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In spite of the overwhelming high-tech marvels and applications that rule our digital lives, the use of the handwritten signature is still recognized worldwide in government, personal and legal entities to be the most important behavioral biometric trait. A number of notable research approaches provide advanced results up to a certain point which allow us to assert with confidence that the performance attained by signature verification (SV) systems is comparable to those provided by any other biometric modality. Up to now, the mainstream trend for offline SV is shared between standard -or handcrafted- feature extraction methods and popular machine learning techniques, with typical examples ranging from sparse representation to Deep Learning. Recent progress in graph mining algorithms provide us with the prospect to re-evaluate the opportunity of utilizing graph representations by exploring corresponding graph features for offline SV. In this paper, inspired by the recent use of image visibility graphs for mapping images into networks, we introduce for the first time in offline SV literature their use as a parameter free, agnostic representation for exploring global as well as local information. Global properties of the sparsely located content of the shape of the signature image are encoded with topological information of the whole graph. In addition, local pixel patches are encoded by sequential visibility motifs-subgraphs of size four, to a low six dimensional motif profile vector. A number of pooling functions operate on the motif codes in a spatial pyramid context in order to create the final feature vector. The effectiveness of the proposed method is evaluated with the use of two popular datasets. The local visibility graph features are considered to be highly informative for SV; this is sustained by the corresponding results which are at least comparable with other classic state-of-the-art approaches.

Wireless ad hoc networks are characterized by their limited capabilities and their routine deployment in unfavorable environments. This creates the strong requirement to regulate energy expenditure. We present a scheme to regulate energy cost through optimized transmission scheduling in a noisy epidemic dissemination environment. Building on the intrinsically cross-layer nature of the adaptive epidemic dissemination process, we strive to deliver an optimized mechanism, where energy cost is regulated without compromising the network infection. Improvement of data freshness and applicability in routing are also investigated. Extensive simulations are used to support our proposal.

A thermal gas flow sensor was developed and evaluated. The presented implementation requires only low-cost manufacturing techniques and readily available components, while maintaining a high level of detection range and sensitivity. Heater and sensing elements were integrated on a flexible substrate and the device was formed by bending the substrate so that the active elements were placed on the external surface of the formed channel, therefore zero flow interference is achieved and a wide variety of fluids can be measured without compromising the sensor integrity. Evaluation was made using air flow rates in the range of 0-65SLPM utilizing electrical measurements and IR imaging techniques simultaneously.

In our everyday life, we frequently experience cases where persons group together. In those cases, context-aware systems capture and process identical context. Therefore, the need to collaboratively address Context Awareness (CA) emerges. In the considered setting, ad hoc networking between mobile nodes enables the exchange of information, thus, CA is facilitated. The synergy between mobile nodes materializes the Collaborative CA (CCA) paradigm. We advance the general CCA concept by performing communication between nodes probabilistically in a way similar to virus (epidemic) spreading. Nodes feature a hierarchical information model, which can be exploited by an information diffusion process. Multiple pieces of information, exchanged as epidemics, can complete the information present at a certain node, which in turn infers and spreads new information. We study this novel scheme extensively through an information model for context and an analytical framework (Markov process) with simulations. Our findings show that the information spreading large benefits from the mobility of nodes and semantic processing of the information model.

Fire is a common disastrous phenomenon that constitutes a serious threat. The SCIER (Sensor and Computing Infrastructure for Environmental Risks is partially funded by the European Community through the FP6 IST Program. The work presented in this paper expresses the ideas of the authors and not necessarily the whole SCIER consortium.) project envisages the deployment of Wireless Sensor Networks at the ''Urban-Rural-Interface'' (URI) aiming to the detection, monitoring and crisis management of such natural hazards. One of its primary objectives is the development of an advanced multisensor data fusion scheme which feeds a CUSUM sequential test used in the early detection of fires. Reasoning about the probability of fire in a geographical area covered by temperature, humidity and vision sensors is achieved through Evidential Reasoning (Dempster-Shafer theory).

The theoretical analysis of a cross-layer mechanism for improving the quality of service of real-time applications in wireless networks is presented. The mechanism coordinates adaptations of the modulation order at the Physical layer and the media encoding mode at the Application layer, to improve packet loss rate, throughput and mean delay. With the use of Continuous Flow Modeling, the system is considered as a "fluid" queue with inflow and outflow rates representing its traffic generation and service rates, respectively. Each data source is modeled as a Markov chain, from the steady-state of which the optimal adaptation thresholds of the cross-layer mechanism are derived. Extensive performance evaluation results show that the optimized operation of the mechanism attains a significant performance improvement compared to both the sub-optimal case, and a legacy system, which adjusts the modulation order and encoding mode separately and independently of each other.

