Mean path length invariance in wave-scattering beyond the diffusive regime

Diffusive random walks feature the surprising property that the average length of all possible random trajectories that enter and exit a finite domain is determined solely by the domain boundary. Changes in the diffusion constant or the mean-free path, that characterize the diffusion process, leave the mean path length unchanged. Here, we demonstrate experimentally that this result can be transferred to the scattering of waves, even when wave interference leads to marked deviations from a diffusion process. Using a versatile microwave setup, we establish the mean path length invariance for the crossover to Anderson localization and for the case of a band gap in a photonic crystal. We obtain these results on the mean path length solely based on a transmission matrix measurement through a procedure that turns out to be more robust to absorption and incomplete measurement in the localized regime as compared to an assessment based on the full scattering matrix.

Machine learning based data retrieval for inverse scattering problems with incomplete data

We are concerned with the inverse scattering problems associated with incomplete measurement data. It is a challenging topic of increasing importance that arise in many practical applications. Based on a prototypical working model, we propose a machine learning based inverse scattering scheme, which integrates a CNN (convolution neural network) for the data retrieval. The proposed method can effectively cope with the reconstruction under limited-aperture and/or phaseless far-field data. Numerical experiments verify the promising features of our new scheme.

ARS: Adaptive Robust Synchronization for Underground Coal Wireless Internet of Things

Clock synchronization is still a vital and challenging task for underground coal wireless internet of things (IoT) due to the uncertainty of underground environment and unreliability of communication links. Instead of considering on-demand driven clock synchronization, this paper proposes a novel Adaptive Robust Synchronization (ARS) scheme with packets loss for mine wireless environment. A clock synchronization framework that is based on Kalman filtering is first proposed, which can adaptively adjust the sampling period of each clock and reduce the communication overhead in single-hop networks. The proposed scheme also solves the problem of outliers in data packets with time-stamps. In addition, this paper extends the ARS algorithm to multi-hop networks. Additionally, the upper and lower bounds of error covariance expectation are analyzed in the case of incomplete measurement. Extensive simulations are conducted in order to evaluate the performance. In the simulation environment, the clock accuracy of ARS algorithm is improved by 7.85% when compared with previous studies for single-hop networks. For multi-hop networks, the proposed scheme improves the accuracy by 12.56%. The results show that the proposed algorithm has high scalability, robustness, and accuracy, and can quickly adapt to different clock accuracy requirements.

Concerns with the use of imputation to assign HLA allele-level typing in research predicting transplant outcomes

Incomplete information on HLA allele typing is a persistent problem when analyzing the role of Human Leukocyte Antigen (HLA) in transplantation. To refine the predictions possible with partial knowledge of HLA typing, some researchers use HaploStats statistics on the frequencies of haplotypes within specified ethnic/national populations to impute complete HLA allele typing. We evaluated methods that use imputation to predict patient outcomes after organ transplantation, with focus on prediction of graft survival conditional on typing information of the donor and recipient. Logical arguments show that imputation yields no predictive power when predictions are conditioned on all observed HLA typing data. Computational experiments indicate that imputation does not have predictive power when applied to risk-assessment models that make predictions conditional on only part of the observable HLA data. We therefore caution against reliance on imputation to overcome incomplete measurement. We encourage high-resolution typing of HLA antigens to improve prediction of transplant outcomes and matching of donors with recipients. Similar considerations should likely apply in other clinical settings.

Dielectrophoresis-Based Method for Measuring the Multiangle Mechanical Properties of Biological Cells

The mechanical properties of cells are closely related to their physiological functions and states. Analyzing and measuring these properties are beneficial to understanding cell mechanisms. However, most measurement methods only involve the unidirectional analysis of cellular mechanical properties and thus result in the incomplete measurement of these properties. In this study, a microfluidic platform was established, and an innovative microfluidic chip was designed to measure the multiangle cellular mechanical properties by using dielectrophoresis (DEP) force. Three unsymmetrical indium tin oxide (ITO) microelectrodes were designed and combined with the microfluidic chip, which were utilized to generate DEP force and stretch cell from different angles. A series of experiments was performed to measure and analyze the multiangle mechanical properties of red blood cells of mice. This work provided a new tool for the comprehensive and accurate measurement of multiangle cellular mechanical properties. The results may contribute to the exploration of the internal physiological structures of cells and the building of accurate cell models.

Study of damage identification for bridges based on deep belief network

To solve the problem of poor anti-noise ability faced by traditional pattern recognition methods in damage identification field, a bridge damage identification method based on deep belief network was proposed. Taken the modal curvature difference as the damage index, three restricted Boltzmann machines were constructed for pre-training. Then, the Softmax classifier and neural network were used to identify the damage location and degree under the environmental cases of no noise, weak noise, and strong noise, respectively. Subsequently, the influence of incomplete measurement modal data on the method was studied. Finally, damage identification based on deep belief network was implemented to a continuous beam bridge and compared with that of the back propagation neural network. The results showed that the proposed method could be highly effective not only on damage location but also on degree identification. Compared with back propagation neural network, deep belief network method may possess better identification ability and stronger anti-noise ability. It also demonstrates good identification effect under the condition of incomplete measurement modal data.