Search

The electroencephalogram (EEG), for measuring the electrophysiological activity of the brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG-based seizure detection algorithms have already yielded high sensitivity, but training those algorithms requires a large amount of labelled data. Data labelling is often done with a lot of human efforts, which is very time-consuming. In this study, we propose a hybrid system integrating an unsupervised learning (UL) module and a supervised learning (SL) module, where the UL module can significantly reduce the workload of data labelling. For preliminary seizure screening, UL synthesizes amplitude-integrated EEG (aEEG) extraction, isolation forest-based anomaly detection, adaptive segmentation, and silhouette coefficient-based anomaly detection evaluation. The UL module serves to quickly locate the determinate subjects (seizure segments and seizure-free segments) and the indeterminate subjects (potential seizure candidates). Afterwards, more robust seizure detection for the indeterminate subjects is performed by the SL using an EasyEnsemble algorithm. EasyEnsemble, as a class-imbalance learning method, can potentially decrease the generalization error of the seizure-free segments. The proposed method can significantly reduce the workload of data labelling while guaranteeing satisfactory performance. The proposed seizure detection system is evaluated using the Children’s Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) scalp EEG dataset, and it achieves a mean accuracy of 92.62%, a mean sensitivity of 95.55%, and a mean specificity of 92.57%. To the best of our knowledge, this is the first epilepsy seizure detection study employing the integration of both the UL and the SL modules, achieving a competitive performance superior or similar to that of the state-of-the-art methods.

With the widespread use of mobile and sensing devices, and the popularity of online map-based services, such as navigation services, the volume of spatio-temporal data is growing rapidly. Conventional big data technologies in existing distributed systems cannot effectively process spatio-temporal big data with temporal continuity and spatial proximity. How to construct an effective index for the application requirements of spatio-temporal data in a distributed environment has become one of the hotspots of spatio-temporal big data research. Many spatio-temporal indexing methods have been proposed to support efficient query processing of spatio-temporal data. In this article, the various spatio-temporal big data indexing methods proposed by domestic and foreign researchers from 2010 to 2020 are classified and summarized according to the distributed environment and application background, and the hot issues that need to be paid attention to in the future are proposed according to the changes in application requirements

Motion trajectories tracked from the points of interest can provide the key relevant features for characterizing the motion patterns in video. As the increasing number of 3-D vision sensors rises, the 3-D motion trajectories that serve as motion representations have been applied successfully to video retrieval and analysis, scene understanding, motion recognition, and so on, in existing works. Most of these works use raw data of motion trajectories directly or draw simple geometric quantities to describe the motion trajectories, whereas these simple descriptions are not intrinsically complete as they cannot feature the orientation changes of moving points along the 3-D motion trajectories. In principle, orientation changes of a single moving point in 3-D space have to been obtained by resorting to high-order derivatives, but the high-order derivatives would result in high sensitivity to noise. This paper tackles the problem by describing the local reference frames along 3-D motion trajectories, while we consider a motion trajectory as a temporal sequence of local reference frames. The maximal blurred segment of the noisy discrete curves is employed to estimate the local reference frames without high-order derivatives involved, and the local reference frame contains complete information of positions and orientations in the 3-D Euclidean space. To describe such local reference frames, we use the rotations and local square root velocities of local reference frames as the proposed descriptor to characterize the position and orientation changes of the moving points along the motion trajectories. In the experiments, we evaluate the effectiveness of the proposed descriptor by applying it to the gesture recognition on two large benchmark data sets that contain hand motion trajectories. The results show that our proposed descriptor can achieve superior performance compared to the existing descriptors and state-of-the-art methods in the 3-D motion trajectory recognition.

Current generation of smartphones is running more and more complex applications that reduce the battery life to as short as several hours. Thus, it becomes very important to understand the diversities of applications installed on smartphones and how batteries are consumed across different applications. This paper presents a large-scale battery study on smartphones focusing on diversities in applications and users. Based on application and battery traces collected on over 80000 Android smartphones for a four-week period, we analyze the battery discharging patterns, the types of mobile applications, and the usage and energy consumption patterns for these applications. During the analysis, we introduce a novel method to calculate the energy consumption rate for each application based on coarse-grained battery data collected with a lightweight monitoring tool. Based on the results, we present some observations and discuss possible improvements on smartphone designs and mobile application development. We also compare our results to some previous studies wherever it is possible.

In this paper, isoelectronic doping with indium (In) in p-GaN is investigated on the basis of the thermal quenching of ultraviolet luminescence (UVL) band. A phenomenological rate-equation model is proposed as a nondestructive diagnostic technique to acquire the dopant ionization energy of p-GaN. In terms of this model, we analyze the evolution of the ionization energy of p-GaN samples grown with different trimethyindium flow rate and find that isoelectronic doping with appropriate In incorporation is able to significantly reduce the ionization energy of the acceptor from 245 to 112 meV, which are consistent with the results of the variable temperature Hall-effect measurements. The comparison between the samples' full width at half maximum of the UVL band indicates that excessive In incorporation will degenerate the conductivity of p-GaN.