Dual-Topology Hamiltonian-Replica-Exchange Overlap Histogramming Method to Calculate Relative Free Energy Difference in Rough Energy Landscape

A novel overlap histogramming method based on Dual-Topology Hamiltonian-Replica-Exchange simulation technique is presented to efficiently calculate relative free energy difference in rough energy landscape, in which multiple conformers coexist and are separated by large energy barriers. The proposed method is based on the realization that both DT-HERM exchange efficiency and confidence of free energy determination in overlap histogramming method depend on the same criteria: neighboring states' energy derivative distribution overlap. In this paper, we demonstrate this new methodology by calculating free energy difference between amino acids: Leucine and Asparagine, which is an identified chanllenging system for free energy simulations.Comment: 14 pages with 4 figure

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

A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed IMFs and residuals are the final prediction results. Six kinds of prediction models are compared, which are DBN prediction model, EMD-DBN prediction model, EEMD-DBN prediction model, CEEMD-DBN prediction model, ESMD-DBN prediction model, and the proposed model in this paper. The same sunspots time series are predicted with six kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error

Feature Extraction Method for Ship-Radiated Noise Based on Extreme-point Symmetric Mode Decomposition and Dispersion Entropy

175-183A novel feature extraction method for ship-radiated noise based on extreme-point symmetric mode decomposition (ESMD) and dispersion entropy (DE) is proposed in the present study. Firstly, ship-radiated noise signals were decomposed into a set of band-limited intrinsic mode functions (IMFs) by ESMD. Then, the correlation coefficient (CC) between each IMF and the original signal were calculated. Finally, the IMF with highest CC was selected to calculate DE as the feature vector. Comparing DE of the IMF with highest CC by empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and ESMD, it is revealed that the proposed method can assist the feature extraction and classification recognition for ship-radiated noise

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

Feature Extraction Method for Ship-Radiated Noise Based on Extreme-point Symmetric Mode Decomposition and Dispersion Entropy

175-183A novel feature extraction method for ship-radiated noise based on extreme-point symmetric mode decomposition (ESMD) and dispersion entropy (DE) is proposed in the present study. Firstly, ship-radiated noise signals were decomposed into a set of band-limited intrinsic mode functions (IMFs) by ESMD. Then, the correlation coefficient (CC) between each IMF and the original signal were calculated. Finally, the IMF with highest CC was selected to calculate DE as the feature vector. Comparing DE of the IMF with highest CC by empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and ESMD, it is revealed that the proposed method can assist the feature extraction and classification recognition for ship-radiated noise

A new method for detecting line spectrum of ship-radiated noise based on a new double duffing oscillator differential system

34-43In order to detect line spectrum of ship-radiated noise under the ocean background noise and improve the method of detecting duffing oscillator intermittent chaos, a method of detecting intermittent chaos based on variable step size dual duffing oscillator differential system is proposed. Based on the duffing oscillator, two independent and incompletely coupled duffing oscillators can be differentiated based on the differential principle by using the proposed method, which reduces the computational complexity and makes the timing diagram more intuitive. In order to further improve the detection efficiency and reduce the computational complexity of the system, the author put forward that a sequence of solving steps can be built by using only one duffing oscillator and the method of detecting the unknown frequency signal can be achieved by changing the step size of the system. Simulation results show that compared with the conventional duffing oscillator detection method, the proposed method has improved the SNR (signal-to-noise ratio) by at least 10.6 dB. Comparing with duffing system chaotic oscillator column and double duffing system chaotic oscillator column detection method, the proposed method is most effective in detecting line spectrum of ship-radiated noise

Multi-Layer Parallel Decoding Algorithm and VLSI Architecture for Quasi-Cyclic LDPC Codes

We propose a multi-layer parallel decoding algorithm and VLSI architecture for decoding of structured quasi-cyclic low-density parity-check codes. In the conventional layered decoding algorithm, the block-rows of the parity check matrix are processed sequentially, or layer after layer. The maximum number of rows that can be simultaneously processed by the conventional layered decoder is limited to the sub-matrix size. To remove this limitation and support layer-level parallelism, we extend the conventional layered decoding algorithm and architecture to enable simultaneously processing of multiple (K) layers of a parity check matrix, which will lead to a roughly K-fold throughput increase. As a case study, we have designed a double-layer parallel LDPC decoder for the IEEE 802.11n standard. The decoder was synthesized for a TSMC 45-nm CMOS technology. With a synthesis area of 0.81 mm2 and a maximum clock frequency of 815 MHz, the decoder achieves a maximum throughput of 3.0 Gbps at 15 iterations