Hierarchical control of multiple resources in distributed real-time and embedded systems

By Hong Lu, Jun Yang, Zhigang Liu, Nicholas Donald Lane, Tanzeem Choudhury, Andrew T. Campbell. Euromicro Conference on Real-Time Systems, 2006

626 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/e35421f86d7af4b7b123c6764b2f2c692f58339f

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Citations in the corpus

Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors (2016, methodology): “They are implemented on mobile phones in various studies; i.e. , Naive Bayes in [37–39], decision tree in [40–42] and KNN in [31,32,39,43].”
Accelerometer-based transportation mode detection on smartphones (2013, methodology): “such as those used as part of the Jigsaw system [19], can be used to further improve the robustness of our approach.”
Classification Accuracies of Physical Activities Using Smartphone Motion Sensors (2012, methodology): “Researchers have used accelerometers on Nokia N95 phones [10,11] and Android phones [12] to detect common activities such walking, stair climbing, jogging, and sitting.”
A Survey of Online Activity Recognition Using Mobile Phones (2015, methodology): “The orientation independence can be achieved mainly in two ways [36,39]: • Using orientation-independent features: To counter mobile phone orientation changes, usually orientation-independent features are used.”
StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones (2014, methodology): “The activity classifier achieves overall 94% of accuracy [29].”
BodyScope: a wearable acoustic sensor for activity recognition (2012, background): “Mobile phones could be a good platform for daily activity recognition [19, 20, 21]; however, they may not be always with the user [25], and therefore may miss some activities.”
Accelerating Mobile Audio Sensing Algorithms through On-Chip GPU Offloading (2017, methodology): “GMM emotion recognition [52], speaker identification [52, 45], ambient sound classification [47], stress detection [46]”
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices (2015, background): “Among the crowdsensing systems cited in the previous section ( [9]–[13], [15]–[19], [30], [31]), only [10], [12], [18] provide privacy mechanisms, of which only [10] addresses the privacy more formally.”
Bewell: A smartphone application to monitor, model and promote wellbeing (2011, result): “Our results find that inference accuracy is inline with previous mobile phone sensing experiments [18], [19] conducted with a larger number of subjects.”
From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices (2016, methodology): “The Jigsaw continuous sensing engine implements a pipelined stream processing architecture that adaptively triggers different sensors at different sampling rates to fit the context accuracy required by different applications [38].”