UAVouch : a distributed drone identity and location validation mechanism

As aplicações emergentes de vigilância, com equipes de VANTs, dependem de comunicação segura para trocar informações, coordenar seus movimentos e cumprir os objetivos da missão. Proteger a rede identificando o acesso de nós mal-intencionados tentando perturbar o sistema é uma tarefa importante, e particularmente sensível no domínio militar. Observando essa necessidade, este artigo apresenta o design e a avaliação do UAVouch: Um esquema distribuído de validação de localização e identidade de drones que combina uma autenticação baseada em chave pública com uma verificação de plausibilidade de movimento para grupos de VANTs. A ideia principal do UAVouch complementa o mecanismo de autenticação, verificando periodicamente a plausibilidade da localização dos VANTs vizinhos, permitindo a detecção de intrusos que não conseguem seguir as trajetórias esperadas. A solução proposta foi avaliada em simulação através de um cenário de vigilância militar, no qual detectou-se ataques de falsificação de posição de nós mal-intencionados com precisão em média acima de 85%.Emerging surveillance applications of UAV teams rely on secure communication to exchange information, coordinate their movements, and fulfill mission objectives. Protecting the network by identifying malicious nodes access trying to disturb the system is an important task, which is particularly sensitive in the military domain. Observing this need, this paper presents the design and evaluation of UAVouch: an identity and location validation scheme combining a public-key based authentication with a movement plausibility check for groups of UAVs. The key idea of UAVouch supplement the authentication mechanism by periodically checking the plausibility of the location of neighboring UAVs, allowing the detection of intruders that are unable to follow expected trajectories. The proposed solution was evaluated in a simulated military surveillance scenario in which it detects malicious nodes’ position falsification attacks with an accuracy on average above 85%

OPERA: waiting for the tau

The OPERA experiment, whose aim is the direct observation of nu_mu ~> nu_tau oscillations in appearance mode in the CNGS high-energy neutrino beam, consists of a high-granularity modular target of nuclear emulsions-lead "bricks", richly instrumented with electronic detectors necessary for the location of neutrino interactions and kinematic analysis. After the first short runs of August 2006, October 2006 and October 2007, the experiment started early this summer its first long physics run (about 150 days). Operating at the final target mass of 1.35 kton, the 2008 run can realistically offer the first chance to observe a tau originated from a flavour oscillated nu_mu. This contribution will discuss the different aspects of the experiment, the challenges of the data handling and the recent achievements. It will also outline the various steps required for the analysis in nuclear emulsions from the "brick finding" to the vertex reconstruction.Comment: 6 pages, 3 figures. Proceedings of the "XXVIII Physics in Collisions", Perugia, Italy, 25-28 June 200

PRESENCE VALIDATION USING SECURED INTERNET PROTOCOL (IP) ADDRESS

Various presence-based validation technologies exist that provide for the ability add identity and presence validation to a laptop-based system. However, these technologies are primarily limited to computers (e.g., desktops and laptops) and do not include location validation. There is a need to extend these capabilities to other devices connected to a network and also to eliminate the need for a hardware-assisted solution. There is also a need to offer network location to avoid any type of attack from devices that are not connected to a local area network (LAN) or even to the same port. This proposal provides a technique to ensure that a device/person is present at a location by observing that the device/person performed an activity on-site, which can be observed by a trusted third-party

Brain image clustering by wavelet energy and CBSSO optimization algorithm

Previously, the diagnosis of brain abnormality was significantly important in the saving of social and hospital resources. Wavelet energy is known as an effective feature detection which has great efficiency in different utilities. This paper suggests a new method based on wavelet energy to automatically classify magnetic resonance imaging (MRI) brain images into two groups (normal and abnormal), utilizing support vector machine (SVM) classification based on chaotic binary shark smell optimization (CBSSO) to optimize the SVM weights. The results of the suggested CBSSO-based KSVM are compared favorably to several other methods in terms of better sensitivity and authenticity. The proposed CAD system can additionally be utilized to categorize the images with various pathological conditions, types, and illness modes

Remote sensing-based tool to assess nitrogen levels in farmers fields, to support farmers' stover+fertilizer use/management

Initial comparison between ground- and aerial-based tools made. Further multi-location validation required. As low-cost tool in comparison to the more specialized leaf-clip sensors, digital photography is promising approach for precision agriculture crop management. Ground and aerial platform-based measurements performed similarly in terms of assessing leaf N content and GY

Three-Dimensional Time Resolved Lagrangian Flow Field Reconstruction Based on Constrained Least Squares and Stable Radial Basis Function

The three-dimensional Time-Resolved Lagrangian Particle Tracking (3D TR-LPT) technique has recently advanced flow diagnostics by providing high spatiotemporal resolution measurements under the Lagrangian framework. To fully exploit its potential, accurate and robust data processing algorithms are needed. These algorithms are responsible for reconstructing particle trajectories, velocities, and differential quantities (e.g., pressure gradients, strain- and rotation-rate tensors, and coherent structures) from raw LPT data. In this paper, we propose a three-dimensional (3D) divergence-free Lagrangian reconstruction method, where three foundation algorithms -- Constrained Least Squares (CLS), stable Radial Basis Function (RBF-QR), and Partition-of-Unity Method (PUM) -- are integrated into one comprehensive reconstruction strategy. Our method, named CLS-RBF PUM, is able to (i) directly reconstruct flow fields at scattered data points, avoiding Lagrangian-to-Eulerian data conversions; (ii) assimilate the flow diagnostics in Lagrangian and Eulerian descriptions to achieve high-accuracy flow reconstruction; (iii) process large-scale LPT data sets with more than hundreds of thousand particles in two dimensions (2D) or 3D; (iv) enable spatiotemporal super-resolution while imposing physical constraints (e.g., divergence-free for incompressible flows) at arbitrary time and location. Validation based on synthetic and experimental LPT data confirmed that our method can consistently achieve the above advantages with accuracy and robustness.Comment: 30 pages, 11 figure

Railway track condition assessment at network level by frequency domain analysis of GPR data

The railway track system is a crucial infrastructure for the transportation of people and goods in modern societies. With the increase in railway traffic, the availability of the track for monitoring and maintenance purposes is becoming significantly reduced. Therefore, continuous non-destructive monitoring tools for track diagnoses take on even greater importance. In this context, Ground Penetrating Radar (GPR) technique results yield valuable information on track condition, mainly in the identification of the degradation of its physical and mechanical characteristics caused by subsurface malfunctions. Nevertheless, the application of GPR to assess the ballast condition is a challenging task because the material electromagnetic properties are sensitive to both the ballast grading and water content. This work presents a novel approach, fast and practical for surveying and analysing long sections of transport infrastructure, based mainly on expedite frequency domain analysis of the GPR signal. Examples are presented with the identification of track events, ballast interventions and potential locations of malfunctions. The approach, developed to identify changes in the track infrastructure, allows for a user-friendly visualisation of the track condition, even for GPR non-professionals such as railways engineers, and may further be used to correlate with track geometric parameters. It aims to automatically detect sudden variations in the GPR signals, obtained with successive surveys over long stretches of railway lines, thus providing valuable information in asset management activities of infrastructure managers