Machine learning/artificial intelligence for sensor data fusion–opportunities and challenges
During Fusion 2019 Conference (https://www. fusion2019. org/program. html), leading
experts presented ideas on the historical, contemporary, and future coordination of artificial …
experts presented ideas on the historical, contemporary, and future coordination of artificial …
Edge intelligence: Architectures, challenges, and applications
Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis in locations close to where data is captured based on …
caching, processing, and analysis in locations close to where data is captured based on …
Edge intelligence: Empowering intelligence to the edge of network
Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …
caching, processing, and analysis proximity to where data are captured based on artificial …
UNet-NILM: A deep neural network for multi-tasks appliances state detection and power estimation in NILM
Over the years, an enormous amount of research has been exploring Deep Neural Networks
(DNN), particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks …
(DNN), particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks …
Deep learning for the internet of things
How can the advantages of deep learning be brought to the emerging world of embedded
IoT devices? The authors discuss several core challenges in embedded and mobile deep …
IoT devices? The authors discuss several core challenges in embedded and mobile deep …
Stfnets: Learning sensing signals from the time-frequency perspective with short-time fourier neural networks
Recent advances in deep learning motivate the use of deep neural networks in Internet-of-
Things (IoT) applications. These networks are modelled after signal processing in the …
Things (IoT) applications. These networks are modelled after signal processing in the …
Scheduling real-time deep learning services as imprecise computations
The paper presents a real-time computing framework for intelligent real-time edge services,
on behalf of local embedded devices that are themselves unable to support extensive …
on behalf of local embedded devices that are themselves unable to support extensive …
Advanced dropout: A model-free methodology for bayesian dropout optimization
Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural
networks (DNNs). We propose advanced dropout, a model-free methodology, to mitigate …
networks (DNNs). We propose advanced dropout, a model-free methodology, to mitigate …
Zygarde: Time-sensitive on-device deep inference and adaptation on intermittently-powered systems
We propose Zygarde--which is an energy--and accuracy-aware soft real-time task
scheduling framework for batteryless systems that flexibly execute deep learning tasks1 that …
scheduling framework for batteryless systems that flexibly execute deep learning tasks1 that …
Handling missing sensors in topology-aware iot applications with gated graph neural network
Reliable data collection, transmission, and delivery on Internet of Things (IoT) systems is
crucial in order to provide high-quality intelligent services. However, sensor data delivery …
crucial in order to provide high-quality intelligent services. However, sensor data delivery …