Image segmentation is an essential processing step for many image analysis applications. In this paper, a novel image segmentation algorithm using fuzzy C-means clustering (FCM) with spatial constraints based on Markov random field (MRF) via Bayesian theory is proposed. Due to disregard of spatial constraint information, the FCM algorithm fails to segment images corrupted by noise. In order to improve the robustness of FCM to noise, a powerful model for the membership functions that incorporates local correlation is given by MRF defined through a Gibbs function. Then spatial information is incorporated into the FCM by Bayesian theory. Therefore, the proposed algorithm has both the advantages of the FCM and MRF, and is robust to noise. Experimental results on the synthetic and real-world images are given to demonstrate the robustness and validity of the proposed algorithm.

Space-time coding (STC) schemes for communication systems employing multiple transmit and receive antennas have received considerable interest recently. On space-time coding, some algorithms with perfect channel state information (CSI) have been proposed. In certain fast varying situation, however, it may be difficult to estimate the channel accurately and it is natural to study the blind detection algorithm without CSI. In this paper, based on subspace, a new blind detection algorithm without CSI is proposed. Using singular value decomposition (SVD) on output signal, noise subspace and signal subspace, which keep orthogonal to each other, are obtained. By searching the intersection of the signal subspace and the limited symbol vector set, symbol detection is achieved. The simulations illustrate that the proposed algorithm significantly improves system performance by receiving more output signals relative to transmit symbols. Furthermore, the presented algorithm is robust to the fading channel that changes between two successive blocks.

For the performance deficiency of the pilot symbol aided channel estimation in orthogonal frequency division multiplexing (OFDM) systems, the wavelets network interpolation channel estimator is proposed. By contrast with conventional methods, wavelets network interpolation channel estimator can guarantee the high transmission rate and lower Bit error rates (BER). Computer simulation results demonstrate that the proposed channel estimation method exhibit an improved performance compared to the conventional linear channel estimation methods and is robust to fading rate, especially in fast fading channels.

A novel blind channel estimation scheme is proposed for OFDM systems employing PSK modulation. This scheme minimizes the number of possible channels by exploiting the constant modulus property, chooses a best fit over the possible channels by exploiting the finite alphabet property of information signals, and achieves competitive performance with low computational complexity. Results comparing the new scheme with the finite-alphabet based channel estimation are presented.

In this paper a new adaptive multi-input multi-output (MIMO) channel estimation and multiuser detection algorithm based kernel space iterative inversion is proposed. The functions of output signals are mapped from a low dimensional space to a high dimensional reproducing kernel Hilbert space. The function of the output signals is represented as a linear combination of a set of basis functions, and a Mercer kernel function is constructed by the distribution function. In order to avoid finding the function f(.) and g(.), the correlation among the output signals is calculated in the low dimension space by the kernel. Moreover, considering the practical application, the algorithm is extended to online iteration of mixture system. The computer simulation results illustrated that the new algorithm increase the performance of channel estimation, the global convergence, and the system stability.

In this letter, the performance of opportunistic-based two-way relaying with beamforming over Nakagami-m fading channels is investigated. We provide an approximate expression for the cumulative distribution function of the end-to-end signal-to-noise ratio to derive the closed-form lower bounds for the outage probability and average bit error probability as well as the closed-form upper bound for the ergodic capacity. Simulation results demonstrate the tightness of the derived bounds.

This paper proposes a learning classifier based on Support Vector Domain Description (SVDD) for two-class problem. First, by the description of the training samples from one class, a sphere boundary containing these samples is obtained; then, this boundary is used to classify the test samples. In addition, instead of the traditional quadratic programming, multiplicative updates is used to solve the Lagrange multiplier in optimizing the solution of the sphere boundary. The experiment on CBCL face database illustrates the effectiveness of this learning algorithm in comparison with Support Vector Machine (SVM) and Sequential Minimal Optimization (SMO).

Focusing on time correlation of real communication channels, a channel quantization algorithm based on finite state vector quantization (FSVQ) is proposed. Firstly channels are partitioned into finite states, then codebooks corresponding to each state are constructed, which are used to quantize channels transferred from corresponding states. Further, the state transition function is designed to ensure the synchronization between transmitter and receiver. The proposed algorithm can achieve improved performance with the same feedback load compared with classical memoryless channel quantizer without consideration of the influence of time correlation. Simulation results verify the effectiveness of the proposed algorithm.

