Clustering Denoising of 2D LiDAR Scanning in Indoor Environment Based on Keyframe Extraction.

In the indoor laser simulation localization and mapping (SLAM) system, the signal emitted by the LiDAR sensor is easily affected by lights and objects with low reflectivity during the transmission process, resulting in more noise points in the laser scan. To solve the above problem, this paper proposes a clustering noise reduction method based on keyframe extraction. First, the dimension of a scan is reduced to a histogram, and the histogram is used to extract the keyframes. The scans that do not contain new environmental information are dropped. Secondly, the laser points in the keyframe are divided into different regions by the region segmentation method. Next, the points are separately clustered in different regions and it is attempted to merge the point sets from adjacent regions. This greatly reduces the dimension of clustering. Finally, the obtained clusters are filtered. The sets with the number of laser points lower than the threshold will be dropped as abnormal clusters. Different from the traditional clustering noise reduction method, the technique not only drops some unnecessary scans but also uses a region segmentation method to accelerate clustering. Therefore, it has better real-time performance and denoising effect. Experiments on the MIT dataset show that the method can improve the trajectory accuracy based on dropping a part of the scans and save a lot of time for the SLAM system. It is very friendly to mobile robots with limited computing resources.

[An EHG-based Preterm Delivery Prediction Algorithm via Convolution Neural Network].

Premature delivery is one of the direct factors that affect the early development and safety of infants. Its direct clinical manifestation is the change of uterine contraction intensity and frequency. Uterine Electrohysterography(EHG) signal collected from the abdomen of pregnant women can accurately and effectively reflect the uterine contraction, which has higher clinical application value than invasive monitoring technology such as intrauterine pressure catheter. Therefore, the research of fetal preterm birth recognition algorithm based on EHG is particularly important for perinatal fetal monitoring. We proposed a convolution neural network(CNN) based on EHG fetal preterm birth recognition algorithm, and a deep CNN model was constructed by combining the Gramian angular difference field(GADF) with the transfer learning technology. The structure of the model was optimized using the clinical measured term-preterm EHG database. The classification accuracy of 94.38% and F1 value of 97.11% were achieved. The experimental results showed that the model constructed in this paper has a certain auxiliary diagnostic value for clinical prediction of premature delivery.

[Classification of Fetal Heart Rate Based on Poincare Plot and LSTM].

Fetal heart rate plays an essential role in maternal and fetal monitoring and fetal health detection. In this study, a method based on Poincare Plot and LSTM is proposed to realize the high performance classification of abnormal fetal heart rate. Firstly, the original fetal heart rate signal of CTU-UHB database is preprocessed via interpolation, then the sequential fetal heart rate signal is converted into Poincare Plot to obtain nonlinear characteristics of the signals, and then SquenzeNet is used to extract the features of Poincare Plot. Finally, the features extracted by SqueezeNet are classified by LSTM. And the accuracy, the true positive rate and the false positive rate are 98.00%, 100.00%, 92.30% respectively on 2 000 test set data. Compared with the traditional fetal heart rate classification method, all respects are improved. The method proposed in this study has good performance in CTU-UHB fetal monitoring database and has certain practical value in the clinical diagnosis of auxiliary fetal heart rate detection.

DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network.

BACKGROUND: Fetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guidelines usually results in significant subjective inter-observer and intra-observer variability. OBJECTIVE: Therefore, computer aided diagnosis (CAD) systems based on advanced artificial intelligence (AI) technology have recently been developed to assist obstetricians in making objective medical decisions. METHODS: In this work, we present an 8-layer deep convolutional neural network (CNN) framework to automatically predict fetal acidemia. After signal preprocessing, the input 2-dimensional (2D) images are obtained using the continuous wavelet transform (CWT), which provides a better way to observe and capture the hidden characteristic information of the FHR signals in both the time and frequency domains. Unlike the conventional machine learning (ML) approaches, this work does not require the execution of complex feature engineering, i.e., feature extraction and selection. In fact, 2D CNN model can self-learn useful features from the input data with the prerequisite of not losing informative features, representing the tremendous advantage of deep learning (DL) over ML. RESULTS: Based on the test open-access database (CTU-UHB), after comprehensive experimentation, we achieved better classification performance using the optimal CNN configuration compared to other state-of-the-art methods: the averaged ten-fold cross-validation of the accuracy, sensitivity, specificity, quality index defined as the geometric mean of the sensitivity and specificity, and the area under the curve yielded results of 98.34, 98.22, 94.87, 96.53 and 97.82%, respectively CONCLUSIONS: Once the proposed CNN model is successfully trained, the corresponding CAD system can be served as an effective tool to predict fetal asphyxia objectively and accurately.

Ultra-highly fluorescent N doped carbon dots-CdTe QDs nanohybrids with excitation-independent emission in the blue-violet region.

Luminescent carbon dots (CDs) are of significant practical application interest such as in optoelectronic devices and sensitive probing in the life science and environment fields. In this study, N doped CDs-CdTe quantum dots (QDs) nanohybrids (CdTe/N-CDs) were synthesized by a plasma heating process using silk fibroin and CdTe QDs as precursors. The synthesis, doping, hybridizing and passivation of the CdTe/N-CDs were carried out in a single-step process. The as-synthesized CdTe/N-CDs dispersed in ethanol exhibited blue-violet photoluminescence with excitation-independent emission characteristics (strong emissions at 405 nm and 429 nm, and a weak emission at 456 nm). Additionally, the optimal excitation wavelength for the CdTe/N-CDs was found at 360-380 nm, which very closely matches the intrinsic wavelength of GaN-based LEDs. Furthermore, the obtained CdTe/N-CDs exhibited a very high quantum yield of ∼84%, showing great potential in developing chip-based high performance optoelectronics devices. The emission mechanism and emission enhancement by related factors including N-bonded configurations in the carbon base and the transfer of photo-excited electrons from the CdTe QDs to the N doped CDs were studied, as well.

N-doped graphene-based copper nanocomposite with ultralow electrical resistivity and high thermal conductivity.

Nanocomposite with a room-temperature ultra-low resistivity far below that of conventional metals like copper is considered as the next generation conductor. However, many technical and scientific problems are encountered in the fabrication of such nanocomposite materials at present. Here, we report the rapid and efficient fabrication and characterization of a novel nitrogen-doped graphene-copper nanocomposite. Silk fibroin was used as a precursor and placed on a copper substrate, followed by the microwave plasma treatment. This resulted nitrogen-doped graphene-copper composite possesses an electrical resistivity of 0.16 µΩ·cm at room temperature, far lower than that of copper. In addition, the composite has superior thermal conductivity (538 W/m·K at 25 °C) which is 138% of copper. The combination of excellent thermal conductivity and ultra-low electrical resistivity opens up potentials in next-generation conductors.