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Found 21 papers, 5 papers with code
Collaborative Secret and Covert Communications for Multi-User Multi-Antenna Uplink UAV Systems: Design and Optimization
To characterize the theoretical benchmark of the above model, we formulate a weighted multi-objective optimization problem to maximize the average of secret and covert transmission rates subject to constraints SOP, DEP, the beamformers of Bob and Carlo, and UAV trajectory parameters.
VGA: Vision and Graph Fused Attention Network for Rumor Detection
Moreover, these methods usually only extract visual features in a basic manner, seldom consider tampering or textual information in images.
GraphAlign: Enhancing Accurate Feature Alignment by Graph matching for Multi-Modal 3D Object Detection
Through the projection calibration between the image and point cloud, we project the nearest neighbors of point cloud features onto the image features.
Machine Learning for Large-Scale Optimization in 6G Wireless Networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from "connected things" to "connected intelligence", featured by ultra high density, large-scale, dynamic heterogeneity, diversified functional requirements and machine learning capabilities, which leads to a growing need for highly efficient intelligent algorithms.
The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery
The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally.
A Near Sensor Edge Computing System for Point Cloud Semantic Segmentation
In this system, a FPGA-based deep learning accelerator core (DPU) is placed next to the LiDAR sensor, to perform point cloud pre-processing and segmentation neural network.
FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding
In this paper, we propose a new projection-based LiDAR semantic segmentation pipeline that consists of a novel network structure and an efficient post-processing step.
LIDAR Semantic Segmentation
Robust 3D Semantic Segmentation
+1
Enabling 3D Object Detection with a Low-Resolution LiDAR
Light Detection And Ranging (LiDAR) has been widely used in autonomous vehicles for perception and localization.
A Surface Geometry Model for LiDAR Depth Completion
Therefore, it is assumed those pixels share the same surface with the nearest LiDAR point, and their respective depth can be estimated as the nearest LiDAR depth value plus a residual error.
Novel Visible Light Communication Assisted Perspective-Four-Line Algorithm for Indoor Localization
To further mitigate the effect of height differences among LEDs on localization accuracy, we then propose a correction algorithm of V-P4L based on the LLS method and a simple optimization method.
DepthNet: Real-Time LiDAR Point Cloud Depth Completion for Autonomous Vehicles
In this paper, a light-weight network is proposed for the task of LiDAR point cloud depth completion.
RoadNet-RT: High Throughput CNN Architecture and SoC Design for Real-Time Road Segmentation
In order to reach real-time process speed, a light-weight, high-throughput CNN architecture namely RoadNet-RT is proposed for road segmentation in this paper.
A Unified Hardware Architecture for Convolutions and Deconvolutions in CNN
In this paper, a scalable neural network hardware architecture for image segmentation is proposed.
PointNet on FPGA for Real-Time LiDAR Point Cloud Processing
LiDAR sensors have been widely used in many autonomous vehicle modalities, such as perception, mapping, and localization.
A Novel Received Signal Strength Assisted Perspective-three-Point Algorithm for Indoor Visible Light Positioning
The basic idea of R-P3P is to joint visual and strength information to estimate the receiver position using 3 LEDs regardless of the LEDs' orientations.
Angle-Dependent Phase Shifter Model for Reconfigurable Intelligent Surfaces: Does the Angle-Reciprocity Hold?
The existing phase shifter models adopted for reconfigurable intelligent surfaces (RISs) have ignored the electromagnetic (EM) waves propagation behavior, thus cannot reveal practical effects of RIS on wireless communication systems.
A CNN Accelerator on FPGA Using Depthwise Separable Convolution
The state-of-the-art CNNs, such as MobileNetV2 and Xception, adopt depthwise separable convolution to replace the standard convolution for embedded platforms.
Road Segmentation Using CNN and Distributed LSTM
In automated driving systems (ADS) and advanced driver-assistance systems (ADAS), an efficient road segmentation is necessary to perceive the drivable region and build an occupancy map for path planning.
ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA
This paper presents a field-programmable gate array (FPGA) design of a segmentation algorithm based on convolutional neural network (CNN) that can process light detection and ranging (LiDAR) data in real-time.
Learning More Robust Features with Adversarial Training
We show that the features learned by neural networks are not robust, and find that the robustness of the learned features is closely related to the resistance against adversarial examples of neural networks.
Real-Time Road Segmentation Using LiDAR Data Processing on an FPGA
In this work, a convolutional neural network model is proposed and trained to perform semantic segmentation using the LiDAR sensor data.
Robotics
