Notes

Man Papillomavirus Contamination during the time of Delivery.
The availability of wireless networked control systems (WNCSs) has increased the interest in controlling multi-agent systems. Multiple feedback loops are closed over a shared communication network in such systems. An event triggering algorithm can significantly reduce network usage compared to the time triggering algorithm in WNCSs, however, the control performance is insecure in an industrial environment with a high probability of the packet dropping. This paper presents the design of a distributed event triggering algorithm in the state feedback controller for multi-agent systems, whose dynamics are subjected to the external interaction of other agents and under a random single packet drop scenario. Distributed event-based state estimation methods were applied in this work for designing a new event triggering algorithm for multi-agent systems while retaining satisfactory control performance, even in a high probability of packet drop condition. Simulation results for a multi-agent application show the main benefits and suitability of the proposed event triggering algorithm for multi-agent feedback control in WNCSs with packet drop imperfection.Drowsiness when in command of a vehicle leads to a decline in cognitive performance that affects driver behavior, potentially causing accidents. Drowsiness-related road accidents lead to severe trauma, economic consequences, impact on others, physical injury and/or even death. Real-time and accurate driver drowsiness detection and warnings systems are necessary schemes to reduce tiredness-related driving accident rates. The research presented here aims at the classification of drowsy and non-drowsy driver states based on respiration rate detection by non-invasive, non-touch, impulsive radio ultra-wideband (IR-UWB) radar. Chest movements of 40 subjects were acquired for 5 m using a lab-placed IR-UWB radar system, and respiration per minute was extracted from the resulting signals. A structured dataset was obtained comprising respiration per minute, age and label (drowsy/non-drowsy). Different machine learning models, namely, Support Vector Machine, Decision Tree, Logistic regression, Gradient Boosting Machine, Extra Tree Classifier and Multilayer Perceptron were trained on the dataset, amongst which the Support Vector Machine shows the best accuracy of 87%. This research provides a ground truth for verification and assessment of UWB to be used effectively for driver drowsiness detection based on respiration.The detection of dissolved gases in seawater plays an important role in oceanic observations and exploration. As a potential technique for oceanic applications, Raman spectroscopy has been successfully applied in hydrothermal vents and cold seep fluids, but it has not yet been used in common seawater due to the technique's lower sensitivity. In this work, we present a highly sensitive underwater in situ Raman spectroscopy system for dissolved gas detection in common seawater. Considering the difficulty of underwater degassing and in situ detection, we designed a near-concentric cavity to improve the sensitivity, with a miniature gas sample chamber featuring an inner volume of 1 mL placed inside the cavity to reach equilibrium in a short period of time. According to the 3σ criteria, the detection limits of this system for CO2, O2, and H2 were calculated to be 72.8, 44.0, and 27.7 ppm, respectively. Using a hollow fiber membrane degasser with a large surface area, the CO2 signal was found to be clearly visible in 30 s at a flow rate of 550 mL/min. Moreover, we deployed the system in Qingdao's offshore seawater, and the field test showed that this system is capable of successfully detecting in situ the multiple gases dissolved in the seawater simultaneously.In this contribution, we are investigating a technique for the representation of electromagnetic fields by recording their thermal footprints on an indicator material using a thermal camera. Fundamentals regarding the interaction of electromagnetic heating, thermodynamics, and fluid dynamics are derived which allow for a precise design of the field illustration method. The synthesis and description of high-loss dielectric materials is discussed and a technique for a simple estimation of the broadband material's imaginary permittivity part is introduced. Finally, exemplifying investigations, comparing simulations and measurements on the fundamental TE10-mode in an X-band waveguide are presented, which prove the above introduced sensing theory.In order to achieve the safe and efficient navigation of mobile robots, it is essential to consider both the environmental geometry and kinodynamic constraints of robots. We propose a trajectory planner for car-like robots on the basis of the Dual-Tree RRT (DT-RRT). DT-RRT utilizes two tree structures in order to generate fast-growing trajectories under the kinodynamic constraints of robots. A local trajectory generator has been newly designed for car-like robots. The proposed scheme of searching a parent node enables the efficient generation of safe trajectories in cluttered environments. The presented simulation results clearly show the usefulness and the advantage of the proposed trajectory planner in various environments.We present fluorescent Janus hydrogel microbeads for continuous glucose sensing with pH calibration. The Janus hydrogel microbeads, that consist of fluorescent glucose and pH sensors, were fabricated with a UV-assisted centrifugal microfluidic device. The microbead can calibrate the pH values of its surroundings and enables accurate measurements of glucose within various pH conditions. As a proof of concept, we succeeded in obtaining the accurate value of glucose concentration in a body-fluid-like sample solution. We believe that our fluorescent microbeads, with pH calibration capability, could be applied to fully implantable sensors for continuous glucose monitoring.In an autonomous vehicle, the lane following algorithm is an important component, which