<p>In system identification, different methods are often classified as parametric or non-parametric methods. For parametric methods, a parametric model of a system is considered and the model parameters are estimated. For non-parametric methods, no parametric model is used and the result of the identification is given as a curve or a function.</p><p>One of the non-parametric methods is the impulse response analysis. This approach is dynamic simulation. This thesis introduces a new paradigm for dynamic simulation, called impulse-based simulation. This approach is based on choosing a Dirac function as input, and as a result, the output will be equal to the impulse response. However, a Dirac function cannot be realized in practice, and an approximation has to be used. As a consequence, the output will deviate from the impulse response. Once the impulse response is estimated, a parametric model can be fitted to the estimation.</p><p>This thesis aims to determine the parameters in a parametric model from an estimated impulse response. The process of investigating the models is a critical aspect of the project. Correlation analysis is used to obtain the weighting function from the estimates of covariance functions.</p><p>Later, a relation formed between the parameters and the estimates (obtained by correlation analysis) in the form of a linear system of equations. Furthermore, simulations are carried out using Monte Carlo for investigating the properties of the two step approach, which involves in correlation analysis to find h-parameters and least squares and total least squares methods to solve for the parameters of the model. In order to evaluate the complete capability of the approach to the noise variation a study of signal to noise ratio and mean, mean square error and variances of the estimated parameters is carried out.</p><p>The results of the Monte Carlo study indicate that two-step approach can give rather accurate parameter estimates. In addition, the least squares and total least squares methods give similar results.</p>

In the case of wideband (aperiodic) signals, the classical signal and noise measures used to characterize stochastic resonance do not work because their way of distinguishing signal from noise fails. In a study published earlier (L. B. Kish, 1996), a new way of measuring and identifying noise and aperiodic (wideband) signals during strongly nonlinear transfer was introduced. The method was based on using cross-spectra between the input and the output. According to the study, in the case of linear transfer and sinusoidal signals, the method gives the same results as the classical method and in the case of aperiodic signals it gives a sensible measure. In this paper we refine the theory and present detailed simulations which validate and refine the conclusions reached in that study. As neural and ion channel signal transfer are nonlinear and aperiodic, the new method has direct applicability in membrane biology and neural science (S.M. Bezrukov and I. Vodyanoy, 1997).

Conventional magnetic resonance imaging (MRI) is a noninvasive and nondestructive technique and ideally suited for applications in clinical studies. In addition to the information of human anatomy provided by MRI, magnetic resonance spectroscopy (MRS) also provided a noninvasive method to investigate the metabolites in the body and is therefore regarded as a valuable method to examine tumors and disorders especially for the brain applications. To diagnose pyogenic brain abscess from other diseases is very important for clinic treatment. Cytosolic amino acids, lactate, alanine and acetate have been recognized as potential abscess markers, especially amino acids. LCModel is a well-known and reliable post-processing tool for MRS which can provide objectively quantitative of metabolite concentration. In this thesis, we would use LCModel to analyze the spectra of amino acids and further to identify and quantitate these metabolites. And we hope that the method would benefit more precisely noninvasive diagnosis and treatment of pyogenic brain abscess. However, due to the possibly poor SNR of in vivo proton MR spectroscopy, it might be difficult to identify these metabolites. In this study, we would validate the accuracy of LCModel in the analysis of amino acids. We used GAVA-simulated resonance spectra with different level noise as our input signals and analyzed by LCModel to understand the influence of concentrations and SNR caused by different level noise. Our goal is to find an optimally reliable method to help the clinic diagnosis of abscess patients.

In the case of wideband (aperiodic) signals, the classical signal and noise measures used to characterize stochastic resonance do not work because their way of distinguishing signal from noise fails. In a study published earlier (L. B. Kish, 1996), a new way of measuring and identifying noise and aperiodic (wideband) signals during strongly nonlinear transfer was introduced. The method was based on using cross-spectra between the input and the output. According to the study, in the case of linear transfer and sinusoidal signals, the method gives the same results as the classical method and in the case of aperiodic signals it gives a sensible measure. In this paper we refine the theory and present detailed simulations which validate and refine the conclusions reached in that study. As neural and ion channel signal transfer are nonlinear and aperiodic, the new method has direct applicability in membrane biology and neural science (S.M. Bezrukov and I. Vodyanoy, 1997).

