Detection filter design for LPV systems—a geometric approach

This paper investigates fault detection and isolation of linear parameter-varying (LPV) systems by using parameter-varying (C,A)-invariant subspace and parameter-varying unobservability subspaces. The so called “detection filter” approach, formulated as the fundamental problem of residual generation (FPRG) for linear time-invariant (LTI) systems, is extended for a class of LPV systems. The question of stability is addressed in the terms of Lyapunov quadratic stability by using linear matrix inequalities. The results are applied to the model of a generic small commercial aircraft.     

Decentralized overlapping control of a formation of unmanned aerial vehicles

Decentralized overlapping feedback laws are designed for a formation of unmanned aerial vehicles. The dynamic model of the formation with an information structure constraint in which each vehicle, except the leader, only detects the vehicle directly in front of it, is treated as an interconnected system with overlapping subsystems. Using the mathematical framework of the inclusion principle, the interconnected system is expanded into a higher dimensional space in which the subsystems appear to be disjoint. Then, at each subsystem, a static state feedback controller is designed to robustly stabilize the perturbed nominal dynamics of the subsystem. The design procedure is based on the application of convex optimization tools involving linear matrix inequalities. As a final step, the decentralized controllers are contracted back to the original interconnected system for implementation.     

Distributed intelligence for multi-camera visual surveillance

Latest advances in hardware technology and state of the art of computer vision and artificial intelligence research can be employed to develop autonomous and distributed monitoring systems. The paper proposes a multi-agent architecture for the understanding of scene dynamics merging the information streamed by multiple cameras. A typical application would be the monitoring of a secure site, or any visual surveillance application deploying a network of cameras. Modular software (the agents) within such architecture controls the different components of the system and incrementally builds a model of the scene by merging the information gathered over extended periods of time. The role of distributed artificial intelligence composed of separate and autonomous modules is justified by the need for scalable designs capable of co-operating to infer an optimal interpretation of the scene. Decentralizing intelligence means creating more robust and reliable sources of interpretation, but also allows easy maintenance and updating of the system. Results are presented to support the choice of a distributed architecture, and to prove that scene interpretation can be incrementally and efficiently built by modular software.     

Relations between compliances of inhomogeneities having the same shape but different elastic constants

The contributions of inhomogeneities having the same shape but different elastic constants, to the overall elastic properties are interrelated. The utility of these relations lies, in particular, in the possibility to extend available results for pores or rigid inclusions to inhomogeneities of arbitrary elastic properties. The relations are exact for ellipsoids and approximate for non-ellipsoidal shapes. The constructed approximation also constitutes approximate connection between the first Eshelby’s problem (the eigenstrain problem) and the second one (the inhomogeneity problem), for non-ellipsoidal shapes. It also yields approximate formulas for the contribution of a non-ellipsoidal inhomogeneity to effective elastic properties.     

Extracting the optimal dimensionality for local tensor discriminant analysis

Supervised dimensionality reduction with tensor representation has attracted great interest in recent years. It has been successfully applied to problems with tensor data, such as image and video recognition tasks. However, in the tensor-based methods, how to select the suitable dimensions is a very important problem. Since the number of possible dimension combinations exponentially increases with respect to the order of tensor, manually selecting the suitable dimensions becomes an impossible task in the case of high-order tensor. In this paper, we aim at solving this important problem and propose an algorithm to extract the optimal dimensionality for local tensor discriminant analysis. Experimental results on a toy example and real-world data validate the effectiveness of the proposed method.     

Wet shells and dry tales: the evolutionary ‘Just-So’ stories behind the structure–function of biominerals

The ability of evolution to shape organic form involves the interactions of multiple systems of constraints, including fabrication, phylogeny and function. The tendency to place function above everything else has characterized some of the historical biological literature as a series of ‘Just-So’ stories that provided untested explanations for individual features of an organism. A similar tendency occurs in biomaterials research, where features for which a mechanical function can be postulated are treated as an adaptation. Moreover, functional adaptation of an entire structure is often discussed based on the local characterization of specimens kept in conditions that are far from those in which they evolved. In this work, environmental- and frequency-dependent mechanical characterization of the shells of two cephalopods, Nautilus pompilius and Argonauta argo, is used to demonstrate the importance of multi-scale environmentally controlled characterization of biogenic materials. We uncover two mechanistically independent strategies to achieve deformable, stiff, strong and tough highly mineralized structures. These results are then used to critique interpretations of adaptation in the literature. By integrating the hierarchical nature of biological structures and the environment in which they exist, biomaterials testing can be a powerful tool for generating functional hypotheses that should be informed by how these structures are fabricated and their evolutionary history.     

