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Found 36 papers, 14 papers with code
MoRAL: Motion-aware Multi-Frame 4D Radar and LiDAR Fusion for Robust 3D Object Detection
In particular, 4D radar and LiDAR fusion methods based on multi-frame radar point clouds have demonstrated the effectiveness in bridging the point density gap.
CaRaFFusion: Improving 2D Semantic Segmentation with Camera-Radar Point Cloud Fusion and Zero-Shot Image Inpainting
This demonstrates the effectiveness of our approach for semantic segmentation using camera-radar fusion under adverse weather conditions.
4D mmWave Radar in Adverse Environments for Autonomous Driving: A Survey
To the best of our knowledge, this is the first survey specifically focusing on 4D mmWave radar in adverse environments for autonomous driving.
Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition
In response, we present advancements in XentricAI, an explainable hand gesture recognition (HGR) system designed to meet these regulatory requirements.
MutualForce: Mutual-Aware Enhancement for 4D Radar-LiDAR 3D Object Detection
Radar and LiDAR have been widely used in autonomous driving as LiDAR provides rich structure information, and radar demonstrates high robustness under adverse weather.
LiRCDepth: Lightweight Radar-Camera Depth Estimation via Knowledge Distillation and Uncertainty Guidance
Recently, radar-camera fusion algorithms have gained significant attention as radar sensors provide geometric information that complements the limitations of cameras.
Interpretable Rule-Based System for Radar-Based Gesture Sensing: Enhancing Transparency and Personalization in AI
The increasing demand in artificial intelligence (AI) for models that are both effective and explainable is critical in domains where safety and trust are paramount.
GET-UP: GEomeTric-aware Depth Estimation with Radar Points UPsampling
However, existing algorithms process the inherently noisy and sparse radar data by projecting 3D points onto the image plane for pixel-level feature extraction, overlooking the valuable geometric information contained within the radar point cloud.
MUFASA: Multi-View Fusion and Adaptation Network with Spatial Awareness for Radar Object Detection
To address this challenge, this paper introduces a comprehensive feature extraction method for radar point clouds.
CaFNet: A Confidence-Driven Framework for Radar Camera Depth Estimation
Depth estimation is critical in autonomous driving for interpreting 3D scenes accurately.
Comparing Lazy Constraint Selection Strategies in Train Routing with Moving Block Control
In this work, we close this gap by providing an extended approach as well as a flexible open-source implementation that can use different solving strategies.
Hamiltonian-based Quantum Reinforcement Learning for Neural Combinatorial Optimization
We model our ansatzes directly on the combinatorial optimization problem's Hamiltonian formulation, which allows us to apply our approach to a broad class of problems.
Enhanced Radar Perception via Multi-Task Learning: Towards Refined Data for Sensor Fusion Applications
Radar and camera fusion yields robustness in perception tasks by leveraging the strength of both sensors.
Efficient Post-Training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments
For an ECG classification task, it was able to terminate all samples early, reducing the mean inference energy by 74. 9% and computations by 78. 3%.
Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks
These findings highlight the importance of considering temporal correlation in sensor data to improve the termination decision.
Temporal Patience: Efficient Adaptive Deep Learning for Embedded Radar Data Processing
Radar sensors offer power-efficient solutions for always-on smart devices, but processing the data streams on resource-constrained embedded platforms remains challenging.
Design Tasks and Their Complexity for the European Train Control System with Hybrid Train Detection
Since expanding the global railway network is time- and resource-consuming, maximizing the rail capacity of the existing infrastructure is desirable.
Multi-Task Cross-Modality Attention-Fusion for 2D Object Detection
In addition, we introduce a Multi-Task Cross-Modality Attention-Fusion Network (MCAF-Net) for object detection, which includes two new fusion blocks.
Detection of Sensor-To-Sensor Variations using Explainable AI
This study proposes a novel approach for detecting sensor-to-sensor variations in sensing devices using the explainable AI (XAI) method of SHapley Additive exPlanations (SHAP).
