A Bayesian sparse inference approach in near-field wideband aeroacoustic imaging

International audienceRecently improved deconvolution methods using sparse regularization achieve high spatial resolution in aeroacoustic imaging in the low Signal-to-Noise Ratio (SNR), but sparse prior and model parameters should be optimized to obtain super resolution and be robust to sparsity constraint. In this paper, we propose a Bayesian Sparse Inference Approach in Aeroacoustic Imaging (BSIAAI) to reconstruct both source powers and positions in poor SNR cases, and simultaneously estimate background noise and model parameters. Double Exponential prior model is selected for source spatial distribution and hyper-parameters are estimated by Joint Maximized A Posterior criterion and Bayesian Expectation and Minimization algorithm. On simulated and wind tunnel data, proposed approach is well applied for near-field wideband monopole and extended source imaging. Comparing to several classical methods, proposed approach is robust to noise, super resolution, wide dynamic range, and source number and SNR are not needed

General Equilibrium Analysis of Hold-Up Problem and Non-Exclusive Franchise Contract

In this paper, we develop a general equilibrium model that examines the emergence of non-exclusive franchise contracts in the presence of the franchisor hold-up problem. Our model of an endogenous franchising network underscores the trade-off between th

Bayesian sparse regularization in near-field wideband aeroacoustic imaging for wind tunnel test

International audienc

Beamforming and Power Splitting Designs for AN-aided Secure Multi-user MIMO SWIPT Systems

In this paper, an energy harvesting scheme for a multi-user multiple-input-multiple-output (MIMO) secrecy channel with artificial noise (AN) transmission is investigated. Joint optimization of the transmit beamforming matrix, the AN covariance matrix, and the power splitting ratio is conducted to minimize the transmit power under the target secrecy rate, the total transmit power, and the harvested energy constraints. The original problem is shown to be non-convex, which is tackled by a two-layer decomposition approach. The inner layer problem is solved through semi-definite relaxation, and the outer problem, on the other hand, is shown to be a single- variable optimization that can be solved by one-dimensional (1- D) line search. To reduce computational complexity, a sequential parametric convex approximation (SPCA) method is proposed to find a near-optimal solution. The work is then extended to the imperfect channel state information case with norm-bounded channel errors. Furthermore, tightness of the relaxation for the proposed schemes are validated by showing that the optimal solution of the relaxed problem is rank-one. Simulation results demonstrate that the proposed SPCA method achieves the same performance as the scheme based on 1-D but with much lower complexity.Comment: 12 pages, 6 figures, submitted for possible publicatio

Dynamic response of human linearvection.

Thesis. 1976. M.S.--Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.Microfiche copy available in Archives and Barker.Bibliography: p.101-102.M.S

2D convolution model using (in)variant kernels for fast acoustic imaging

International audienceAcoustic imaging is an advanced technique for acoustic source localization and power reconstruction using limited measurements at microphone sensors. The acoustic imaging methods often involve in two aspects: one is to build up a forward model of acoustic power propagation which requires tremendous matrix multiplications due to large dimension of the power propagation matrix; the other is to solve an inverse problem which is usually ill-posed and time consuming. In this paper, our main contribution is to propose to use 2D convolution model for fast acoustic imaging. We find out that power propagation ma-trix seems to be a quasi-Symmetric Toeplitz Block Toeplitz (STBT) matrix in the far-field condition, so that the (in)variant convolution kernels (sizes and values) can be well derived from this STBT matrix. For method validation, we use simulated and real data from the wind tunnel S2A (France) experiment for acoustic imaging on vehicle surface