Predicting blur visual discomfort for natural scenes by the loss of positional information

The perception of blur due to accommodation failures, insufficient optical correction or imperfect image reproduction is a common source of visual discomfort, usually attributed to an anomalous and annoying distribution of the image spectrum in the spatial frequency domain. In the present paper, this discomfort is related to a loss of the localization accuracy of the observed patterns. It is assumed, as a starting perceptual principle, that the visual system is optimally adapted to pattern localization in a natural environment. Thus, since the best possible accuracy of the image patterns localization is indicated by the positional Fisher Information, it is argued that blur discomfort is strictly related to a loss of this information. Following this concept, a receptive field functional model is adopted to predict the visual discomfort. It is a complex-valued operator, orientation-selective both in the space domain and in the spatial frequency domain. Starting from the case of Gaussian blur, the analysis is extended to a generic type of blur by applying a positional Fisher Information equivalence criterion. Out-of-focus blur and astigmatic blur are presented as significant examples. The validity of the proposed model is verified by comparing its predictions with subjective ratings. The model fits linearly with the experiments reported in independent databases, based on different protocols and settings

A Wiener-Laguerre model of VIV forces given recent cylinder velocities

Slender structures immersed in a cross flow can experience vibrations induced by vortex shedding (VIV), which cause fatigue damage and other problems. VIV models in engineering use today tend to operate in the frequency domain. A time domain model would allow to capture the chaotic nature of VIV and to model interactions with other loads and non-linearities. Such a model was developed in the present work: for each cross section, recent velocity history is compressed using Laguerre polynomials. The compressed information is used to enter an interpolation function to predict the instantaneous force, allowing to step the dynamic analysis. An offshore riser was modeled in this way: Some analyses provided an unusually fine level of realism, while in other analyses, the riser fell into an unphysical pattern of vibration. It is concluded that the concept is promissing, yet that more work is needed to understand orbit stability and related issues, in order to further progress towards an engineering tool

Design of Waveform Set for Multiuser Ultra-Wideband Communications

The thesis investigates the design of analogue waveform sets for multiuser and UWB communications using suitably chosen Hermite-Rodriguez basis functions. The non-linear non-convex optimization problem with time and frequency domains constraints has been transformed into suitable forms and then solved using a standard optimization package. The proposed approach is more flexible and efficient than existing approaches in the literature. Numerical results show that orthogonal waveform sets with high spectral efficiency can be produced

Design of Neural Network Filters

Emnet for n rv rende licentiatafhandling er design af neurale netv rks ltre. Filtre baseret pa neurale netv rk kan ses som udvidelser af det klassiske line re adaptive l-ter rettet mod modellering af uline re sammenh nge. Hovedv gten l gges pa en neural netv rks implementering af den ikke-rekursive, uline re adaptive model med additiv st j. Formalet er at klarl gge en r kke faser forbundet med design af neural netv rks arkitekturer med henblik pa at udf re forskellige \black-box " modellerings opgaver sa som: System identi kation, invers modellering og pr diktion af tidsserier. De v senligste bidrag omfatter: Formulering af en neural netv rks baseret kanonisk lter repr sentation, der danner baggrund for udvikling af et arkitektur klassi kationssystem. I hovedsagen drejer det sig om en skelnen mellem globale og lokale modeller. Dette leder til at en r kke kendte neurale netv rks arkitekturer kan klassi ceres, og yderligere abnes der mulighed for udvikling af helt nye strukturer. I denne sammenh ng ndes en gennemgang af en r kke velkendte arkitekturer. I s rdeleshed l gges der v gt pa behandlingen af multi-lags perceptron neural netv rket

Multi-layer functional approximation of non-linear unsteady aerodynamic response

Non-linear unsteady aerodynamic effects present major modelling difficulties in the analysis of aeroelastic response and in the subsequent design of appropriate controllers. As the direct use of the basic fluid mechanic equations is still not practical for aeroelastic applications, approximate models of the non-linear unsteady aerodynamic response are required. A rigorous mathematical framework, that can account for the complex non-linearities and time-history effects of the unsteady aerodynamic response, is provided by the use of functional representations. A recent development, based on functional approximation theory, has provided a new functional form; namely, multi-layer functionals. Moreover, the multi-layer functional representation for time-invariant, infinite memory systems is shown to be realisable in terms of temporal neural networks. In this work, a multi-layer functional representation of non-linear motion-induced unsteady aerodynamic response is presented. A discrete-time, finite memory temporal neural network, in the form of a finite impulse response (FIR) neural network, is used as a practical realisation of a multi-layer functional. This model form permits the identification of parametric input-output models of the non-linear motion-induced unsteady aerodynamic response. Identification of an appropriate FIR neural network model is facilitated by means of a supervised training process using multiple sets of motion-induced unsteady aerodynamic response. The training process is based on a conventional genetic algorithm to optimise the FIR neural network architecture, and is combined with a simplification of the simulated annealing algorithm to update weight and bias values

Orbital Angular Momentum Waves: Generation, Detection and Emerging Applications

Orbital angular momentum (OAM) has aroused a widespread interest in many fields, especially in telecommunications due to its potential for unleashing new capacity in the severely congested spectrum of commercial communication systems. Beams carrying OAM have a helical phase front and a field strength with a singularity along the axial center, which can be used for information transmission, imaging and particle manipulation. The number of orthogonal OAM modes in a single beam is theoretically infinite and each mode is an element of a complete orthogonal basis that can be employed for multiplexing different signals, thus greatly improving the spectrum efficiency. In this paper, we comprehensively summarize and compare the methods for generation and detection of optical OAM, radio OAM and acoustic OAM. Then, we represent the applications and technical challenges of OAM in communications, including free-space optical communications, optical fiber communications, radio communications and acoustic communications. To complete our survey, we also discuss the state of art of particle manipulation and target imaging with OAM beams