On the dual structure of the auditory brainstem response in dogs

Objective: To use the over-complete discrete wavelet transform (OCDWT) to further examine the dual structure of auditory brainstem response (ABR) in the dog. Methods: ABR waveforms recorded from 20 adult dogs at supra-threshold (90 and 70 dBnHL) and threshold (0-15 dBSL) levels were decomposed using a six level OCDWT and reconstructed at individual scales (frequency ranges) A6 (0-391 Hz), D6 (391-781 Hz), and D5 (781-1563 Hz). Results: At supra-threshold stimulus levels, the A6 scale (0-391 Hz) showed a large amplitude waveform with its prominent wave corresponding in latency with ABR waves II/III; the D6 scale (391-781 Hz) showed a small amplitude waveform with its first four waves corresponding in latency to ABR waves I, II/III, V, and VI; and the D5 scale (781-1563 Hz) showed a large amplitude, multiple peaked waveform with its first six waves corresponding in latency to ABR waves I, II, III, IV, V, and VI. At threshold stimulus levels (0-15 dBSL), the A6 scale (0-391 Hz) continued to show a relatively large amplitude waveform, but both the D6 and D5 scales (391781 and 781-1563 Hz, respectively) now showed relatively small amplitude waveforms. Conclusions: A dual structure exists within the ABR of the dog, but its relative structure changes with stimulus level. Significance: The ABR in the dog differs from that in the human both in the relative contributions made by its different frequency components, and the way these components change with stimulus level. (c) 2006 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Vanishing of Ext and Tor over complete intersections

Let (R,m) be a local complete intersection, that is, a local ring whose m-adic completion is the quotient of a complete regular local ring by a regular sequence. Let M and N be finitely generated R-modules. This dissertation concerns the vanishing of Tor(M, N) and Ext(M, N). In this context, M satisfies Serre's condition (S_{n}) if and only if M is an nth syzygy. The complexity of M is the least nonnegative integer r such that the nth Betti number of M is bounded by a polynomial of degree r-1 for all sufficiently large n. We use this notion of Serre's condition and complexity to study the vanishing of Tor_{i}(M, N). In particular, building on results of C. Huneke, D. Jorgensen and R. Wiegand [32], and H. Dao [21], we obtain new results showing that good depth properties on the R-modules M, N and MtensorN force the vanishing of Tor_{i}(M, N) for all i>0. We give examples showing that our results are sharp. We also show that if R is a one-dimensional domain and M and MtensorHom(M,R) are torsion-free, then M is free if and only if M has complexity at most one. If R is a hypersurface and Ext^{i}(M, N) has finite length for all i>>0, then the Herbrand difference [18] is defined as length(Ext^{2n}(M, N))-(Ext^{2n-1}(M, N)) for some (equivalently, every) sufficiently large integer n. In joint work with Hailong Dao, we generalize and study the Herbrand difference. Using the Grothendieck group of finitely generated R-modules, we also examined the number of consecutive vanishing of Ext^{i}(M, N) needed to ensure that Ext^{i}(M, N) = 0 for all i>>0. Our results recover and improve on most of the known bounds in the literature, especially when R has dimension two.

Size and temperature dependent stability and phase transformation in single-crystal zirconium nanowire

A novel size dependent FCC (face-centered-cubic) -> HCP (hexagonally-closed-pack) phase transformation and stability of an initial FCC zirconium nanowire are studied. FCC zirconium nanowires with cross-sectional dimensions < 20 are found unstable in nature, and they undergo a FCC -> HCP phase transformation, which is driven by tensile surface stress induced high internal compressive stresses. FCC nanowire with cross-sectional dimensions > 20 , in which surface stresses are not enough to drive the phase transformation, show meta-stability. In such a case, an external kinetic energy in the form of thermal heating is required to overcome the energy barrier and achieve FCC -> HCP phase transformation. The FCC-HCP transition pathway is also studied using Nudged Elastic Band (NEB) method, to further confirm the size dependent stability/metastability of Zr nanowires. We also show size dependent critical temperature, which is required for complete phase transformation of a metastable-FCC nanowire.

