This paper describes a contemporary concept store, offering a technology-rich blend of entertainment and interactivity targeted to help customers in their shopping experiences while shortening the time they waste queuing.

Our research explores novel forms of smart spaces that support full body interaction with smart objects instrumented with audio, light, and motion sensors and actuators, virtual worlds on medium-large displays, and smart lights, and are integrated with cloud services for remote supervision and analysis of user behaviour. In this paper, we present the architecture and coordination infrastructure created in the Playful Supervised Smart Space (P3S) project, as well as initial designs for the Smart Object and Smart Space Gateway Components.

In this paper, an Android SPEech proCessing plaTform as smaRtphone Application (SPECTRA) is presented. Such application, developed by the authors, has three main functions: i) Gender Recognition (GR), ii) Speaker Recognition (SR) and iii) Language Recognition (LR). All these recognition functions are performed simultaneously by using unsupervised Support Vector Machine (SVM) classifiers. An innovative point of this paper lies in the automatic re-training of the employed SVMs which are able to dynamically update themselves when a (new) audio from a (new) speaker is provided. This allow to build more robust classifiers, which results in better recognition performances. In terms of accuracy, the GR reaches about 98% of correct classifications, SR performs around 80% while LR shows an accuracy of about 74%.

The StreetSmart Project develops information services that integrate multiple (touch and touchless) interaction paradigms on personal devices and large public displays. It exploits personalization techniques in order to offer new engaging user experiences involving large amounts of multimedia contents.

This paper considers IRTR (Improved Real-Time TV-channel Recognition), a new method aimed at recognizing in real-time (live) what people is watching on TV, similarly to the action performed by Audience investigations, but without any TV user active interaction. IRTR uses only the audio signal of the TV program recorded through smartphones and is independent of the specific smartphone technology. It is performed through two main steps: i) fingerprint extraction and ii) TV channel real-time identification. This paper proposes a likelihood estimation-based algorithm aimed at performing the second step. The computational time of the proposed approach has been evaluated through real measures and shows really satisfying results.

Recent multimedia and location-based services (LBSs) employ information about location, orientation and context of a mobile device. Moreover, the wide spread adoption of Smartphones, usually equipped with powerful processors, accelerometers, compasses and Global and Hybrid Positioning Systems (GPS/HPSs) receivers, has favored the increasing of location- and context-based services over the last years. In this work a novel Location Recognition approach aimed at supporting location aware services, in particular Check-In services, is presented. The proposed method uses both the GPS/HPS positioning information and, in an opportunistic way, the presence of Wi-Fi Access Points (APs). The method is based on the concept of radio FingerPrint (FP) whose definition proposed in this paper, to the best of authors' knowledge, has never been applied previously. The method is suited to be employed where traditional approaches - usually based only on GPS/HPS - fail, as in case of indoor or dense urban environments. Finally, the proposal, practically realized over different Android OS Smartphones, has been tested in terms of performance. The experimental results are very satisfying and open the doors to a wide employment of the method.

This paper identifies requirements for querying and accessing context information in mobile and pervasive computing environments. Furthermore it studies existing query languages showing that they satisfy only a subset of these requirements or cover some of them only to a limited extent. A new context query language is presented to overcome these shortcomings, improving the state of the art in several respects: heterogeneous representations of context information, definition of complex filtering mechanisms, elaborate aggregation functions and ontology integration, all in one language.

Mobile phones are being increasingly equipped with sensors that ease retrieval of context information about a user. Context data can be aggregated with information centrally available to mobile operators and service providers, to infer higher-level information such as user "situations", easier to integrate with services. We have been conducting an internal trial monitoring the context of different users in their business life and designing rules to infer high level situations: logical location, activity and social state. In this paper we present the infrastructure and the rule-based reasoning process used for this experiment.

TCP Westwood (TCPW) is a recently proposed sender side modification of TCP congestion control. TCPW relies an bandwidth share estimation techniques to enhance congestion control over high speed and/or wireless networks. The bandwidth share estimation methods turn out to be critical to guarantee both throughput improvement and friendliness towards widely used TCP protocols such as NewReno. In this paper we propose a new bandwidth share estimation technique, called "adaptive bandwidth share estimation", or ABSE. ABSE adapts to changing network congestion level, round trip times, and other relevant network conditions, as well as to the rate at which such changes occur. We compare the throughput gain and friendliness of ABSE with that of NewReno and previous estimation methods used for TCPW. We test the new technique using different simulation scenarios, including RED, to show the benefit of our proposed ABSE estimation method.

The rapid advancement in wireless communication technology has spurred significant interest in the design and development of enhanced TCP protocols. Among them, TCP Westwood (TCPW) is a sender side only modification to improve TCP performance particularly over heterogeneous networks. The key idea of TCPW is to use rate estimation methods to set the congestion window and slow start threshold after a packet loss. When packet losses are not only due to buffer overflow, but random errors as well, TCPW estimation methods have been shown to provide significant performance improvement. The earliest estimation method, called bandwidth estimation (BE), however, may result in over-estimation under certain circumstances, and thus may be unfriendly toward non-TCPW traffic. TCPW CRB (combined rate and bandwidth estimation) and TCPW ABSE (adaptive bandwidth share estimation), have been later introduced to address this concern. The schemes provide better control of the tradeoffs among efficiency, friendliness, and implementation complexity. CRB may slightly sacrifice the efficiency gain to ensure friendliness. ABSE adaptivity mechanisms are more sophisticated and provide both better efficiency and friendliness.We summarize ABSE, which adapts to congestion level, as well as round drip time, and other network dynamics, thus providing enhanced and robust performance under various network conditions. Extensive experiments show that TCPW ABSE is able to enhance TCP performance significantly over "large leaky pipes", while maintaining friendliness toward TCP NewReno. We show that TCPW ABSE is robust to packet and ACK compression due to cross traffic on forward and backward paths. We also show that ABSE is robust to buffer size variations, which are inevitable in today's networks.

We propose a refinement of TCP Westwood which allows management of the efficiency/friendliness-to-NewReno tradeoff. We show that the refined TCP Westwood is able to achieve higher efficiency yet at the same time maintain friendliness. TCP Westwood (TCPW) implements a novel window congestion control algorithm based on available bandwidth estimation. The performance of TCPW has been promising, exceeding that of TCP NewReno in high speed and/or wired/wireless networks. However, under certain circumstances, TCP NewReno may experience some performance degradation because TCPW possesses more information and thus can take better advantage of available bandwidth. We propose combining the original TCPW sampling strategy that produces available bandwidth estimates (BE), with a new strategy that produces rate estimates (RE). Our studies show that RE works best when packet losses are mostly due to congestion. If on the other hand, the packet losses are mostly due to link errors, BE gives better performance. To achieve the "best of all worlds", we introduce a method we call combined rate and bandwidth estimation (CRB). A connection first infers the predominant cause of packet losses, and then uses the most appropriate estimation method. We also introduce the efficiency/friendliness tradeoff graph that provides better tradeoff visualization. In our experiments, we found that CRB provides a better compromise between efficiency and friendliness, and the means to manage such a tradeoff.