Analysis and correction of the helium speech effect by autoregressive signal processing

SIGLELD:D48902/84 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

A historical view on the maximum entropy

How to find unknown distributions is questioned in many pieces of research. There are several ways to figure them out, but the main question is which acts more reasonably than others. In this paper, we focus on the maximum entropy principle as a suitable method of discovering the unknown distribution, which recommends some prior information based on the available data set. We explain its features by reviewing some papers. Furthermore, we recommend some articles to study around the generalized maximum entropy issue, which is more suitable when autocorrelation data exists. Then, we list the beneficial features of the maximum entropy as a result. Finally, some disadvantages of entropy are expressed to have a complete look at the maximum entropy principle, and we list its drawbacks as the final step

A communications system perspective for dynamic mode atomic force microscopy, with applications to high-density storage and nanoimaging

In recent times, the atomic force microscope (AFM) has been used in various fields like biology, chemistry, physics and medicine for obtaining atomic level images. The AFM is a high-resolution microscope which can provide the resolution on the order of fractions of a nanometer. It has applications in the field of material characterization, probe based data storage, nano-imaging etc. The prevalent mode of using the AFM is the static mode where the cantilever is in continuous contact with the sample. This is harsh on the probe and the sample. The problem of probe and sample wear can be partly addressed by using the dynamic mode operation with the high quality factor cantilevers. In the dynamic mode operation, the cantilever is forced sinusoidally using a dither piezo. The oscillating cantilever gently taps the sample which reduces the probe-sample wear. In this dissertation, we demonstrate that viewing the dynamic mode operation from a communication systems perspective can yield huge gains in nano-interrogation speed and fidelity. In the first part of the dissertation, we have considered a data storage system that operates by encoding information as topographic profiles on a polymer medium. A cantilever probe with a sharp tip (few nm radius) is used to create and sense the presence of topographic profiles, resulting in a density of few Tb per square inch. The usage of the static mode is harsh on the probe and the media. In this work, the high quality factor dynamic mode operation, which alleviates the probe-media wear, is analyzed. The read operation is modeled as a communication channel which incorporates system memory due to inter-symbol interference and the cantilever state. We demonstrate an appropriate level of abstraction of this complex nanoscale system that obviates the need for an involved physical model. Next, a solution to the maximum likelihood sequence detection problem based on the Viterbi algorithm is devised. Experimental and simulation results demonstrate that the performance of this detector is several orders of magnitude better than the performance of other existing schemes. In the second part of the dissertation, we have considered another interesting application of the dynamic mode AFM in the field of nano-imaging. Nano-imaging has played a vital role in biology, chemistry and physics as it enables interrogation of material with sub-nanometer resolution. However, current nano-imaging techniques are too slow to be useful in the high speed applications of interest such as studying the evolution of certain biological processes over time that involve very small time scales. In this work, we present a high speed one-bit imaging technique using the dynamic mode AFM with a high quality factor cantilever. We propose a communication channel model for the cantilever based nano-imaging system. Next, we devise an imaging algorithm that incorporates a learned prior from the previous scan line while detecting the features on the current scan line. Experimental results demonstrate that our proposed algorithm provides significantly better image resolution compared to current nano-imaging techniques at high scanning speed. While modeling the probe-based data storage system and the cantilever based nano-imaging system, it has been observed that the channel models exhibit the behavior similar to intersymbol-interference (ISI) channel with data dependent time-correlated noise. The Viterbi algorithm can be adapted for performing maximum likelihood sequence detection in such channels. However, the problem of finding an analytical upper bound on the bit error rate of the Viterbi detector in this case has not been fully investigated. In the third part of the dissertation, we have considered a subset of the class of ISI channels with data dependent Gauss-Markov noise. We derive an upper bound on the pairwise error probability (PEP) between the transmitted bit sequence and the decoded bit sequence that can be expressed as a product of functions depending on current and previous states in the (incorrect) decoded sequence and the (correct) transmitted sequence. In general, the PEP is asymmetric. The average BER over all possible bit sequences is then determined using a pairwise state diagram. Simulations results demonstrate that analytic bound on BER is tight in high SNR regime

Statistical methods for scale-invariant and multifractal stochastic processes.

