Search

With the rapid development of radio frequency identification (RFID) technology, in a variety of special environments (such as factories, warehouses, prisons), the demand for optimal deployment of RFID reader antennas has attracted extensive attention. At present, the deployment of RFID reader antenna is very difficult, there are many constraints and it is difficult to find the optimal solution, this paper proposes an improved element herding algorithm(IEHO) In the RFID network planning, the weighted coefficient method is used to optimize the four objectives of coverage, load balance, economic benefit and reader conflict at the same time. The experimental results show that compared with the bacterial foraging optimization algorithm (MC-BFO), particle swarm optimization (PSO) and constrained bat algorithm (BA-OM), the coverage increases by 9%, 17% and 22% respectively. The load balance is increased by 10.91% and 56.60% respectively, and the reader interference problem is well improved.

This paper addresses the problems in channel equalization of high data rates scenarios. Traditional equalization algorithms tend to fall into local optimum, and the complexity is usually high, requiring a long time to converge, thus not suitable for high data rate scenarios. This paper proposes a low complexity and efficient equalizer using artificial neural network (ANN) trained by modified fruit fly optimization algorithm (FOA). After that, we applied it to four different channels and compared it with the ANN trained with back propagation (BP) and ANN trained with particle swarm optimization (PSO) algorithms under the same conditions. It is obvious from the simulation results that the proposed algorithm outperforms the other two algorithms. The algorithm proposed in this paper requires a shorter convergence time and can converge to a smaller value, and the complexity of the algorithm is very low.

The channel matrix of massive MIMO-OFDM systems is sparse in the delay domain. The channel estimation based on compressed sensing uses the sparsity of the channel matrix to improve the channel estimation accuracy and reduce the pilot overhead. A channel estimation algorithm based on block compressive sampling match pursuit (B-CoSaMP) is proposed. The proposed algorithm combines the spatial correlation of the MIMO channel caused by multi-antenna propagation with the close antenna spacing at the base station, and generates an equivalent channel impulse response based on block sparsity to improve the channel estimation performance. The simulation results show that the proposed algorithm has low bit error rate and small normalized mean square error (NMSE), which is suitable for channel estimation of massive MIMO-OFDM systems.

Tag collision arbitration for the multiple tags is a significant issue for fast identification. This paper proposes a tree-based backoff (TBB) protocol to reduce the number of the iterations implemented in the procedure of the tag collision arbitration. Compared with traditional tree-based protocol, simulated results show that the TBB protocol reduces the computational complexity efficiently by employing backoff mechanism and finally accelerates tags identification.

Redundant bits spent on serial number transmission and the heavy iterations cost are big obstacles for fast identification in RFID system. This paper proposes a skip dynamic tree-based (SDT) anti-collision protocol to overcome this problem. This proposed protocol including two mechanisms: (1) dynamic mechanism, which only transmits the known part of the serial number and saves the bits. (2) skip mechanism, which return to the parent node instead of the root node to reduce the iterations. Simulated results show that the performance of SDT protocol surpasses the traditional tree-based protocol greatly and finally achieve the goal of fast identification in RFID system.

Disparity estimation is an essential step in stereoscopic video coding. Existing disparity estimation methods are based on single disparity model and lack the robustness for estimating the real-time view disparities. To overcome this problem, this paper proposes an adaptive disparity estimation scheme which employs adaptive selection of disparity models based on the rate distortion optimization function. By exploiting the information obtained from the disparity models, the proposed technique estimates and compensates the disparities among different views in stereoscopic video coding efficiently. Experimental results show that the proposed technique contributes to improve the accurate of disparity estimation and save the bits spent on the coding.

This paper proposes an affine motion compensated prediction (AMCP) method to predict the complex changes between the successive frames in panoramic video coding. A panoramic video is an image-based rendering (IBR) technique [1] which provides users with a large field of view (e.g. 360 degree) on surrounding dynamic scenes. It includes not only the translational motions but also the non-translational motions, such as zooming and rotation etc. However, the traditional motion compensated prediction is a translational motion compensated prediction (TMCP) which cannot predict nontranslational changes between panoramic images accurately. The AMCP can model the nontranslational motion effects of panoramic video accurately by using six motion coefficients which are estimated by Gauss Newton iterative minimization algorithm [2]. Simulated results show that the gain of coding performance is up to about 1.3 dB when using AMCP compared with TMCP in panoramic video coding.