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In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator (RSSI) value and the in-phase and quadrature-phase (IQ) components of the received BLE signals at a single time instance to simultaneously estimate the angle of arrival (AoA) at all APs. Through supervised learning on simulated data, various machine learning (ML) architectures are trained to perform AoA estimation using varying subsets of anchor points. In the final stage of the system, the estimated AoA values are fed to a positioning engine which uses the least squares (LS) algorithm to estimate the position of the tag. The proposed architectures are trained and rigorously tested on several simulated room scenarios and are shown to achieve a localization accuracy of 70 cm. Moreover, the proposed systems possess generalization capabilities by being robust to modifications in the room’s content or anchors’ configuration. Additionally, some of the proposed architectures have the ability to distribute the computational load over the APs.

In large companies, whose business is critically dependent on the eectiveness of their R&D function, the provision of eective means to access and share all forms of technical information is an acute problem. It is often easier to repeat an activity than it is to determine whether work has been carried out before.In this paper we present experiences in implementing and evaluating the MEMOIR system. MEMOIR is an open framework, i.e., it is extensible and adaptable to an organization's infrastructure and applications, and it provides its user interface via standard Web browsers. It uses trails, open hypermedia link services and a set of software agents to assist users in accessing and navigating vast amounts of information in Intranet environments. Additionally, MEMOIR exploits trail data to support users in ®nding colleagues with similar interests. The MEMOIR system has been installed and evaluated by two end-user organizations. This paper describes the results obtained in this evaluation. 7

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This paper presents a new extension to the variable duration Hidden Markov model, capable of classifying musical pattens that have been extracted from raw audio data, into a set predefined classes. Each musical pattern is converted into a sequence of music intervals by means of a fundamental frequency tracking procedure and it is subsequently given as input to a set of variable duration Hidden Markov models. Each of these models has been trained to recognize patterns of the respective predefined class. Classification is determined based on the highest recognition probability. This new type of variable duration Hidden Markov model provides increased classification accuracy because a) it deals effectively with errors originating during the feature extraction stage and b) it accounts for variations due to the expressive performance of instrument players. To demonstrate its effectiveness, the novel classification scheme has been employed in the context of Greek traditional music, to monophonic musical patterns of a popular instrument, the Greek Traditional clarinet. The classification results demonstrate that the new approach outperforms previous work based on conventional Hidden Markov models.

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