One major challenge to implement orthogonal frequency division multiplexing (OFDM) systems over doubly selective channels is the non-negligible intercarrier interference (ICI), which significantly degrades the system performance. Existing solutions to cope with ICI include zero-forcing (ZF), minimum mean square error (MMSE) and other linear or nonlinear equalization methods. However, these schemes fail to achieve a satisfactory tradeoff between performance and computational complexity. To address this problem, in this paper we propose two novel nonlinear ICI cancellation techniques, which are referred to as parallel interference cancelation (PIC) and hybrid interference cancelation (HIC). Taking advantage of the special structure of basis expansion model (BEM) based channel matrices, our proposed schemes enjoy low computational complexity and are capable of cancelling ICI effectively. Moreover, since the proposed schemes can flexibly select different basis functions and be independent of the channel statistics, they are applicable to practical OFDM based systems such as DVB-T2 over doubly selective channels. Theoretical analysis and simulation results both confirm their performance-complexity advantages in comparison with some existing methods.

A simple yet effective time domain correlation channel estimation method is proposed for multiple-input multiple-output (MIMO) systems over dispersive channels. It is known that the inherent co-channel interference (CCI) and inter-symbol interference (ISI) coexist when the signals propagate through MIMO frequency selective channels, which renders the MIMO channel estimation intractable. By elaborately devising the quasi-orthogonal training sequences between multiple antennas which have constant autocorrelation property with different cyclic shifts in the time domain, the interferences induced by ISI and CCI can be simultaneously maintained at a constant and identical value under quasi-static channels. As a consequence, it is advisable to implement the joint ISI and CCI cancelation by solving the constructed linear equation on the basis of the correlation output with optional correlation window. Finally, a general and simplified closed-form expression of the estimated channel impulse response can be acquired without matrix inversion. Additionally, the layered space-time (LST) minimum mean square error (MMSE) (LST-MMSE) frequency domain equalization is briefly described. We also provide some meaningful discussions on the beginning index of the variable correlation window and on the cyclic shift number of m-sequence of other antennas relative to the first antenna. Simulation results demonstrate that the proposed channel estimation approach apparently outperforms the existing schemes with a remarkable reduction in computational complexity.

Multiple image group cosegmentation (MGC) aims at segmenting common object from multiple group of images, which is a new cosegmentation research topic. The existing MGC methods formulate MGC as label assignment problem (Markov Random Field framework), which is observed to be sensitive to parameter setting. Meanwhile, it is also observed that large object variations and complicated backgrounds dramatically decrease the existing MGC performance. To this end, we propose a new object proposal based MGC model, with the aim of avoiding tedious parameter setting, and improving MGC performance. Our main idea is to formulate MGC as new region proposal selection task. A new energy function in term of proposal is proposed. Two aspects such as the foreground consistency within each single image group, and the group consistency among image groups are considered. The energy minimization method is designed in EM framework. Two steps such as the loop belief propagation and foreground propagation are iteratively implemented for the minimization. We verify our method on ICoseg dataset. Six existing cosegmentation methods are used for the comparison. The experimental results demonstrate that the proposed method can not only improve MGC performance in terms of larger IOU values, but is also robust to the parameter setting.

In this paper, the issue of carrier frequency offset (CFO) compensation in interleaved orthogonal frequency division multiple access (OFDMA) uplink system is investigated. To mitigate the effect of multiple access interference (MAI) caused by CFOs of different users, a new parallel interference cancellation (PIC) compensation algorithm is proposed. This scheme uses minimum mean square error (MMSE) criterion to obtain the estimation of interference users, then circular convolutions are employed to restore MAI and compensate CFO. To tackle the complexity problem of circular convolutions, an efficient MAI restoration and cancellation method is developed. Simulations illustrate the good performance and low computational complexity of the proposed algorithm.

In this paper, we propose a novel joint rate distortion optimization (JRDO) model for intra prediction coding. The spatial prediction dependency is exploited by modeling the distortion propagation with a linear fitting function. A novel JRDO based Lagrange multiplier (LM) is derived from this model. To adapt to different blocks' distortion propagation characteristics, we also introduce a generalized multiple Lagrange multiplier (MLM) framework where some candidate LMs are used in the RDO process. Experiment results show that our proposed JRDO-MLM scheme is superior to the H.264/AVC encoder.