Fuzzy distributed cooperative tracking for a swarm of unmanned aerial vehicles with heterogeneous goals

This article proposes a systematic analysis for a tracking problem which ensures cooperation amongst a swarm of unmanned aerial vehicles (UAVs), modelled as nonlinear systems with linear and angular velocity constraints, in order to achieve different goals. A distributed Takagi–Sugeno (TS) framework design is adopted for the representation of the nonlinear model of the dynamics of the UAVs. The distributed control law which is introduced is composed of both node and network level information. Firstly, feedback gains are synthesised using a parallel distributed compensation (PDC) control law structure, for a collection of isolated UAVs; ignoring communications among the swarm. Then secondly, based on an alternation-like procedure, the resulting feedback gains are used to determine Lyapunov matrices which are utilised at network level to incorporate into the control law, the relative differences in the states of the vehicles, and to induce cooperative behaviour. Eventually stability is guaranteed for the entire swarm. The control synthesis is performed using tools from linear control theory: in particular the design criteria are posed as linear matrix inequalities (LMIs). An example based on a UAV tracking scenario is included to outline the efficacy of the approach.     

Inverse lighting design using a coverage optimization strategy

Lighting design is an essential process in computer cinematography, games, architectural design and various other applications for correctly illuminating or highlighting parts of a scene and enhancing storytelling. When targeting specific illumination goals and constraints, this process can be tedious and counter-intuitive even for experienced users and thus automatic, goal-driven methods have emerged for the estimation of a lighting configuration to match the desired result. We present a general automatic approach to such an inverse lighting design problem, where the number of light sources along with their position and emittance are computed given a set of user-specified lighting goals. To this end, we employ a special hierarchical light clustering that operates in the lighting goal coverage domain and overcomes limitations of previous approaches in environments with high occlusion or structural complexity. Our approach is independent of the underlying light transport model and can quickly converge to usable solutions. We validate our results and provide comparative evaluation with the current state of the art.     

An overview of depth cameras and range scanners based on time-of-flight technologies

Time-of-flight (TOF) cameras are sensors that can measure the depths of scene points, by illuminating the scene with a controlled laser or LED source and then analyzing the reflected light. In this paper, we will first describe the underlying measurement principles of time-of-flight cameras, including: (1) pulsed-light cameras, which measure directly the time taken for a light pulse to travel from the device to the object and back again, and (2) continuous-wave-modulated light cameras, which measure the phase difference between the emitted and received signals, and hence obtain the travel time indirectly. We review the main existing designs, including prototypes as well as commercially available devices. We also review the relevant camera calibration principles, and how they are applied to TOF devices. Finally, we discuss the benefits and challenges of combined TOF and color camera systems.     

Hyperconnected attribute filters based on k-flat zones

In this paper, we present a new method for attribute filtering, combining contrast and structural information. Using hyperconnectivity based on k-flat zones, we improve the ability of attribute filters to retain internal details in detected objects. Simultaneously, we improve the suppression of small, unwanted detail in the background. We extend the theory of attribute filters to hyperconnectivity and provide a fast algorithm to implement the new method. The new version is only marginally slower than the standard Max-Tree algorithm for connected attribute filters, and linear in the number of pixels or voxels. It is two orders of magnitude faster than anisotropic diffusion. The method is implemented in the form of a filtering rule suitable for handling both increasing (size) and nonincreasing (shape) attributes. We test this new framework on nonincreasing shape filters on both 2D images from astronomy, document processing, and microscopy, and 3D CT scans, and show increased robustness to noise while maintaining the advantages of previous methods.     

DoS attacks exploiting signaling in UMTS and IMS

The Universal Mobile Telecommunication Standard (UMTS) is continuously evolving to meet the growing demand of modern mobile and Internet applications for high capacity and advanced features in security and quality of service. Although admittedly enhanced in terms of security when compared to 2G systems, UMTS still has weaknesses that can lead to security incidents. In this paper, we investigate the vulnerabilities of the UMTS security architecture that can be exploited by a malicious individual to mount Denial of Service (DoS) attacks. Our focus is on signaling-oriented attacks above the physical layer. We describe and analyze several novel attacks that can be triggered against both core UMTS architecture as well as hybrid UMTS/WLAN realms. An additional contribution of this paper is the presentation of an extensive survey of similar attacks in UMTS and related protocol infrastructures such as IP Multimedia Subsystem (IMS). Finally, we offer some suggestions that would provide greater tolerance to the system against DoS attacks.     

Three-dimensional face recognition in the presence of facial expressions: an annotated deformable model approach

In this paper, we present the computational tools and a hardware prototype for 3D face recognition. Full automation is provided through the use of advanced multistage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact metadata. We present our results on the largest known, and now publicly available, face recognition grand challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, this is the highest performance reported on the FRGC v2 database for the 3D modality     

Forecasting and trading the EUR/USD exchange rate with Gene Expression and Psi Sigma Neural Networks

The motivation for this paper is to investigate the use of two promising classes of artificial intelligence models, the Psi Sigma Neural Network (PSI) and the Gene Expression algorithm (GEP), when applied to the task of forecasting and trading the EUR/USD exchange rate. This is done by benchmarking their results with a Multi-Layer Perceptron (MLP), a Recurrent Neural Network (RNN), a genetic programming algorithm (GP), an autoregressive moving average model (ARMA) plus a naïve strategy. We also examine if the introduction of a time-varying leverage strategy can improve the trading performance of our models.     

The effect of two types of C-S-H on the elasticity of cement-based materials: Results from nanoindentation and micromechanical modeling

It has long been recognized, in cement chemistry, that two types of calcium-silicate-hydrate (C-S-H) exist in cement-based materials, but less is known about how the two types of C-S-H affect the mechanical properties. By means of nanoindentation tests on nondegraded and calcium leached cement paste, the paper confirms the existence of two types of C-S-H, and investigates the distinct role played by the two phases on the elastic properties of cement-based materials. It is found that (1) high-density C-S-H are mechanically less affected by calcium leaching than low density C-S-H, and (2) the volume fractions occupied by the two phases in the C-S-H matrix are not affected by calcium leaching. The nanoindentation results also provide quantitative evidence, suggesting that the elastic properties of the C-S-H phase are intrinsic material properties that do not depend on mix proportions of cement-based materials. The material properties and volume fractions are used in a novel two-step homogenization model, that predicts the macroscopic elastic properties of cement pastes with high accuracy. Combined with advanced physical chemistry models that allow, for a given w/c ratio, determination of the volume fractions of the two types of C-S-H, the model can be applied to any cement paste, with or without Portlandite, Clinker, and so on. In particular, from an application of the model to decalcified cement pastes, it is shown that that the decalcification of the C-S-H phase is the primary source of the macroscopic elastic modulus degradation, that dominates over the effect of the dissolution of Portlandite in cement-based material systems.