Explore the vast range of titles available.

Estimating the pose of a plane given a set of point correspondences is a core problem in computer vision with many applications including Augmented Reality (AR), camera calibration and 3D scene reconstruction and interpretation. Despite much progress over recent years there is still the need for a more efficient and more accurate solution, particularly in mobile applications where the run-time budget is critical. We present a new analytic solution to the problem which is far faster than current methods based on solving Pose from n Points (PnP) and is in most cases more accurate. Our approach involves a new way to exploit redundancy in the homography coefficients. This uses the fact that when the homography is noisy it will estimate the true transform between the model plane and the image better at some regions on the plane than at others. Our method is based on locating a point where the transform is best estimated, and using only the local transformation at that point to constrain pose. This involves solving pose with a local non-redundant 1st-order PDE. We call this framework Infinitesimal Plane-based Pose Estimation (IPPE), because one can think of it as solving pose using the transform about an infinitesimally small region on the surface. We show experimentally that IPPE leads to very accurate pose estimates. Because IPPE is analytic it is both extremely fast and allows us to fully characterise the method in terms of degeneracies, number of returned solutions, and the geometric relationship of these solutions. This characterisation is not possible with state-of-the-art PnP methods.

We modeled the mobility of mobile phone users in order to study the fundamental spreading patterns that characterize a mobile virus outbreak. We find that although Bluetooth viruses can reach all susceptible handsets with time, they spread slowly because of human mobility, offering ample opportunities to deploy antiviral software. In contrast, viruses using multimedia messaging services could infect all users in hours, but currently a phase transition on the underlying call graph limits them to only a small fraction of the susceptible users. These results explain the lack of a major mobile virus breakout so far and predict that once a mobile operating system's market share reaches the phase transition point, viruses will pose a serious threat to mobile communications.

Body area networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed.

A novel and compact wideband multiple input multiple output (MIMO) antenna for mobile terminals is presented. The proposed antenna operates over a wide frequency range of 1.79–3.77 GHz suitable for long-term evolution (LTE), worldwide interoperability for microwave access (WiMAX) and wireless local area network (WLAN) handheld devices. The MIMO antenna structure consists of two identical radiators with a small size of 10 × 17.7 mm2 and a parasitic element with a T-shape. The radiators, which comprise four branches with a meandered strip feed, are symmetrically located with respect to the T-shaped parasitic element. The parasitic element is a novel design appended at the ground plane. By using this parasitic element, a better isolation performance has been achieved between the two radiators. The overall performance of the antenna in terms of s-parameters, radiation pattern, gain and envelope correlation coefficient is investigated and verified through measurements. The results obtained show that the proposed antenna has attractive physical properties due to being small, compact and embeddable in mobile handsets, and has good characteristics of wideband, isolation, gain and radiation pattern.

Passive radars utilising illuminators of opportunity are being actively studied in the field of radar. In the last few years, many illuminator sources have been employed for passive radar applications. However, there have been no published results on the use of the long-term evolution (LTE) signal as a novel source for passive radar applications. For the first time an experimental investigation into the feasibility of LTE-based passive radars for detecting a moving target is presented. A theoretical analysis was conducted on a captured LTE signal in the atmosphere. This was then followed by a field experiment. The preliminary results show that the LTE signal is capable of being an illuminator for passive radars.

In this paper, we present an introduction and critical experimental evaluation of a reservoir computing (RC) approach for ambient assisted living (AAL) applications. Such an empirical analysis jointly addresses the issues of efficiency, by analyzing different system configurations toward the embedding into computationally constrained wireless sensor devices, and of efficacy, by analyzing the predictive performance on real-world applications. First, the approach is assessed on a validation scheme where training, validation and test data are sampled in homogeneous ambient conditions, i.e., from the same set of rooms. Then, it is introduced an external test set involving a new setting, i.e., a novel ambient, which was not available in the first phase of model training and validation. The specific test-bed considered in the paper allows us to investigate the capability of the RC approach to discriminate among user movement trajectories from received signal strength indicator sensor signals. This capability can be exploited in various AAL applications targeted at learning user indoor habits, such as in the proposed indoor movement forecasting task. Such a joint analysis of the efficiency/efficacy trade-off provides novel insight in the concrete successful exploitation of RC for AAL tasks and for their distributed implementation into wireless sensor networks.

The multimedia applications such as audio, video transmission in Mobile Ad Hoc Network (MANET) requires that the path in which such data transmits must be delay sensitive, reliable and energy efficient. An existing MANET routing protocol Ad Hoc On demand Multipath Distance Vector (AOMDV) fails to perform well in terms of QoS metrics such as delay, jitter, packet delivery ratio (PDR) etc., where there is high mobility and heavy traffic. The paths which are stored in the Routing table are not reliable and energy efficient. It is possible to modify the route discovery of AODV so that more than one node disjoint, link reliable and energy efficient paths are stored in the routing table. The proposed protocol Multiconstrained and Multipath QoS Aware Routing Protocol (MMQARP) is novel, which takes care of QoS parameters dynamically and simultaneously along with path finding, so that only link reliable, energy efficient paths are available for data transmission. The extensive simulation study shows that the proposed protocol performs better in terms of PDR, delay and jitter compared to AOMDV protocol.

In this study, we exploit genetic algorithms to design a rectenna required to harvest ambient radio-frequency (RF) energy from four different RF bands in critical (ultra-low power) conditions. For this purpose a set of multi-resonant annular-ring patch antennas are pixel-wise described inside an electromagnetic simulator to provide the ‘population’ of individuals among which the genetic tool is able to select the most adapted one with respect to the design specifications. The further use of circuit-level non-linear simulation tool, based on Harmonic balance technique, allows the rigorous multi-band design of the whole rectenna system in RF stationary conditions at several received power levels. The result is a novel compact, lightweight and highly efficient tetra-band rectenna, able to harvest RF energy from GSM 900, GSM 1800, UMTS and WiFi sources available in the ambient. At these frequency bands high radiation efficiency is desired as an essential prerequisite for optimally handling very low-power densities. Very good agreement with measurements of both the radiating and rectifying designs is demonstrated in real office scenarios. Finally the rectenna is connected to a power management unit and the resulting assembly is tested in terms of stored energy while harvesting from a mobile cell-phone call.

Recently, mobile social networks (MSN) have gained tremendous attention, which free users from face-to-monitor life, while still can share information and stay in touch with their friends on the go. However most MSN applications regard mobile terminals just as entry points to existing social networks, in which centralized servers (for storage and processing of all application/context data) and continual Internet connectivity are prerequisites for mobile users to exploit MSN services, even though they are within proximity area (like campus, event spot, and community, etc.), and can directly exchange data through various wireless technologies (e.g., Bluetooth, WiFi Direct, etc.). In this paper, we focus on mobile social networking in proximity (MSNP), which is explicitly defined in our paper as: MSNP is wireless peer-to-peer (P2P) network of spontaneously and opportunistically connected nodes, and uses geo-proximity as the primary filter in determining who is discoverable on the social network. In this paper, first, primary support approaches related to MSNP available in literature, are summarized and compared, including MSN, mobile P2P and opportunistic networks. And then, we offer the special characteristics of MSNP, open issues and potential solutions. A networking technologies and platform independent architecture is proposed for developing MSNP applications, and proof-of-concept implementation of WiFi direct based MSNP application is also provided. Our primary goal is to identify the characteristics, technical challenges and potential solutions for future MSNP applications, capable to flexibly adapt to different application domains and deployment requirements.