Síntese hidrotérmica e caracterização estrutural de titanatos nanotubulares para aplicação na captura do dióxido de carbono

Nanostructured materials have been spreading successfully over past years due its size and unusual properties, resulting in an exponential growth of research activities devoted to nanoscience and nanotechnology, which has stimulated the search for different methods to control main properties of nanomaterials and make them suitable for applications with high added value. In the late 90 s an alternative and low cost method was proposed from alkaline hydrothermal synthesis of nanotubes. Based on this context, the objective of this work was to prepare different materials based on TiO2 anatase using hydrothermal synthesis method proposed by Kasuga and submit them to an acid wash treatment, in order to check the structural behavior of final samples. They were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), adsorption/desorption of N2, thermal analysis (TG/DTA) and various spectroscopic methods such as absorption spectroscopy in the infrared (FT-IR), Raman spectroscopy and X-ray photoelectron spectroscopy (XPS). All the information of characterizations confirmed the complete conversion of anatase TiO2 in nanotubes titanates (TTNT). Observing the influence of acid washing treatment in titanates structure, it was concluded that the nanotubes are formed during heat treatment, the sample which was not subjected to this process also achieved a complete phase transformation, as showed in crystallography and morphology results, however the surface area of them practically doubled after the acid washing. By spectroscopy was performed a discussion about chemical composition of these titanates, obtaining relevant results. Finally, it was observed that the products obtained in this work are potential materials for various applications in adsorption, catalysis and photocatalysis, showing great promise in CO2 capture

Transformation into Platform - How can an organisation move from a single-sided business to platforms? A case study at Lyyti

The research studies the transformation from a single-sided offering to a multi-sided platform. The study aims to define platforms and their benefits, creating a theoretical framework by applying change management models with the platform theory, and by finding critical change points of the transformation. The empirical research was done by utilizing action research. The researcher worked as project manager in the case company, and studied the transformation project by working actively and leading the project team. The result of the project was a study of how the company would be able to manage the transformation. The results clearly showed that the company didn’t have the capabilities to finish the transformation. As a conclusion, the study showed that the critical change points that led to the project failure were, that the project was managed with insufficient change managerial efforts, which later resulted as lack of commitment to re-allocating the resources to complete the transformation. Many of the critical change points were results of combined change managerial and platform-related issues.

Knowledge-based urban development processes of an emerging knowledge city : Brisbane, Australia

In the global knowledge economy, to attract and retain knowledge-intensive industries and workers, cities produce various development strategies. Such strategising is an important development mechanism for cities to complete their transformation into knowledge cities. This paper discusses the critical connections between knowledge city foundations and integrated knowledge-based urban development strategies, and scrutinises Brisbane’s strategies in attracting and retaining investment and talent. The paper introduces a knowledge-based urban development assessment framework and uses this framework to provide a clearer understanding of Brisbane’s knowledge-based development processes and knowledge city transformation experience. The assessment framework particularly focuses on examining Brisbane’s four development processes, institutional, economic, socio-cultural and urban development, in detail. The findings reveal that although Brisbane is still in early stages of its transformation into a fully-fledged knowledge city, global orientation and achievements of Brisbane in strategising knowledge-based urban development are noteworthy.

Class-specific sparse codes for representing activities

In this paper we investigate the effectiveness of class specific sparse codes in the context of discriminative action classification. The bag-of-words representation is widely used in activity recognition to encode features, and although it yields state-of-the art performance with several feature descriptors it still suffers from large quantization errors and reduces the overall performance. Recently proposed sparse representation methods have been shown to effectively represent features as a linear combination of an over complete dictionary by minimizing the reconstruction error. In contrast to most of the sparse representation methods which focus on Sparse-Reconstruction based Classification (SRC), this paper focuses on a discriminative classification using a SVM by constructing class-specific sparse codes for motion and appearance separately. Experimental results demonstrates that separate motion and appearance specific sparse coefficients provide the most effective and discriminative representation for each class compared to a single class-specific sparse coefficients.

Sparse coding based VLAD for efficient image retrieval

Representing images and videos in the form of compact codes has emerged as an important research interest in the vision community, in the context of web scale image/video search. Recently proposed Vector of Locally Aggregated Descriptors (VLAD), has been shown to outperform the existing retrieval techniques, while giving a desired compact representation. VLAD aggregates the local features of an image in the feature space. In this paper, we propose to represent the local features extracted from an image, as sparse codes over an over-complete dictionary, which is obtained by K-SVD based dictionary training algorithm. The proposed VLAD aggregates the residuals in the space of these sparse codes, to obtain a compact representation for the image. Experiments are performed over the `Holidays' database using SIFT features. The performance of the proposed method is compared with the original VLAD. The 4% increment in the mean average precision (mAP) indicates the better retrieval performance of the proposed sparse coding based VLAD.

