Two-Point Oscillation for a Class of Second-Order Damped Linear Differential Equations

Using the comparison theorem, the two-point oscillation for linear differential equation with damping term is considered, where . Results are obtained that or imply the two-point oscillation of the equation

Characterizing pump line phase offset of a single-soliton Kerr comb by dual comb interferometry

We experimentally demonstrate phase retrieval of a single-soliton Kerr comb using electric field cross-correlation implemented via dual-comb interferometry. The phase profile of the Kerr comb is acquired through the heterodyne beat between the Kerr comb and a reference electro-optical comb with a pre-characterized phase profile. The soliton Kerr comb has a nearly flat phase profile, and the pump line is observed to show a phase offset which depends on the pumping parameters. The experimental results are in agreement with numerical simulations. Our all-linear approach enables rapid measurements (3.2 $\mu$s) with low input power (20 $\mu$W)

Packet Scheduling Study for Heterogeneous Traffic in Downlink 3GPP LTE System

Long Term Evolution (LTE) network deploys Orthogonal Frequency Division Multiple Access (OFDMA) technology for downlink multi-carrier transmission. To meet the Quality of Service (QoS) requirements for LTE networks, packet scheduling has been employed. Packet scheduling determines when and how the user’s packets are transmitted to the receiver. Therefore effective design of packet scheduling algorithm is an important discussion. The aims of packet scheduling are maximizing system throughput, guaranteeing fairness among users, andminimizing either or both PacketLoss Ratio (PLR)and packet delay. Inthis paper, the performance of two packet scheduling algorithms namely Log Maximum-Largest Weighted Delay First (LOG-MLWDF) and Max Delay Unit (MDU), developed for OFDM(Orthogonal Frequency Division Multiplexing)networks, has been investigated in LTE downlink networks, and acomparison of those algorithmswith a well-known scheduling algorithm namely Maximum-Largest Weighted Delay First(MLWDF) has been studied.The performance evaluation was in terms of system throughput, PLR and fairness index. This study was performed forboth real time (voice and video streaming)and non-real time (best effort)perspectives. Results show that for streaming flows,LOG-MLWDF shows best PLR performance among the considered scheduling schemes, and for best effort flows, it outperforms theother two algorithms in terms of packet delay and throughput

FaFCNN: A General Disease Classification Framework Based on Feature Fusion Neural Networks

There are two fundamental problems in applying deep learning/machine learning methods to disease classification tasks, one is the insufficient number and poor quality of training samples; another one is how to effectively fuse multiple source features and thus train robust classification models. To address these problems, inspired by the process of human learning knowledge, we propose the Feature-aware Fusion Correlation Neural Network (FaFCNN), which introduces a feature-aware interaction module and a feature alignment module based on domain adversarial learning. This is a general framework for disease classification, and FaFCNN improves the way existing methods obtain sample correlation features. The experimental results show that training using augmented features obtained by pre-training gradient boosting decision tree yields more performance gains than random-forest based methods. On the low-quality dataset with a large amount of missing data in our setup, FaFCNN obtains a consistently optimal performance compared to competitive baselines. In addition, extensive experiments demonstrate the robustness of the proposed method and the effectiveness of each component of the model\footnote{Accepted in IEEE SMC2023}

Landslide Surface Displacement Prediction Based on VSXC-LSTM Algorithm

Landslide is a natural disaster that can easily threaten local ecology, people's lives and property. In this paper, we conduct modelling research on real unidirectional surface displacement data of recent landslides in the research area and propose a time series prediction framework named VMD-SegSigmoid-XGBoost-ClusterLSTM (VSXC-LSTM) based on variational mode decomposition, which can predict the landslide surface displacement more accurately. The model performs well on the test set. Except for the random item subsequence that is hard to fit, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the trend item subsequence and the periodic item subsequence are both less than 0.1, and the RMSE is as low as 0.006 for the periodic item prediction module based on XGBoost\footnote{Accepted in ICANN2023}