An algebraic approach to the estimation of the order of FIR filters from complete and partial magnitude and phase specifications

Published versio

A new image thresholding method based on Gaussian mixture model

2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Energy flow polynomials: A complete linear basis for jet substructure

We introduce the energy flow polynomials: a complete set of jet substructure observables which form a discrete linear basis for all infrared- and collinear-safe observables. Energy flow polynomials are multiparticle energy correlators with specific angular structures that are a direct consequence of infrared and collinear safety. We establish a powerful graph-theoretic representation of the energy flow polynomials which allows us to design efficient algorithms for their computation. Many common jet observables are exact linear combinations of energy flow polynomials, and we demonstrate the linear spanning nature of the energy flow basis by performing regression for several common jet observables. Using linear classification with energy flow polynomials, we achieve excellent performance on three representative jet tagging problems: quark/gluon discrimination, boosted W tagging, and boosted top tagging. The energy flow basis provides a systematic framework for complete investigations of jet substructure using linear methods.Comment: 41+15 pages, 13 figures, 5 tables; v2: updated to match JHEP versio

Hybrid Neural Network Predictive-Wavelet Image Compression System

This paper considers a novel image compression technique called hybrid predictive wavelet coding. The new proposed technique combines the properties of predictive coding and discrete wavelet coding. In contrast to JPEG2000, the image data values are pre-processed using predictive coding to remove interpixel redundancy. The error values, which are the difference between the original and the predicted values, are discrete wavelet coding transformed. In this case, a nonlinear neural network predictor is utilised in the predictive coding system. The simulation results indicated that the proposed technique can achieve good compressed images at high decomposition levels in comparison to JPEG2000

Pole Placement and Reduced-Order Modelling for Time-Delayed Systems Using Galerkin Approximations

The dynamics of time-delayed systems (TDS) are governed by delay differential equa- tions (DDEs), which are infinite dimensional and pose computational challenges. The Galerkin approximation method is one of several techniques to obtain the spectrum of DDEs for stability and stabilization studies. In the literature, Galerkin approximations for DDEs have primarily dealt with second-order TDS (second-order Galerkin method), and the for- mulations have resulted in spurious roots, i.e., roots that are not among the characteristic roots of the DDE. Although these spurious roots do not affect stability studies, they never- theless add to the complexity and computation time for control and reduced-order modelling studies of DDEs. A refined mathematical model, called the first-order Galerkin method, is proposed to avoid spurious roots, and the subtle differences between the two formulations (second-order and first-order Galerkin methods) are highlighted with examples. For embedding the boundary conditions in the first-order Galerkin method, a new pseudoinverse-based technique is developed. This method not only gives the exact location of the rightmost root but also, on average, has a higher number of converged roots when compared to the existing pseudospectral differencing method. The proposed method is combined with an optimization framework to develop a pole-placement technique for DDEs to design closed-loop feedback gains that stabilize TDS. A rotary inverted pendulum system apparatus with inherent sensing delays as well as deliberately introduced time delays is used to experimentally validate the Galerkin approximation-based optimization framework for the pole placement of DDEs. Optimization-based techniques cannot always place the rightmost root at the desired location; also, one has no control over the placement of the next set of rightmost roots. However, one has the precise location of the rightmost root. To overcome this, a pole- placement technique for second-order TDS is proposed, which combines the strengths of the method of receptances and an optimization-based strategy. When the method of receptances provides an unsatisfactory solution, particle swarm optimization is used to improve the location of the rightmost pole. The proposed approach is demonstrated with numerical studies and is validated experimentally using a 3D hovercraft apparatus. The Galerkin approximation method contains both converged and unconverged roots of the DDE. By using only the information about the converged roots and applying the eigenvalue decomposition, one obtains an r-dimensional reduced-order model (ROM) of the DDE. To analyze the dynamics of DDEs, we first choose an appropriate value for r; we then select the minimum value of the order of the Galerkin approximation method system at which at least r roots converge. By judiciously selecting r, solutions of the ROM and the original DDE are found to match closely. Finally, an r-dimensional ROM of a 3D hovercraft apparatus in the presence of delay is validated experimentally