In this letter, a dual-hop wireless communication network with opportunistic amplify and forward (O-AF) relay is investigated over independent and non-identically distributed Nakagami-m fading channels. Employing Maclaurin series expansion around zero to derive the approximate probability density function of the normalized instantaneous signal-to-noise ratio (SNR), the asymptotic symbol error rate (SER) and outage probability expressions are presented. Simulation results indicate that the derived expressions well match the results of Monte-Carlo simulations at medium and high SNR regions. By comparing the O-AF with all AF relaying analyzed previously, it can be concluded that the former has significantly better performance than the latter in many cases.

The subspace algorithm can be utilized for the blind detection of space-time block codes (STBC) without knowledge of channel state information (CSI) both at the transmitter and receiver. However, its performance degrades when the channels are correlated. In this letter, we analyze the impact of channel correlation from the orthogonality loss between the transmit signal subspace (TSS) and the statistical noise subspace (SNS). Based on the decoding property of the subspace algorithm, we propose a revised detection in favor of the channel correlation matrix (CCM) only known to the receiver. Then, a joint transmit-receive preprocessing scheme is derived to obtain a further performance improvement when the CCM is available both at the transmitter and receiver. Analysis and simulation results indicate that the proposed methods can significantly improve the blind detection performance of STBC over the correlated channels.

This letter investigates the performance of amplify-and-forward relaying systems using maximum ratio transmission at the source. A closed-form expression for the outage probability and a closed-form lower bound for the average bit error probability of the system are derived. Also, the approximate expressions for the outage probability and average bit error probability in the high signal-to-noise ratio regime are given, based on which the optimal power allocation strategies to minimize the outage probability and average bit error probability are developed. Furthermore, numerical results illustrate that optimizing the allocation of power can improve the system performance, especially in the high signal-to-noise ratio regime.

Antenna selection is a practical way to decrease system complexity and the hardware cost of radio frequency (RF) chains in multiple input multiple output (MIMO) system. In this study, we give a simple characterization of the optimal diversity and multiplexing tradeoff (DMT) curve of the MIMO system with antenna subset selection at both the transmitter and the receiver for Rayleigh fading channel.

In this paper, we consider an amplify-and-forward (AF) relay network where a source node transmits information to a destination node through the cooperation of multiple relay nodes. It is shown in prior works that the outage behavior and average throughput of the selection AF (S-AF) scheme where only the best relay node is chosen to assist can outperform the conventional all-participate AF (AP-AF) scheme. Assuming multiple antennas at the destination node and single antennas at other nodes in this paper, we propose a relay selection scheme according to the criterion of maximizing receive signal to noise ratio (SNR), where a group of relays is chosen to assist in the transmission simultaneously in a manner similar to cyclic delay diversity (CDD). Compared with S-AF, the proposed scheme achieves better outage behavior and average throughput. It can be seen from simulation results that the performance improvement of symbol error rate (SER) is significant compared with S-AF.

For suppressing inter symbol interference, the support vector machine mutliuser detector (SVM-MUD) was adopted as a nonlinear method in direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. To solve the problems of the complexity of SVM-MUD model and the number of support vectors, based on recursive least squares support vector machine (RLS-SVM) and Riemannian geometry, a new algorithm for nonlinear multiuser detector is proposed. The algorithm introduces the forgetting factor to get the support vectors at the first training samples, then, uses Riemannian geometry to train the support vectors again and gets less improved support vectors. Simulation results illustrated that the algorithm simplifies SVM-MUD model at the cost of only a little more bit error rate and decreases the computational complexity. At the same time, the algorithm has an excellent effect on suppressing multipath interference.

In this letter, a new joint precoding and decoding design scheme for multiuser MIMO downlink is proposed which dispenses with iterative operations and can achieve better performance. This scheme introduces zero-force processing into minimum mean square error (MMSE) design scheme to avoid iterative operations. We derived closed-form precoders and decoders and transmit power allocation strategy of proposed design scheme, validated performance of proposed design scheme by computer simulation. The simulation results show that the proposed design scheme can achieve better bit error rate (BER) and sum capacity performance compared to an existing non-iterative design scheme.

A new data-aided carrier frequency offset (CFO) estimation technique is presented for correlative coded OFDM systems in the presence of strong multipath. Different from traditional data-aided estimation techniques, the technique estimates CFO by detecting amplitude of pilots rather than their phase shift and removes effects on CFO estimation due to intercarrier interference by an iterative compensation method. A theoretical analysis of its performance has been derived and simulation results comparing the new technique with a traditional data-aided estimation technique are presented.