The search functionality is under construction.
The search functionality is under construction.

Author Search Result

[Author] Hao FANG(3hit)

1-3hit
  • Neuron-Network-Based Mixture Probability Model for Passenger Walking Time Distribution Estimation

    Hao FANG  Chi-Hua CHEN  Dewang CHEN  Feng-Jang HWANG  

     
    LETTER-Artificial Intelligence, Data Mining

      Pubricized:
    2022/01/28
      Vol:
    E105-D No:5
      Page(s):
    1112-1115

    Aiming for accurate data-driven predictions for the passenger walking time, this study proposes a novel neuron-network-based mixture probability (NNBMP) model with repetition learning (RL) to estimate the probability density distribution of passenger walking time (PWT) in the metro station. Our conducted experiments for Fuzhou metro stations demonstrate that the proposed NNBMP-RL model achieved the mean absolute error, mean square error, and mean absolute percentage error of 0.0078, 1.33 × 10-4, and 19.41%, respectively, and it outperformed all the seven compared models. The developed NNBMP model fitting accurately the PWT distribution in the metro station is readily applicable to the microscopic analyses of passenger flow.

  • Low-Complexity Memory Access Architectures for Quasi-Cyclic LDPC Decoders

    Ming-Der SHIEH  Shih-Hao FANG  Shing-Chung TANG  Der-Wei YANG  

     
    PAPER-Computer System

      Vol:
    E95-D No:2
      Page(s):
    549-557

    Partially parallel decoding architectures are widely used in the design of low-density parity-check (LDPC) decoders, especially for quasi-cyclic (QC) LDPC codes. To comply with the code structure of parity-check matrices of QC-LDPC codes, many small memory blocks are conventionally employed in this architecture. The total memory area usually dominates the area requirement of LDPC decoders. This paper proposes a low-complexity memory access architecture that merges small memory blocks into memory groups to relax the effect of peripherals in small memory blocks. A simple but efficient algorithm is also presented to handle the additional delay elements introduced in the memory merging method. Experiment results on a rate-1/2 parity-check matrix defined in the IEEE 802.16e standard show that the LDPC decoder designed using the proposed memory access architecture has the lowest area complexity among related studies. Compared to a design with the same specifications, the decoder implemented using the proposed architecture requires 33% fewer gates and is more power-efficient. The proposed new memory access architecture is thus suitable for the design of low-complexity LDPC decoders.

  • Blind Channel Estimation for SIMO-OFDM Systems without Cyclic Prefix

    Shih-Hao FANG  Ju-Ya CHEN  Ming-Der SHIEH  Jing-Shiun LIN  

     
    LETTER-Communication Theory and Signals

      Vol:
    E93-A No:1
      Page(s):
    339-343

    A blind channel estimation algorithm based on the subspace method for single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) systems is proposed in this letter. With the aid of a repetition index, the conventional algorithm is a special case of our algorithm. Compared with related studies, the proposed algorithm reduces the computational complexity of the SVD operation and is suitable for cyclic-prefix-free systems. In particular, the necessary condition of the proposed signal matrix to be full rank can be satisfied with fewer OFDM blocks. Simulation results demonstrate that the proposed algorithm outperforms conventional methods in normalized mean-square error.