GpoSolver: a Matlab/C++ toolbox for global polynomial optimization

Global polynomial optimization can be a powerful tool when applied to engineering problems. One of the most successful methods for solving such problems is based on convex linear matrix inequality (LMI) relaxations. Software implementations of this approach can be found for example in Matlab toolboxes GloptiPoly and YALMIP. Matlab language makes it very easy when it comes to modelling polynomial problems. However, when using these toolboxes, Matlab is also required for the problem solving. GpoSolver aims at bridging this gap by providing a Matlab-based problem modelling toolbox supplemented by a problem-solving back end in a form of a C++ template library. Once a problem is conveniently modelled and parametrized in Matlab, a C++ class is automatically generated by GpoSolver. This class can be easily included into an existing codebase and used to solve different instances of the problem based on the supplied parameters.     

Advanced methods for the estimation of an unknown projection from a map

This article presents three new methods (M5, M6, M7) for the estimation of an unknown map projection and its parameters. Such an analysis is beneficial and interesting for historic, old, or current maps without information about the map projection; it could improve their georeference. The location similarity approach takes into account the residuals on the corresponding features; the minimum is found using the non-linear least squares. For the shape similarity approach, the minimized objective function ? takes into account the spatial distribution of the features, together with the shapes of the meridians, parallels and other 0D-2D elements. Due to the non-convexity and discontinuity, its global minimum is determined using the global optimization, represented by the differential evolution. The constant values of projection φ _k , λ _k , φ ₁, λ ₀, and map constants R,ΔX,ΔY, α (in relation to which the methods are invariant) are estimated. All methods are compared and the results are presented for the synthetic data as well as for 8 early maps from the Map Collection of the Charles University and the David Rumsay Map Collection. The proposed algorithms have been implemented in the new version of the detectproj software.     

Practical Product Importance Sampling for Direct Illumination

We present a practical algorithm for sampling the product of environment map lighting and surface reflectance. Our method builds on wavelet‐based importance sampling, but has a number of important advantages over previous methods. Most importantly, we avoid using precomputed reflectance functions by sampling the BRDF on‐the‐fly. Hence, all types of materials can be handled, including anisotropic and spatially varying BRDFs, as well as procedural shaders. This also opens up for using very high resolution, uncompressed, environment maps. Our results show that this gives a significant reduction of variance compared to using lower resolution approximations. In addition, we study the wavelet product, and present a faster algorithm geared for sampling purposes. For our application, the computations are reduced to a simple quadtree‐based multiplication. We build the BRDF approximation and evaluate the product in a single tree traversal, which makes the algorithm both faster and more flexible than previous methods.     

Passivating Defects of Perovskite Solar Cells with Functional Donor-Acceptor–Donor Type Hole Transporting Materials

In this study, a series of donor–acceptor–donor (D-A-D) type small molecules based on the fluorene and diphenylethenyl enamine units, which are distinguished by different acceptors, as holetransporting materials (HTMs) for perovskite solar cells is presented. The incorporation of the malononitrile acceptor units is found to be beneficial for not only carrier transportation but also defects passivation via Pb–N interactions. The highest power conversion efficiency of over 22% is achieved on cells based on V1359, which is higher than that of spiro-OMeTAD under identical conditions. This st shows that HTMs prepared via simplified synthetic routes are not only a low-cost alternative to spiro-OMeTAD but also outperform in efficiency and stability state-of-art materials obtained via expensive cross-coupling methods.     

Multifunctional Gadolinium‐Doped Mesoporous TiO2 Nanobeads: Photoluminescence,Enhanced Spin Relaxation,and Reactive Oxygen Species Photogeneration,Beneficial for Cancer Diagnosis and Treatment

Materials with controllable multifunctional abilities for optical imaging (OI) and magnetic resonant imaging (MRI) that also can be used in photodynamic therapy are very interesting for future applications. Mesoporous TiO₂ sub‐micrometer particles are doped with gadolinium to improve photoluminescence functionality and spin relaxation for MRI, with the added benefit of enhanced generation of reactive oxygen species (ROS). The Gd‐doped TiO₂ exhibits red emission at 637 nm that is beneficial for OI and significantly improves MRI relaxation times, with a beneficial decrease in spin–lattice and spin–spin relaxation times. Density functional theory calculations show that Gd³⁺ ions introduce impurity energy levels inside the bandgap of anatase TiO₂, and also create dipoles that are beneficial for charge separation and decreased electron–hole recombination in the doped lattice. The Gd‐doped TiO₂ nanobeads (NBs) show enhanced ability for ROS monitored via ^?OH radical photogeneration, in comparison with undoped TiO₂ nanobeads and TiO₂ P25, for Gd‐doping up to 10%. Cellular internalization and biocompatibility of TiO₂@x Gd NBs are tested in vitro on MG‐63 human osteosarcoma cells, showing full biocompatibility. After photoactivation of the particles, anticancer trace by means of ROS photogeneration is observed just after 3 min irradiation.     

An analysis of the parametrization by data driven local coordinates for multivariable linear systems

In this paper, we study a novel parametrization for state-space systems, namely data driven local coordinates (DDLC) which have recently been introduced and applied. Even though DDLC has meanwhile become the default parametrization used in the system identification toolbox of the software package MATLAB, an analysis of properties of DDLC, which are relevant to identification, has not been performed up to now. In this paper, we provide insights into the geometry and topology of the DDLC construction and show a number of results which are important for actual identification such as maximum likelihood-type estimation.     

Navigation Aided Image Processing in UAV Surveillance: Preliminary Results and Design of an Airborne Experimental System

This paper describes an airborne reconfigurable measurement system being developed at Swedish Defence Research Agency (FOI), Sensor Technology, Sweden. An image processing oriented sensor management architecture for UAV (unmanned aerial vehicles) IR/EO-surveillance is presented. Some preliminary results of navigation aided image processing in UAV applications are demonstrated, such as SLAM (simultaneous localization and mapping), structure from motion and geolocation, target tracking, and detection of moving objects. The design goal of the measurement system is to emulate a UAV-mounted sensor gimbal using a stand-alone system. The minimal configuration of the system consists of a gyro-stabilized gimbal with IR and CCD sensors and an integrated high-performance navigation system. The navigation system combines dGPS real-time kinematics (RTK) data with data from an inertial measurement unit (IMU) mounted with reference to the optical sensors. The gimbal is to be used as an experimental georeferenced sensor platform, using a choice of carriers, to produce military relevant image sequences for studies of image processing and sensor control on moving surveillance and reconnaissance platforms. Furthermore, a high resolution synthetic environment, developed for sensor simulations in the visual and infrared wavelengths, is presented. © 2004 Wiley Periodicals, Inc.