Development of an image-based method for the detection of archaeological buried relics using multi-temporal satellite imagery

Remote sensing has been successfully used for the exposure of shallow buried relics such as archaeological remains. The detection is mainly based on photointerpretation of high-resolution satellite or aerial images. Photointerpretation for archaeological purposes is focused on the identification of crop marks using visible and near infrared (VNIR) spectrum (e.g. vegetation indices) response, which is sensitive to vegetation stress. Detection of such marks is always performed through images of adequate spatial resolution, and therefore this procedure might be problematic in cases when there is a lack of accessibility to such kinds of data. This paper addresses this problem and illustrates an image-based method intended for the detection of crop marks using satellite data of inadequate spatial resolution. The overall methodology consists of seven separate steps. The method needs two areas of interest to be selected in the image, preferably in close proximity to one another. The first area is characterized as the ‘archaeological area under investigation'while the second is a vegetated non-archaeological area. These two areas are simultaneously examined in detail using spectral signatures, soil lines, and their phenological cycle characteristics. The proposed methodology has been successfully applied in three different areas in Cyprus and Greece, where the authors have already used the technique for validation purposes.     

D-cores: measuring collaboration of directed graphs based on degeneracy

Community detection and evaluation is an important task in graph mining. In many cases, a community is defined as a subgraph characterized by dense connections or interactions between its nodes. A variety of measures are proposed to evaluate different quality aspects of such communities—in most cases ignoring the directed nature of edges. In this paper, we introduce novel metrics for evaluating the collaborative nature of directed graphs—a property not captured by the single node metrics or by other established community evaluation metrics. In order to accomplish this objective, we capitalize on the concept of graph degeneracy and define a novel D-core framework, extending the classic graph-theoretic notion of $k$ -cores for undirected graphs to directed ones. Based on the D-core, which essentially can be seen as a measure of the robustness of a community under degeneracy, we devise a wealth of novel metrics used to evaluate graph collaboration features of directed graphs. We applied the D-core approach on large synthetic and real-world graphs such as Wikipedia, DBLP, and ArXiv and report interesting results at the graph as well at the node level.     

Secure and robust multipath routings for advanced metering infrastructure

A Smart Grid is the modernization of the electricity grid using communication technology with the prime goals of reducing energy consumption as well as cost increasing reliability and creating new services for all participants. It comprises key components such as the Advanced Metering Infrastructure (AMI), which includes Neighborhood area network (NAN). When multi-hopping is considered in wireless communication, especially in WiFi and ZigBee, the range of the communication can be extended to communicate with the gateway collector in AMI network. Wireless mesh AMI network may have smart meters, a NAN gateway, and fixed as well as mobile repeaters. Though many techniques have been developed to secure on-demand routing protocols in wireless multi-hop ad-hoc networks, these protocols have shortcomings. In this paper, we propose two robust and secure multipath routing protocols for wireless mesh AMI networks. We have analyzed their robustness to various attacks. The simulation results show that the proposed protocols are better than existing secure routing protocols.     

Traction control design for off-road mobility using an SPH-DAE cosimulation framework

Multibody System Dynamics - We describe an analytical framework implemented in a general-purpose mobility simulation platform for enabling the design of control policies for improved rover mobility...     

Fast Minor Testing in Planar Graphs

Minor Containment is a fundamental problem in Algorithmic Graph Theory used as a subroutine in numerous graph algorithms. A model of a graph H in a graph G is a set of disjoint connected subgraphs of G indexed by the vertices of H, such that if {u,v} is an edge of H, then there is an edge of G between components C _u and C _v . A graph H is a minor of G if G contains a model of H as a subgraph. We give an algorithm that, given a planar n-vertex graph G and an h-vertex graph H, either finds in time $\mathcal{O}(2^{\mathcal{O}(h)} \cdot n +n^{2}\cdot\log n)$ a model of H in G, or correctly concludes that G does not contain H as a minor. Our algorithm is the first single-exponential algorithm for this problem and improves all previous minor testing algorithms in planar graphs. Our technique is based on a novel approach called partially embedded dynamic programming.     