Compiler Optimization for Quantum Computing Using Reinforcement Learning
Any quantum computing application, once encoded as a quantum circuit, must be compiled before being executable on a quantum computer.
Uncertainty-based Meta-Reinforcement Learning for Robust Radar Tracking
There, we show that our method outperforms related Meta-RL approaches on unseen tracking scenarios in peak performance by 16% and the baseline by 35% while detecting OOD data with an F1-Score of 72%.
MEET: A Monte Carlo Exploration-Exploitation Trade-off for Buffer Sampling
This is enabled by the uncertainty estimation of the Q-Value function, which guides the sampling to explore more significant transitions and, thus, learn a more efficient policy.
Utilizing Explainable AI for improving the Performance of Neural Networks
To address this, Explainable Artificial Intelligence (XAI) has been developing as a field that aims to improve the transparency of the model and increase their trustworthiness.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Cross-modal Learning of Graph Representations using Radar Point Cloud for Long-Range Gesture Recognition
Gesture recognition is one of the most intuitive ways of interaction and has gathered particular attention for human computer interaction.
Label-Aware Ranked Loss for robust People Counting using Automotive in-cabin Radar
In this paper, we introduce the Label-Aware Ranked loss, a novel metric loss function.
As Accurate as Needed, as Efficient as Possible: Approximations in DD-based Quantum Circuit Simulation
Quantum computers promise to solve important problems faster than conventional computers.
Quantum Physics
Considering Decoherence Errors in the Simulation of Quantum Circuits Using Decision Diagrams
However, most of those simulators mimic perfect quantum computers and, hence, ignore the fragile nature of quantum mechanical effects which frequently yield to decoherence errors in real quantum devices.
Quantum Physics
Stochastic Quantum Circuit Simulation Using Decision Diagrams
Recent years have seen unprecedented advance in the design and control of quantum computers.
Quantum Physics
Characteristics of Reversible Circuits for Error Detection
In this work, we consider error detection via simulation for reversible circuit architectures.
Hardware Architecture Emerging Technologies
Random Stimuli Generation for the Verification of Quantum Circuits
Verification of quantum circuits is essential for guaranteeing correctness of quantum algorithms and/or quantum descriptions across various levels of abstraction.
Quantum Physics Emerging Technologies
Verifying Results of the IBM Qiskit Quantum Circuit Compilation Flow
In this paper, we propose an efficient scheme for quantum circuit equivalence checking---specialized for verifying results of the IBM Qiskit quantum circuit compilation flow.
Quantum Circuit Equivalence Checking
Quantum Physics
Just Like the Real Thing: Fast Weak Simulation of Quantum Computation
In this work, we focus on weak simulation that aims to produce outputs which are statistically indistinguishable from those of error-free quantum computers.
Quantum Physics
Advanced Equivalence Checking for Quantum Circuits
Experimental evaluations confirm that the resulting methodology allows one to conduct equivalence checking dramatically faster than ever before--in many cases just a single simulation run is sufficient.
Quantum Circuit Equivalence Checking
Quantum Physics
Emerging Technologies
Mapping Quantum Circuits to IBM QX Architectures Using the Minimal Number of SWAP and H Operations
By this, we do not only provide a method that maps quantum circuits to IBM's QX architectures with a minimal number of SWAP and H operations, but also show by experimental evaluation that the number of operations added by IBM's heuristic solution exceeds the lower bound by more than 100% on average.
Quantum Circuit Mapping
Quantum Physics
fiction: An Open Source Framework for the Design of Field-coupled Nanocomputing Circuits
As a class of emerging post-CMOS technologies, Field-coupled Nanocomputing (FCN) devices promise computation with tremendously low energy dissipation.
Emerging Technologies
Advanced Simulation of Quantum Computations
There also exist solutions based on decision diagrams (i. e. graph-based approaches) that try to tackle the exponential complexity by exploiting redundancies in quantum states and operations.
Quantum Physics Emerging Technologies
