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“Nothing About Us Without Us” Transforming Participatory Research and Ethics in Human Systems Engineering
Published in Rod D. Roscoe, Erin K. Chiou, Abigail R. Wooldridge, Advancing Diversity, Inclusion, and Social Justice Through Human Systems Engineering, 2019
Rua M. Williams, Juan E. Gilbert
As discussed previously, our survey of wearable technologies for autism intervention found that 80% of these technologies focused on behavior shaping and social skills. 30% of studies reviewed applied wearable technologies to existing behavioral intervention. These augmented behavioral interventions leveraged wearable computing as tools for patient redirection, behavior classification, prediction, and automated discipline. In most cases, the user of these technologies is the therapist, not the autistic wearer. A combined 50% of studies used wearable technologies as a platform to deliver social skill instruction; monitoring and even sometimes directing gaze behavior, prosody, and conversational proximity. In this context, the autistic person is wearing the technology, but the device is in control. They are not the user; they are being used.
Virtual Desktop Infrastructure
Published in Curtis Franklin, Brian J. S. Chee, Securing the Cloud, 2019
Curtis Franklin, Brian J. S. Chee
In the case of Citrix, you need to be running a CUDA core–based graphics processing unit (GPU) in a XenServer®-based hypervisor; and in the case of Microsoft, you need to be running on at least a Windows Server® 2008 R2 SPl–based Hyper-V machine. Citrix describes this arrangement as “GPU Passthru,” whereas Microsoft is sharing the GPU as a system resource. The obvious markets for this sort of advanced graphics processing include scientific computing (i.e., MATLAB®, AutoCAD®, ArcGIS®, etc.) as well as providing thin clients or remote clients a user experience similar to those on full-sized workstations locally connected to servers using the newer Aero 3D interfaces, HD video playback, and USB redirection for peripherals.
Overview, Motivations and Frameworks
Published in F. Richard Yu, Tao Huang, Garima Ameta, Yunjie Liu, Integrated Networking, Caching, and Computing, 2018
F. Richard Yu, Tao Huang, Garima Ameta, Yunjie Liu
Nevertheless, due to the fact that the cloud is usually distant from mobile devices, the low‐latency requirements of some latency‐sensitive (real‐time) applications may not be fulfilled by cloud computing. Moreover, migration of a large amount of computation tasks over a long distance is sometimes infeasible and uneconomical. To tackle this issue, fog computing [47,56,57,59] has been proposed to provide UE with proximity to resourceful computation servers. The terminology fog (From cOre to edGe) computing was first coined in 2012 by Cisco [60]. It is a distributed computing paradigm in which network entities with different computation and storage abilities and various hierarchical levels are placed within a short distance from the cellular wireless access network, connecting user devices to the cloud or Internet. It is worth noting that fog computing is not a replacement but a complement of cloud computing, due to the fact that the gist of fog computing is providing low‐latency services to meet the demands of real‐time applications, such as smart traffic monitoring, live streaming, etc. However, when the applications requiring a tremendous amount of computation or permanent storage are concerned, the fog computing infrastructures are only acting as gateways or routers for data redirection to the cloud computing framework [57].
Management of local multi-sensors applied to SHM and long-term infrared monitoring: Cloud2IR implementation
Published in Quantitative InfraRed Thermography Journal, 2019
Antoine Crinière, Jean Dumoulin, Laurent Mevel
For each experiment we have different sensors and needs, as the data management is already achieved by DaMaLoS the effort is then focused on the sensor drivers if they are not already built. The resources can now be oriented to specific treatment as computing algorithm or for the infared camera the redirection of their video flux to a streaming server (RTSP), this is achieved with the help of the Live555 library. Another example of Live555 deployment can be found in [27]. Once each driver is attached to DaMaLoS through the sensor interface, the data management is automatically done and Hdf5 files are directly shaped.