple-access channel (MAC) are jointly considered as an equivalent channel, referred to as an IDMA channel. Given channel signal-to-noise ratio (SNR), the sum capacity of the IDMA channel is only determined by a K-user spread-interleave (SI) matrix. First, it is shown that to maximize the sum capacity of the IDMA channel, rows or columns of its K-user SI matrix should be pairwise orthogonal. The optimal K-user SI matrix is constructed. Second, for the IDMA system with each user employing the same spreading sequence followed by random interleaving, it is shown that, as the number of users approaches infinity, the sum capacity of the IDMA channel converges to a determinate value, which is achieved by a balanced spreading sequence. Moreover, when both the number of users and the data length approach infinity, this determinate value of sum capacity is achieved by an arbitrary spreading sequence. Furthermore, for a finite number of users, an optimal spreading sequence is derived by minimizing an expected column correlation of the K-user SI matrix. It shows that this optimal spreading sequence provides the maximum ergodic sum capacity.

This letter proposes an algorithm of determining the BER-minimized block delay for detection and the associated precoder design once the channel state information and limited transmission power are given. Simulation cases demonstrate the adjusting capability of the proposed algorithm for achieving best BER performance of the joint linear transceiver design.

We give characterizations of stable scaling functions with compact band regions, which have the oversampling property.

Trusted network access protocols are proposed for the security and authorization of network-access requests. Because they differ greatly from traditional security protocols on security demands, they can not be analyzed with previous strand space works directly. To solve this problem, we first give some extensions necessary to verify them in this letter. Moreover, we point out Zhuo Ma et al.'s MN-TAP protocol is unsecure based on the Strand Space Model (SSM), and then improve the MN-TAP protocol and show that the improved MN-TAP protocol is secure in the SSM.

Optical code-division multiple-access (OCDMA) is a promising technique for multimedia transmission in fiber-optic local-area networks (LANs). Variable-weight optical orthogonal codes (OOCs) can be used for OCDMA networks supporting multiple quality of services (QoS). Most constructions for optimal variable-weight OOCs have focused on the case where the number of distinct Hamming weights of all codewords is equal to two, and the codewords of weight 3 are normally included. In this letter, four explicit constructions of optimal (υ,{4,5,6},1,Q)-OOCs are presented, and more new optimal (υ,{4,5,6},1,Q)-OOCs are obtained via recursive constructions. These improve the existing results on optimal variable-weight OOCs with at least three distinct Hamming weights and minimum Hamming weight 4.

Recent advances in 3-D technologies draw an interest on the just noticeable difference in depth (JNDD) that describes a perceptual threshold of depth differences. In this letter, we address a new application of the JNDD to the depth image enhancement. In the proposed algorithm, a depth image is first segmented into multiple layers and then the depth range of the layer is expanded if the depth difference between adjacent layers is smaller than the JNDD. Therefore, viewers can effectively perceive the depth differences between layers and thus the human depth perception can be improved. The proposed algorithm can be applied to any depth-based 3-D display applications.