In this paper, we propose a simple, yet effective, multiuser detection scheme for a two-hop cooperative CDMAs. In phase 1, the minimum mean square error (MMSE) detector at the destination is used to identify reliable decisions of direct transmissions from the sources and return them to the relays. Then, in phase 2, based on the reliable decisions, the relays and the destination successively utilize the maximum likelihood (ML) detectors to estimate the residual symbols. Due to the destination estimating the symbols separately from direct transmissions and the relaying signals, as a result the destination does not need the information about the relays' decision performance for the construction of the ML detector. Hence, the proposed scheme is more feasible than existing approaches for practical implementation. In addition, due to the ML detectors in phase 2 only estimating the residual symbols, the number of computations performed by the ML detectors can be reduced significantly. The results of simulations and complexity analysis demonstrate the efficiency and effectiveness of the proposed scheme.

To eliminate casting shadows of moving objects, which cause difficulties in vision applications, a novel method is proposed based on Visual background extractor by altering its updating mechanism using relevant spatiotemporal information. An adaptive threshold and a spatial adjustment are also employed. Experiments on typical surveillance scenes validate this scheme.

In order to simultaneously combat both of the inter-carrier interferences (ICIs) and multiple access interferences (MAIs) to achieve reliable performance in multi-carrier code division multiple access (MC-CDMA) systems, this letter proposes a maximum likelihood based scheme for joint frequency offset estimation and multiuser symbol detection. To reduce the computational complexity called for by the joint decision statistic without extra mechanisms, the genetic algorithm (GA) is employed to solve the nonlinear optimization involved. Due to the robustness of the GA, the joint decision statistic can be efficiently solved, and, as shown by furnished simulation results, the proposed approach can offer satisfactory performance in various scenarios.

Existing moving object's trajectory prediction algorithms suffer from the data sparsity problem, which affects the accuracy of the trajectory prediction. Aiming to the problem, we present an Entropy-based Sparse Trajectories Prediction method enhanced by Matrix Factorization (ESTP-MF). Firstly, we do trajectory synthesis based on trajectory entropy and put synthesized trajectories into the trajectory space. It can resolve the sparse problem of trajectory data and make the new trajectory space more reliable. Secondly, under the new trajectory space, we introduce matrix factorization into Markov models to improve the sparse trajectory prediction. It uses matrix factorization to infer transition probabilities of the missing regions in terms of corresponding existing elements in the transition probability matrix. It aims to further solve the problem of data sparsity. Experiments with a real trajectory dataset show that ESTP-MF generally improves prediction accuracy by as much as 6% and 4% compared to the SubSyn algorithm and STP-EE algorithm respectively.

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.

This letter proposes a novel scheme of joint antenna combination and symbol detection in multi-input multi-output (MIMO) systems, which simultaneously determines the antenna combination coefficients to lower the RF chains and designs the minimum bit error rate (MBER) detector to mitigate the interference. The joint decision statistic, however, is highly nonlinear and the particle swarm optimization (PSO) algorithm is employed to reduce the computational overhead. Simulations show that the new approach yields satisfactory performance with reduced computational overhead compared with pervious works.

This paper presents a simple, yet effective hybrid of the minimum mean square error (MMSE) multi-user detection (MUD) and successive interference cancellation (SIC) for direct-sequence code division multiple access (DS-CDMA) systems. The proposed hybrid MUD first divides the users into groups, with each group consisting of users with a close power level. The SIC is then used to distinguish users among different groups, while the MMSE MUD is used to detect signals within each group. To further improve the performance impaired by the propagation errors, an information reuse scheme is also addressed, which can be used in conjunction with the hybrid MMSE/SIC MUD to adequately cancel the multiple access interferences (MAIs) so as to attain more accurate detections. Furthermore, the asymptotic multiuser efficiency (AME), a measure to characterize the near-far resistance capability, is also conducted to provide further insights into the new detectors. Furnished simulations, in both additive white Gaussian noise (AWGN) channels and slow flat Rayleigh fading channels, show that the performances of the proposed hybrid MMSE/SIC detectors, with or without the decision aided scheme, are superior to that of the SIC and, especially, the one with decision aided is close to that of the MMSE MUD but with substantially lower computational complexity.

In wireless sensor networks (WSN), communication interference and the energy limitation of sensor nodes seriously hamper the network performance such as throughput and network lifetime. In this paper, we focus on the Successive Interference Cancellation (SIC) and Wireless Energy Transmission (WET) technology aiming to design a heuristic power control algorithm and an efficient cross-layer strategy to realize concurrency communication and improve the network throughput, channel utilization ratio and network lifetime. We realize that the challenge of this problem is that joint consideration of communication interference and energy shortage makes the problem model more complicated. To solve the problem efficiently, we adopt link scheduling strategy, time-slice scheduling scheme and energy consumption optimization protocol to construct a cross-layer optimization problem, then use an approximate linearization method to transform it into a linear problem which yields identical optimal value and solve it to obtain the optimal work strategy of wireless charging equipment (WCE). Simulation results show that adopting SIC and WCE can greatly improve communication capability and channel utilization ratio, and increase throughput by 200% to 500% while prolonging the network lifetime.

A simple, yet effective geometric method is presented to construct the signature sequences for multicarrier code-division multiple access (MC-CDMA) systems. By minimizing the correlation of the effective signature vectors, the signature sequences are recursively determined via projection onto a properly constructed subspace. Conducted simulations verify the effectiveness of the method.