Conference Papers

Game-theoretic learning in a distributed-information setting: Distributed convergence to mean-centric equilibria

Swenson B., Kar S., Xavier J.

Conference Record - Asilomar Conference on Signals, Systems and Computers

2015

Abstract:

The paper considers distributed learning in large-scale games via fictitious-play type algorithms. Given a preassigned communication graph structure for information exchange among the players, this paper studies a distributed implementation of the Empirical Centroid Fictitious Play (ECFP) algorithm that is well-suited to large-scale games in terms of complexity and memory requirements. It is shown that the distributed algorithm converges to an equilibrium set denoted as the mean-centric equilibria (MCE) for a reasonably large class of games.

GeeBot: A Robotic Platform for Refugee Integration

Simão H., Avelino J., Duarte N., Figueiredo R.

HRI '18 Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction

2018

Abstract:

The refugee crisis is one of society’s leading challenges. After a journey for survival, refugees and host institutions face barriers that hinder the integration process. To design solutions, we interviewed two groups: host institutions and past refugees. We identified critical issues, from legal concerns, like unfamiliarity of their Refugee Status, to grocery shopping. Our envisioned solution is GeeBot, a low-cost egg-shaped robot that institutions would lend to arriving families for eighteen months. GeeBot will be a translator with teaching functions, an information provider, and an active promoter of interaction between native and refugee populations.

Generalized delay-secrecy-throughput trade-offs in mobile ad-hoc networks

Shintre S., Sassatelli L., Barros J.

Proceedings - 2011 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC'11

2011

Abstract:

In this paper, we first present a theoretical framework aimed at generalizing the scaling laws of delay, secrecy and throughput in mobile ad-hoc networks for various network models and scheduling policies available in the literature. We derive new scaling laws for throughput-delay trade-offs for routing policies such as Spray-and-Wait. A model based on threshold secrecy constraint is developed and it is shown that scaling is not impacted provided the eavesdropper density is lower than the node density.

Geographic attribution of an electricity system renewable energy target: Local economic, social and environmental tradeoffs

Schell K.R., Claro J., Fischbeck P.

Renewable and Sustainable Energy Reviews

2015

Abstract:

The 2014 Intergovernmental Panel on Climate Change (IPCC) report asserts that investment in low-carbon electricity production will need to rise by several hundred billion dollars annually, before 2030, in order to stabilize greenhouse gas concentrations in the atmosphere by 2100. In recognition of this urgent need to mitigate climate change, many governments have already established policies to spur renewable energy investment in the electricity sector. One such policy measure is a renewable energy target (RET), which sets a target percentage of electricity production to be generated from renewable sources by a specified date. Variations on this policy have been implemented around the world, from the EU 20-20-20 to diverse renewable portfolio standards in U.S. states and municipalities. This work analyzes economic, environmental and social aspects of a geographic attribution (i.e. Isolated, Regional or Country) of an RET to gain insights on the associated tradeoffs. In the case study of the Azores Islands, Portugal, the regional geographic attribution of an RET captures the best of all three tradeoffs.

Geometry-based vehicle-to-vehicle channel modeling for large-scale simulation

Boban M., Barros J., Tonguz O.K.

IEEE Transactions on Vehicular Technology

2014

Abstract:

Due to the dynamic nature of vehicular traffic and the road surroundings, vehicle-to-vehicle (V2V) propagation characteristics vary greatly on both small and large scale. Recent measurements have shown that both large static objects (e.g., buildings and foliage) and mobile objects (surrounding vehicles) have a profound impact on V2V communication. At the same time, system-level vehicular ad hoc network (VANET) simulators by and large employ simple statistical propagation models, which do not account for surrounding objects explicitly. We designed Geometry-based Efficient propagation Model for V2V communication (GEMV 2 ), which uses outlines of vehicles, buildings, and foliage to distinguish the following three types of links: line of sight (LOS), non-LOS (NLOS) due to vehicles, and NLOS due to static objects. For each link, GEMV 2 calculates the large-scale signal variations deterministically, whereas the small-scale signal variations are calculated stochastically based on the number and size of surrounding objects. We implement GEMV 2 in MATLAB and show that it scales well by using it to simulate radio propagation for city-wide networks with tens of thousands of vehicles on commodity hardware. We make the source code of GEMV 2 freely available. Finally, we validate GEMV 2 against extensive measurements performed in urban, suburban, highway, and open-space environments.

