Currently, the switches of the lights and other electronic devices in the classroom are mainly relied on manual control, as
a result, many lights are on while no one or only few people in the classroom. It is important to change the current
situation and control the electronic devices intelligently according to the number and the distribution of the students in
the classroom, so as to reduce the considerable waste of electronic resources. This paper studies the problem of people
counting in classroom based on video surveillance. As the camera in the classroom can not get the full shape contour
information of bodies and the clear features information of faces, most of the classical algorithms such as the pedestrian
detection method based on HOG (histograms of oriented gradient) feature and the face detection method based on
machine learning are unable to obtain a satisfied result. A new kind of dual background updating model based on sparse
and low-rank matrix decomposition is proposed in this paper, according to the fact that most of the students in the
classroom are almost in stationary state and there are body movement occasionally. Firstly, combining the frame
difference with the sparse and low-rank matrix decomposition to predict the moving areas, and updating the background
model with different parameters according to the positional relationship between the pixels of current video frame and
the predicted motion regions. Secondly, the regions of moving objects are determined based on the updated background
using the background subtraction method. Finally, some operations including binarization, median filtering and
morphology processing, connected component detection, etc. are performed on the regions acquired by the background
subtraction, in order to induce the effects of the noise and obtain the number of people in the classroom. The experiment
results show the validity of the algorithm of people counting.
Influence of splicing the highly birefringent photonic crystal fiber (HB-PCF) with single mode fiber (SMF) under two
different experimental conditions is studied in details. The result shows the birefringence of the HB-PCF can be either
increased or decreased significantly, depending on the connection conditions of the HB-PCF end, which are classified as
case I (the end is closely butted by another fiber) and case II (the end is in open air). From the experiment and
theoretical analysis, it has shown that in case I the retardation change of the spliced section of HB-PCF with 0.2mm in
length can be 3.2 times larger than the original value. However, in case II the retardation may be reduced to 72.12% of
the original one. The obtained result is important for the design and fabrication of optical fiber devices and sensors based
on HB-PCFs.
We discussed how to design the typical trip-clad high-order mode fiber (HOMF) profiles to achieve the required
dispersion properties based on LP02 mode, to compensate all modern transmission fibers, without sacrificing other
important properties, such as effective area. Finally, HOMF compensating 100km eLEAF fiber has been designed. Its
dispersion at 1550nm is -1217ps/nm/km, and the relative dispersion slope (RDS) is 0.02nm-1. Only ~345m of HOMF is
needed to achieve full dispersion and dispersion slope compensation of the span, while maintaining effective area above
52μm2 over the entire C-band.
The ultrasonic estimation of fetal weigh before delivery is of most significance for obstetrical clinic. Estimating fetal
weight more accurately is crucial for prenatal care, obstetrical treatment, choosing appropriate delivery methods,
monitoring fetal growth and reducing the risk of newborn complications. In this paper, we introduce a method which
combines golden section and artificial neural network (ANN) to estimate the fetal weight. The golden section is
employed to optimize the hidden layer node number of the back propagation (BP) neural network. The method greatly
improves the accuracy of fetal weight estimation, and simultaneously avoids choosing the hidden layer node number
with subjective experience. The estimation coincidence rate achieves 74.19%, and the mean absolute error is 185.83g.
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