A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation: Application to Image Denoising

In big data image/video analytics, we encounter the problem of learning an over-complete dictionary for sparse representation from a large training dataset, which cannot be processed at once because of storage and computational constraints. To tackle the problem of dictionary learning in such scenarios, we propose an algorithm that exploits the inherent clustered structure of the training data and make use of a divide-and-conquer approach. The fundamental idea behind the algorithm is to partition the training dataset into smaller clusters, and learn local dictionaries for each cluster. Subsequently, the local dictionaries are merged to form a global dictionary. Merging is done by solving another dictionary learning problem on the atoms of the locally trained dictionaries. This algorithm is referred to as the split-and-merge algorithm. We show that the proposed algorithm is efficient in its usage of memory and computational complexity, and performs on par with the standard learning strategy, which operates on the entire data at a time. As an application, we consider the problem of image denoising. We present a comparative analysis of our algorithm with the standard learning techniques that use the entire database at a time, in terms of training and denoising performance. We observe that the split-and-merge algorithm results in a remarkable reduction of training time, without significantly affecting the denoising performance.

Comparison of Matching Pursuit Algorithm with Other Signal Processing Techniques for Computation of the Time-Frequency Power Spectrum of Brain Signals

Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. In addition, these signals have very transient structures related to spiking or sudden onset of a stimulus, which have durations not exceeding tens of milliseconds. Further, brain signals are highly nonstationary because both behavioral state and external stimuli can change on a short time scale. It is therefore essential to study brain signals using techniques that can represent both rhythmic and transient components of the signal, something not always possible using standard signal processing techniques such as short time fourier transform, multitaper method, wavelet transform, or Hilbert transform. In this review, we describe a multiscale decomposition technique based on an over-complete dictionary called matching pursuit (MP), and show that it is able to capture both a sharp stimulus-onset transient and a sustained gamma rhythm in local field potential recorded from the primary visual cortex. We compare the performance of MP with other techniques and discuss its advantages and limitations. Data and codes for generating all time-frequency power spectra are provided.

Biologically-inspired object recognition system with features from complex wavelets

In this paper, a novel cortex-inspired feed-forward hierarchical object recognition system based on complex wavelets is proposed and tested. Complex wavelets contain three key properties for object representation: shift invariance, which enables the extraction of stable local features; good directional selectivity, which simplifies the determination of image orientations; and limited redundancy, which allows for efficient signal analysis using the multi-resolution decomposition offered by complex wavelets. In this paper, we propose a complete cortex-inspired object recognition system based on complex wavelets. We find that the implementation of the HMAX model for object recognition in [1, 2] is rather over-complete and includes too much redundant information and processing. We have optimized the structure of the model to make it more efficient. Specifically, we have used the Caltech 5 standard dataset to compare with Serre's model in [2] (which employs Gabor filter bands). Results demonstrate that the complex wavelet model achieves a speed improvement of about 4 times over the Serre model and gives comparable recognition performance. © 2011 IEEE.

On sparsity and overcompleteness in image models

Computational models of visual cortex, and in particular those based on sparse coding, have enjoyed much recent attention. Despite this currency, the question of how sparse or how over-complete a sparse representation should be, has gone without principled answer. Here, we use Bayesian model-selection methods to address these questions for a sparse-coding model based on a Student-t prior. Having validated our methods on toy data, we find that natural images are indeed best modelled by extremely sparse distributions; although for the Student-t prior, the associated optimal basis size is only modestly over-complete.

Hydrodynamic and sediment fluxes through the inlets of the Ria Formosa

Tese de dout., Ciências do Mar, da Terra e do Ambiente (Ciências do Mar-Oceanografia Física), Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2011

Eficácia do herbicida amicarbazone em aplicação conjunta com a colheita de cana-de-açúcar no controle das principais plantas daninhas da cultura

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Efeito das placas estabilizadora rígida, resiliente e não oclusiva na redução dos sinais e sintomas de pacientes com desordens de origem muscular

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)