Browsing by Subject "sampling"
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Item Open Access Effective biodegradation of 2,4,6-trinitrotoluene using a novel bacterial strain isolated from TNT-contaminated soil(2013) Gumuscu, B.; Tekinay, T.In this environmental-sample based study, rapid microbial-mediated degradation of 2,4,6-trinitrotoluene (TNT) contaminated soils is demonstrated by a novel strain, Achromobacter spanius STE 11. Complete removal of 100mgL-1 TNT is achieved within only 20h under aerobic conditions by the isolate. In thisbio-conversion process, TNT is transformed to 2,4-dinitrotoluene (7mgL-1), 2,6-dinitrotoluene (3mgL-1), 4-aminodinitrotoluene (49mgL-1) and 2-aminodinitrotoluene (16mgL-1) as the key metabolites. A. spanius STE 11 has the ability to denitrate TNT in aerobic conditions as suggested by the dinitrotoluene and NO3 productions during the growth period. Elemental analysis results indicate that 24.77mgL-1 nitrogen from TNT was accumulated in the cell biomass, showing that STE 11 can use TNT as its sole nitrogen source. TNT degradation was observed between pH 4.0-8.0 and 4-43°C; however, the most efficient degradation was at pH 6.0-7.0 and 30°C. © 2013 Elsevier Ltd.Item Open Access Signal representation and recovery under measurement constraints(Bilkent University, 2012) Özçelikkale Hünerli, AyçaWe are concerned with a family of signal representation and recovery problems under various measurement restrictions. We focus on finding performance bounds for these problems where the aim is to reconstruct a signal from its direct or indirect measurements. One of our main goals is to understand the effect of different forms of finiteness in the sampling process, such as finite number of samples or finite amplitude accuracy, on the recovery performance. In the first part of the thesis, we use a measurement device model in which each device has a cost that depends on the amplitude accuracy of the device: the cost of a measurement device is primarily determined by the number of amplitude levels that the device can reliably distinguish; devices with higher numbers of distinguishable levels have higher costs. We also assume that there is a limited cost budget so that it is not possible to make a high amplitude resolution measurement at every point. We investigate the optimal allocation of cost budget to the measurement devices so as to minimize estimation error. In contrast to common practice which often treats sampling and quantization separately, we have explicitly focused on the interplay between limited spatial resolution and limited amplitude accuracy. We show that in certain cases, sampling at rates different than the Nyquist rate is more efficient. We find the optimal sampling rates, and the resulting optimal error-cost trade-off curves. In the second part of the thesis, we formulate a set of measurement problems with the aim of reaching a better understanding of the relationship between geometry of statistical dependence in measurement space and total uncertainty of the signal. These problems are investigated in a mean-square error setting under the assumption of Gaussian signals. An important aspect of our formulation is our focus on the linear unitary transformation that relates the canonical signal domain and the measurement domain. We consider measurement set-ups in which a random or a fixed subset of the signal components in the measurement space are erased. We investigate the error performance, both We are concerned with a family of signal representation and recovery problems under various measurement restrictions. We focus on finding performance bounds for these problems where the aim is to reconstruct a signal from its direct or indirect measurements. One of our main goals is to understand the effect of different forms of finiteness in the sampling process, such as finite number of samples or finite amplitude accuracy, on the recovery performance. In the first part of the thesis, we use a measurement device model in which each device has a cost that depends on the amplitude accuracy of the device: the cost of a measurement device is primarily determined by the number of amplitude levels that the device can reliably distinguish; devices with higher numbers of distinguishable levels have higher costs. We also assume that there is a limited cost budget so that it is not possible to make a high amplitude resolution measurement at every point. We investigate the optimal allocation of cost budget to the measurement devices so as to minimize estimation error. In contrast to common practice which often treats sampling and quantization separately, we have explicitly focused on the interplay between limited spatial resolution and limited amplitude accuracy. We show that in certain cases, sampling at rates different than the Nyquist rate is more efficient. We find the optimal sampling rates, and the resulting optimal error-cost trade-off curves. In the second part of the thesis, we formulate a set of measurement problems with the aim of reaching a better understanding of the relationship between geometry of statistical dependence in measurement space and total uncertainty of the signal. These problems are investigated in a mean-square error setting under the assumption of Gaussian signals. An important aspect of our formulation is our focus on the linear unitary transformation that relates the canonical signal domain and the measurement domain. We consider measurement set-ups in which a random or a fixed subset of the signal components in the measurement space are erased. We investigate the error performance, both We are concerned with a family of signal representation and recovery problems under various measurement restrictions. We focus on finding performance bounds for these problems where the aim is to reconstruct a signal from its direct or indirect measurements. One of our main goals is to understand the effect of different forms of finiteness in the sampling process, such as finite number of samples or finite amplitude accuracy, on the recovery performance. In the first part of the thesis, we use a measurement device model in which each device has a cost that depends on the amplitude accuracy of the device: the cost of a measurement device is primarily determined by the number of amplitude levels that the device can reliably distinguish; devices with higher numbers of distinguishable levels have higher costs. We also assume that there is a limited cost budget so that it is not possible to make a high amplitude resolution measurement at every point. We investigate the optimal allocation of cost budget to the measurement devices so as to minimize estimation error. In contrast to common practice which often treats sampling and quantization separately, we have explicitly focused on the interplay between limited spatial resolution and limited amplitude accuracy. We show that in certain cases, sampling at rates different than the Nyquist rate is more efficient. We find the optimal sampling rates, and the resulting