Global localization by soft object recognition from 3D Partial Views

Ribeiro F., Brandao S., Costeira J.P., Veloso M.

IEEE International Conference on Intelligent Robots and Systems

2015

Abstract:

Global localization is a widely studied problem, and in essence corresponds to the online robot pose estimation based on a given map with landmarks, an odometry model, and real robot sensory observations and motion. In most approaches, the map provides the position of visible objects, which are then recognized to provide the robot pose estimation. Such object recognition with noisy sensory data is challenging. In this paper, we present an effective global localization technique using soft 3D object recognition to estimate the pose with respect to the landmarks in the given map. A depth sensor acquires a partial view for each observed object, from which our algorithm extracts the robot pose relative to the objects, based on a library of 3D Partial View Heat Kernel descriptors. Our approach departs from methods that require classification and registration against complete 3D models, which are prone to errors due to noisy sensory data and object misclassifications in the recognition stage. We experimentally validate our method in different robot paths with different common 3D environment objects. We also show the improvement of our method compared to when the partial view information is not used.

Gray-space spectrum sharing between multiple rotating radars and cellular network hotspots

Saruthirathanaworakun R., Peha J.M., Correia L.M.

IEEE Vehicular Technology Conference

2013

Abstract:

This paper considers gray-space spectrum sharing when rotating radars are primary spectrum users, and multiple cells from one or more cellular networks are secondary users. A cellular network may share spectrum to supplement its dedicated spectrum, or provide a broadband hotspot service. A secondary device is allowed to transmit as long as cumulative interference is not harmful to nearby radars, probably because no radar is pointing its directional antenna at the device at this moment. This paper presents mechanisms that would support such sharing, and quantifies performance when spectrum is considered 100% utilized under traditional spectrum management. It is shown that the sharing allows cells to sustain significant mean data rates. For example, if 5% of a cellular network’s cells need more capacity than dedicated spectrum can provide, a cell can get almost 1.2 bps/Hz on average from shared spectrum. By evaluating quality of service, it is found that shared spectrum could be used efficiently for applications such as non-interactive video streaming, peer-to-peer file sharing, large file transfers, and web browsing, but not for applications such as real-time transfers of small files, and VoIP.

Harbornet: A real-world testbed for vehicular networks

Ameixieira C., Cardote A., Neves F., Meireles R., Sargento S., Coelho L., Afonso J., Areias B., Mota E., Costa R., Matos R., Barros J.

IEEE Communications Magazine

2014

Abstract:

We present a real-world testbed for research and development in vehicular networking that has been deployed successfully in the seaport of Leixoes in Portugal. The testbed allows cloudbased code deployment, remote network control and distributed data collection from moving container trucks, cranes, tow boats, patrol vessels, and roadside units, thereby enabling a wide range of experiments and performance analyses. After describing the testbed architecture and its various modes of operation, we give concrete examples of its use and offer insights on how to build effective testbeds for wireless networking with moving vehicles.

Heartbeat classification using morphological and dynamic features of ECG signals

Ye C., Vijaya Kumar B.V.K., Coimbra M.T.

IEEE Transactions on Biomedical Engineering

2012

Abstract:

In this paper, we propose a new approach for heartbeat classification based on a combination of morphological and dynamic features. Wavelet transform and independent component analysis (ICA) are applied separately to each heartbeat to extract morphological features. In addition, RR interval information is computed to provide dynamic features. These two different types of features are concatenated and a support vector machine classifier is utilized for the classification of heartbeats into one of 16 classes. The procedure is independently applied to the data from two ECG leads and the two decisions are fused for the final classification decision. The proposed method is validated on the baseline MIT-BIH arrhythmia database and it yields an overall accuracy (i.e., the percentage of heartbeats correctly classified) of 99.3% (99.7% with 2.4% rejection) in the “class-oriented” evaluation and an accuracy of 86.4% in the “subject-oriented” evaluation, comparable to the state-of-the-art results for automatic heartbeat classification.