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Point Cloud Analysis of Railway Infrastructure: A Systematic Literature Review

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Twirre: Architecture for autonomous mini-UAVs using interchangeable commodity components

Twirre is a new architecture for mini-UAV platforms designed for autonomous flight in both GPS-enabled and GPS-deprived applications. The architecture consists of low-cost hardware and software components. High-level control software enables autonomous operation. Exchanging or upgrading hardware components is straightforward and the architecture is an excellent starting point for building low-cost autonomous mini-UAVs for a variety of applications. Experiments with an implementation of the architecture are in development, and preliminary results demonstrate accurate indoor navigation

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Twirre: Architecture for autonomous mini-UAVs using interchangeable commodity components

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Carnival Futures

Carnival Futures: Notting Hill Carnival 2020 is a King’s Cultural Institute project led by Nicole Ferdinand (Culture, Media and Creative Industries at King’s College London) which sought to engage cultural organisations and other stakeholders in planning for the future of the Notting Hill Carnival. The content of this report is intended as a contribution to current research and to identifying future directions for the development of the Notting Hill Carnival. The material and views expressed are produced by various stakeholders in a series of workshops.

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A novel validation approach for validating the simulation model of a passengers' airport terminal

In this paper we propose a novel approach for validating a simulation model for a passengers' airport terminal. The validation approach is based on a "historical data" and "model-to-model" validation approach, and the novelty is represented by the fact that the model used as comparison uses historical data from different data sources and technologies. The proposed validation approach , which is presented as part of the IMHOTEP project, implements various data fusion and data analytics methods to generate the passenger "Activity-Travel-Diary", which is the model that is then compared with the results from the simulation model. The data used for developing the "Activity-Travel-Diary" comes from different sources and technologies such as: passengers data (personal mobile phone, apps), airport data (airport Wi-Fi, GPS, scanning facilities), and flight Information (flight schedules, gate allocation etc.). The simulation model is based on an agent-based simulation paradigm and includes all the passengers flows and operations within a terminal airport. The proposed validation approach is implemented in a real-life case study, Palma de Mallorca Airport, and preliminary results of the validation (calibration) process of the simulation model are presented.

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A novel validation approach for validating the simulation model of a passengers' airport terminal

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Mapping welfare

Several studies have suggested that precision livestock farming (PLF) is a useful tool foranimal welfare management and assessment. Location, posture and movement of an individual are key elements in identifying the animal and recording its behaviour. Currently, multiple technologies are available for automated monitoring of the location of individual animals, ranging from Global Navigation Satellite Systems (GNSS) to ultra-wideband (UWB), RFID, wireless sensor networks (WSN) and even computer vision. These techniques and developments all yield potential to manage and assess animal welfare, but also have their constraints, such as range and accuracy. Combining sensors such as accelerometers with any location determining technique into a sensor fusion systemcan give more detailed information on the individual cow, achieving an even more reliable and accurate indication of animal welfare. We conclude that location systems are a promising approach to determining animal welfare, especially when applied in conjunction with additional sensors, but additional research focused on the use of technology in animal welfare monitoring is needed.

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Prenatal anxiety recognition model integrating multimodal physiological signal

Anxiety among pregnant women can significantly impact their overall well-being. However, the development of data-driven HCI interventions for this demographic is often hindered by data scarcity and collection challenges. In this study, we leverage the Empatica E4 wristband to gather physiological data from pregnant women in both resting and relaxed states. Additionally, we collect subjective reports on their anxiety levels. We integrate features from signals including Blood Volume Pulse (BVP), Skin Temperature (SKT), and Inter-Beat Interval (IBI). Employing a Support Vector Machine (SVM) algorithm, we construct a model capable of evaluating anxiety levels in pregnant women. Our model attains an emotion recognition accuracy of 69.3%, marking achievements in HCI technology tailored for this specific user group. Furthermore, we introduce conceptual ideas for biofeedback on maternal emotions and its interactive mechanism, shedding light on improved monitoring and timely intervention strategies to enhance the emotional health of pregnant women.

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Prenatal anxiety recognition model integrating multimodal physiological signal

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Qualitative models to learn about star properties, star states, and the balance between fusion and gravity

This paper presents three qualitative models that were developed for the Stargazing Live! program. This program consists of a mobile planetarium that aims to inspire and motivate learners using real telescope data during the experience. To further consolidate the learning experience three lessons are available that teachers can use as follow up activities with their learners. The lessons implement a pedagogical approach that focuses on learning by creating qualitative models with the aim to have learners learn subject specific concepts as well as generic systems thinking skills. The three lessons form an ordered set with increasing complexity and were developed in close collaboration with domain experts.

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Qualitative models to learn about star properties, star states, and the balance between fusion and gravity

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Machine learning based sensor fusion for localization applications

Poster presentation on conference Alice and Eve 2020.

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Zoekresultaten

Producten 87

Point Cloud Analysis of Railway Infrastructure: A Systematic Literature Review

Twirre: Architecture for autonomous mini-UAVs using interchangeable commodity components

Carnival Futures

A novel validation approach for validating the simulation model of a passengers' airport terminal

Mapping welfare

Prenatal anxiety recognition model integrating multimodal physiological signal

Qualitative models to learn about star properties, star states, and the balance between fusion and gravity

Machine learning based sensor fusion for localization applications

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Producten 87

Point Cloud Analysis of Railway Infrastructure: A Systematic Literature Review

Twirre: Architecture for autonomous mini-UAVs using interchangeable commodity components

Carnival Futures

A novel validation approach for validating the simulation model of a passengers' airport terminal

Mapping welfare

Prenatal anxiety recognition model integrating multimodal physiological signal

Qualitative models to learn about star properties, star states, and the balance between fusion and gravity

Machine learning based sensor fusion for localization applications