Non-Contact Physiological Measurement System for Wearing Masks During the Epidemic

Physiological signals indicate a person’s physical and mental state at any given time. Accordingly, many studies extract physiological signals from the human body with non-contact methods, and most of them require facial feature points. However, under COVID-19, wearing a mask has become a must in many places, so how non-contact physiological information measurements can still be performed correctly even when a mask covers the facial information has become a focus of research. In this study, RGB and thermal infrared cameras were used to execute non-contact physiological information measurement systems for heart rate, blood pressure, respiratory rate, and forehead temperature for people wearing masks due to the pandemic. Using the green (G) minus red (R) signal in the RGB image, the region of interest (ROI) is established in the forehead and nose bridge regions. The photoplethysmography (PPG) waveforms of the two regions are obtained after the acquired PPG signal is subjected to the optical flow method, baseline drift calibration, normalization, and bandpass filtering. The relevant parameters in Deep Neural Networks (DNN) for the regression model can correctly predict the heartbeat and blood pressure. In addition, the temperature change in the ROI of the mask after thermal image processing and filtering can be used to correctly determine the number of breaths. Meanwhile, the thermal image can be used to read the temperature average of the ROI of the forehead, and the forehead temperature can be obtained smoothly. The experimental results show that the above-mentioned physiological signals of a subject can be obtained in 6-s images with the error for both heart rate and blood pressure within 2%~3% and the error of forehead temperature within ±0.5°C.     

CHL1 encodes a component of the low-affinity nitrate uptake system in Arabidopsis and shows cell type-specific expression in roots

The Arabidopsis CHL1 (AtNRT1) gene confers sensitivity to the herbicide chlorate and encodes a nitrate-regulated nitrate transporter. However, how CHL1 participates in nitrate uptake in plants is not yet clear. In this study, we examined the in vivo function of CHL1 with in vivo uptake measurements and in situ hybridization experiments. Under most conditions tested, the amount of nitrate uptake by a chl1 deletion mutant was found to be significantly less than that of the wild type. This uptake deficiency was reversed when a CHL1 cDNA clone driven by the cauliflower mosaic virus 35S promoter was expressed in transgenic chl1 plants. Furthermore, tissue-specific expression patterns showed that near the root tip, CHL1 mRNA is found primarily in the epidermis, but further from the root tip, the mRNA is found in the cortex or endodermis. These results are consistent with the involvement of CHL1 in nitrate uptake at different stages of root cell development. A functional analysis in Xenopus oocytes indicated that CHL1 is a low-affinity nitrate transporter with a K(m) value of approximately 8.5 mM for nitrate. This finding is consistent with the chlorate resistance phenotype of chl1 mutants. However, these results do not fit the current model of a single, constitutive component for the low-affinity uptake system. To reconcile this discrepancy and the complex uptake behavior observed, we propose a "two-gene" model for the low-affinity nitrate uptake system of Arabidopsis.     

Out‐of‐Core Simplification and Crack‐Free LOD Volume Rendering for Irregular Grids

We propose a novel out‐of‐core simplification and level‐of‐detail (LOD) volume rendering algorithm for large irregular grids represented as tetrahedral meshes. One important feature of our algorithm is that it creates a space decomposition as required by I/O‐efficient simplification and volume rendering, and simplifies both the internal and boundary portions of the sub‐volumes progressively by edge collapses using the (extended) quadric error metric, while ensuring any selected LOD mesh to be crack‐free (i.e., any neighboring sub‐volumes in the LOD have consistent boundaries, and all the cells in the LOD do not have negative volumes), with all computations performed I/O‐ejficiently. This has been an elusive goal for out‐of‐core progressive meshes and LOD visualization, and our novel solution achieves this goal with a theoretical guarantee to be crack‐free for tetrahedral meshes. As for selecting a desirable LOD mesh for volume rendering, our technique supports selective refinement LODs (where different places can have different error bounds), in addition to the basic uniform LODs (where the error bound is the same in all places). The proposed scalar‐value range and view‐dependent selection queries for selective refinement are especially effective in producing images of the highest quality with a much faster rendering speed. The experiments demonstrate the efficacy of our new technique.     

High quality surface remeshing with equilateral triangle grid

This study proposes a robust and efficient 3D surface remeshing algorithm for mesh quality optimization. Instead of the global mesh relaxation method proposed in the previous study conducted on remeshing, this study proposes an equilateral triangle grid-resampling scheme for achieving mesh optimization more efficiently. In order to improve the feasibility of resampling by directly using an equilateral triangle grid, the surface structure of the original model is correctly extracted by an automatic surface segmentation technique before the resampling step is executed. Results of this study show that the proposed remeshing algorithm can automatically and substantially improve the quality of triangulation, as well as automatically preserve shape features under an acceptable level of measurement error in the shape approximation, which is suitable for a mesh with a specific topology.     

