Tamed village “democracy”: elections, governance and clientelism in a contemporary Chinese village

The thesis is an exploration of the elections and governance in a contemporary Chinese village. It is a qualitative case study of one village in Shandong Province, China, using in-depth interviews with villagers, village candidates, township officials as well as national, provincial, township and village documents. It reveals how the clientelist system functions in and shapes the process of the village elections and governance. Drawing upon the qualitative data and empirical evidence collected in the field site, the thesis challenges the liberal-democratic view that the implementation of direct village elections and self-governance, which is generally considered to be “village democracy”, has empowered villagers to resist the state and may mark the beginning of a bottom-up democratization in China. In contrast, it argues that even procedurally “free and fair” village elections largely fail to deliver meaningful results, and that village governance, although in the name of self-governance, actually continues to be dominated by the Chinese local state. This is because clientelist structures, embodied in vertical patron-client alliances between political elites and villagers, have strongly influenced the actors and functioned to facilitate and supplement the authoritarian control of the state. The thesis also contests interpretations of village elections and self-governance that stress the state’s formal administrative capacity over controlling and manipulating village politics. While it shows some of the formal mechanisms by which township government control village affairs, it demonstrates also that after the implementation of the “village democracy” the state is still able to maintain its authoritarian capacity by taking advantage of the informal clientelist interaction between local state officials and the village elites

Large-Scale MIMO Detection for 3GPP LTE: Algorithms and FPGA Implementations

Large-scale (or massive) multiple-input multiple-output (MIMO) is expected to be one of the key technologies in next-generation multi-user cellular systems, based on the upcoming 3GPP LTE Release 12 standard, for example. In this work, we propose - to the best of our knowledge - the first VLSI design enabling high-throughput data detection in single-carrier frequency-division multiple access (SC-FDMA)-based large-scale MIMO systems. We propose a new approximate matrix inversion algorithm relying on a Neumann series expansion, which substantially reduces the complexity of linear data detection. We analyze the associated error, and we compare its performance and complexity to those of an exact linear detector. We present corresponding VLSI architectures, which perform exact and approximate soft-output detection for large-scale MIMO systems with various antenna/user configurations. Reference implementation results for a Xilinx Virtex-7 XC7VX980T FPGA show that our designs are able to achieve more than 600 Mb/s for a 128 antenna, 8 user 3GPP LTE-based large-scale MIMO system. We finally provide a performance/complexity trade-off comparison using the presented FPGA designs, which reveals that the detector circuit of choice is determined by the ratio between BS antennas and users, as well as the desired error-rate performance.Comment: To appear in the IEEE Journal of Selected Topics in Signal Processin

GPU Accelerated Scalable Parallel Decoding of LDPC Codes

This paper proposes a flexible low-density parity-check (LDPC) decoder which leverages graphic processor units (GPU) to provide high decoding throughput. LDPC codes are widely adopted by the new emerging standards for wireless communication systems and storage applications due to their near-capacity error correcting performance. To achieve high decoding throughput on GPU, we leverage the parallelism embedded in the check-node computation and variable-node computation and propose a parallel strategy of partitioning the decoding jobs among multi-processors in GPU. In addition, we propose a scalable multi-codeword decoding scheme to fully utilize the computation resources of GPU. Furthermore, we developed a novel adaptive performance-tuning method to make our decoder implementation more flexible and scalable. The experimental results show that our LDPC decoder is scalable and flexible, and the adaptive performance-tuning method can deliver the peak performance based on the GPU architecture.Renesas MobileSamsungNational Science Foundatio

Prediction of underwater acoustic signals based on ESMD and ELM

357-362The local predictability of underwater acoustic signals plays an important role in underwater acoustic signal processing, as it is the basis for solving non-stationary signal detection. A prediction model of underwater acoustic signals based on extreme-point symmetric mode decomposition (ESMD) and extreme learning machine (ELM) is proposed. First, underwater acoustic signals are decomposed by ESMD to obtain a set of intrinsic model functions (IMFs). After IMFs are grouped, the training samples and forecast samples are obtained. Then, prediction model for training samples is established by using ELM to obtain the input layer, output layer weight vector and offset matrix. The trained ELM is used to predict the forecast sample to obtain component. Finally, the reconstructed IMFs and residuals are the final prediction results. The experimental results show that the proposed model is a good predictive model having better prediction accuracy and smaller error

Prediction of underwater acoustic signals based on ESMD and ELM

357-362The local predictability of underwater acoustic signals plays an important role in underwater acoustic signal processing, as it is the basis for solving non-stationary signal detection. A prediction model of underwater acoustic signals based on extreme-point symmetric mode decomposition (ESMD) and extreme learning machine (ELM) is proposed. First, underwater acoustic signals are decomposed by ESMD to obtain a set of intrinsic model functions (IMFs). After IMFs are grouped, the training samples and forecast samples are obtained. Then, prediction model for training samples is established by using ELM to obtain the input layer, output layer weight vector and offset matrix. The trained ELM is used to predict the forecast sample to obtain component. Finally, the reconstructed IMFs and residuals are the final prediction results. The experimental results show that the proposed model is a good predictive model having better prediction accuracy and smaller error