Recent advances in device-free wireless sensing (DFS) have shown that it may eventually evolve traditional wireless networks into smart networks which could sense surrounding target location and ...activity information without equipping the target with any devices. Despite its promising application prospects, one challenging problem to be solved is that the performance of the DFS system degrades significantly in complex scenarios, such as through-wall and non-line-of-sight (NLOS) scenarios. To alleviate this problem, this paper seeks to explore and exploit more informative features from not only the time domain and frequency domain, but also the spatial structural domain. We partition the time domain and frequency domain measurement matrices into basic structure blocks, adopt self-organizing map networks to cluster the blocks into a number of categories, so as to make it feasible to characterize the block distributions. We further adopt coherence histograms to characterize the distribution of the blocks by considering the spatial relationship between adjacent blocks. Thanks to the additional information provided by the spatial structural domain, extensive experimental results achieved in through-wall and NLOS scenarios confirm the outstanding performance of the proposed multi-domain features based DFS system.
As an emerging technique with promising application prospects, device-free localization (DFL) could estimate the location of target within the deployment area of wireless networks (WNs) while ...eliminating the need to equip the target with a wireless device. However, one major disadvantage of this technique is that it needs several wireless links travelling through the deployment area to guarantee good performance. To overcome this problem, a novel multidimensional wireless-link-information-based DFL scheme is proposed. Different from a traditional DFL scheme that scans wireless links sequentially with one frequency and one transmission power level, the proposed scheme makes full use of multiple frequencies and multiple transmission power levels to enrich the link measurement information. Meanwhile, motivated by the fact that the location information of the target is not only sparse but also changes slowly and continuously over time, we present a novel recursive compressive sensing algorithm to reconstruct the location information from undersampled measurements. The experimental results demonstrate the outstanding performance of the proposed scheme.
Dilated cardiomyopathy (DCM) is a primary cause of heart failure (HF), with the incidence of HF increasing consistently in recent years. DCM pathogenesis involves a combination of inherited ...predisposition and environmental factors. Endocrine-disrupting chemicals (EDCs) are exogenous chemicals that interfere with endogenous hormone action and are capable of targeting various organs, including the heart. However, the impact of these disruptors on heart disease through their effects on genes remains underexplored. In this study, we aimed to explore key DCM-related genes using machine learning (ML) and the construction of a predictive model. Using the Gene Expression Omnibus (GEO) database, we screened differentially expressed genes (DEGs) and performed enrichment analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to DCM. Through ML techniques combining maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression, we identified key genes for predicting DCM (IL1RL1, SEZ6L, SFRP4, COL22A1, RNASE2, HB). Based on these key genes, 79 EDCs with the potential to affect DCM were identified, among which 4 (3,4-dichloroaniline, fenitrothion, pyrene, and isoproturon) have not been previously associated with DCM. These findings establish a novel relationship between the EDCs mediated by key genes and the development of DCM.
•Certain EDCs can affect DCM development and occurrence.•An EDCs-genes-DCM network was created using bioinformatics and ML.•ML facilitated the establishment of a model for repeatable predictive DCM.•Potential DCM-causing EDCs were discovered through key genes in the model.
Device-Free simultaneous wireless Localization and Activity Recognition (DFLAR) is a promising novel technique that empowers wireless networks with the ability to perceive the location and activity ...of a target within its deployment area while not equipping the target with a device. This technique turns traditional wireless networks into smart context-aware networks and will play an important role in many smart applications, e.g., smart city, smart space, and smart house. Essentially, DFLAR utilizes the shadowing effect incurred by the target on wireless links to realize localization and activity recognition. The feature utilized to characterize the shadowing effect is crucial for DFLAR. Traditional methods use time-domain features to characterize the shadowing effect. In this paper, we explore the method of realizing DFLAR with a wavelet feature. Compared with the time-domain feature, the wavelet feature could characterize link measurement in both the time and frequency domains, which could provide in-depth robust discriminative information and, therefore, improve the performance of the DFLAR system. Meanwhile, we also design a two-stage strategy to realize multitarget DFLAR with the feature map built by one target only, which reduces the training complexity remarkably. The experimental results in a clutter indoor scenario show that it could achieve location estimation and activity recognition accuracy of higher than 90%.
