Remote flood monitoring system based on plastic optical fibres and wireless motes

This paper summarises an effort in the development of a remote flood monitoring system based on plastic optical fibre (POF) sensors and a wireless mote network. The wireless mote, comprising of a network of MICA2DOT™ units, was used as a platform to monitor and record the signal from the POF sensors and transmit this information to a base station wirelessly. A prototype of the integrated wireless POF sensor unit has been constructed, rendering it possible to deploy the autonomous unit remotely at multiple monitoring points as required. A flood monitoring simulation was carried out in a 24 m × 10 m × 0.9 m wave basin where four of these wireless optical fibre mote sensors were used to detect the rising water level in the basin. The novelty of the work lies in the successful integration of the wireless platform to a POF-based liquid level sensor and the subsequent demonstration of the prototype of the system for the purposes of flood monitoring applications.The sensing principle of the POF sensor developed here is well-known and is based on the loss of total internal reflection of the optical signal as the sensor probe comes in contact with the liquid. Compared to optical fibre-based sensors reported previously in the literature, the probe profile used in this study differs in terms of its simplicity in design, while exhibiting an excellent signal intensity loss ratio without the need for additional attachments to the probe such as optical prisms. The tests carried out showed that the POF sensor is capable of detecting a variety of fluids. Exhibiting good signal stability, the sensor also detects the liquid level reliably when the liquid rises or falls to the predetermined level. The responsiveness of the optical fibre sensor was evaluated by simulating different rates at which the liquid rises by immersing the sensor tip into the liquid and vice-versa at various speeds ranging from 1 mm/min to 500 mm/min.     

An expert system for fault diagnosis in internal combustion engines using probability neural network

An expert system for fault diagnosis in internal combustion engines using adaptive order tracking technique and artificial neural networks is presented in this paper. The proposed system can be divided into two parts. In the first stage, the engine sound emission signals are recorded and treated as the tracking of frequency-varying bandpass signals. Ordered amplitudes can be calculated with a high-resolution adaptive filter algorithm. The vital features of signals with various fault conditions are obtained and displayed clearly by order figures. Then the sound energy diagram is utilized to normalize the features and reduce computation quantity. In the second stage, the artificial neural network is used to train the signal features and engine fault conditions. In order to verify the effect of the proposed probability neural network (PNN) in fault diagnosis, two conventional neural networks that included the back-propagation (BP) network and radial-basic function (RBF) network are compared with the proposed PNN network. The experimental results indicated that the proposed PNN network achieved the best performance in the present fault diagnosis system.     

3D strain mapping in rocks using digital volumetric speckle photography technique

Speckle photography technique is a versatile displacement/deformation mapping technique that can be applied to almost any material. It has its genesis in the laser speckle interferometry technique whereby the natural speckles created by illuminating an optically rough surface using a coherent laser beam are used as displacement gauging elements. It evolves into the white light speckle photography technique whereby a random pattern of any type is used as a displacement measurement transducer. With the advent of digital cameras and ubiquitous usage of computers, the digital version of the technique is developed. Up to now, the technique is essentially limited to 2D applications. Recently, we extended the technique into the 3D domain by using the volumetric imaging capabilities of CT or MRI. In this paper, we apply this technique to measuring the internal deformation of rocks. It so happens that certain rocks have natural density variation at various places such that when imaged by CT these impurities can be treated as 3D speckles. The elements of volumetric speckle photography technique are as follows. A reference volumetric image of the rock is recorded by a micro-CT scan and stored as a reference. Under load, the deformed CT image of the rock is also recorded. Both volume images are divided into subsets of certain voxel arrays. Each corresponding pair of the subsets is “compared” via a two-step 3D Fourier transform analysis. The result is a 3D map of displacement vectors representing the collective displacement experienced by all the speckles within the subset of voxels. The strain distribution of the entire rock specimen can then be calculated using appropriate displacement strain relations. The application of this technique to strain mapping of red sandstone and argillite rocks is presented.     

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.     

Multiple evolutionary origins of the fungus causing Panama disease of banana: concordant evidence from nuclear and mitochondrial gene genealogies

Panama disease of banana, caused by the fungus Fusarium oxysporum f. sp. cubense, is a serious constraint both to the commercial production of banana and cultivation for subsistence agriculture. Previous work has indicated that F. oxysporum f. sp. cubense consists of several clonal lineages that may be genetically distant. In this study we tested whether lineages of the Panama disease pathogen have a monophyletic origin by comparing DNA sequences of nuclear and mitochondrial genes. DNA sequences were obtained for translation elongation factor 1alpha and the mitochondrial small subunit ribosomal RNA genes for F. oxysporum strains from banana, pathogenic strains from other hosts and putatively nonpathogenic isolates of F. oxysporum. Cladograms for the two genes were highly concordant and a partition-homogeneity test indicated the two datasets could be combined. The tree inferred from the combined dataset resolved five lineages corresponding to "F. oxysporum f. sp. cubense" with a large dichotomy between two taxa represented by strains most commonly isolated from bananas with Panama disease. The results also demonstrate that the latter two taxa have significantly different chromosome numbers. F. oxysporum isolates collected as nonpathogenic or pathogenic to other hosts that have very similar or identical elongation factor 1alpha and mitochondrial small subunit genotypes as banana pathogens were shown to cause little or no disease on banana. Taken together, these results indicate Panama disease of banana is caused by fungi with independent evolutionary origins.     

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.