Explorations into Appendicular Ontogeny using a Cross-Sectional, Contemporary U.S. Sample

Investigations into the subadult skeleton have been restricted by sample availability in biological anthropology. Alternatively, the same source of longitudinal data has been repeatedly used, which does not reflect the variability of growth and development (i.e., ontogeny) or the United States (U.S.) population. Small and/or homogenous samples have often resulted in limited or inappropriate modeling choices to investigate the growth and development and variation of the subadult skeleton. Recent technological advancements have made virtual anthropology possible. The use of computed tomography (CT) scans has opened the doors to increasing sample sizes of minority groups and in turn increasing the variation of skeletal information. One repository, the Subadult Virtual Anthropology Database (SVAD), has focused on increasing and diversifying subadult skeletal data to increase the possibilities of subadult research in biological anthropology. The articles in this (non)dissertation collection use the SVAD (M=610, F=416) and the Forensic Anthropology Data Bank (FDB; M=285, F=161) to evaluate three different perspectives of appendicular (i.e., long bone) ontogeny: absolute, relative, and index. First, relative long bone lengths and nonlinear modeling are used as the first-ever evaluation of long bone growth through adult stabilization. Second, the brachial and crural indices are used to explore the chronological ontogenetic trajectories of each index and their ecogeographic patterns. Third, absolute long bone breadth and length measurements are used to create linear and nonlinear equations for estimating subadult stature for forensic application. In doing so, this is the first comprehensive collection of studies that explore three distinct perspectives of long bone ontogeny and variation from the same source of subadult skeletal data, demonstrating the need for additional contemporary subadult samples and novel modeling approaches

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

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

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

International audienc

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

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