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Remote Sensing: Leveraging Cloud IoT and AI/ml Services

Remote Sensing: Leveraging Cloud IoT and AI/ML Services

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Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III

作者： Bennett, Kelly W. Robertson, James US Army Res Lab Sensors & Electron Devices Directorate Adelphi MD 20783 USA Clearhaven Technol LLC Severna Pk MD 21146 USA

ISBN: (数字)9781510643307

ISBN: (纸本)9781510643307

Artificial Intelligence/Machine Learning (AI/ml) services implemented at the tactical edge on multiple, distributed low power sensors can take advantage of Cloud IoT services and processes to learn in complex data environments supporting evolving mission tasks and continuous improvement of algorithms through Cloud automation and management. Remote sensors and their associated functionality must exhibit resilience against adversaries and deceptive techniques, and operate securely in all domains. Amazon Web Services (AWS) IoT Greengrass uses machine learning models that are built in the cloud and deployed locally on remote sensors and IoT devices. Limited datasets can be used to train models and be refined as more data is available. AWS SageMaker can be used for scene detection using Images and other signals of interest resulting in alerts and notifications from deployed sensors. The quality of machine learning models can be improved through captured data from IoT Greengrass being returned to the Cloud and processed by AWS SageMaker. Labeling of data can be streamlined through Cloud services such as Amazon SageMaker Ground Truth using auto-segment, automatic 3D cuboid snapping, and other automated labeling features. Using AWS Cloud security services in addition to AI/ml and IoT services provides additional advantages supporting identity and certificate management, sensor and data analytics, and intrusion detection and prevention. The number of low-cost sensors supporting Cloud-based IoT remote sensing using AI/ml algorithms at the edge continues to grow. Raspberry PI, NVIDIA Jetson Nano and others, equipped with appropriate sensor devices, are readily available for building a distributed remote sensing network and communicating with the Cloud. This paper provides a detailed description of components and the initial results of building a small distributed remote sensing network using NVIDIA Jetson Nano edge devices equipped with inexpensive acoustic and image sensors u

关键词： Amazon Web Services Cloud Computing IoT al/ml algorithms AWS AI/ml Services Big data Disruptive Computing Technologies AWS Greengrass AWS SageMaker AWS PyTorch

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Multicore processors and GPUs: the power of parallel computing in the Cloud 2

Multicore processors and GPUs: the power of parallel computi...

引用

Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II Part of SPIE Defense + Commercial Sensing Conference

作者： Bennett, Kelly W. Robertson, James US Army Res Lab Sensors & Electron Devices Directorate Adelphi MD 20783 USA Clearhaven Technol LLC Severna Pk MD 21146 USA

ISBN: (纸本)9781510636040

Sensors used in intelligence, surveillance and reconnaissance (ISR) operations and activities have the ability to generate vast amounts of data. High-volume analytical capabilities are needed to process data from multi-modal sensors to develop and test complex computational and deep learning models in support of the U.S. Army Multi-Domain Operations (MDO). The Army Research Laboratory designs, develops and tests Artificial Intelligence and Machine Learning (AI/ml) algorithms employing large repositories of in-house data. To efficiently process the data as well as design, build, train and deploy models, parallel and distributed algorithms are needed. Deep learning frameworks provide language-specific, container-based building blocks associated with deep learning neural networks applied to specific target applications. This paper discusses applications of AI/ml deep learning frameworks and Software Development Kits (SDKs) and demonstrates and compares specific multi-core processor and NVidia Graphics Processing Unit (GPU) implementations for desktop and Cloud environments. Frameworks selected for this research include PyTorch and Matlab. Amazon Web Services (AWS) SageMaker was used to launch Machine Learning instances ranging from general purpose computing to GPU instances. Detailed processes, example code, performance enhancements, best practices and lessons learned are included for publicly available acoustic and image datasets. Research results indicate parallel implementations of data preprocessing steps saved significant time but more expensive GPUs did not provide any processing time advantages for the machine learning algorithms tested.

关键词： Amazon Web Services Cloud Computing al/ml algorithms Cloud-based GPU platform Big data Disruptive Computing Technologies AWS SageMaker

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