External chemical reactors for steam reforming and water gas shift reactions are needed for a proton exchange membrane (PEM) fuel cell system using syngas fuel. For the preheating of syngas and stable steam reforming reaction at 600 °C, residual hydrogen from a fuel cell and a certain amount of additional syngas are burned. The combustion temperature is calculated and the molar ratio of the syngas into burner and steam reformer is determined. Based on thermodynamics and electrochemistry, the electric power density and energy conversion efficiency of a PEM fuel cell based syngas are expressed. The effects of the temperature, the hydrogen utilization factor at the anode, and the molar ratio of the syngas into burner and steam reformer on the performance of a PEM fuel cell are discussed. To achieve the maximum power density or efficiency, the key parameters are determined. This manuscript presents the detailed operating process of a PEM fuel cell, the allocation of the syngas for combustion and electric generation, and the feasibility of a PEM fuel cell using syngas.

No abstract

No abstract

Massive multiple-input multiple-output (MIMO) is becoming a key technology for future wireless communications. Channel feedback for massive MIMO is a challenging task due to the increased dimension of MIMO channel matrix. By exploiting the channel sparsity, channel estimation based on compressive sensing (CS) aims to reduce the feedback overhead in massive MIMO systems. In this paper, various CS algorithms for channel estimation in massive MIMO systems are summarized, and a novel CS algorithm, i.e. modified sparsity adaptive matching pursuit (MSAMP), is proposed hereupon. Moreover, various measurement matrices are introduced in the CS scheme. The performances of channel estimation and recovery are simulated and compared. It is inferred from simulation that, SAMP is very appropriate for reconstructing sparse channel information in massive MIMO system, especially the modified SAMP can give higher reconstruction, and some measurement matrix is suitable for certain optimal CS algorithms.

No abstract

No abstract

Wireless Sensor and Actuator Networks (WSANs) are widely used in smart control system as home automation, military service, etc. They consist of hundreds of heterogeneous nodes. Due to this high density, finding an optimal deployment becomes a NP-Hard task. So, determining different node positions, that ensure the highest QoS of these networks, is the most significant challenges. In this paper, we study this problem for light control application. We expose our models for coverage, connectivity and lighting metrics. These proposed models, are adapted and validated by real measurements. The optimization approach is based on Genetic Algorithm for both regular and random deployment. The proposed approach is evaluated for different lighting space shapes, and the results are presented and compared to other studies.

No abstract