Device-free wireless localization (DFL) is a technique that can locate a target by analyzing its shadowing effect on wireless links, which causes the variation of link measurements, while removing ...the requirement of equipping the target with a device. It can provide fundamental data for pervasive computing, smart environment, and traffic surveillance applications. The observation model, which represents the relationship between wireless link measurement and target location, is very important for DFL, since it characterizes the shadowing effect of the target on wireless links and, therefore, determines the performance of the DFL system. In this paper, inspired by measurement results, we propose a saddle surface (SaS) model to describe the shadowing effect. The SaS model characterizes the elaborate information within the spatial impact area and provides more useful observation information for the location estimation algorithm. We incorporate the SaS model into the particle filter framework for location estimation. Extensive experiments in indoor and outdoor scenarios are carried out to evaluate the performance of the proposed schemes. The tracking errors of 0.78 and 0.21 m in the given two scenarios demonstrate the better performance of the proposed SaS model compared with existing models.
It is a challenging problem to realize robust localization in complex indoor environments where non-line-of-sight (NLOS) occurs due to reflection and diffraction. To solve this problem, a ...localization algorithm under the Bayesian framework is proposed in this paper. We adopt the 802.15.4a chirp-spread-spectrum ranging hardware to measure the distances between the mobile node and the anchor nodes, and realize the location estimation by incorporating the range measurements into the localization algorithm. We propose a novel joint-state estimation localization algorithm which adopts a Markov model for NLOS state estimation and a particle filter for location state estimation. For utilizing the positive effect of the NLOS measurements while restraining their negative effect, we present a scheme to build the feasible region of the particles based on the NLOS and line-of-sight (LOS) measurements and calculate the particle weight based only on the LOS measurements. The results of the indoor experiment demonstrate the effectiveness of our approach.
Hand gesture classification and finger angle estimation are both critical for intuitive human-computer interaction. However, most approaches study them in isolation. We thus propose a dual-output ...deep learning model to enable simultaneous hand gesture classification and finger angle estimation. Data augmentation and deep learning were used to detect spatial-temporal features via a wristband with ten modified barometric sensors. Ten subjects performed experimental testing by flexing/extending each finger at the metacarpophalangeal joint while the proposed model was used to classify each hand gesture and estimate continuous finger angles simultaneously. A data glove was worn to record ground-truth finger angles. Overall hand gesture classification accuracy was 97.5% and finger angle estimation R 2 was 0.922, both of which were significantly higher than shallow existing learning approaches used in isolation. The proposed method could be used in applications related to the human-computer interaction and in control environments with both discrete and continuous variables.
As an emerging technique with a promising application prospect, the device-free localization (DFL) technique has drawn considerable attention due to its ability of realizing wireless localization ...without the need of equipping the target with any device. The DFL technique detects the shadowed links and realizes localization with the received signal strength (RSS) measurements of these links. However, one major disadvantage of the DFL technique is that the RSS signal is too sensitive, and a slight variation of the environment will cause the variation of RSS measurements, which incurs the misjudgment of shadowed links and degradation of localization performance. To solve this problem, a robust DFL scheme based on differential RSS is proposed. The scheme utilizes the novel differential RSS to judge whether a link is shadowed, which not only eliminates the need of acquiring reference RSS measurements but also overcomes the negative effect incurred by the environment. Meanwhile, an outlier detection scheme is presented to filter out the outlier links that are far away from the target. We present the observation model of the shadowed links and incorporate it into the particle filter framework to realize location estimation robustly. Experimental results demonstrate the outstanding performance of the proposed scheme.
Cav1.2 Ca
channels, a type of voltage-gated L-type Ca
channel, are ubiquitously expressed, and the predominant Ca
channel type, in working cardiac myocytes. Cav1.2 channels are regulated by the ...direct interactions with calmodulin (CaM), a Ca
-binding protein that causes Ca
-dependent facilitation (CDF) and inactivation (CDI). Ca
-free CaM (apoCaM) also contributes to the regulation of Cav1.2 channels. Furthermore, CaM indirectly affects channel activity by activating CaM-dependent enzymes, such as CaM-dependent protein kinase II and calcineurin (a CaM-dependent protein phosphatase). In this article, we review the recent progress in identifying the role of apoCaM in the channel 'rundown' phenomena and related repriming of channels, and CDF, as well as the role of Ca
/CaM in CDI. In addition, the role of CaM in channel clustering is reviewed.
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