2,029 research outputs found
Integration of mobile devices in home automation with use of machine learning for object recognition
The number of smart homes is increasingly expanding, with even more connected devices and available control options. Mobile devices have unfortunately been up to now generally regarded as mere remote controls in these environments.
This paper addresses this shortcoming, by presenting a novel integration architecture and prototype where the potential of mobile devices sensors can be better explored in home automation platforms, in particular by detecting objects in the information collected by their cameras that subsequently allow for users to interact with them in an intuitive way. The detection is performed at the mobile side, using a lightweight machine learning solution.
The obtained accuracy and processing time are comparable to that obtained at server side. But the advantage here is that the interactive experience of users can be dramatically improved, with the absence of round-trip time required if server processing would be used.info:eu-repo/semantics/publishedVersio
Remote sensing, AI and innovative prediction methods for adapting cities to the impacts of the climate change
Urban areas are not only one of the biggest contributors to climate change,
but also they are one of the most vulnerable areas with high populations who
would together experience the negative impacts. In this paper, I address some
of the opportunities brought by satellite remote sensing imaging and artificial
intelligence (AI) in order to measure climate adaptation of cities
automatically. I propose an AI-based framework which might be useful for
extracting indicators from remote sensing images and might help with predictive
estimation of future states of these climate adaptation related indicators.
When such models become more robust and used in real-life applications, they
might help decision makers and early responders to choose the best actions to
sustain the wellbeing of society, natural resources and biodiversity. I
underline that this is an open field and an ongoing research for many
scientists, therefore I offer an in depth discussion on the challenges and
limitations of AI-based methods and the predictive estimation models in
general
Artificial Intelligence Technology
This open access book aims to give our readers a basic outline of today’s research and technology developments on artificial intelligence (AI), help them to have a general understanding of this trend, and familiarize them with the current research hotspots, as well as part of the fundamental and common theories and methodologies that are widely accepted in AI research and application. This book is written in comprehensible and plain language, featuring clearly explained theories and concepts and extensive analysis and examples. Some of the traditional findings are skipped in narration on the premise of a relatively comprehensive introduction to the evolution of artificial intelligence technology. The book provides a detailed elaboration of the basic concepts of AI, machine learning, as well as other relevant topics, including deep learning, deep learning framework, Huawei MindSpore AI development framework, Huawei Atlas computing platform, Huawei AI open platform for smart terminals, and Huawei CLOUD Enterprise Intelligence application platform. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence
Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes
This paper is about alerting acoustic event detection and sound source
localisation in an urban scenario. Specifically, we are interested in spotting
the presence of horns, and sirens of emergency vehicles. In order to obtain a
reliable system able to operate robustly despite the presence of traffic noise,
which can be copious, unstructured and unpredictable, we propose to treat the
spectrograms of incoming stereo signals as images, and apply semantic
segmentation, based on a Unet architecture, to extract the target sound from
the background noise. In a multi-task learning scheme, together with signal
denoising, we perform acoustic event classification to identify the nature of
the alerting sound. Lastly, we use the denoised signals to localise the
acoustic source on the horizon plane, by regressing the direction of arrival of
the sound through a CNN architecture. Our experimental evaluation shows an
average classification rate of 94%, and a median absolute error on the
localisation of 7.5{\deg} when operating on audio frames of 0.5s, and of
2.5{\deg} when operating on frames of 2.5s. The system offers excellent
performance in particularly challenging scenarios, where the noise level is
remarkably high.Comment: 6 pages, 9 figure
Accident Detection in Live Surveillance
With the increase in number of vehicles in the country vehicle detection is an important in road traffic management system. Different traffic accident causes such as vehicle overspeeding, wrong way driving, collision and accident can be detected by CCTV installed on roads. The results obtained from traffic parameters can be applied for vehicle tracking, vehicle classification, parking area monitoring, road traffic monitoring and management etc. The main objective of this project is to decrease the deaths caused by accident occurring because over speeding, wrong war driving by ensuring public safety and also a building a better system for managing the traffic on the roads. The aim of this paper is to develop a system that can detect the vehicle accident which are caused by overspeeding, wrong way driving and collision detection on city roads. A prototype system is developed and tested
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