The Impact of Network Structure on the Stability of Greedy Protocols

A packet-switching network is stable if the number of packets in the network remains bounded at all times. A very natural question that arises in the context of stability properties of such networks is how network structure precisely affects these properties. In this work we embark on a systematic study of this question in the context of Adversarial Queueing Theory, which assumes that packets are adversarially injected into the network. We consider size, diameter, maximum vertex degree, minimum number of disjoint paths that cover all edges of the network and network subgraphs as crucial structural parameters of the network, and we present a comprehensive collection of structural results, in the form of stability and instability bounds on injection rate of the adversary for various greedy protocols: —Increasing the size of a network may result in dropping its instability bound. This is shown through a novel, yet simple and natural, combinatorial construction of a size-parameterized network on which certain compositions of greedy protocols are running. The convergence of the drop to 0.5 is found to be fast with and proportional to the increase in size. —Maintaining the size of a network small may already suffice to drop its instability bound to a substantially low value. This is shown through a construction of a FIFO network with size 22, which becomes unstable at rate 0.704. This represents the current state-of-the-art trade-off between network size and instability bound. —The diameter, maximum vertex degree and minimum number of edge-disjoint paths that cover a network may be used as control parameters for the stability bound of the network. This is shown through an improved analysis of the stability bound of any arbitrary FIFO network, which takes these parameters into account. —How much can network subgraphs that are forbidden for stability affect the instability bound? Through improved combinatorial constructions of networks and executions, we improve the state-of-the-art instability bound induced by certain known forbidden subgraphs on networks running a certain greedy protocol. —Our results shed more light and contribute significantly to a finer understanding of the impact of structural parameters on stability and instability properties of networks.     

An optimized sequential pattern matching methodology for sequence classification

In this paper we present a novel methodology for sequence classification, based on sequential pattern mining and optimization algorithms. The proposed methodology automatically generates a sequence classification model, based on a two stage process. In the first stage, a sequential pattern mining algorithm is applied to a set of sequences and the sequential patterns are extracted. Then, the score of every pattern with respect to each sequence is calculated using a scoring function and the score of each class under consideration is estimated by summing the specific pattern scores. Each score is updated, multiplied by a weight and the output of the first stage is the classification confusion matrix of the sequences. In the second stage an optimization technique, aims to finding a set of weights which minimize an objective function, defined using the classification confusion matrix. The set of the extracted sequential patterns and the optimal weights of the classes comprise the sequence classification model. Extensive evaluation of the methodology was carried out in the protein classification domain, by varying the number of training and test sequences, the number of patterns and the number of classes. The methodology is compared with other similar sequence classification approaches. The proposed methodology exhibits several advantages, such as automated weight assignment to classes using optimization techniques and knowledge discovery in the domain of application.

A knowledge-based approach to user interface adaptation from preferences and for special needs

Moving between devices is omnipresent, but not for people with disabilities or those who require specific accessibility options. Setting up assistive technologies or finding settings to overcome a certain barrier can be a demanding task for people without technical skills. Context-sensitive adaptive user interfaces are advancing, although migrating access features from one device to another is very rarely addressed. In this paper, we describe the knowledge-based component of the Global Public Inclusive Infrastructure that infers how a device shall be best configured at the operating system layer, the application layer and the web layer to meet the requirements of a user including possible special needs or disabilities. In this regard, a mechanism to detect and resolve conflicting accessibility policies as well as recommending preference substitutes is a main requirement, as elaborated in this paper. As the proposed system emulates decision-making of accessibility experts, we validated the automatic deduced configurations against manual configurations of ten accessibility experts. The assessment result shows that the average matching score of the developed system is high. Thus, the proposed system can be considered capable of making precise decisions towards personalizing user interfaces based on user needs and preferences.     

Interactive mixed reality white cane simulation for the training of the blind and the visually impaired

This paper presents a mixed reality tool developed for the training of the visually impaired based on haptic and auditory feedback. The proposed approach focuses on the development of a highly interactive and extensible Haptic Mixed Reality training system that allows visually impaired to navigate into real size Virtual Reality environments. The system is based on the use of the CyberGrasp™ haptic device. An efficient collision detection algorithm based on superquadrics is also integrated into the system so as to allow real time collision detection in complex environments. A set of evaluation tests is designed in order to identify the importance of haptic, auditory and multimodal feedback and to compare the MR cane against the existing Virtual Reality cane simulation system.     

Privacy preserving broadcast message authentication protocol for VANETs

Vehicular Ad-hoc Networks (VANETs) will start becoming deployed within the next decade. Among other benefits, it is expected that VANETs will support applications and services targeting the increase of safety on the road, and assist in improving the efficiency of the road transportation network. However, several serious challenges remain to be solved before efficient and secure VANET technology becomes available, one of them been efficient authentication of messages in a VANET. There is a significant body of research work addressing this issue, however, while progress has been made, the challenge is still far from having been resolved and reliable and secure systems ready for deployment becoming available. In this paper, we propose the Privacy Preserving Broadcast Message Authentication (PPBMA) scheme, which, instead of performing asymmetric verification, uses Message Authentication Code (MAC) functionality and HASH operations to authenticate messages. Moreover, we use two-level key hash chain, which assists avoiding message losses. Simulation results demonstrate that PPBMA has superior performances in terms of packet loss rate and message delivery latency when compared to existing solutions. Due to this advantage, it can support emergency and routine messages alike, while existing solutions can only support routine messages.