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Grammenos D.,Institute of Computer Science FORTH
Interactions | Year: 2016

Future Designers aims to be an experience that broadens one's thinking-not a lesson. The presented material is not meant to be learned or remembered. Like the fifth use of the designer's pillow, it is intended as a step to move further ahead. Probably the best short description of the course comes from one of the participating teachers, who exclaimed that "it feels like a rollercoaster for the mind!" The underlying philosophy behind Future Designers can be summed up this way: If at some point in your life you realize that you cannot change the world, the next best thing that you can do is to try to change those who one day may change it-the Future Designers. © 2016 ACM.

Nikitaki S.,A+ Network | Tsagkatakis G.,Institute of Computer Science FORTH | Tsakalides P.,University of Crete
IEEE Transactions on Mobile Computing | Year: 2015

Fingerprint-based location sensing technologies play an increasingly important role in pervasive computing applications due to their accuracy and minimal hardware requirements. However, typical fingerprint-based schemes implicitly assume that communication occurs over the same channel (frequency) during the training and the runtime phases. When this assumption is violated, the mismatches between training and runtime fingerprints can significantly deteriorate the localization performance. Additionally, the exhaustive calibration procedure required during training limits the scalability of this class of methods. In this work, we propose a novel, scalable, multi-channel fingerprint-based indoor localization system that employs modern mathematical concepts based on the Sparse Representations and Matrix Completion theories. The contribution of our work is threefold. First, we investigate the impact of channel changes on the fingerprint characteristics and the effects of channel mismatch on state-of-the-art localization schemes. Second, we propose a novel fingerprint collection technique that significantly reduces the calibration time, by formulating the map construction as an instance of the Matrix Completion problem. Third, we propose the use of sparse Bayesian learning to achieve accurate location estimation. Experimental evaluation on real data highlights the superior performance of the proposed framework in terms of reconstruction error and localization accuracy. © 2015 IEEE.

Milioris D.,University of Crete | Milioris D.,University Paris - Sud | Milioris D.,French Institute for Research in Computer Science and Automation | Tzagkarakis G.,CEA Saclay Nuclear Research Center | And 7 more authors.
Ad Hoc Networks | Year: 2014

Accurate location awareness is of paramount importance in most ubiquitous and pervasive computing applications. Numerous solutions for indoor localization based on IEEE802.11, bluetooth, ultrasonic and vision technologies have been proposed. This paper introduces a suite of novel indoor positioning techniques utilizing signal-strength (SS) fingerprints collected from access points (APs). Our first approach employs a statistical representation of the received SS measurements by means of a multivariate Gaussian model by considering a discretized grid-like form of the indoor environment and by computing probability distribution signatures at each cell of the grid. At run time, the system compares the signature at the unknown position with the signature of each cell by using the Kullback-Leibler Divergence (KLD) between their corresponding probability densities. Our second approach applies compressive sensing (CS) to perform sparsity-based accurate indoor localization, while reducing significantly the amount of information transmitted from a wireless device, possessing limited power, storage, and processing capabilities, to a central server. The performance evaluation which was conducted at the premises of a research laboratory and an aquarium under real-life conditions, reveals that the proposed statistical fingerprinting and CS-based localization techniques achieve a substantial localization accuracy. © 2013 Elsevier B.V. All rights reserved.

Milioris D.,University of Crete | Milioris D.,University Paris - Sud | Milioris D.,French Institute for Research in Computer Science and Automation | Tzagkarakis G.,SAP | And 3 more authors.
European Signal Processing Conference | Year: 2011

Accurate indoor localization is a significant task for many ubiquitous and pervasive computing applications, with numerous solutions based on IEEE802.11, bluetooth, ultrasound and infrared technologies being proposed. The inherent sparsity present in the problem of location estimation motivates in a natural fashion the use of the recently introduced theory of compressive sensing (CS), which states that a signal having a sparse representation in an appropriate basis can be reconstructed with high accuracy from a small number of random linear projections. In the present work, we exploit the framework of CS to perform accurate indoor localization based on signal-strength measurements, while reducing significantly the amount of information transmitted from a wireless device with limited power, storage, and processing capabilities to a central server. Equally importantly, the inherent property of CS acting as a weak encryption process is demonstrated by showing that the proposed approach presents an increased robustness to potential intrusions of an unauthorized entity. The experimental evaluation reveals that the proposed CS-based localization technique is superior in terms of an increased localization accuracy in conjunction with a low computational complexity when compared with previous statistical fingerprint-based methods. © 2011 EURASIP.

Saveta T.,Institute of Computer Science FORTH | Daskalaki E.,Institute of Computer Science FORTH | Flouris G.,Institute of Computer Science FORTH | Fundulaki I.,Institute of Computer Science FORTH | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

One of the main challenges in the Data Web is the identification of instances that refer to the same real-world entity. Choosing the right framework for this purpose remains tedious, as current instance matching benchmarks fail to provide end users and developers with the necessary insights pertaining to how current frameworks behave when dealing with real data. In this paper, we present LANCE, a domain-independent instance matching benchmark generator which focuses on benchmarking instance matching systems for Linked Data. LANCE is the first Linked Data benchmark generator to support complex semantics-aware test cases that take into account expressive OWL constructs, in addition to the standard test cases related to structure and value transformations. LANCE supports the definition of matching tasks with varying degrees of difficulty and produces a weighted gold standard, which allows a more fine-grained analysis of the performance of instance matching tools. It can accept any linked dataset and its accompanying schema as input to produce a target dataset implementing test cases of varying levels of difficulty. We provide a comparative analysis with LANCE benchmarks to assess and identify the capabilities of state of the art instance matching systems as well as an evaluation to demonstrate the scalability of LANCE’s test case generator. © Springer International Publishing Switzerland 2015.

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