Intelligent video surveillance: a review through deep learning techniques for crowd analysis
Big data applications are consuming most of the space in industry and research area.
Among the widespread examples of big data, the role of video streams from CCTV cameras …
Among the widespread examples of big data, the role of video streams from CCTV cameras …
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: 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 …
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 …
Transforming large-size to lightweight deep neural networks for IoT applications
Deep Neural Networks (DNNs) have gained unprecedented popularity due to their high-
order performance and automated feature extraction capability. This has encouraged …
order performance and automated feature extraction capability. This has encouraged …
Fastdeepiot: Towards understanding and optimizing neural network execution time on mobile and embedded devices
Deep neural networks show great potential as solutions to many sensing application
problems, but their excessive resource demand slows down execution time, pausing a …
problems, but their excessive resource demand slows down execution time, pausing a …
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 …
An uncertainty-aware deep reinforcement learning framework for residential air conditioning energy management
Most existing methods for controlling the energy consumption of air conditioning (AC), focus
on either scheduling the switching (on/off) of compressors or optimizing the overall energy …
on either scheduling the switching (on/off) of compressors or optimizing the overall energy …
Probabilistic electrical load forecasting for buildings using Bayesian deep neural networks
Deep learning techniques are increasingly applied in building electrical load analysis
thanks to the enrichment of information-intensive sensory data. However, uncertainty is …
thanks to the enrichment of information-intensive sensory data. However, uncertainty is …
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 …