Progression and Dissemination of Pulmonary Mycobacterium Avium Infection in a Susceptible Immunocompetent Mouse Model

Pulmonary infections caused by the group of nontuberculosis mycobacteria (NTM), Mycobacterium avium complex (MAC), are a growing public health concern with incidence and mortality steadily increasing globally. Granulomatous inflammation is the hallmark of MAC lung infection, yet reliable correlates of disease progression, susceptibility, and resolution are poorly defined. Unlike widely used inbred mouse strains, mice that carry the mutant allele at the genetic locus sst1 develop human-like pulmonary tuberculosis featuring well-organized caseating granulomas. We characterized pulmonary temporospatial outcomes of intranasal and left intrabronchial M. avium spp. hominissuis (M.av) induced pneumonia in B6.Sst1S mice, which carries the sst1 mutant allele. We utilized traditional semi-quantitative histomorphological evaluation, in combination with fluorescent multiplex immunohistochemistry (fmIHC), whole slide imaging, and quantitative digital image analysis. Followingintrabronchiolar infection with the laboratory M.av strain 101, the B6.Sst1S pulmonary lesions progressed 12–16 weeks post infection (wpi), with plateauing and/or resolving disease by 21 wpi. Caseating granulomas were not observed during the study. Disease progression from 12–16 wpi was associated with increased acid-fast bacilli, area of secondary granulomatous pneumonia lesions, and Arg1+ and double positive iNOS+/Arg1+ macrophages. Compared to B6 WT, at 16 wpi, B6.Sst1S lungs exhibited an increased area of acid-fast bacilli, larger secondary lesions with greater Arg1+ and double positive iNOS+/Arg1+ macrophages, and reduced T cell density. This morphomolecular analysis of histologic correlates of disease progression in B6.Sst1S could serve as a platform for assessment of medical countermeasures against NTM infection.     

Europe should stop taxing innovation

After nearly 48 years of failure to create the EU patent, language issues and the design of a centralised patent-litigation court still dominate headlines. But behind these issues there are high financial stakes and control power to play for. The recent EU Council deal on an ‘enhanced’ European patent system does not solve the above problems, and has not eliminated lingering governance issues. The risk for Europe is that a final patent agreement might be reached that does not cure the system of its major ills, and thus does not bring about any significant improvement for those that need it most: entrepreneurs and innovative companies starting out on the innovation process. The creation of an effective single EU patent requires (i) English-only post-grant translation, (ii) the end of nationally granted patents, (iii) phasing-out of the current ‘European patent’, (iv) lower fees for young innovative companies, and (v) a radical shake-up of the governance of the European Patent Office.     

Cross-Property Connection between Work-Hardening Coefficient and Electrical Resistivity of Stainless Steel During Plastic Deformation

Present paper focuses on the cross-property connection between the changes in electrical resistivity and work hardening coefficient in the process of plastic deformation. The possibility of the cross-property connection is provided by the fact that both quantities are governed by the same parameter - growth of the dislocation density caused by the applied stresses. Experimental measurements on stainless steel 304 are in a good agreement with analytical estimates.     

Synthesis of crystalline metal nitride and metal carbide nanostructures by sol–gel chemistry

Attention toward nanosized metal nitrides and carbides is rapidly increasing thanks to their chemical characteristics that make them as valid and sustainable alternatives to noble metals in catalysis and to air-sensitive metals or oxides for applications under harsh conditions. They are mostly used as bulk phase or micron sized powders, due to an intrinsic difficulty to synthesize them as nanoparticles in a systematic and scalable fashion. However, nanosized metal nitrides and carbides could exhibit improved performances, e.g. in catalysis due to a higher surface area, and can be shaped more easily than corresponding larger grains for further specific applications. Recently, sol–gel chemistry has closed this gap and now enables the simple, cheap, and sustainable production of metal nitride and carbide nanoparticles.In the present review we give an overview on recent sol–gel based pathways for the synthesis of metal nitride and carbide nanoparticles, believing that a better knowledge of the potentialities of these still hardly touched materials stimulates research interest and applications.