Alfredo Nantes

Bio: Alfredo Nantes is an academic researcher from Queensland University of Technology. The author has contributed to research in topics: Bluetooth & Virtual machine. The author has an hindex of 13, co-authored 31 publications receiving 538 citations.

A toolbox for animal call recognition

Abstract: Monitoring the natural environment is increasingly important as habit degradation and climate change reduce the world's biodiversity. We have developed software tools and applications to assist ecologists with the collection and analysis of acoustic data at large spatial and temporal scales. One of our key objectives is automated animal call recognition, and our approach has three novel attributes. First, we work with raw environmental audio, contaminated by noise and artefacts and containing calls that vary greatly in volume depending on the animal's proximity to the microphone. Second, initial experimentation suggested that no single recognizer could deal with the enormous variety of calls. Therefore, we developed a toolbox of generic recognizers to extract invariant features for each call type. Third, many species are cryptic and offer little data with which to train a recognizer. Many popular machine learning methods require large volumes of training and validation data and considerable time and exper...

Real-time traffic state estimation in urban corridors from heterogeneous data

Abstract: In recent years, rapid advances in information technology have led to various data collection systems which are enriching the sources of empirical data for use in transport systems. Currently, traffic data are collected through various sensors including loop detectors, probe vehicles, cell-phones, Bluetooth, video cameras, remote sensing and public transport smart cards. It has been argued that combining the complementary information from multiple sources will generally result in better accuracy, increased robustness and reduced ambiguity. Despite the fact that there have been substantial advances in data assimilation techniques to reconstruct and predict the traffic state from multiple data sources, such methods are generally data-driven and do not fully utilize the power of traffic models. Furthermore, the existing methods are still limited to freeway networks and are not yet applicable in the urban context due to the enhanced complexity of the flow behavior. The main traffic phenomena on urban links are generally caused by the boundary conditions at intersections, un-signalized or signalized, at which the switching of the traffic lights and the turning maneuvers of the road users lead to shock-wave phenomena that propagate upstream of the intersections. This paper develops a new model-based methodology to build up a real-time traffic prediction model for arterial corridors using data from multiple sources, particularly from loop detectors and partial observations from Bluetooth and GPS devices.

Spatiotemporal Analysis of Bluetooth Data: Application to a Large Urban Network

Abstract: The emergence of new technologies allows better monitoring of traffic conditions and understanding of urban network dynamics. Bluetooth technology is becoming widespread, as it represents a cost-effective means for capturing road traffic in both arterials and motorways. Although the extraction of travel time from Bluetooth data is fairly straightforward, data reliability and processing is still challenging with the issues of penetration rate, mode discrimination, and detection quality. This paper presents a methodological contribution to the use of Bluetooth data for the spatiotemporal analysis of a large urban network (Brisbane, Australia). It introduces the concept of the Bluetooth origin–destination (B-OD) matrix, which is built from a network of 79 Bluetooth detectors located within the Brisbane urban area. The B-OD matrix describes the dynamics of a subpopulation of vehicles, between pairs of detectors. The results show that the characteristics of urban networks can be effectively represented through B-OD matrices. A comparison with loop detector data enables an assessment of the results' significance. Then, the spatiotemporal structure of the network is analyzed with two different clustering analyses, namely, latent Dirichlet allocation (LDA) and $K$ -means. While LDA is used to detect a temporal pattern, the $K$ -means algorithm highlights Bluetooth fundamental diagram (BFD) classes. The results show that Bluetooth data has the potential to be a reliable data source for traffic monitoring. By highlighting hidden structures of a large area, the algorithm outputs allow us to provide the road operators with a fine spatiotemporal analysis of their network, in terms of traffic conditions.

Bluetooth Data in an Urban Context: Retrieving Vehicle Trajectories

Abstract: Bluetooth sensors have recently been developed throughout the world for traffic information gathering. Primarily designed for travel time analysis, this article presents a method for vehicular trajectories retrieval. After a short description of some of the challenges at hand in using Bluetooth data in an urban network, a procedure to extract trip information from such data is proposed. It is further analyzed and illustrated at work on a real dataset collected in Brisbane. Last, this article shows that using spatially constrained shortest path analysis, this trip information, once extracted, can be used for the reconstruction of the trajectories. The performance of the process is assessed using both a simulated dataset and one from the real-world acquired in Brisbane, showing encouraging results, with up to 84% of accurately recovered trajectories.

A framework for the semi-automatic testing of video games

Abstract: Game environments are complex interactive systems that require extensive analysis and testing to ensure that they are at a high enough quality to be released commercially. In particular, the last build of the product needs an additional and extensive beta test carried out by people that play the game in order to establish its robustness and playability. This entails additional costs from the viewpoint of a company as it requires the hiring of play testers. In the present work we propose a general software framework that integrates Artificial Intelligence (AI) Agents and Computer Vision (CV) technologies to support the test team and help to improve and accelerate the test process. We also present a prototype shadow alias detection algorithm that illustrates the effectiveness of the framework in developing automated visual debugging technology that will ease the heavy cost of beta testing games.

Acoustic Indices for Biodiversity Assessment and Landscape Investigation

Abstract: Jérôme Sueur1), Almo Farina2), Amandine Gasc1,3), Nadia Pieretti2), Sandrine Pavoine3,4) 1) Muséum national d’Histoire naturelle, Département Systématique et Évolution, UMR 7205-CNRS ISYEB, 45 rue Buffon, Paris, France. sueur@mnhn.fr 2) Department of Basic Sciences and Foundations, Urbino University, Urbino, Italy 3) Muséum national d’Histoire naturelle, Département Ecologie et Gestion de la Biodiversité, UMR 7204 CNRS-UPMC CESCO, 55-61 rue Buffon, 75005 Paris, France 4) Mathematical Ecology Research Group, Department of Zoology, University of Oxford, South Parks Road, Oxford, OX1 3PS, UK

Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps.

Abstract: In this paper, we investigate the day-to-day regularity of urban congestion patterns. We first partition link speed data every 10 min into 3D clusters that propose a parsimonious sketch of the congestion pulse. We then gather days with similar patterns and use consensus clustering methods to produce a unique global pattern that fits multiple days, uncovering the day-to-day regularity. We show that the network of Amsterdam over 35 days can be synthesized into only 4 consensual 3D speed maps with 9 clusters. This paves the way for a cutting-edge systematic method for travel time predictions in cities. By matching the current observation to historical consensual 3D speed maps, we design an efficient real-time method that successfully predicts 84% trips travel times with an error margin below 25%. The new concept of consensual 3D speed maps allows us to extract the essence out of large amounts of link speed observations and as a result reveals a global and previously mostly hidden picture of traffic dynamics at the whole city scale, which may be more regular and predictable than expected.