A stochastic self-triggered model predictive control (SSMPC) algorithm is proposed for linear systems subject to exogenous disturbances and probabilistic constraints. The main idea behind the ...self-triggered framework is that at each sampling instant, an optimization problem is solved to determine both the next sampling instant and the control inputs to be applied between the two sampling instants. Although the self-triggered implementation achieves communication reduction, the control commands are necessarily applied in open-loop between sampling instants. To guarantee probabilistic constraint satisfaction, necessary and sufficient conditions are derived on the nominal systems by using the information on the distribution of the disturbances explicitly. Moreover, based on a tailored terminal set, a multi-step open-loop MPC optimization problem with infinite prediction horizon is transformed into a tractable quadratic programming problem with guaranteed recursive feasibility. The closed-loop system is shown to be stable. Numerical examples illustrate the efficacy of the proposed scheme in terms of performance, constraint satisfaction, and reduction of both control updates and communications with a conventional time-triggered scheme.
WiFi technology has been applied to various places due to the increasing requirement of high-speed Internet access. Recently, besides network services, WiFi sensing is appealing in smart homes since ...it is device free, cost effective and privacy preserving. Though numerous WiFi sensing methods have been developed, most of them only consider single smart home scenario. Without the connection of powerful cloud server and massive users, large-scale WiFi sensing is still difficult. In this article, we first analyze and summarize these obstacles, and propose an efficient large-scale WiFi sensing framework, namely, EfficientFi. The EfficientFi works with edge computing at WiFi access points and cloud computing at center servers. It consists of a novel deep neural network that can compress fine-grained WiFi channel state information (CSI) at edge, restore CSI at cloud, and perform sensing tasks simultaneously. A quantized autoencoder and a joint classifier are designed to achieve these goals in an end-to-end fashion. To the best of our knowledge, the EfficientFi is the first Internet of Things-cloud-enabled WiFi sensing framework that significantly reduces communication overhead while realizing sensing tasks accurately. We utilized human activity recognition (HAR) and identification via WiFi sensing as two case studies, and conduct extensive experiments to evaluate the EfficientFi. The results show that it compresses CSI data from 1.368 Mb/s to 0.768 kb/s with extremely low error of data reconstruction and achieves over 98% accuracy for HAR.
To improve the utilization of fertilizer and water resources at the same time, a new slow-release PK compound fertilizer (SRCF) with the function of water retention was prepared. Element analysis ...results showed that the product contained 20.46% potassium (shown by K
2
O) and 15.45% phosphorus (shown by P
2
O
5
), which were trapped in the matrix of carboxymethylcellulose-graft-poly(acrylic acid-co-itaconic acid) superabsorbent polymer. Major factors affecting the water absorbency of SRCF such as weight ratio of itaconic acid (IA) to acrylic acid (AA), contents of crosslinker, K
2
HPO
4
, and carboxymethylcellulose (CMC) were investigated and optimized. The water absorbency of the product was 130 times its own weight if it was allowed to swell in tap water at room temperature for 1 h. Fourier transform infrared spectroscopy (FTIR) and Thermogravimetric/Differential thermal analysis (TG/DTA) confirmed that AA and IA monomers were graft-copolymerized onto CMC backbone and presented the improved thermal stability. The water evaporation of the fertilizer-containing superabsorbents, as well as their nutrients release in sandy soil was carried out, and a possible slow-release mechanism was proposed. Additionally, compressive modulus measurements revealed that the introduction of CMC could improve the mechanical properties of the superabsorbents. These studies showed that the product with good slow-release and water retention properties, being economical and environment-friendly, could be expected to have wide potential applications in modern agriculture and horticulture.
The use of multichannel data in line spectral estimation (or frequency estimation) is common for improving the estimation accuracy in array processing, structural health monitoring, wireless ...communications, and more. Recently proposed atomic norm methods have attracted considerable attention due to their provable superiority in accuracy, flexibility, and robustness compared with conventional approaches. In this paper, we analyze atomic norm minimization for multichannel frequency estimation from noiseless compressive data, showing that the sample size per channel that ensures exact estimation decreases with the increase of the number of channels under mild conditions. In particular, given <inline-formula> <tex-math notation="LaTeX">L </tex-math></inline-formula> channels, order <inline-formula> <tex-math notation="LaTeX">K \left ({\log K}\right) \left ({1+\frac {1}{L}\log N}\right) </tex-math></inline-formula> samples per channel, selected randomly from <inline-formula> <tex-math notation="LaTeX">N </tex-math></inline-formula> equispaced samples, suffice to ensure with high probability exact estimation of <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> frequencies that are normalized and mutually separated by at least <inline-formula> <tex-math notation="LaTeX">\frac {4}{N} </tex-math></inline-formula>. Numerical results are provided corroborating our analysis.
Fingerprinting based indoor positioning system is gaining more research interest under the umbrella of location-based services. However, existing works have certain limitations in addressing issues ...such as noisy measurements, high computational complexity, and poor generalization ability. In this work, a random vector functional link network based approach is introduced to address these issues. In the proposed system, a subset of informative features from many randomized noisy features is selected to both reduce the computational complexity and boost the generalization ability. Moreover, the feature selector and predictor are jointly learned iteratively in a single framework based on an augmented Lagrangian method. The proposed system is appealing as it can be naturally fit into parallel or distributed computing environment. Extensive real-world indoor localization experiments are conducted on users with smartphone devices and results demonstrate the superiority of the proposed method over the existing approaches.
