A timely detection of seizures for newborn infants with electroencephalogram (EEG) has been a common yet life-saving practice in the Neonatal Intensive Care Unit (NICU).
The emergence of Neural Radiance Fields (NeRF) has promoted the development of synthesized high-fidelity views of the intricate real world.
However, partially due to the irregular non-Euclidean data in graphs, the pretext tasks are generally designed under homophily assumptions and cornered in the low-frequency signals, which results in significant loss of other signals, especially high-frequency signals widespread in graphs with heterophily.
For example, when we learn mathematics at school, we build upon our knowledge of addition to learn multiplication.
Event-based sensors (e. g., DVS cameras) are capable of higher dynamic range, higher temporal resolution, lower time latency, and better power efficiency compared to conventional devices (e. g., RGB cameras).
We theoretically prove that clients will use as much data as they can possibly possess to participate in federated learning under certain conditions with our incentive mechanism
Cameras are essential for perception systems due to the advantages of object detection and recognition provided by modern computer vision algorithms, comparing to other sensors, such as LiDARs and radars.
Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding.
A Doppler value is predicted based on an integrated extended Kalman filter (EKF) navigator where the pseudorange-state-based measurements of GNSS and INS are fused.
The results indicate that the IMU individuality, the vehicle loads, and the road conditions have little impact on the robustness and precision of the OdoNet, while the IMU biases and the mounting angles may notably ruin the OdoNet.
So as to reach satisfactory results, the system fuses road detection results obtained from three variants of RoadSeg, processing different LIDAR features.
Low-dimension graph embeddings have proved extremely useful in various downstream tasks in large graphs, e. g., link-related content recommendation and node classification tasks, etc.
Camera-based end-to-end driving neural networks bring the promise of a low-cost system that maps camera images to driving control commands.
Recently, the emergence of chip-level inertial sensors has expanded the relevant applications from positioning, navigation, and mobile mapping to location-based services, unmanned systems, and transportation big data.
In this work, a low complexity, general architecture based on the deep real-valued convolutional neural network (DRVCNN) is proposed to build the nonlinear behavior of the power amplifier.
However, it is challenging to use low-cost IoT devices for robust unsupervised localization (i. e., localization without training data that have known location labels).
no code implementations • 7 Apr 2020 • You Li, Yuan Zhuang, Xin Hu, Zhouzheng Gao, Jia Hu, Long Chen, Zhe He, Ling Pei, Kejie Chen, Maosong Wang, Xiaoji Niu, Ruizhi Chen, John Thompson, Fadhel Ghannouchi, Naser El-Sheimy
Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges.
Networking and Internet Architecture Signal Processing
Random mismatch errors in the resistor networks are one of the dominant nonlinearity sources for high resolution and high accuracy resistor DACs.