This paper extends the work on cross-layer design which combines adaptive modulation and coding at the physical layer and hybrid automatic repeat request protocol at the data link layer. By contrast with previous works on this topic, the present development and the performance analysis as well, is based on rate compatible punctured turbo codes. Rate compatibility provides incremental redundancy in transmission of parity bits for error correction at the data link layer. Turbo coding and iterative decoding gives lower packet error rate values in low signal-to-noise ratio regions of the adaptive modulation and coding (AMC) schemes. Thus, the applied cross-layer design results in AMC schemes can achieve better spectral efficiency than convolutional one while it retains the QoS requirements at the application layer. Numerical results in terms of spectral efficiency for both turbo and convolutional rate compatible punctured codes are presented. For a more comprehensive presentation, the performance of rate compatible LDPC is contrasted with turbo case as well as the performance complexity is discussed for each of the above codes.

The aim of this work is to develop a wireless sensor network for both structural health monitoring of civil infrastructure and seismic event detection and recording. The network is consisted of a small number of nodes, on which high resolution ADXL accelerometers are connected. The nodes are placed within the building in pre-determined locations, in order to acquire and record data generated by seismic activity. A massive installation of such networks could monitor the seismic activity of wide areas within a city, allowing civil safeguard to act quickly in case of serious earthquakes events. The nodes are stationary and the network is self-organized and self-configured. We mainly focus on the routing protocol of the network and the time synchronization of the data. Since all data are gathered to a master node placed at the base of the building, successful transmission to that node is of great importance. Therefore, the developed routing protocol emphasizes at the delivery robustness of the transmitted data, trading off latency. Data travel through a multi-hop order and no periodic routing advertisement is required. Regarding the time synchronization, the desired accuracy is on the order of 1ms. In order to achieve such accuracy, a time synchronization protocol has been developed. Clock differences and drifts between the nodes are calculated and taken into account when synchronizing the acquired data, thus accomplishing even better accuracy (tens of μs). Experimental evaluation in laboratory specially designed set-up, proved that the performance of both protocols meet the needs of the propose application. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

During the past years, several attempts have been made to develop functionality for mobility management support and QoS provision in the realm of the IP networks. Since IP was not designed to support such functionality, new protocols have been specified and implemented to tackle these issues. Mobile IP is currently the dominant protocol that allows users to retain connectivity while roaming in IP networks. RSVP (Resource reSerVation Protocol) is a well established protocol for reserving network resources to support QoS requirements. These protocols, when deployed separately, can work quite efficiently. However, if their functionality is combined, several inefficiencies arise in terms of QoS deterioration and misuse of the network resources. To minimize these inefficiencies, we propose a new approach that limits mobility and QoS related network modifications inside the domain, in which a user moves. The deployment of our scheme enhances the network resource usage efficiency, while minimizing the duration of the QoS deterioration experienced after a terminal movement. To quantify the advantages of our proposal, we have developed an analytical and a simulation model that we also present in this paper.

A method for the detection of abnormal behavior in HVAC systems is presented. The method combines deterministic subspace identification for each zone independently to create a system model that produces the anticipated zone’s temperature and the sequential test CUSUM algorithm to detect drifts of the rate of change of the difference between the real and the anticipated measurements. Simulation results regarding the detection of infiltration heat losses and the detection of exogenous heat gains such as fire demonstrate the effectiveness of the proposed method.

This work presents a comparison between direct and indirect neural control methods based on the radial basis function (RBF) architecture. As far as direct control schemes are concerned, a novel direct inverse neural RBF controller taking into account the applicability domain criterion (INCAD) is utilized. ? model predictive control (MPC) formulation based on RBF networks is tested as an example of indirect method. The performances of the two control schemes are evaluated and compared on a highly nonlinear control problem, namely control of a continuous stirred tank reactor (CSTR) with multiple stable and unstable steady states. Results show that the INCAD controller is able to provide satisfactory performance, while performing almost instant calculation of the control actions. MPC on the other hand, outperforms the INCAD in terms of speed of responses, due to the built-in optimization capability; however, the lengthy procedure of solving online the optimization problem impedes the practical use of MPC on systems with fast dynamics.