Fast Min-Sum Algorithms for Decoding of LDPC over GF(q)

In this paper, we present a fast min-sum algorithm for decoding LDPC codes over GF(q). Our algorithm is different from the one presented by David Declercq and Marc Fossorier in ISIT 05 only at the way of speeding up the horizontal scan in the min-sum algorithm. The Declercq and Fossorier's algorithm speeds up the computation by reducing the number of configurations, while our algorithm uses the dynamic programming instead. Compared with the configuration reduction algorithm, the dynamic programming one is simpler at the design stage because it has less parameters to tune. Furthermore, it does not have the performance degradation problem caused by the configuration reduction because it searches the whole configuration space efficiently through dynamic programming. Both algorithms have the same level of complexity and use simple operations which are suitable for hardware implementations.Comment: Accepted by IEEE Information Theory Workshop, Chengdu, China, 200

Noise control and sound quality evaluation of outdoor unit of split air-conditioner

Low-noise, energy-saving and improved sound quality has been the major concern for the eco-design of household appliance product. In this paper, taking off from vibro-acoustic coupling, a composite noise control scheme (CNCS) combined with dynamic vibration absorbing technology, the sound absorption technique, controlling noise in the outlet, and particle damping technique is developed to control the sound radiation from an outdoor unit of a split air-conditioner based on sound sources identification. With the help of developed experiment platform, application effect of each technology and CNCS are measured, experimental evaluation shows that the developed CNCS can reduce the sound pressure level (SPL) more than 10 dB. To clarify the application effect of CNCS in sound quality further, it should be evaluated subjectively by using experiments. Experimental result shows that the stability and smoothness of the unit’s noise have been greatly improved in time domain signal of sound pressure, the fluctuation strength is greatly improved for the controlled unit, the value at the left ear is reduced from 0.0195 vacil to 0.0146 vacil, and the right ear value is 0.0141 vacil instead of 0.0251 vacil. In addition, the sharpness has also been significantly reduced after CNCS, the value at the left ear decreases from 2.11 acum to 1.97 acum, and 2.01 acum to 1.86 acum for the right ear. So, CNCS is a pragmatic technique to control noise, vibration and improving sound quality

Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems

In the paper, we propose a novel approach for solving Bayesian inverse problems with physics-informed invertible neural networks (PI-INN). The architecture of PI-INN consists of two sub-networks: an invertible neural network (INN) and a neural basis network (NB-Net). The invertible map between the parametric input and the INN output with the aid of NB-Net is constructed to provide a tractable estimation of the posterior distribution, which enables efficient sampling and accurate density evaluation. Furthermore, the loss function of PI-INN includes two components: a residual-based physics-informed loss term and a new independence loss term. The presented independence loss term can Gaussianize the random latent variables and ensure statistical independence between two parts of INN output by effectively utilizing the estimated density function. Several numerical experiments are presented to demonstrate the efficiency and accuracy of the proposed PI-INN, including inverse kinematics, inverse problems of the 1-d and 2-d diffusion equations, and seismic traveltime tomography