Link Quality Estimation of Cross-Technology Communication: The Case with Physical-Level Emulation

Research on cross-technology communication ( CTC ) has made rapid progress in recent years. While the CTC links are complex and dynamic, how to estimate the quality of a CTC link remains an open and challenging problem. Through our observation and study, we find that none of the existing approaches can be applied to estimate the link quality of CTC. Built upon the physical-level emulation, transmission over a CTC link is jointly affected by two factors: the emulation error and the channel distortion. Furthermore, the channel distortion can be modeled and observed through the signal strength and the noise strength. We, in this article, propose a new link metric called C-LQI and a joint link model that simultaneously takes into account the emulation error and the channel distortion in the In-phase and Quadrature ( IQ ) domain. We accurately describe the superimposed impact on the received signal. We further design a lightweight link estimation approach including two different methods to estimate C-LQI and in turn the packet reception rate ( PRR ) over the CTC link. We implement C-LQI and compare it with two representative link estimation approaches. The results demonstrate that C-LQI reduces the relative estimation error by 49.8% and 51.5% compared with s-PRR and EWMA, respectively.

Robust, Primitive, and Unsupervised Quality Estimation for Segmentation Ensembles

A multitude of image-based machine learning segmentation and classification algorithms has recently been proposed, offering diagnostic decision support for the identification and characterization of glioma, Covid-19 and many other diseases. Even though these algorithms often outperform human experts in segmentation tasks, their limited reliability, and in particular the inability to detect failure cases, has hindered translation into clinical practice. To address this major shortcoming, we propose an unsupervised quality estimation method for segmentation ensembles. Our primitive solution examines discord in binary segmentation maps to automatically flag segmentation results that are particularly error-prone and therefore require special assessment by human readers. We validate our method both on segmentation of brain glioma in multi-modal magnetic resonance - and of lung lesions in computer tomography images. Additionally, our method provides an adaptive prioritization mechanism to maximize efficacy in use of human expert time by enabling radiologists to focus on the most difficult, yet important cases while maintaining full diagnostic autonomy. Our method offers an intuitive and reliable uncertainty estimation from segmentation ensembles and thereby closes an important gap toward successful translation of automatic segmentation into clinical routine.

Air Quality Estimation Using Dendritic Neural Regression with Scale-Free Network-Based Differential Evolution

With the rapid development of the global economy, air pollution, which restricts sustainable development and threatens human health, has become an important focus of environmental governance worldwide. The modeling and reliable prediction of air quality remain substantial challenges because uncertainties residing in emissions data are unknown and the dynamic processes are not well understood. A number of machine learning approaches have been used to predict air quality to help alleviate air pollution, since accurate air quality estimation may result in significant social-economic development. From this perspective, a novel air quality estimation approach is proposed, which consists of two components: newly-designed dendritic neural regression (DNR) and customized scale-free network-based differential evolution (SFDE). The DNR can adaptively utilize spatio-temporal information to capture the nonlinear correlation between observations and air pollutant concentrations. Since the landscape of the weight space in DNR is vast and multimodal, SFDE is used as the optimization algorithm due to its powerful search ability. Extensive experimental results demonstrate that the proposed approach can provide stable and reliable performances in the estimation of both PM2.5 and PM10 concentrations, being significantly better than several commonly-used machine learning algorithms, such as support vector regression and long short-term memory.