1000 resultados para Spectrum decomposition


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A theoretical method to calculate multidimensional Franck-Condon factors including Duschinsky effects is described and used to simulate the photoelectron spectrum of the anion SO. Geometry optimizations and harmonic vibrational frequency calculations have been performed on the XA(1) state of SO2 and (XB1)-B-2 state of SO2. Franck-Condon analyses and spectral simulation were carried out on the first photoelectron band of SO2. The theoretical spectra obtained by employing CCSD(T)/6-31 I+G(2d,p) values are in excellent agreement with the experiment. In addition, the equilibrium geometric parameters, r(c)(OS) = 0.1508 +/- 0.0005 nm and theta(e)(O-S-0) = 113.5 +/- 0.5 degrees, of the (XB1)-B-2 state of SO2, are derived by employing an iterative Franck-Condon analysis procedure in the spectral simulation. (c) 2005 Elsevier B.V. All rights reserved.

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B.M. Brown, M.S.P. Eastham, I. Wood: Conditions for the spectrum associated with a leaky wire to contain the interval [? ?2/4, ?), Arch. Math., 90, 6 (2008), 554-558

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The increased diversity of Internet application requirements has spurred recent interests in transport protocols with flexible transmission controls. In window-based congestion control schemes, increase rules determine how to probe available bandwidth, whereas decrease rules determine how to back off when losses due to congestion are detected. The parameterization of these control rules is done so as to ensure that the resulting protocol is TCP-friendly in terms of the relationship between throughput and loss rate. In this paper, we define a new spectrum of window-based congestion control algorithms that are TCP-friendly as well as TCP-compatible under RED. Contrary to previous memory-less controls, our algorithms utilize history information in their control rules. Our proposed algorithms have two salient features: (1) They enable a wider region of TCP-friendliness, and thus more flexibility in trading off among smoothness, aggressiveness, and responsiveness; and (2) they ensure a faster convergence to fairness under a wide range of system conditions. We demonstrate analytically and through extensive ns simulations the steady-state and transient behaviors of several instances of this new spectrum of algorithms. In particular, SIMD is one instance in which the congestion window is increased super-linearly with time since the detection of the last loss. Compared to recently proposed TCP-friendly AIMD and binomial algorithms, we demonstrate the superiority of SIMD in: (1) adapting to sudden increases in available bandwidth, while maintaining competitive smoothness and responsiveness; and (2) rapidly converging to fairness and efficiency.

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The increasing diversity of Internet application requirements has spurred recent interest in transport protocols with flexible transmission controls. In window-based congestion control schemes, increase rules determine how to probe available bandwidth, whereas decrease rules determine how to back off when losses due to congestion are detected. The control rules are parameterized so as to ensure that the resulting protocol is TCP-friendly in terms of the relationship between throughput and loss rate. This paper presents a comprehensive study of a new spectrum of window-based congestion controls, which are TCP-friendly as well as TCP-compatible under RED. Our controls utilize history information in their control rules. By doing so, they improve the transient behavior, compared to recently proposed slowly-responsive congestion controls such as general AIMD and binomial controls. Our controls can achieve better tradeoffs among smoothness, aggressiveness, and responsiveness, and they can achieve faster convergence. We demonstrate analytically and through extensive ns simulations the steady-state and transient behavior of several instances of this new spectrum.

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The analysis of energy detector systems is a well studied topic in the literature: numerous models have been derived describing the behaviour of single and multiple antenna architectures operating in a variety of radio environments. However, in many cases of interest, these models are not in a closed form and so their evaluation requires the use of numerical methods. In general, these are computationally expensive, which can cause difficulties in certain scenarios, such as in the optimisation of device parameters on low cost hardware. The problem becomes acute in situations where the signal to noise ratio is small and reliable detection is to be ensured or where the number of samples of the received signal is large. Furthermore, due to the analytic complexity of the models, further insight into the behaviour of various system parameters of interest is not readily apparent. In this thesis, an approximation based approach is taken towards the analysis of such systems. By focusing on the situations where exact analyses become complicated, and making a small number of astute simplifications to the underlying mathematical models, it is possible to derive novel, accurate and compact descriptions of system behaviour. Approximations are derived for the analysis of energy detectors with single and multiple antennae operating on additive white Gaussian noise (AWGN) and independent and identically distributed Rayleigh, Nakagami-m and Rice channels; in the multiple antenna case, approximations are derived for systems with maximal ratio combiner (MRC), equal gain combiner (EGC) and square law combiner (SLC) diversity. In each case, error bounds are derived describing the maximum error resulting from the use of the approximations. In addition, it is demonstrated that the derived approximations require fewer computations of simple functions than any of the exact models available in the literature. Consequently, the regions of applicability of the approximations directly complement the regions of applicability of the available exact models. Further novel approximations for other system parameters of interest, such as sample complexity, minimum detectable signal to noise ratio and diversity gain, are also derived. In the course of the analysis, a novel theorem describing the convergence of the chi square, noncentral chi square and gamma distributions towards the normal distribution is derived. The theorem describes a tight upper bound on the error resulting from the application of the central limit theorem to random variables of the aforementioned distributions and gives a much better description of the resulting error than existing Berry-Esseen type bounds. A second novel theorem, providing an upper bound on the maximum error resulting from the use of the central limit theorem to approximate the noncentral chi square distribution where the noncentrality parameter is a multiple of the number of degrees of freedom, is also derived.