This thesis focuses on stochastic modeling, and statistical methods, in finance and in climate science. Two financial markets, short-term interest rates and electricity prices, are analyzed. We find that the evidence of mean reversion in short-term interest rates is week, while the “log-returns” of electricity prices have significant anti-correlations. More importantly, empirical analyses confirm the multifractal nature of these financial markets, and we propose multifractal models that incorporate the specific conditional mean reversion and level dependence. A second topic in the thesis is the analysis of regional (5◦ × 5◦ and 2◦ × 2◦ latitude- longitude) globally gridded surface temperature series for the time period 1900-2014, with respect to a linear trend and long-range dependence. We find statistically significant trends in most regions. However, we also demonstrate that the existence of a second scaling regime on decadal time scales will have an impact on trend detection. The last main result is an approximative maximum likelihood (ML) method for the log- normal multifractal random walk. It is shown that the ML method has applications beyond parameter estimation, and can for instance be used to compute various risk measures in financial markets

The Use of EEG Signals For Biometric Person Recognition

This work is devoted to investigating EEG-based biometric recognition systems. One potential advantage of using EEG signals for person recognition is the difficulty in generating artificial signals with biometric characteristics, thus making the spoofing of EEG-based biometric systems a challenging task. However, more works needs to be done to overcome certain drawbacks that currently prevent the adoption of EEG biometrics in real-life scenarios: 1) usually large number of employed sensors, 2) still relatively low recognition rates (compared with some other biometric modalities), 3) the template ageing effect. The existing shortcomings of EEG biometrics and their possible solutions are addressed from three main perspectives in the thesis: pre-processing, feature extraction and pattern classification. In pre-processing, task (stimuli) sensitivity and noise removal are investigated and discussed in separated chapters. For feature extraction, four novel features are proposed; for pattern classification, a new quality filtering method, and a novel instance-based learning algorithm are described in respective chapters. A self-collected database (Mobile Sensor Database) is employed to investigate some important biometric specified effects (e.g. the template ageing effect; using low-cost sensor for recognition). In the research for pre-processing, a training data accumulation scheme is developed, which improves the recognition performance by combining the data of different mental tasks for training; a new wavelet-based de-noising method is developed, its effectiveness in person identification is found to be considerable. Two novel features based on Empirical Mode Decomposition and Hilbert Transform are developed, which provided the best biometric performance amongst all the newly proposed features and other state-of-the-art features reported in the thesis; the other two newly developed wavelet-based features, while having slightly lower recognition accuracies, were computationally more efficient. The quality filtering algorithm is designed to employ the most informative EEG signal segments: experimental results indicate using a small subset of the available data for feature training could receive reasonable improvement in identification rate. The proposed instance-based template reconstruction learning algorithm has shown significant effectiveness when tested using both the publicly available and self-collected databases

Coordinated control and network integration of wave power farms

Significant progress has been made in the development of wave energy converters (WECs) during recent years, with prototypes and farms of WECs being installed in different parts of the world. With increasing sizes of individual WECs and farms, it becomes necessary to consider the impacts of connecting these to the electricity network and to investigate means by which these impacts may be mitigated. The time-varying and the unpredictable nature of the power generated from wave power farms supplemented by the weak networks to which most of these farms will be connected to, makes the question of integrating a large quantity of wave power to the network more challenging. The work reported here focuses on the fluctuations in the rms-voltage introduced by the connection of wave power farms. Two means to reduce these rms-voltage fluctuations are proposed. In the first method, the physical placement of the WECs within a farm is selected prior to the development of the farm to reduce the fluctuations in the net real power generated. It is shown that spacing the WECs or the line of WECs within a farm at a distance greater than half the peak wavelength and orienting the farm at 90◦ to the dominant wave direction produces a much smoother power output. The appropriateness of the following conclusions has been tested and proven for a wave power farm developed off the Outer Hebrides, using real wave field and network data. The second method uses intelligent reactive power control algorithms, which have already been tested with wind and hydro power systems, to reduce voltage fluctuations. The application of these intelligent control methods to a 6 MW wave power farm connected to a realistic UK distribution network verified that these approaches improve the voltage profile of the distribution network and help the connection of larger farms to the network, without any need for network management or upgrades. Using these control methods ensured the connection of the wave power farm to the network for longer than when the conventional control methods are used, which is economically beneficial for the wave power farm developer. The use of such intelligent voltage - reactive power (volt/VAr) control methods with the wave power farm significantly affects the operation of other onshore voltage control devices found prior to the connection of the farm. Thus, it is essential that the control of the farm and the onshore control devices are coordinated. A voltage estimation method, which uses a one-step-ahead demand predictor, is used to sense the voltage downstream of the substation at the bus where the farm is connected. The estimator uses only measurements made at the substation and historical demand data. The estimation method is applied to identify the operating mode of a wave power farm connected to a generic 11 kV distribution network in the UK from the upstream substation. The developed method introduced an additional level of control and can be used at rural substations to optimise the operation of the network, without any new addition of measuring devices or communication means