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Found 17 papers, 4 papers with code
Direct Punjabi to English speech translation using discrete units
Speech-to-speech translation is yet to reach the same level of coverage as text-to-text translation systems.
E$^3$-UAV: An Edge-based Energy-Efficient Object Detection System for Unmanned Aerial Vehicles
We first present an effective evaluation metric for actual tasks and construct a transparent energy consumption model based on hundreds of actual flight data to formalize the relationship between energy consumption and flight parameters.
Spatial-Temporal Synchronous Graph Transformer network (STSGT) for COVID-19 forecasting
COVID-19 has become a matter of serious concern over the last few years.
Understanding Time Variations of DNN Inference in Autonomous Driving
Understanding the time variations of the DNN inference becomes a fundamental challenge in real-time scheduling for autonomous driving.
Fall Detection from Audios with Audio Transformers
Our paper provides a novel, non-wearable, non-intrusive, and scalable solution for fall detection, deployed on an autonomous mobile robot equipped with a microphone.
A Pulse-and-Glide-driven Adaptive Cruise Control System for Electric Vehicle
As the adaptive cruise control system (ACCS) on vehicles is well-developed today, vehicle manufacturers have increasingly employed this technology in new-generation intelligent vehicles.
HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection
We present the HIT-UAV dataset, a high-altitude infrared thermal dataset for object detection applications on Unmanned Aerial Vehicles (UAVs).
Learning Pruned Structure and Weights Simultaneously from Scratch: an Attention based Approach
As a deep learning model typically contains millions of trainable weights, there has been a growing demand for a more efficient network structure with reduced storage space and improved run-time efficiency.
4C: A Computation, Communication, and Control Co-Design Framework for CAVs
Finally, we present several challenges to achieving the vision of the 4C framework.
Reinforcement Learning for Adaptive Video Compressive Sensing
We apply reinforcement learning to video compressive sensing to adapt the compression ratio.
Computing Systems for Autonomous Driving: State-of-the-Art and Challenges
The recent proliferation of computing technologies, e. g., sensors, computer vision, machine learning, hardware acceleration, and the broad deployment of communication mechanisms, e. g., DSRC, C-V2X, 5G, have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors.
Distributed, Parallel, and Cluster Computing Robotics
A Case For Adaptive Deep Neural Networks in Edge Computing
However, there is limited understanding of: (a) whether and how evolving operational conditions (increased CPU and memory utilization at the edge or reduced data transfer rates between the edge and the cloud) affect the performance of already deployed DNNs, and (b) whether a new partition configuration is required to maximize performance.
Unsupervised Domain Adaptation for Object Detection via Cross-Domain Semi-Supervised Learning
In this work, we show that such adversarial-based methods can only reduce the domain style gap, but cannot address the domain content distribution gap that is shown to be important for object detectors.
pCAMP: Performance Comparison of Machine Learning Packages on the Edges
However, little research has been done to evaluate these packages on the edges, making it difficult for end users to select an appropriate pair of software and hardware.
OpenEI: An Open Framework for Edge Intelligence
At the same time, we have witnessed the proliferation of AI algorithms and models which accelerate the successful deployment of intelligence mainly in cloud services.
CAVBench: A Benchmark Suite for Connected and Autonomous Vehicles
Meanwhile, several pioneer efforts have focused on the edge computing system and architecture design for the CAVs scenario and provided various heterogeneous platform prototypes for CAVs.
Distributed, Parallel, and Cluster Computing Performance
Teaching Autonomous Driving Using a Modular and Integrated Approach
Then, once the students have understood these modules, the experimental platforms for integration we have developed allow the students to fully understand how the modules interact with each other.
