In this thesis, both previously studied phase masks and newly optimized ones are to be tested and validated experimentally. As it is not feasible to manufacture thin transparent masks for each phase function studied, a liquid crystal on silicon (LCOS) spatial light modulator (SLM) is used as the pupil-phase mask in experiment. These advanced electro- optic devices can produce arbitrary phase functions on its reflective surface by leveraging the birefringent properties of liquid crystals. While the SLM allows for these experiments to be possible, they also introduce challenges which would not be present in a static, transparent phase mask. In this chapter, the experimental setup is discussed which seeks emulate a PSF engineered imaging system imaging a distant scene with a laser present. Challenges of using the SLM are discussed as well as methods from overcoming them. Lastly, the setup will be implemented to test the previously mentioned axicon, cubic and vortex phase functions.
It is widely accepted that the pointspreadfunction (PSF) and their Fourier transforms, the modulationtransfer functions (MTF), is the fundamental analysis tool for initial imaging system specification and design, perfomence analysis, and subsequent detail analysis of image it produces. A general schematic of sampled W-band PMMW imaging system is shown in Figure 2. The imaging subsystem PSF’s (lens + DRW antenna array), the sampling subsystem PSF’s, and the reconstruction subsystem PSF’s are the components that apply to all system. In this paper, we consider analysis up to sampling subsystem. The imaging system is assumed to be linear and shift invariant; thus the convolution of subsystems PSF’s provide the overall PSF of the sampled imaging system used for performance analysis. The output final output of the system in Figure 2 is given by:
In 1998, Dognitz and Wagnières reported the first use of the SFD method for measur- ing the tissue optical properties . A wide-field light source modulated with radially- varying square wave was employed and both the diffuse reflectivity and the modulation depth of the backscattering light were used to recover the optical properties at a single point in space. In 2005, Cuccia et al. proposed a single-frequency sinusoidal modulation based imaging, where images of the direct current (DC) and alternating current (AC) components of the modulated reflectance were simultaneously extracted using a phase- shifting demodulation technique, and pixel-by-pixel recovery of the optical properties was achieved from joint use of the AC and DC amplitudes [2, 15]. This approach has been a mainstream technique for SFD imaging. Since then, further studies on improved techniques such as the fast demodulation and depth-resolved recovery, etc., as well as clinical applications, have been comprehensively reported [16–23].
Based on the elastoplastic behavior, Chopra (1995) (and Chopra and Goel (1999)) has defined the equivalent linear behavior of an elastoplastic oscillator by using the secant stiffness for the Capacity Spectrum Method (CSM). This equivalent linearization consist in an equivalent linear oscillator whose frequency is linked to the slope of the straight line connecting the origin and the point of maximal deformation of the deformation-force curve during the earthquake, and the equivalent damping ratio based on the dissipated energy within the hysteretic loop corresponding to the maximal deformation. This concept has two disadvantages, at least, at the author’s point of view:
According to DilipDatta and coworkers, preparation of timetable for specific university is a very complex task. Therefore, they could introduce multi objective Evolutionary Algorithm based class timetable optimizer to reduce time.On the other hand, Jonathan Lee and coworkers could address some of the key challenges of timetabling by an automatic software engineering process as task– based conceptual graph (TBCG) . There hard and soft constraints can be easily inserted or removed while the specifications are maintainable. However, there were some drawbacks as necessity of generalized methodology, specialists’ skills while the problems are varying by concerning type of institute. Further, AnujaChowdhary and his colleagues also introduced an automated timetabling system of handling soft and hard constraints wisely with the limitations of mentioning the logic of the system. In a different research, NelishiaPillay says even though there are number of researches found in timetabling few of them only developed as software.Their paper provides an overview of methodologies such as Bee algorithm, Constraint programming, Cyclic transfers, Evolutionary algorithms, Integer programming, Neural networks, Simulated annealing and so on. Yet another research, Edmund Bruke and coworkers, could compare and contrast some recent approaches of scheduling problems handled by the University of Nottingham . As a result, they identified many present effective university-timetabling systems customized by the desired university and recent research directions in automated timetabling. Another aspect of automatic timetabling is defining constraints. Ben Peachter and his colleagues could introduce two major concepts behind them in their research.
within an image subset. A considerable amount of research has been undertaken in the DIC community to quantitatively address the assessment of image texture, the role played by sub- set size, and the impact of both of these factors on the precision and accuracy of DIC measurements (e.g., Sutton et al. 2009; Pan et al. 2008,2009,2010). In this section, the relevance of MTF on image texture with particular emphasis on its subsequent effect on the precision of DIC results will be illustrated using numerically-generated images to demonstrate the underlying philosophy. The images used for this discussion consist of a 1000 by 1000 pixel black canvass, onto which 5000 points of white light were randomly assigned to sub-pixel locations uni- formly throughout the image ( Fig. 3 ). Following the procedure described in detail by Take (2015), each point of light that makes up the speckle pattern of Fig. 13 has a non-integer pixel position of the centre and a shape deﬁned by a Gaussian inten- sity proﬁle of size of 10 pixels, a maximum intensity I max out of
Modulationtransferfunction (MTF) is objectively evaluate the optical system imaging quality standards, the fundamentally overcome the point of star, resolution, geometric aberration, and other traditional image quality inspection methods deficiencies[1-2].
modified fit, the final fit parameters were a = 5.99709, b = 5.10619, c = -2.32603, d = 5.21272, and e = 1.07624. Here the fitted curve g(x) runs over point (0.25, 1.0) and has a value greater than 1.0 at zero frequency. It is not clear whether it is valid to extrapolate the SWRF by assuming a model of SWRF as per Equation (3), and it is less clear how to attack this problem. The ‘corrected’ SWRF [f(x)] was obtained by dividing g(x) with e. These are results of the second version of the calculation.
The effect of a medical imaging system with certain input functions is the qualitative and quantitative study to understand about the characteristics and physical behavior of that system. There are several ways to study the system by a test input function. The input functions characterize the behavior of the system. A ‘physical’ test function and the output signal of an experiment can be analyzed to determine the transform characteristic through the system (Macovski, 1983). The MTF of an X-ray imaging system is determined by measuring the line spreadfunction (LSF) (Samei et al., 1998). The LSF is the response of the imaging system to a test device with an exact narrow slit. Therefore, a method for measuring the MTF which is carried out with slit method is a method that has been widely studied in the past (Morishita et al., 1995).
The PSF on the imaging system is almost known and provided in the form of the Optical TransferFunction (OTF). Shan et al.  used a probability model of the latent image, PSF, and noise. Their results are particularly useful when applied to motion- blurred images. Pan et al.  suggested a method to estimate the blur mask from a blurred image by regularizing the sparsity speciality of natural images. In this work, a regularization algorithm of deconvolution kernel were presented, that is based on randomly generating the PSF using non- reference algorithm depending on adaptive RL algorithm, the results gives high-quality deblurred images even when the image is severely blurred.
The research reported concerns mathematical modelling and practical measurement of the PointSpreadFunction (PSF) of focused and defocused image acquisition systems, such as digital TV cameras. This measure of image blur can be utilised to optimize image processing functions such as edge detection [1-3] and Depth From Defocus (DFD) depth estimation [4-11]. An image acquisition system typically consists of optical components (such as lenses and apertures) and electronic components (such as the 2-D CCD array, anti-aliasing and communication circuits). Each of the components in the optical and electronic paths can be considered as spatial low-pass filters. Considering the system in the terminology of system theory, the transfer functions of each of its components can be estimated and then all combined to find the overall transferfunction. Alternatively, and the approach taken in this work, the transferfunction for the entire system can be measured. