Magnetic anomaly map of the Weddell Sea Region, Antarctic (Scale 1:2 500 000)

This paper describes a 1 : 2 500 000 scale aeromagnetic anomaly map produced by the joint efforts of VNIIOkeangeologia, Polar Marine Geological Research Expedition (PMGRE) and the Alfred Wegener Institute for Polar and Marine Research (AWl) for the Weddell Sea region covering 1 850 000 km' of West Antarctica. Extensive regional magnetic survey flights with line-spacing of about 20 km and 5 km were carried out by the PMGRE between 1977 and 1989. In course of these investigations the PMGRE flew 9 surveys with flight-line spacing of 20 km and 6 surveys with flight-line spacing of 5 km mainly over the mountain areas of southern Palmer Land, western Dronning Maud Land, Coats Land and Pensacola Mountains, over the Ronne lee Shelf and the Filchner Ice Shelf and the central part of the Weddell Sea. More than 215 000 line-kilometers of total field aeromagnetic data have been acquired by using an Ilyushin Il-14 ski-equipped aircraft. Survey operations were centered on the field base stations Druzhnaya-1, -2, and -3, from which the majority of the Weddell Sea region network was completed. The composite map of the Weddell Sea region is prepared in colour, showing magnetic anomaly contours at intervals of 50-100 nT with supplemental contours at an interval of 25 nT in low gradient areas, on a polar stereographic projection. The compiled colour magnetic anomaly map of the Weddell Sea region demonstrates that features of large areal extent, such as geologic provinces, fold-belts, ancient eratonic fragments and other regional structural features can be readily delineated. The map allows a comparison of regional magnetic features with similar-scale geological structures on geological and geophysical maps. It also provides a database for the future production of the ''Digital Magnetic Anomaly Map of Antarctica'' in the framework of the Scientific Committee on Antarctic Research/International Association of Geomagnetism and Aeronomy (SCAR/IAGA) compilation.

Basic and other measurements of radiation from the Baseline Surface Radiation Network (BSRN) Station Ny-Ålesund in the years 1992 to 2013, reference list of 253 datasets

At Ny-Ålesund (78.9° N), Svalbard, surface radiation measurements of up- and downward short- and longwave radiation are operated since August 1992 in the frame of the Baseline Surface Radiation Network (BSRN), complemented with surface and upper air meteorology since August 1993. The long-term observations are the base for a climatological presentation of the surface radiation data. Over the 21-year observation period, ongoing changes in the Arctic climate system are reflected. Particularly, the observations indicate a strong seasonality of surface warming and related changes in different radiation parameters. The annual mean temperature at Ny-Ålesund has risen by +1.3 ± 0.7 K per decade, with a maximum seasonal increase during the winter months of +3.1 ± 2.6 K per decade. At the same time, winter is also the season with the largest long-term changes in radiation, featuring an increase of +15.6 ± 11.6 W/m**2 per decade in the downward longwave radiation. Furthermore, changes in the reflected solar radiation during the months of snow melt indicate an earlier onset of the warm season by about 1 week compared to the beginning of the observations. The online available dataset of Ny-Ålesund surface radiation measurements provides a valuable data source for the validation of satellite instruments and climate models.

Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detection

Microcalcifications Detection using PFCM and ANN

This work presents a method to detect Microcalcifications in Regions of Interest from digitized mammograms. The method is based mainly on the combination of Image Processing, Pattern Recognition and Artificial Intelligence. The Top-Hat transform is a technique based on mathematical morphology operations that, in this work is used to perform contrast enhancement of microcalcifications in the region of interest. In order to find more or less homogeneous regions in the image, we apply a novel image sub-segmentation technique based on Possibilistic Fuzzy c-Means clustering algorithm. From the original region of interest we extract two window-based features, Mean and Deviation Standard, which will be used in a classifier based on a Artificial Neural Network in order to identify microcalcifications. Our results show that the proposed method is a good alternative in the stage of microcalcifications detection, because this stage is an important part of the early Breast Cancer detection

PDR with a foot-mounted IMU and ramp detection

The localization of persons in indoor environments is nowadays an open problem. There are partial solutions based on the deployment of a network of sensors (Local Positioning Systems or LPS). Other solutions only require the installation of an inertial sensor on the person’s body (Pedestrian Dead-Reckoning or PDR). PDR solutions integrate the signals coming from an Inertial Measurement Unit (IMU), which usually contains 3 accelerometers and 3 gyroscopes. The main problem of PDR is the accumulation of positioning errors due to the drift caused by the noise in the sensors. This paper presents a PDR solution that incorporates a drift correction method based on detecting the access ramps usually found in buildings. The ramp correction method is implemented over a PDR framework that uses an Inertial Navigation algorithm (INS) and an IMU attached to the person’s foot. Unlike other approaches that use external sensors to correct the drift error, we only use one IMU on the foot. To detect a ramp, the slope of the terrain on which the user is walking, and the change in height sensed when moving forward, are estimated from the IMU. After detection, the ramp is checked for association with one of the existing in a database. For each associated ramp, a position correction is fed into the Kalman Filter in order to refine the INS-PDR solution. Drift-free localization is achieved with positioning errors below 2 meters for 1,000-meter-long routes in a building with a few ramps.

Performance assessment of a kinetically-powered network for herd localization

Developing a herd localization system capable to operate unattended in communication-challenged areas arises from the necessity of improving current systems in terms of cost, autonomy or any other facilities that a certain target group (or overall users) may demand. A network architecture of herd localization is proposed with its corresponding hardware and a methodology to assess performance in different operating conditions. The system is designed taking into account an eventual environmental impact hence most nodes are simple, cheap and kinetically powered from animal movements-neither batteries nor sophisticated processor chips are needed. Other network elements integrating GPS and batteries operate with selectable duty cycles, thus reducing maintenance duties. Equipment has been tested on Scandinavian reindeer in Lapland and its element modeling is integrated into a simulator to analyze such localization network applicability for different use cases. Performance indicators (detection frequency, localization accuracy and delay) are fitted to assess the overall performance; system relative costs are enclosed also for a range of deployments.

Structural health monitoring network system with wireless communications inside closed aerospace structures

Structural Health Monitoring (SHM) requires integrated "all in one" electronic devices capable of performing analysis of structural integrity and on-board damage detection in aircraft?s structures. PAMELA III (Phased Array Monitoring for Enhanced Life Assessment, version III) SHM embedded system is an example of this device type. This equipment is capable of generating excitation signals to be applied to an array of integrated piezoelectric Phased Array (PhA) transducers stuck to aircraft structure, acquiring the response signals, and carrying out the advanced signal processing to obtain SHM maps. PAMELA III is connected with a host computer in order to receive the configuration parameters and sending the obtained SHM maps, alarms and so on. This host can communicate with PAMELA III through an Ethernet interface. To avoid the use of wires where necessary, it is possible to add Wi-Fi capabilities to PAMELA III, connecting a Wi-Fi node working as a bridge, and to establish a wireless communication between PAMELA III and the host. However, in a real aircraft scenario, several PAMELA III devices must work together inside closed structures. In this situation, it is not possible for all PAMELA III devices to establish a wireless communication directly with the host, due to the signal attenuation caused by the different obstacles of the aircraft structure. To provide communication among all PAMELA III devices and the host, a wireless mesh network (WMN) system has been implemented inside a closed aluminum wingbox. In a WMN, as long as a node is connected to at least one other node, it will have full connectivity to the entire network because each mesh node forwards packets to other nodes in the network as required. Mesh protocols automatically determine the best route through the network and can dynamically reconfigure the network if a link drops out. The advantages and disadvantages on the use of a wireless mesh network system inside closed aerospace structures are discussed.

Defending against cybercrime: advances in the detection of malicious servers and the analysis of client-side vulnerabilities

Esta tesis se centra en el análisis de dos aspectos complementarios de la ciberdelincuencia (es decir, el crimen perpetrado a través de la red para ganar dinero). Estos dos aspectos son las máquinas infectadas utilizadas para obtener beneficios económicos de la delincuencia a través de diferentes acciones (como por ejemplo, clickfraud, DDoS, correo no deseado) y la infraestructura de servidores utilizados para gestionar estas máquinas (por ejemplo, C & C, servidores explotadores, servidores de monetización, redirectores). En la primera parte se investiga la exposición a las amenazas de los ordenadores victimas. Para realizar este análisis hemos utilizado los metadatos contenidos en WINE-BR conjunto de datos de Symantec. Este conjunto de datos contiene metadatos de instalación de ficheros ejecutables (por ejemplo, hash del fichero, su editor, fecha de instalación, nombre del fichero, la versión del fichero) proveniente de 8,4 millones de usuarios de Windows. Hemos asociado estos metadatos con las vulnerabilidades en el National Vulnerability Database (NVD) y en el Opens Sourced Vulnerability Database (OSVDB) con el fin de realizar un seguimiento de la decadencia de la vulnerabilidad en el tiempo y observar la rapidez de los usuarios a remiendar sus sistemas y, por tanto, su exposición a posibles ataques. Hemos identificado 3 factores que pueden influir en la actividad de parches de ordenadores victimas: código compartido, el tipo de usuario, exploits. Presentamos 2 nuevos ataques contra el código compartido y un análisis de cómo el conocimiento usuarios y la disponibilidad de exploit influyen en la actividad de aplicación de parches. Para las 80 vulnerabilidades en nuestra base de datos que afectan código compartido entre dos aplicaciones, el tiempo entre el parche libera en las diferentes aplicaciones es hasta 118 das (con una mediana de 11 das) En la segunda parte se proponen nuevas técnicas de sondeo activos para detectar y analizar las infraestructuras de servidores maliciosos. Aprovechamos técnicas de sondaje activo, para detectar servidores maliciosos en el internet. Empezamos con el análisis y la detección de operaciones de servidores explotadores. Como una operación identificamos los servidores que son controlados por las mismas personas y, posiblemente, participan en la misma campaña de infección. Hemos analizado un total de 500 servidores explotadores durante un período de 1 año, donde 2/3 de las operaciones tenían un único servidor y 1/2 por varios servidores. Hemos desarrollado la técnica para detectar servidores explotadores a diferentes tipologías de servidores, (por ejemplo, C & C, servidores de monetización, redirectores) y hemos logrado escala de Internet de sondeo para las distintas categorías de servidores maliciosos. Estas nuevas técnicas se han incorporado en una nueva herramienta llamada CyberProbe. Para detectar estos servidores hemos desarrollado una novedosa técnica llamada Adversarial Fingerprint Generation, que es una metodología para generar un modelo único de solicitud-respuesta para identificar la familia de servidores (es decir, el tipo y la operación que el servidor apartenece). A partir de una fichero de malware y un servidor activo de una determinada familia, CyberProbe puede generar un fingerprint válido para detectar todos los servidores vivos de esa familia. Hemos realizado 11 exploraciones en todo el Internet detectando 151 servidores maliciosos, de estos 151 servidores 75% son desconocidos a bases de datos publicas de servidores maliciosos. Otra cuestión que se plantea mientras se hace la detección de servidores maliciosos es que algunos de estos servidores podrán estar ocultos detrás de un proxy inverso silente. Para identificar la prevalencia de esta configuración de red y mejorar el capacidades de CyberProbe hemos desarrollado RevProbe una nueva herramienta a través del aprovechamiento de leakages en la configuración de la Web proxies inversa puede detectar proxies inversos. RevProbe identifica que el 16% de direcciones IP maliciosas activas analizadas corresponden a proxies inversos, que el 92% de ellos son silenciosos en comparación con 55% para los proxies inversos benignos, y que son utilizado principalmente para equilibrio de carga a través de múltiples servidores. ABSTRACT In this dissertation we investigate two fundamental aspects of cybercrime: the infection of machines used to monetize the crime and the malicious server infrastructures that are used to manage the infected machines. In the first part of this dissertation, we analyze how fast software vendors apply patches to secure client applications, identifying shared code as an important factor in patch deployment. Shared code is code present in multiple programs. When a vulnerability affects shared code the usual linear vulnerability life cycle is not anymore effective to describe how the patch deployment takes place. In this work we show which are the consequences of shared code vulnerabilities and we demonstrate two novel attacks that can be used to exploit this condition. In the second part of this dissertation we analyze malicious server infrastructures, our contributions are: a technique to cluster exploit server operations, a tool named CyberProbe to perform large scale detection of different malicious servers categories, and RevProbe a tool that detects silent reverse proxies. We start by identifying exploit server operations, that are, exploit servers managed by the same people. We investigate a total of 500 exploit servers over a period of more 13 months. We have collected malware from these servers and all the metadata related to the communication with the servers. Thanks to this metadata we have extracted different features to group together servers managed by the same entity (i.e., exploit server operation), we have discovered that 2/3 of the operations have a single server while 1/3 have multiple servers. Next, we present CyberProbe a tool that detects different malicious server types through a novel technique called adversarial fingerprint generation (AFG). The idea behind CyberProbe’s AFG is to run some piece of malware and observe its network communication towards malicious servers. Then it replays this communication to the malicious server and outputs a fingerprint (i.e. a port selection function, a probe generation function and a signature generation function). Once the fingerprint is generated CyberProbe scans the Internet with the fingerprint and finds all the servers of a given family. We have performed a total of 11 Internet wide scans finding 151 new servers starting with 15 seed servers. This gives to CyberProbe a 10 times amplification factor. Moreover we have compared CyberProbe with existing blacklists on the internet finding that only 40% of the server detected by CyberProbe were listed. To enhance the capabilities of CyberProbe we have developed RevProbe, a reverse proxy detection tool that can be integrated with CyberProbe to allow precise detection of silent reverse proxies used to hide malicious servers. RevProbe leverages leakage based detection techniques to detect if a malicious server is hidden behind a silent reverse proxy and the infrastructure of servers behind it. At the core of RevProbe is the analysis of differences in the traffic by interacting with a remote server.

A Survey on Underwater Acoustic Sensor Network Routing Protocols

Underwater acoustic sensor networks (UASNs) have become more and more important in ocean exploration applications, such as ocean monitoring, pollution detection, ocean resource management, underwater device maintenance, etc. In underwater acoustic sensor networks, since the routing protocol guarantees reliable and effective data transmission from the source node to the destination node, routing protocol design is an attractive topic for researchers. There are many routing algorithms have been proposed in recent years. To present the current state of development of UASN routing protocols, we review herein the UASN routing protocol designs reported in recent years. In this paper, all the routing protocols have been classified into different groups according to their characteristics and routing algorithms, such as the non-cross-layer design routing protocol, the traditional cross-layer design routing protocol, and the intelligent algorithm based routing protocol. This is also the first paper that introduces intelligent algorithm-based UASN routing protocols. In addition, in this paper, we investigate the development trends of UASN routing protocols, which can provide researchers with clear and direct insights for further research.

A link density clustering algorithm based on automatically selecting density peaks for overlapping community detection

Peer reviewed

Excitatory–inhibitory network in the visual cortex: Psychophysical evidence

At early stages in visual processing cells respond to local stimuli with specific features such as orientation and spatial frequency. Although the receptive fields of these cells have been thought to be local and independent, recent physiological and psychophysical evidence has accumulated, indicating that the cells participate in a rich network of local connections. Thus, these local processing units can integrate information over much larger parts of the visual field; the pattern of their response to a stimulus apparently depends on the context presented. To explore the pattern of lateral interactions in human visual cortex under different context conditions we used a novel chain lateral masking detection paradigm, in which human observers performed a detection task in the presence of different length chains of high-contrast-flanked Gabor signals. The results indicated a nonmonotonic relation of the detection threshold with the number of flankers. Remote flankers had a stronger effect on target detection when the space between them was filled with other flankers, indicating that the detection threshold is caused by dynamics of large neuronal populations in the neocortex, with a major interplay between excitation and inhibition. We considered a model of the primary visual cortex as a network consisting of excitatory and inhibitory cell populations, with both short- and long-range interactions. The model exhibited a behavior similar to the experimental results throughout a range of parameters. Experimental and modeling results indicated that long-range connections play an important role in visual perception, possibly mediating the effects of context.

A new modularity measure for Fuzzy Community detection problems based on overlap and grouping functions

One of the main challenges of fuzzy community detection problems is to be able to measure the quality of a fuzzy partition. In this paper, we present an alternative way of measuring the quality of a fuzzy community detection output based on n-dimensional grouping and overlap functions. Moreover, the proposed modularity measure generalizes the classical Girvan–Newman (GN) modularity for crisp community detection problems and also for crisp overlapping community detection problems. Therefore, it can be used to compare partitions of different nature (i.e. those composed of classical, overlapping and fuzzy communities). Particularly, as is usually done with the GN modularity, the proposed measure may be used to identify the optimal number of communities to be obtained by any network clustering algorithm in a given network. We illustrate this usage by adapting in this way a well-known algorithm for fuzzy community detection problems, extending it to also deal with overlapping community detection problems and produce a ranking of the overlapping nodes. Some computational experiments show the feasibility of the proposed approach to modularity measures through n-dimensional overlap and grouping functions.

Improving 3D Keypoint Detection from Noisy Data Using Growing Neural Gas

3D sensors provides valuable information for mobile robotic tasks like scene classification or object recognition, but these sensors often produce noisy data that makes impossible applying classical keypoint detection and feature extraction techniques. Therefore, noise removal and downsampling have become essential steps in 3D data processing. In this work, we propose the use of a 3D filtering and down-sampling technique based on a Growing Neural Gas (GNG) network. GNG method is able to deal with outliers presents in the input data. These features allows to represent 3D spaces, obtaining an induced Delaunay Triangulation of the input space. Experiments show how the state-of-the-art keypoint detectors improve their performance using GNG output representation as input data. Descriptors extracted on improved keypoints perform better matching in robotics applications as 3D scene registration.

Conceptual Modelling of Complex Network Management Systems

Society, as we know it today, is completely dependent on computer networks, Internet and distributed systems, which place at our disposal the necessary services to perform our daily tasks. Moreover, and unconsciously, all services and distributed systems require network management systems. These systems allow us to, in general, maintain, manage, configure, scale, adapt, modify, edit, protect or improve the main distributed systems. Their role is secondary and is unknown and transparent to the users. They provide the necessary support to maintain the distributed systems whose services we use every day. If we don’t consider network management systems during the development stage of main distributed systems, then there could be serious consequences or even total failures in the development of the distributed systems. It is necessary, therefore, to consider the management of the systems within the design of distributed systems and systematize their conception to minimize the impact of the management of networks within the project of distributed systems. In this paper, we present a formalization method of the conceptual modelling for design of a network management system through the use of formal modelling tools, thus allowing from the definition of processes to identify those responsible for these. Finally we will propose a use case to design a conceptual model intrusion detection system in network.

An Electronic Reconfigurable Neural Architecture for Intrusion Detection

The explosive growth of the traffic in computer systems has made it clear that traditional control techniques are not adequate to provide the system users fast access to network resources and prevent unfair uses. In this paper, we present a reconfigurable digital hardware implementation of a specific neural model for intrusion detection. It uses a specific vector of characterization of the network packages (intrusion vector) which is starting from information obtained during the access intent. This vector will be treated by the system. Our approach is adaptative and to detecting these intrusions by using a complex artificial intelligence method known as multilayer perceptron. The implementation have been developed and tested into a reconfigurable hardware (FPGA) for embedded systems. Finally, the Intrusion detection system was tested in a real-world simulation to gauge its effectiveness and real-time response.