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This thesis investigates the optimisation of Coarse-Fine (CF) spectrum sensing architectures under a distribution of SNRs for Dynamic Spectrum Access (DSA). Three different detector architectures are investigated: the Coarse-Sorting Fine Detector (CSFD), the Coarse-Deciding Fine Detector (CDFD) and the Hybrid Coarse-Fine Detector (HCFD). To date, the majority of the work on coarse-fine spectrum sensing for cognitive radio has focused on a single value for the SNR. This approach overlooks the key advantage that CF sensing has to offer, namely that high powered signals can be easily detected without extra signal processing. By considering a range of SNR values, the detector can be optimised more effectively and greater performance gains realised. This work considers the optimisation of CF spectrum sensing schemes where the security and performance are treated separately. Instead of optimising system performance at a single, constant, low SNR value, the system instead is optimised for the average operating conditions. The security is still provided such that at the low SNR values the safety specifications are met. By decoupling the security and performance, the system’s average performance increases whilst maintaining the protection of licensed users from harmful interference. The different architectures considered in this thesis are investigated in theory, simulation and physical implementation to provide a complete overview of the performance of each system. This thesis provides a method for estimating SNR distributions which is quick, accurate and relatively low cost. The CSFD is modelled and the characteristic equations are found for the CDFD scheme. The HCFD is introduced and optimisation schemes for all three architectures are proposed. Finally, using the Implementing Radio In Software (IRIS) test-bed to confirm simulation results, CF spectrum sensing is shown to be significantly quicker than naive methods, whilst still meeting the required interference probability rates and not requiring substantial receiver complexity increases.

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M66 an X-ray induced mutant of winter wheat (Triticum aestivum) cv. Guardian exhibits broad-spectrum resistance to powdery mildew (Blumeria graminis f. sp. tritici), yellow rust (Puccinia striiformis f. sp. tritici), and leaf rust (Puccinia recondita f. sp. tritici), along with partial resistance to stagnonospora nodorum blotch (caused by the necrotroph Stagonosporum nodorum) and septoria tritici blotch (caused by the hemibiotroph Mycosphaerella graminicola) compared to the parent plant ‘Guardian’. Analysis revealed that M66 exhibited no symptoms of infection following artificial inoculation with Bgt in the glasshouse after adult growth stage (GS 45). Resistance in M66 was associated with widespread leaf flecking which developed during tillering. Flecking also occurred in M66 leaves without Bgt challenge; as a result grain yields were reduced by approximately 17% compared to ‘Guardian’ in the absence of disease. At the seedling stage, M66 exhibited partial resistance. M66, along with Tht mutants (Tht 12, Tht13), also exhibit increased tolerance to environmental stresses (abiotic), such as drought and heat stress at seedling and adult growth stages, However, adult M66 exhibited increased susceptibility to the aphid Schizaphis graminum compared to ‘Guardian’. Resistance to Bgt in M66 was characterized with increased and earlier H2O2 accumulation at the site of infection which resulted in increased papilla formation in epidermal cells, compared to ‘Guardian’. Papilla formation was associated with reduced pathogen ingress and haustorium formation, indicating that the primary cause of resistance in M66 was prevention of pathogen penetration. Heat treatment at 46º C prior to challenge with Bgt also induced partial disease resistance to Blumeria graminis f. sp. tritici in ‘Guardian’ and M66 seedlings. This was characterized by a delay in primary infection, due to increased production of ROS species, such as hydrogen peroxide, ROS-scavenging enzymes and Hsp70, resulting in cross-linking of cell wall components prior to inoculation. This actively prevented the fungus from penetrating the epidermal cell wall. Proteomics analysis using 2-D gel electrophoresis identified primary and secondary disease resistance effects in M66 including detection of ROS scavenging enzymes (4, 24 hai), such as ascorbate peroxidase and a superoxidase dismutase isoform (CuZnSOD) in M66 which were absent from ‘Guardian’. Chitinase (PR protein) was also upregulated (24 hai) in M66 compared to ‘Guardian’.Monosomic and ditelosomic analysis of M66 revealed that the mutation in M66 is located on the long arm of chromosome 2B (2BL). Chromosome 2BL is known to have key genes involved in resistance to pathogens such as those causing stripe rust and powdery mildew. The TaMloB1 gene, an orthologue of the barley Mlo gene, is also located on chromosome 2BL. Sanger sequencing of part of the coding sequence revealed no deletions in the TaMloB1 gene between ‘Guardian’ and M66.

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The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings.

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Diagnosis and treatment of comorbid neuropsychiatric illness is often a secondary focus of treatment in individuals with autism spectrum disorder (ASD), given that substantial impairment may be caused by core symptoms of ASD itself. However, psychiatric comorbidities, including depressive disorders, are common and frequently result in additional functional impairment, treatment costs, and burden on caregivers. Clinicians may struggle to appropriately diagnose depression in ASD due to communication deficits, atypical presentation of depression in ASD, and lack of standardized diagnostic tools. Specific risk and resilience factors for depression in ASD across the lifespan, including level of functioning, age, family history, and coping style, have been suggested, but require further study. Treatment with medications or psychotherapy may be beneficial, though more research is required to establish guidelines for management of symptoms. This review will describe typical presentations of depression in individuals with ASD, review current information on the prevalence, assessment, and treatment of comorbid depression in individuals with ASD, and identify important research gaps.

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info:eu-repo/semantics/published