HCFContext: Smartphone Context Inference via Sequential History-based Collaborative Filtering

Sadhu, Vidyasagar; Zonouz, Saman; Sritapan, Vincent; Pompili, Dario

doi:10.1109/percom.2019.8767396

Cited by 10 publications

(2 citation statements)

References 24 publications

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“…Similarly in case of a flying object, for certain failures, gliding may be the best solution instead of the much obvious landing. On the other hand Artificial Intelligence (AI) based data driven techniques are increasingly being used to solve many complex problems relating to autonomous vehicles [4][5][6][7], smartphones [8][9][10][11], etc.…”

Section: Introduction Overview and Motivationmentioning

confidence: 99%

On-board Deep-learning-based Unmanned Aerial Vehicle Fault Cause Detection and Identification

Sadhu¹,

Zonouz²,

Pompili³

2020

Preprint

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With the increase in use of Unmanned Aerial Vehicles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or post incident forensics analysis. The cause of crash could be either a fault in the sensor/actuator system, a physical damage/attack, or a cyber attack on the drone's software. In this paper, we propose novel architectures based on deep Convolutional and Long Short-Term Memory Neural Networks (CNNs and LSTMs) to detect (via Autoencoder) and classify drone mis-operations based on sensor data. The proposed architectures are able to learn high-level features automatically from the raw sensor data and learn the spatial and temporal dynamics in the sensor data. We validate the proposed deeplearning architectures via simulations and experiments on a real drone. Empirical results show that our solution is able to detect (with over 90% accuracy) and classify various types of drone mis-operations (with about 99% accuracy (simulation data) and upto 88% accuracy (experimental data)).

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Section: Introduction Overview and Motivationmentioning

confidence: 99%

On-board Deep-learning-based Unmanned Aerial Vehicle Fault Cause Detection and Identification

Sadhu¹,

Zonouz²,

Pompili³

2020

Preprint

Self Cite

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“…This type of navigation should be capable of detecting, identifying and classifying environmental features [22]. GPS signal will be lost underwater even though certain expensive GPS equipment can work with a limited range [23].…”

Section: Introductionmentioning

confidence: 99%

Real-time Image Enhancement for Vision-based Autonomous Underwater Vehicle Navigation in Murky Waters

Chen

Rahmati

Sadhu

et al. 2019

Proceedings of the International Conference on Underwater Networks &Amp; Systems

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Classic vision-based navigation solutions, which are utilized in algorithms such as Simultaneous Localization and Mapping (SLAM), usually fail to work underwater when the water is murky and the quality of the recorded images is low. That is because most SLAM algorithms are feature-based techniques and often it is impossible to extract the matched features from blurry underwater images. To get more useful features, image processing techniques can be used to dehaze the images before they are used in a navigation/localization algorithm. There are many well-developed methods for image restoration, but the degree of enhancement and the resource cost of the methods are different. In this paper, we propose a new visual SLAM, specifically-designed for the underwater environment, using Generative Adversarial Networks (GANs) to enhance the quality of underwater images with underwater image quality evaluation metrics. This procedure increases the efficiency of SLAM and gets a better navigation and localization accuracy. We evaluate the proposed GANs-SLAM combination by using different images with various levels of turbidity in the water. Experiments were conducted and the data was extracted from the Carnegie Lake in Princeton, and the Raritan river both in New Jersey, USA.

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Elastic AI: system support for adaptive machine learning in pervasive computing systems

Cichiwskyj

Schmeißer

Qian

et al. 2021

CCF Trans. Pervasive Comp. Interact.

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Artificial intelligence (AI) is an important part of today’s pervasive computing systems. Still, there is no end-to-end system platform that allows to deploy, update, manage and execute AI models in pervasive systems. We propose such a system platform in this paper. Most importantly, we reuse concepts and techniques from twenty years of pervasive computing research on how to enable runtime adaptation and apply it to AI. This allows to specify adaptive AI models that are able to react to a multitude of dynamic changes, e.g. with respect to available devices, networking conditions, but also application requirements and sensor data sources. Developers can optimise their applications iteratively, starting with a generic setup and refining it step by step towards their specific pervasive computing scenario. To show the applicability of our platform, we apply it to two pervasive use cases and evaluate them, achieving up to four times faster inference and three times lower energy consumption compared to a classical AI deployment.

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HCFContext: Smartphone Context Inference via Sequential History-based Collaborative Filtering

Cited by 10 publications

References 24 publications

On-board Deep-learning-based Unmanned Aerial Vehicle Fault Cause Detection and Identification

On-board Deep-learning-based Unmanned Aerial Vehicle Fault Cause Detection and Identification

Real-time Image Enhancement for Vision-based Autonomous Underwater Vehicle Navigation in Murky Waters

Elastic AI: system support for adaptive machine learning in pervasive computing systems

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