Multi-hop wireless transmission, where radios forward the message of other radios, is becoming popular both in cellular as well as sensor networks. This research is concerned with the statistical modeling of multi-hop wireless networks that do cooperative transmission (CT). CT is a physical layer wireless communication scheme in which spatially separated wireless nodes collaborate to form a virtual array antenna for the purpose of increased reliability. The dissertation has two major parts. The first part addresses a special form of CT known as the Opportunistic Large Array (OLA). The second part addresses the signal-to-noise ratio (SNR) estimation for the purpose of recruiting nodes for CT. In an OLA transmission, the nodes from one level transmit the message signal concurrently without any coordination with each other, thereby producing transmit diversity. The receiving layer of nodes receives the message signal and repeats the process using the decode-and-forward cooperative protocol. The key contribution of this research is to model the transmissions that hop from one layer of nodes to another under the effects of channel variations, carrier frequency offsets, and path loss. It has been shown for a one-dimensional network that the successive transmission process can be modeled as a quasi-stationary Markov chain in discrete time. By studying various properties of the Markov chain, the system parameters, for instance, the transmit power of relays and distance between them can be optimized. This optimization is used to improve the performance of the system in terms of maximum throughput, range extensions, and minimum delays while delivering the data to the destination node using the multi-hop wireless communication system. A major problem for network sustainability, especially in battery-assisted networks, is that the batteries are drained pretty quickly during the operation of the network. However, in dense sensor networks, this problem can be alleviated by using a subset of nodes which take part in CT, thereby saving the network energy. SNR is an important parameter in determining which nodes to participate in CT. The more distant nodes from the source having least SNR are most suitable to transmit the message to next level. However, practical real-time SNR estimators are required to do this job. Therefore, another key contribution of this research is the design of optimal SNR estimators for synchronized as well as non-synchronized receivers, which can work with both the symbol-by-symbol Rayleigh fading channels as well as slow flat fading channels in a wireless medium.

Purpose: This thesis examines the effect of listener characteristics (i.e., cognition and vocabulary) and language-based factors (i.e., lexical frequency and phonological similarity) on speech recognition accuracy in adverse listening conditions. Method: Fifty listeners (40 females and 10 males) aged 18-33 years and with normal hearing (puretone thresholds ≤ 20 dB HL, 0.25-8 kHz) participated. They completed a speech perception experiment, which required listeners to repeat back non-sensical English phrases presented at a variety of signal-to-noise ratios (-5, -2, +1, and +4 dB SNRs). In addition, all listeners undertook assessments of vocabulary knowledge (PPVT-IV) and cognition (WAIS -IV). The primary dependent variable was individual content word recognition accuracy, and results were analysed using binomial mixed effects modelling. Results: Listeners demonstrated variability in their speech recognition abilities, and their vocabulary and cognitive scores. Statistical analysis revealed that listener-based factors affected word recognition. Listeners with faster processing speed and larger working memories exhibited higher word recognition accuracy. Surprisingly, listeners with higher non-verbal intelligence scores exhibited lower word recognition accuracy. Vocabulary knowledge interacted with SNR, such that as the listening conditions became more favourable, listeners with larger receptive vocabularies identified more words correctly. Similarly, main effects were also present for language-based factors. The more phonologically distinct a word was, the more likely it was to be correctly identified; higher frequency words were more likely to be accurately recognised. In addition, higher frequency words were identified more accurately at higher SNR levels. Finally, listener- and language-based factors interacted. The positive effect of working memory on word recognition was reversed as word frequency increased; on the other hand non-verbal intelligence’s negative influence on word recognition was reversed as word frequency increased. Conclusion: In the current cohort, listener and language-based factors interacted in the process of word recognition in noise. These results provide an insight into the underlying speech recognition mechanisms in adverse conditions. Further understanding of how these listener differences affect an individual’s speech processing may lead to the development of improved signal processing techniques and rehabilitation strategies.

High signal-to-noise ratio (SNR) is typically required for higher resolution and faster speed in magnetic resonance imaging (MRI). Planar microcoils as receiver probes in MRI systems offer the potential to be configured into array elements for fast imaging as well as to enable the imaging of extremely small objects. Microcoils, however, are thermal noise dominant and suffer limited SNR. Cryo-cooling for the microcoils can reduce the thermal noise, however conventional cryostats are not optimum for the microcoils because they typically use a thick vacuum gap to keep samples to be imaged to near room temperature during cryo-cooling. This vacuum gap is typically larger than the most sensitive region of the microcoils that defines the imaging depth, which is approximately the same as the diameters of the microcoils. Here microfluidic technology is utilized to locally cryo-cool the microcoils and minimize the thermal isolation gap so that the imaging surface is within the imaging depth of the microcoils. The first system consists of a planar microcoil with microfluidically cryo-cooling channels, a thin N2 gap and an imaging. The microcoil was locally cryo-cooled while maintaining the sample above 8°C. MR images using a 4.7 Tesla MRI system shows an average SNR enhancement of 1.47 fold. Second, the system has been further developed into a cryo-cooled microcoil system with inductive coupling to cryo-cool both the microcoil and the on-chip microfabricated resonating capacitor to further improve the Q improvement. Here inductive coupling was used to eliminate the physical connection between the microcoil and the tuning network so that a single cryocooling microfluidic channel could enclose both the microcoil and the capacitor with minimum loss in cooling capacity. Q improvement was 2.6 fold compared to a conventional microcoil with high-Q varactors and transmission line connection. Microfluidically tunable capacitors with the 653% tunability and Q of 1.3 fold higher compared to a conventional varactor have been developed and demonstrated as matching/tuning networks as a proof of concept. These developed microfluidically cryo-cooling system and tunable capacitors for improving SNR will potentially allow MR microcoils to have high-resolution images over small samples.

