Deterministic regression methods for unbiased estimation of time-varying autoregressive parameters from noisy observations

A great deal of interest has been paid to autoregressive parameter estimation in the noise-free case or when the observation data are disturbed by random noise. Tracking time-varying autoregressive (TVAR) parameters has been also discussed, but few papers deal with this issue when there is an additive zero-mean white Gaussian measurement noise. In this paper, one considers deterministic regression methods (or evolutive methods) where the TVAR parameters are assumed to be weighted combinations of basis functions. However, the additive white measurement noise leads to a weight-estimation bias when standard least squares methods are used. Therefore, we propose two alternative blind off-line methods that allow both the variance of the additive noise and the weights to be estimated. The first one is based on the errors-in-variable issue whereas the second consists in viewing the estimation issue as a generalized eigenvalue problem. A comparative study with other existing methods confirms the effectiveness of the proposed methods.     

Single-Cell Microgels for Diagnostics and Therapeutics

Cell encapsulation within hydrogel droplets is transforming what is feasible in multiple fields of biomedical science such as tissue engineering and regenerative medicine, in vitro modeling, and cell-based therapies. Recent advances have allowed researchers to miniaturize material encapsulation complexes down to single-cell scales, where each complex, termed a single-cell microgel, contains only one cell surrounded by a hydrogel matrix while remaining <100 μm in size. With this achievement, studies requiring single-cell resolution are now possible, similar to those done using liquid droplet encapsulation. Of particular note, applications involving long-term in vitro cultures, modular bioinks, high-throughput screenings, and formation of 3D cellular microenvironments can be tuned independently to suit the needs of individual cells and experimental goals. In this progress report, an overview of established materials and techniques used to fabricate single-cell microgels, as well as insight into potential alternatives is provided. This focused review is concluded by discussing applications that have already benefited from single-cell microgel technologies, as well as prospective applications on the cusp of achieving important new capabilities.     

Exploration of Electrochemical Reactions at Organic–Inorganic Halide Perovskite Interfaces via Machine Learning in In Situ Time‐of‐Flight Secondary Ion Mass Spectrometry

The instability of hybrid organic–inorganic perovskite (HOIP) devices is one of the significant challenges preventing commercialization. Exploring these phenomena is severely limited by the complexity of the intrinsic electrochemistry of HOIPs, the presence of multiple volatile and mobile ionic species, and the possible role of environmentally induced reactions at surfaces and triple‐phase junctions. Here, in situ studies of the electrochemistry of methylammonium lead bromide perovskite with the Au electrode interface are reported via light‐ and voltage‐dependent time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) imaging of lateral perovskite heterostructures. While ToF‐SIMS allows for the visualization of the chemical composition along the surface and its evolution with light and electrical bias, the interpretation of the multidimensional data obtained is often limited due to strong correlations between chemical signatures and the need to track multiple peaks at once. Here, a machine learning workflow combining the Hough transform and non‐negative matrix factorization and non‐negative tensor decomposition is developed to avoid this limitation and extract salient features of associated chemical changes and to separate the light‐ and voltage‐dependent dynamics. Combining these in situ characterizations and the machine learning workflow provides comprehensive information on the chemical nature of moving species, ion accumulation, and interfacial electrochemical reactions in HOIP devices.     

Microwave radiometry for cement kiln temperature measurements.

The maximum temperature inside a cement kiln is a critical operating parameter, but is often difficult or impossible to measure. We present here the first data that show a correlation between cement kiln temperature measured using a microwave radiometer and product chemistry over an eight-hour period. The microwave radiometer senses radiation in the 12-13 GHz range and has been described previously [Stephan and Pearce (2002), JMPEE 37: 112-124].     

Partial differential equation-based approach for empirical mode decomposition: application on image analysis

The major problem with the empirical mode decomposition (EMD) algorithm is its lack of a theoretical framework. So, it is difficult to characterize and evaluate this approach. In this paper, we propose, in the 2-D case, the use of an alternative implementation to the algorithmic definition of the so-called "sifting process" used in the original Huang's EMD method. This approach, especially based on partial differential equations (PDEs), was presented by Niang in previous works, in 2005 and 2007, and relies on a nonlinear diffusion-based filtering process to solve the mean envelope estimation problem. In the 1-D case, the efficiency of the PDE-based method, compared to the original EMD algorithmic version, was also illustrated in a recent paper. Recently, several 2-D extensions of the EMD method have been proposed. Despite some effort, 2-D versions for EMD appear poorly performing and are very time consuming. So in this paper, an extension to the 2-D space of the PDE-based approach is extensively described. This approach has been applied in cases of both signal and image decomposition. The obtained results confirm the usefulness of the new PDE-based sifting process for the decomposition of various kinds of data. Some results have been provided in the case of image decomposition. The effectiveness of the approach encourages its use in a number of signal and image applications such as denoising, detrending, or texture analysis.     

Transformation champ proche — champ lointain bidimensionnelle et monodimensionnelle appliquée à des mesures de SER

In high frequency thercs measurement of large target must be done at important distances, so the distance of measurement (Fraunhofer’s distance) R is given by R ≥ 2d² /λ where d is the tranversal length of the target and λ the radiation wavelength. In this paper we present a method for target that are large relative to the curvature of the spherical wavefront and the curvature of the cylindrical wavefront. A two-dimensional analytical algorithm transforms the spherical wave (near-field) measurement into the desired plane wave (far-field) and a one-dimensionnal analytical algorithm transforms the cylindrical wave (near-field) measurement into the desired plane wave (far-field). We present some results on simulations and measurements. A comparison of one-dimensional and two-dimensional methods demonstrates the efficiency of the two dimensional methods.     

Multichannel AR parameter estimation from noisy observations as an errors-in-variables issue

In various applications from radar processing to mobile communication systems based on CDMA or OFDM, M-AR multichannel processes are often considered and may be combined with Kalman filtering. However, the estimations of the M-AR parameter matrices and the autocorrelation matrices of the additive noise and the driving process from noisy observations are key problems to be addressed. In this paper, we suggest solving them as an errors-in-variables issue. In that case, the noisy-observation autocorrelation matrix compensated by a specific diagonal block matrix and whose kernel is defined by the M-AR parameter matrices must be positive semi-definite. Hence, the parameter estimation consists in searching every diagonal block matrix that satisfies this property, in reiterating this search for a higher model order and then in extracting the solution that belongs to both sets. A comparative study is then carried out with existing methods including those based on the Extended Kalman Filter (EKF) and the Sigma-Point Kalman Filters (SPKF). It illustrates the relevance and advantages of the proposed approaches.     

Virtual RF system platform dedicated to heterogeneous complex SoC design

In this paper, we propose to develop a hierarchical library associated with various simulators that can be used in a single platform, called TrustMe-ViP, which enables a unique simulation framework and full model interoperability. Such platform is dedicated to complex SoC design, such as trusted personal devices where cost and time-to-market are very important constraints. To validate this methodology, we present the estimation of BER and power consumption for a Bluetooth transceiver.     

Defect formation in epitaxial crystal growth

Factors affecting the nucleation and propagation of dislocations, stacking faults, microtwins, and inversion domain boundaries in epitaxially grown semiconductor layers are reviewed, with examples for heteroepitaxial MBE-grown layers on substrates having varying degrees of mismatch or different crystal symmetry. Mechanisms for generation of defects at the heterointerface and in the epilayer are discussed. For epilayers with bulk mismatch from 0 to 4%, methods for reducing defect density in the epitaxial layer are considered. Examples of structural details in the epilayers and at heterointerfaces, particularly those which may be revealed by transmission electron microscopy, are given.