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Found 20 papers, 3 papers with code
Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling
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).
RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models
The emergence of Neural Radiance Fields (NeRF) has promoted the development of synthesized high-fidelity views of the intricate real world.
Multi-View Graph Representation Learning Beyond Homophily
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.
Learning to Learn: How to Continuously Teach Humans and Machines
For example, when we learn mathematics at school, we build upon our knowledge of addition to learn multiplication.
MDOE: A Spatiotemporal Event Representation Considering the Magnitude and Density of Events
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).
Incentivizing Federated Learning
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
Emergent Visual Sensors for Autonomous Vehicles
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.
Graph Representation Learning Beyond Node and Homophily
Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding.
Supporting GNSS Baseband Using Smartphone IMU and Ultra-Tight Integration
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.
OdoNet: Untethered Speed Aiding for Vehicle Navigation Without Hardware Wheeled Odometer
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.
Fusion of neural networks, for LIDAR-based evidential road mapping
So as to reach satisfactory results, the system fuses road detection results obtained from three variants of RoadSeg, processing different LIDAR features.
Pair-view Unsupervised Graph Representation Learning
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.
KNN-enhanced Deep Learning Against Noisy Labels
Inspired by the robustness of K-Nearest Neighbors (KNN) against data noise, in this work, we propose to apply deep KNN for label cleanup.
Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning
Camera-based end-to-end driving neural networks bring the promise of a low-cost system that maps camera images to driving control commands.
Inertial Sensing Meets Artificial Intelligence: Opportunity or Challenge?
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.
A General Architecture for Behavior Modeling of Nonlinear Power Amplifier using Deep Convolutional Neural Network
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.
Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization
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).
Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
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
What happens to a ToF LiDAR in fog?
This article focuses on analyzing the performance of a typical time-of-flight (ToF) LiDAR under fog environment.
Research of Segmented 8bit Voltage-Mode R-2R Ladder DAC
Random mismatch errors in the resistor networks are one of the dominant nonlinearity sources for high resolution and high accuracy resistor DACs.