In this thesis, reduction of the Peak-to-Average Power Ratio (PAPR) of Orthogonal Frequency Division Multiplexing (OFDM) is studied. A new PAPR reduction method is proposed that is based on block coding the input data and modifying the codeword until the PAPR is reduced below a certain threshold. The method makes use of the error correction capability of the block code employed. The performance of the algorithm has been investigated through theoretical models and computer simulations. For performance evaluation, a new gain parameter is defined. The gain parameter considers the SNR loss caused by modification of the codeword together with the PAPR reduction achieved. The gain parameter is used to compare a plain OFDM system with the system employing the PAPR reduction algorithm. The algorithm performance is examined through computer simulations and it is found that power reductions around 2-3 dB are obtained especially for low to moderate number of channels and relatively strong codes.

This thesis is composed of three published articles. Each chapter is devoted to an article. In the first part the origin of some of the single radio pulsars with relatively low magnetic fields (B &lt / 1e12 G) and with characteristic ages (tau) less than 1e7 years is questioned. We proposed that such pulsars might occur as a result of the disruption of high-mass X-ray binary systems after a second supernova explosion. In these binaries, mass accretion on to the surface of X-ray pulsars may lead to the decrease in the magnetic field from its value at birth (B similar to 1e12 &ndash / 1e13 G) down to B &lt / 1e12 G similar to the processes in low-mass X-ray binaries. In the second part we put together many observational data of SGRs and AXPs and analyzed them with the main purpose of the removal of contradiction between the real age (t) of these objects and their characteristic times of period change (tau). SGRs and AXPs are neutron stars that undergo star-quakes. Magnetic activity increases from time to time. We suggest that as a result of these processes plasma is ejected from the NS and propeller mechanism starts to work. Due to propeller effect dP/dt increases, tau decreases. Indeed, high dP/dt values are observed in SGRs and in half of the AXPs. Then, for a long time NS looses its activity, its dP/dt decreases, tau increases and rapid cooling begins. It seems that there is a possible transition between each NS stage (AXP,SGR,dim). This transient cycle may be repeated once or several times until the spin period of the neutron star becomes P &gt / 10 - 12 s. Observational data and mainly the data of AXP 1E 1048-5937 and DRQNS RX J1308.8+2127 support this idea. In the third part dependence of the X-ray luminosity (Lx) of young single pulsars, due to ejection of relativistic particles, on electric field intensity, rate of rotational energy loss (dE/dt), magnetic field, period and some other parameters of neutron stars are discussed. Influence of the magnetic field and effects of some other parameters of neutron stars on the Lx - dE/dt and the Lx - tau(characteristic time) relations are considered. Evolutionary factors also play an important role in our considerations. Only the pulsars whose X-ray luminosity in the 2 &ndash / 10 keV energy band is greater than 1033 erg/s have pulsar wind nebula around them. The pulsars from which gamma-ray radiation has been observed have low X-ray luminosity in general.

This project consists of analyzing and finding solutions to the effect of non-linear distortionon the performance of a Massive Multiple Input Multiple Output (MIMO) system interms of Spectral Efficiency (SE) and Symbol Error Rate (SER). Massive MIMO is one ofthe technologies that are considered the backbone of the 5th generation of wireless communicationsand therefore this technology has gathered much interest from researchersand companies alike [19], as it is proven that this kind of system greatly improves thecapacity of the wireless connection [8]. Since Massive MIMO is still a relatively newtechnology and it is yet to be implemented for commercial use, there are several challengesthat arise when trying to implement such a system. One of these problems arisefrom the fact that the Power Amplifiers (PAs) in the transmitters of Massive MIMO systemsare non-linear and thus impose a distortion on the transmitted signals of the system[12]. The thesis aims to study this non-linear effect on the performance of massive MIMOsystems by first modelling the distortion effect on the transmitted signals using two differentnon-linear models. Moreover, closed-form expressions for one of the models areformed to facilitate the simulation of the non-linear model and facilitate the analysis ofthe distortion effect on the performance metrics. Then the established system model issimulated and based on the results, the effect of each of the power amplifier non-lineardistortion models on the performance metrics of the Massive MIMO system is studied.Furthermore, based on the analysis of the simulation results, a compensation mechanismis introduced to the Massive MIMO system in order to mitigate the distortion effect onthe system performance in terms of SER and SE.