is a basic function of autonomous driving. However, the existing lane following system has a few shortcomings first, the control method it adopts requires an accurate system model, and different vehicles have different parameters, which needs a lot of parameter calibration work. The second is that it may fail on road sections where the lateral acceleration requirements of vehicles are large, such as large curves. Third, its decision-making system is defined based on rules, which has disadvantages it is difficult to formulate; human subjective factors cannot guarantee objectivity; coverage is difficult to guarantee. In recent years, the deep deterministic policy gradient (DDPG) algorithm has been widely used in the field of autonomous driving due to its strong nonlinear fitting ability and generalization performance. However, the DDPG algorithm has overestimated state action values and large cumulative errors, low training efficiency and other issues. Therefore, this paper improves the DDPG algorithm based on the double critic networks and priority experience replay mechanism. Then this paper proposes a lane following method based on this algorithm. Experiment shows that the algorithm can achieve excellent following results under various road conditions.Gas identification/classification through pattern recognition techniques based on gas sensor arrays often requires the equilibrium responses or the full traces of time-series data of the sensor array. Leveraging upon the diverse gas sensing kinetics behaviors measured via the sensor array, a computational intelligence- based meta-model is proposed to automatically conduct the feature extraction and subsequent gas identification using time-series data during the transitional phase before reaching equilibrium. The time-series data contains implicit temporal dependency/correlation that is worth being characterized to enhance the gas identification performance and reliability. In this context, a tailored approach so-called convolutional long short-term memory (CLSTM) neural network is developed to perform the identification task incorporating temporal characteristics within time-series data. This novel approach shows the enhanced accuracy and robustness as compared to the baseline models, i.e., multilayer perceptron (MLP) and support vector machine (SVM) through the comprehensive statistical examination. Specifically, the classification accuracy of CLSTM reaches as high as 96%, regardless of the operating condition specified. More importantly, the excellent gas identification performance of CLSTM at early stages of gas exposure indicates its practical significance in future real-time applications. The promise of the proposed method has been clearly illustrated through both the internal and external validations in the systematic case investigation.Video games have proven useful in physical rehabilitation therapy. Accessibility, however, is limited for some groups such as the elderly or patients with Parkinson's disease (PD). We explore the potential of fully immersive video games as a rehabilitation tool in PD patients. Four patients with mild-moderate PD (3 males1 female, 53-71 years) participated in the study. Training consisted in two immersive virtual reality video gaming sessions. Outcomes were evaluated using System Usability Scale (SUS), Simulator Sickness Questionnaire (SSQ), Game Experience Questionnaire-post game (GEQ), an ad hoc satisfaction questionnaire and perceived effort. All participants completed the sessions without adverse effects (100%), without SSQ symptoms reported. Post-gaming SUS was >75% in both sessions (range 75-80%). Post-gaming GEQ scores were 3.3-4.0/4 in both sessions. Immersive virtual reality video gaming is feasible in patients with mild-moderate PD, with positive usability and patient satisfaction, and no adverse effects.Intra-abdominal pressure (IAP) is defined as the steady-state pressure within the abdominal cavity. Elevated IAP has been implicated in many medical complications. This article reviews the current state-of-the-art in innovative sensors for the measurement of IAP. A systematic review was conducted on studies on the development and application of IAP sensors. Publications from 2010 to 2021 were identified by performing structured searches in databases, review articles, and major textbooks. Sixteen studies were eligible for the final systematic review. Of the 16 articles that describe the measurement of IAP, there were 5 in vitro studies (31.3%), 7 in vivo studies (43.7%), and 4 human trials (25.0%). In addition, with the advancement of wireless communication technology, an increasing number of wireless sensing systems have been developed. Among the studies in this review, five presented wireless sensing systems (31.3%) to monitor IAP. In this systematic review, we present recent developments in different types of intra-abdominal pressure sensors and discuss their inherent advantages due to their small size, remote monitoring, and multiplexing.In this article, a multisensor joint localization system is proposed based on modified cubature Kalman filtering, which aims to improve the accuracy of state estimation under a moderate computational burden in the presence of high process noise. Specifically, first, the covariance of process noise is matched based on adaptive filtering. Smad pathway The inertial measurement unit (IMU), odometer (ODM), and ultra-wideband (UWB) information acquired by the associated sensors is then employed to augment the system state and are fused to lower the influence of process noise. In the presented localization setting, all sensors (IMU/ODM/UWB) are set to work in parallel under the federated Kalman filter (FKF) framework, which can correct the cumulative error of the internal sensor and and can improve the computational efficiency. Two sets of numerical simulations were performed to show that the proposed method can obtain accurate state estimation with a slightly increased computational burden.
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