Mobile medical care is a hot issue in current medical research. Due to the inconvenience of going to hospital for fetal heart monitoring and the limited medical resources, real-time monitoring of fetal health on portable devices has become an urgent need for pregnant women, which helps to protect the health of the fetus in a more comprehensive manner and reduce the workload of doctors. For the feature acquisition of the fetal heart rate (FHR) signal, the traditional feature-based classification methods need to manually read the morphological features from the FHR curve, which is time-consuming and costly and has a certain degree of calibration bias. This paper proposes a classification method of the FHR signal based on neural networks, which can avoid manual feature acquisition and reduce the error caused by human factors. The algorithm will directly learn from the FHR data and truly realize the real-time diagnosis of FHR data. The convolution neural network classification method named “MKNet” and recurrent neural network named “MKRNN” are designed. The main contents of this paper include the preprocessing of the FHR signal, the training of the classification model, and the experiment evaluation. Finally, MKNet is proved to be the best algorithm for real-time FHR signal classification.
The effect of traffic flow prediction plays an important role in routing selection. Traditional traffic flow forecasting methods mainly include linear, nonlinear, neural network, and Time Series Analysis method. However, all of them have some shortcomings. This paper analyzes the existing algorithms on traffic flow prediction and characteristics of city traffic flow and proposes a road traffic flow prediction method based on transfer probability. This method first analyzes the transfer probability of upstream of the target road and then makes the prediction of the traffic flow at the next time by using the traffic flow equation. Newton Interior-Point Method is used to obtain the optimal value of parameters. Finally, it uses the proposed model to predict the traffic flow at the next time. By comparing the existing prediction methods, the proposed model has proven to have good performance. It can fast get the optimal value of parameters faster and has higher prediction accuracy, which can be used to make real-time traffic flow prediction.
Global warming is an important environment issue rapidly becoming a part of popular culture. Scientists are 95-100% certain that it is primarily caused by increasing concentrations of greenhouse gases produced by human activities. Policies dealing with global warming depend on accurate and precise monitoring of the emissions of greenhouse gases. However, most greenhouse gas emissions management comes from bottom-up reporting, in which a company reports its energy use and other resource consumption metrics and maps out its carbon footprint. This is not a sufficient good way. A big data network of sensors has been constructed by us in Neimenggu province of China, which will gather huge amount of more accurate information on the dynamics of greenhouse gases in local areas and measure changes over time. Application specific instruction set processor is integrated in those sensor devices, to perform preliminary process of collected data. In this work, we have presented our efforts at constructing the big data sensor network for monitoring the greenhouse gases emission and mainly focused on building the ASIP's retargetable compiler framework designed and dedicated for our sensor network.
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