Caries detection in panoramic dental x-ray images

The detection of dentalcaries,in a preliminar stage are of most importance. There is a long history of dental caries. Over a million years ago, hominids such as Australopithecus suffered from cavities. Archaeological evidence shows that tooth decay is an ancient disease dating far into prehistory. Skulls dating from a million years ago through the Neolithic period show signs of caries. The increase of caries during the Neolithic period may be attributed to the increase of plant foods containing carbohydrates. The beginning of rice cultivation in South Asia is also believed to have caused an increase in caries. DentalCaries,alsoknownasdentaldecayortoothdecay,isdefinedasadisease of the hard tissues of the teeth caused by the action of microorganisms, found in plaque,onfermentablecarbohydrates(principallysugars). Attheindividuallevel, dental caries is a preventable disease. Given its dynamic nature the dental caries disease, once established, can be treated or reversed prior to significant cavitation taking place. There three types of dental caries [59], the first type is the Enamel Caries, that is preceded by the formation of a microbial dental plaque. Secondly the Dentinal Caries which begins with the natural spread of the process along the natural spread of great numbers of the dentinal tubules. Thirdly the Pulpal Caries that corresponds to the root caries or root surface caries. Primary diagnosis involves inspection of all visible tooth surfaces using a good light source, dental mirror and explorer. Dental radiographs (X-rays) may show dental caries before it is otherwise visible, particularly caries between the teeth. Large dental caries are often apparent to the naked eye, but smaller lesions can be difficult to identify. Visual and tactile inspection along with radiographs are employed frequently among dentists. At times, caries may be difficult to detect. Bacteriacanpenetratetheenameltoreachdentin,butthentheoutersurfacemaybe at first site intact. These caries, sometimes referred to as "hidden caries", in the preliminary stage X-ray are the only way to detect them, despite of the visual examinationofthetoothshowntheenamelintactorminimallyperforated. Without X-rays wouldn’t be possible to detect these problems until they had become severe and caused serious damage. [...

Poisson relationships with applications to digital signal processing

Imperial Users onl

Efficient physics signal selectors for the first trigger level of the Belle II experiment based on machine learning

A neural network based z-vertex trigger is developed for the first level trigger of the upgraded flavor physics experiment Belle II at the high luminosity B factory SuperKEKB in Tsukuba, Japan. Using the hit and drift time information from the central drift chamber, a pool of expert neural networks estimates the 3D track parameters of the single tracks found by a 2D Hough finder. The neural networks are already implemented on parallel FPGA hardware for real time data processing and running pipelined in the online first level trigger of Belle II. Due to the anticipated high luminosity of up to 8 × 10³⁵ cm⁻²s⁻¹, Belle II will have to face severe levels of background tracks with vertices displaced along the beamline. The neural z-vertex algorithm presented in this thesis allows to reject displaced background tracks such that the requirements of the standard track trigger can be strongly relaxed. Especially for physics decay channels with a low track multiplicity in the final states, like τ pair production, or initial state radiation events with reduced center of mass energies, the trigger efficiencies can be significantly increased. As an upgrade of the present 2D Hough finder in the neural network preprocessing, a model independent 3D track finder is developed that uses the additional stereo hit information of the drift chamber. Thus, the trigger efficiencies improve for tracks in the phase space of low transverse momenta and shallow polar angles. Since the cross sections of the physics signal events typically increase towards shallow polar angles, this enlarged acceptance of the track trigger provides a substantial gain in the signal efficiencies. By using an adapted pool of expert networks, the enlarged phase space provided by the 3D finder can be efficiently covered. Studies on simulated MC background, on simulated initial state radiation events, and on recorded data from early Belle II runs demonstrate the high performance of the novel trigger algorithms. With the 3D finder an increase of the track finding rate of about 50 % is confirmed for signal tracks, while displaced background tracks are actively suppressed prior to the neural network. Based on z-vertex cuts on the tracks processed by the neural networks, a two track event efficiency of more than 99 % can be achieved with a purity of around 80 %