optimal error-cost trade-off curves. In the second part of the thesis, we formulate a set of measurement problems with the aim of reaching a better understanding of the relationship between geometry of statistical dependence in measurement space and total uncertainty of the signal. These problems are investigated in a mean-square error setting under the assumption of Gaussian signals. An important aspect of our formulation is our focus on the linear unitary transformation that relates the canonical signal domain and the measurement domain. We consider measurement set-ups in which a random or a fixed subset of the signal components in the measurement space are erased. We investigate the error performance, both in the average, and also in terms of guarantees that hold with high probability, as a function of system parameters. Our investigation also reveals a possible relationship between the concept of coherence of random fields as defined in optics, and the concept of coherence of bases as defined in compressive sensing, through the fractional Fourier transform. We also consider an extension of our discussions to stationary Gaussian sources. We find explicit expressions for the mean-square error for equidistant sampling, and comment on the decay of error introduced by using finite-length representations instead of infinite-length representations.Item Open Access Signal representation and recovery under partial information, redundancy, and generalized finite extent constraints(Bilkent University, 2009) Öktem, Sevinç FigenWe study a number of fundamental issues and problems associated with linear canonical transforms (LCTs) and fractional Fourier transforms (FRTs). First, we study signal representation under generalized finite extent constraints. Then we turn our attention to signal recovery problems under partial and redundant information in multiple transform domains. In the signal representation part, we focus on sampling issues, the number of degrees of freedom, and the timefrequency support of the set of signals which are confined to finite intervals in two arbitrary linear canonical domains. We develop the notion of bicanonical width product, which is the generalization of the ordinary time-bandwidth product, to refer to the number of degrees of freedom of this set of signals. The bicanonical width product is shown to be the area of the time-frequency support of this set of signals, which is simply given by a parallelogram. Furthermore, these signals can be represented in these two LCT domains with the minimum number of samples given by the bicanonical width product. We prove that with these samples the discrete LCT provides a good approximation to the continuous LCT due to the underlying exact relation between them. In addition, the problem of finding the minimum number of samples to represent arbitrary signals is addressed based on the LCT sampling theorem. We show that this problem reduces to a simple geometrical problem, which aims to find the smallest parallelogram enclosing a given time-frequency support. By using this equivalence, we see that the bicanonical width product provides a better fit to the actual number of degrees of freedom of a signal as compared to the time-bandwidth product. We give theoretical bounds on the representational efficiency of this approach. In the process, we accomplish to relate LCT domains to the time-frequency plane. We show that each LCT domain is essentially a scaled FRT domain, and thus any LCT domain can be labeled by the associated fractional order, instead of its three parameters. We apply these concepts knowledge to the analysis of optical systems with arbitrary numbers of apertures. We propose a method to find the largest number of degrees of freedom that can pass through the system. Besides, we investigate the minimum number of samples to represent the wave at any plane in the system. In the signal recovery part of this thesis, we study a class of signal recovery problems where partial information in two or more fractional Fourier domains are available. We propose a novel linear algebraic approach to these problems and use the condition number as a measure of redundant information in given samples. By analyzing the effect of the number of known samples and their distributions on the condition number, we explore the redundancy and information relations between the given data under different partial information conditions.Item Open Access Türkçe şiirde lirik ve ideoloji okumaları ve Marksist bir ara konum : Niyazi Akıncıoğlu(Bilkent University, 2013) Sezen, GüneşIn this thesis, the relationship between lyric poetry and society put forward theoretically by Adorno has been approached in relation with Eagleton’s layered ideology, and a sampling study which focuses especially on Turkish modern poetry has been attempted to be conducted. The samplings made with the emphasis, contrary to the reception until today of lyric poetry, that lyric poetry does not allude only to a subjective and individual poem, but makes references subjectively to society and thus different ideological layers, have revealed various outcomes for each of the poets discuused. It has been discovered that, especially during times of escalated social and political change and/or oppression, poetry tends to at first alternate between going rhetorical through increased volume or going through a lyric silence, and that this situation leads in Turkish poetry to various presentations somewhere between these two dominant types. The perception of environment and life developed around the axis of dream and reality by Cenab Şahabettin and Tevfik Fikret in the pre-modern period, has been used during the modern period after going through some changes. Symbols have been observed to make references in Ahmet Hâşim’s lyric poems to an imaginary place and time, and in yahya Kemal’s, in accordance with the longing for the old culture and aesthetics, to historical places. Even though, the use of religious and mystical metaphors in Asaf Hâlet’s poems has been considered to be a means rather than an end, the fact that such metaphors have been the choice has been deemed significant. Betçet Necatigil has been read with the view that oppression, modernization and urbanization has depicted a micro-universe to the poetry and life of a poet hanging in the balance; in which he has taken refuge. In Nâzım Hikmet and Niyazi Akıncıoğlu’s poems, structures have been observed that have been constructed outside the frame of mind and poetic horizon of two poets—one socialist realist and the other, Marxist. It has been emphasized that the former, in terms of female discourse, has used a patriarchal unconscious discourse in some of his poems; while it has been observed through the poems of the latter that he has digressed from realistic discourse with the attempt to create an epic and abstract universe and poetry. All the sampling in this study has been an attempt to put forward an approach to lyric poetry which is divergent from the points of view exhibited until now.