Dual-stage servos with multiple microactuators are an effective way to increase the servo performance for higher track density of hard disk drives. The multiple microactuators can be used to perform ...simultaneous read/write on multiple disk platters so as to achieve a higher data rate using the parallel processing. Also, faster, independent access to data is possible with multiple actuators. However, the interactions among microactuators may degrade system performance or even cause instability. In this paper, we mainly deal with the interaction problem for piezoelectric microactuators to achieve higher positioning accuracy. The control scheme for the microactuators, designed to reduce the interaction impact, is obtained by applying the generalized Kalman-Yakubovic-Popov lemma in conjunction with the Youla parameterization approach. Our simulation and experiment results demonstrate that the proposed control strategy can improve the position accuracy due to microactuator interaction by 40%.
This paper studies the sensor selection problem for random field estimation in wireless sensor networks. The authors first prove that selecting a set of I sensors that minimize the estimation error ...under the D-optimal criterion is NP-complete. The authors propose an iterative algorithm to pursue a suboptimal solution. Furthermore, in order to improve the bandwidth and energy efficiency of the wireless sensor networks, the authors propose a best linear unbiased estimator for a Gaussian random field with quantized measurements and study the corresponding sensor selection problem. In the case of unknown covariance matrix, the authors propose an estimator for the covariance matrix using measurements and also analyze the sensitivity of this estimator. Simulation results show the good performance of the proposed algorithms.
Wi-Fi sensing technology has shown superiority in smart homes among various sensors for its cost-effective and privacy-preserving merits. It is empowered by channel state information (CSI) extracted ...from Wi-Fi signals and advanced machine learning models to analyze motion patterns in CSI. Many learning-based models have been proposed for kinds of applications, but they severely suffer from environmental dependency. Though domain adaptation methods have been proposed to tackle this issue, it is not practical to collect high-quality, well-segmented, and balanced CSI samples in a new environment for adaptation algorithms, but randomly captured CSI samples can be easily collected. In this article, we first explore how to learn a robust model from these low-quality CSI samples, and propose AutoFi, an annotation-efficient Wi-Fi sensing model based on a novel geometric self-supervised learning algorithm. The AutoFi fully utilizes unlabeled low-quality CSI samples that are captured randomly, and then transfers the knowledge to specific tasks defined by users, which is the first work to achieve cross-task transfer in Wi-Fi sensing. The AutoFi is implemented on a pair of Atheros Wi-Fi APs for evaluation. The AutoFi transfers knowledge from randomly collected CSI samples into human gait recognition and achieves state-of-the-art performance. Furthermore, we simulate cross-task transfer using public data sets to further demonstrate its capacity for cross-task learning. For the UT-HAR and Widar data sets, the AutoFi achieves satisfactory results on activity recognition and gesture recognition without any prior training. We believe that AutoFi takes a huge step toward automatic Wi-Fi sensing without any developer engagement. Our codes have been included in https://github.com/xyanchen/Wi-Fi-CSI-Sensing-Benchmark .
Quantitative flow ratio (QFR) is a novel noninvasive tool for the functional assessment of coronary stenosis. Whether or not QFR could predict graft outcomes after coronary artery bypass grafting ...procedure is unknown. This study aimed to investigate the association of QFR value with graft outcomes after coronary artery bypass grafting surgery.
The QFR values were retrospectively obtained from patients receiving coronary artery bypass grafting surgery from 2017 to 2019 in the Graft Patency Between No-Touch Vein Harvesting Technique and Conventional Approach in Coronary Artery Bypass Graft Surgery (PATENCY) trial. QFR calculation was conducted in eligible coronary arteries, defined as those with ≥50% stenosis and a diameter ≥1.5 mm. A threshold of QFR ≤0.80 was considered functionally significant stenosis. The primary outcome was graft occlusion at 12 months evaluated by computed tomography angiography.
Two thousand twenty-four patients with 7432 grafts (2307 arterial grafts and 5125 vein grafts) were included. For the arterial grafts, the risk of 12-month occlusion was significantly increased in the QFR >0.80 group than in the QFR ≤0.80 group (7.1% vs 2.6%; P = .001; unadjusted model: odds ratio, 3.08; 95% CI, 1.65-5.75; fully adjusted model: odds ratio, 2.67; 95% CI, 1.44-4.97). No significant association was observed in the vein grafts (4.6% vs 4.3%; P = .67; unadjusted model: odds ratio, 1.10; 95% CI, 0.82-1.47; fully adjusted model: odds ratio, 1.12; 95% CI, 0.83-1.51). Results were stable across sensitivity analyses with a QFR threshold of 0.78 and 0.75.
Target vessel QFR >0.80 was associated with a significantly higher risk of arterial graft occlusion at 12 months after coronary artery bypass grafting surgery. No significant association was found between target lesion QFR and vein graft occlusion.
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In this paper, two novel stator inter-turn fault sensing schemes, based on infrared (IR) thermopile sensor array (IRSA) and Hall-effect sensor array (HESA) are proposed. These circular sensing arrays ...are mounted along the inner wall of the stator's casing facing the end-winding region. This enables a direct measurement of temperature and magnetic flux distribution along the end-winding region in a non-contact way. Thus, the deviation in thermal and magnetic symmetry introduced by an inter-turn short circuit fault can be readily assessed. The proposed sensing schemes provides an intuitive and straight forward approach to inter-turn fault monitoring compared to the conventional methods, which depend on detecting fault induced secondary effects on external signals (motor currents, casing vibration, and temperature). The practicality and the diagnostic ability of the end-winding sensor array approaches in detecting the stator inter-turn faults is demonstrated using a 1.5 kW induction motor test rig. Compared to IRSA, HESA is found to be more versatile and with better early fault detection capability. Hence, an embedded online monitoring algorithm based on HESA is developed and demonstrated using the test rig.
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