1,720,970 research outputs found
Smart Mobility and Sensing: Case Studies Based on a Bike Information Gathering Architecture
No full textMapping services and travel planner applications are experiencing a great success in supporting people while they plan a route or while they move across the city, playing a key role in the smart mobility scenario. Nevertheless, they are based on the same algorithms, on the same elements (in terms of time, distance, means of transports, etc.), providing a limited set of personalization. To fill this gap, we propose PUMA, a Personal Urban Mobility Assistant that aims to let the user add different factors of personalization, such as sustainability, street and personal safety, wellness and health, etc. In this paper we focus on the use of smart bikes (equipped with specific sensors) as means of transports and as a mean to collect data about the urban environment. We describe a cloud based architecture, personas and travel scenario to prove the feasibility of our approach
Monitoring Electric Vehicles on The Go
Full text linkElectric vehicles (EV) feature detailed monitoring and control over the CAN bus. Some of this data is made available to users on the On-Board Diagnostic version II (OBDII) bus thus providing an opportunity for large scale high-frequency data collection. This paper introduces a connected monitoring system for OBDII equipped vehicles. The system comprises a low cost hardware design and monitoring algorithms designed to optimize the number of variables collected and their collection frequency. The algorithm aims at collecting a high quantity of Battery Management System (BMS) data in electric vehicles together with power-usage data to enable short and long term estimation for battery state of health (SOH) and state of charge (SOC). The proposed system has been implemented and tested on a Nissan Leaf and lead to the acquisition of 1.7 million records over 120 hours of driving
Poster Abstract: C-Continuum: Edge-to-Cloud computing for distributed AI
No full textMobile autonomous systems are supposed to deeply impact in manufacturing, space exploration, rescue, defense, transportation, and everyday life. Autonomous air-ground vehicles, for example, will become normal tools in the next few years, providing a natural platform for distributed artificial intelligence applications including, for example, disaster rescue and recovery, area surveying, autonomous driving, etc. The raise of autonomous cooperating robots will pose new challenges in networking, distributed systems and resource management. Heavy computational tasks will be dispatched to the closest edge node for processing and the core-cloud will be involved as last resort in an effort to reduce latency and increase the global system capacity leveraging application and resource locality. Massive amounts of data and computations will be required. For example, in the autonomous driving scenario Intel estimates that each driver-less vehicle will produce over 4 TeraBytes of data each day1. While most of this data is consumed in-car, cooperating autonomous vehicles will have to exchange some percentage of the 4TB and eventually offload some computation and data to the local edge or the core-cloud. This is particularly relevant when locally gathered and labeled data can be used to refine the model and ultimately increase the global 'intelligence'. This approach is often taken by autonomous driving automakers.</p
Identifying Degradation Indicators for Electric Vehicle Battery Based on Field Testing Data
No full textState of health estimation of battery is crucial to ensure the safety and durability of electric vehicles. This paper presents six methods to extract the battery health indicator from electric vehicle field testing data. The methods for extracting health indicators from the discharge cycle show the ability to cope with the variable driving condition. In total, 157 health indicators are extracted from the collected data. Pearson correlation coefficient and Spearman's rank correlation coefficient are used to measure the correlation between the health indicators and the state of health. The results suggest that health indicators extracted by the presented methods have high correlations to the battery state of health
On assessing the accuracy of air pollution models exploiting a strategic sensors deployment
No full textThis paper presents a preliminary experiment done to identify potential problems and issues in setting up a testbed for air pollution measurement and modeling. Our final testbed, part of a joint research activity between the University of Bologna and the Macao Polytechnic Institute, will be composed of three lines of the air pollution sensors Canarin II and it will be used to produce spatio-temporal open data to test third-party air pollution models. Here, we present a preliminary experiment based on a single line of sensors, showing interesting insights into the actual open challenge of air pollution modeling techniques validation, taking into account the effects of air pollutant emissions sources, meteorology, atmospheric concentrations and urban vegetation
Monitoring cultural heritage buildings via low-cost edge computing/sensing platforms: The Biblioteca Joanina de Coimbra case study
No full textClimate change and higher level of pollutants are affecting our heritage affect the conservation abilities and endangering centuries old buildings and artifacts. Governments and conservation agencies alike are scrambling to monitor and prevent disasters that would result in the loss of precious cultural artifacts. Sensing technologies, on one side, and new restoration techniques on the other, are in being used in this effort however they result in high costs and complex installation logistics. In this paper we present a pollution monitoring study for the Biblioteca Joanina, a baroque library situated in University of Coimbra that has been inducted in the UNESCO world’s heritage list in 2013. In contrast with current practices the study has been conducted using a low-cost sensing platform, namely CANARIN II, built in a joint effort by the Sorbonne Université, the Macao Polytechnic Institute and the Asian Institute of Technology (Thailand). Our study shows that is possible to use low-cost platform for cultural heritage monitoring and preservation and how it can support decision makers in execute simple policy changes and yet achieve substantial impacts in the preservation efforts
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Race Against the Machine: A Fully-Annotated, Open-Design Dataset of Autonomous and Piloted High-Speed Flight
Full text linkUnmanned aerial vehicles, and multi-rotors in particular, can now perform dexterous tasks in impervious environments, from infrastructure monitoring to emergency deliveries. Autonomous drone racing has emerged as an ideal benchmark to develop and evaluate these capabilities. Its challenges include accurate and robust visual-inertial odometry during aggressive maneuvers, complex aerodynamics, and constrained computational resources. As researchers increasingly channel their efforts into it, they also need the tools to timely and equitably compare their results and advances. With this dataset, we want to (i) support the development of new methods and (ii) establish quantitative comparisons for approaches originating from the broader robotics and artificial intelligence communities. We want to provide a one-stop resource that is comprehensive of (i) aggressive autonomous and piloted flight, (ii) high-resolution, high-frequency visual, inertial, and motion capture data, (iii) commands and control inputs, (iv) multiple light settings, and (v) corner-level labeling of drone racing gates. We also release the complete specifications to recreate our flight platform, using commercial off-the-shelf components and the open-source flight controller Betaflight, to democratize drone racing research
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