A memetic algorithm for minimizing total weighted tardiness on parallel batch machines with incompatible job families and dynamic job arrival

This paper addresses a scheduling problem motivated by scheduling of diffusion operations in the wafer fabrication facility. In the target problem, jobs arrive at the batch machines at different time instants, and only jobs belonging to the same family can be processed together. Parallel batch machine scheduling typically consists of three types of decisions—batch forming, machine assignment, and batch sequencing. We propose a memetic algorithm with a new genome encoding scheme to search for the optimal or near-optimal batch formation and batch sequence simultaneously. Machine assignment is resolved in the proposed decoding scheme. Crossover and mutation operators suitable for the proposed encoding scheme are also devised. Through the experiment with 4860 problem instances of various characteristics including the number of machines, the number of jobs, and so on, the proposed algorithm demonstrates its advantages over a recently proposed benchmark algorithm in terms of both solution quality and computational efficiency.     

Cognitive,social and teaching presence in a virtual world and a text chat

Using a framework of cognitive, social, and teaching presence, the nature of learning experiences in a three-dimensional virtual world environment (Second Life) and a text-chat learning environment without visuals (TeachNet) were investigated. A mixed method of code frequencies, coherence graphs, interviews, and a survey was used. The results revealed that the TeachNet debates included more cognitive presence codes that indicate higher levels of cognitive processing than in SL debates. The teams were significantly different from each other in the collaboration style for developing arguments and in the ways to use utterances associated with cognitive, social, and teaching presences, and the groups’ collaboration style became more established with more experience with the tasks. The three critical factors–tool, tasks and group cohesion–that affect cognitive, teaching, and social presence are discussed.     

Extending 3D Lucas–Kanade tracking with adaptive templates for head pose estimation

The Lucas–Kanade tracker (LKT) is a commonly used method to track target objects over 2D images. The key principle behind the object tracking of an LKT is to warp the object appearance so as to minimize the difference between the warped object’s appearance and a pre-stored template. Accordingly, the 2D pose of the tracked object in terms of translation, rotation, and scaling can be recovered from the warping. To extend the LKT for 3D pose estimation, a model-based 3D LKT assumes a 3D geometric model for the target object in the 3D space and tries to infer the 3D object motion by minimizing the difference between the projected 2D image of the 3D object and the pre-stored 2D image template. In this paper, we propose an extended model-based 3D LKT for estimating 3D head poses by tracking human heads on video sequences. In contrast to the original model-based 3D LKT, which uses a template with each pixel represented by a single intensity value, the proposed model-based 3D LKT exploits an adaptive template with each template pixel modeled by a continuously updated Gaussian distribution during head tracking. This probabilistic template modeling improves the tracker’s ability to handle temporal fluctuation of pixels caused by continuous environmental changes such as varying illumination and dynamic backgrounds. Due to the new probabilistic template modeling, we reformulate the head pose estimation as a maximum likelihood estimation problem, rather than the original difference minimization procedure. Based on the new formulation, an algorithm to estimate the best head pose is derived. The experimental results show that the proposed extended model-based 3D LKT achieves higher accuracy and reliability than the conventional one does. Particularly, the proposed LKT is very effective in handling varying illumination, which cannot be well handled in the original LKT.     

Implementation of a robust complex extended Kalman filter with LabVIEW for detection in a distorted signal

This article proposes the PC-based LabVIEW as the software to develop the algorithm of the robust complex extended Kalman filter (RCEKF) to detect the parameters of the voltage signal in power systems. The hardware used is a sample-and-hold card and a data acquisition (DAQ) card to extract the data from an outside system to the PC, and the program will compute the amplitude, frequency, and phase of the voltage signal with RCEKF. To validate the performance of RCEKF, the voltage signal from a function generator was applied to check the feasibility of the algorithm. This application was also used in the Taiwan Power Company (TPC) secondary substation in Sijhou, Taiwan.     

Gaussian radial basis function neural network controller of a synchronous reluctance motor in electric motorcycle applications

In this article, a sliding mode control (SMC) design based on a Gaussian radial basis function neural network (GRBFNN) is proposed for the synchronous reluctance motor (SynRM) system in electrical motorcycle applications. The conventional SMC assumes that the upper lumped boundaries of parameter variations and external disturbances are known, and the sign function is used. This causes high-frequency chattering and the high-gain phenomenon. In order to avoid these drawbacks, the proposed method utilizes the Lyapunov stability method and the steep descent rule to guarantee the convergence asymptotically, and reduce the magnitude of the chattering or avoid it completely. Finally, numerical simulations are shown to illustrate the good performance of our controller design.     

A virtual subnet scheme on clustering algorithms for mobile ad hoc networks

Resolving the broadcast storm problem is an important issue in mobile ad hoc networks (MANETs). In this paper, we propose an approach for constructing a virtual subnet whose nodes are logically related. The virtual subnet can be spread upon clusters of a MANET. An intelligent agent with a routing filtering table is proposed to assist the best known clustering algorithms, the original Least ID algorithm and the original Highest Connection Cluster (HCC) algorithm, to improve group communication efficiency. Our simulation covers the network factors of hop count, deprave rate, and delay time. The simulation results show that when the proposed intelligent agent is used with the HCC algorithm, the delay time was reduced by 81.84% as compared with flooding, and by 49.25% as compared with the Ad Hoc On-Demand Distance Vector (AODV) routing algorithm. The delay time for the Least ID algorithm assisted by the proposed agents reduced by 81.84% compared to that of flooding and by 50% compared to that of AODV.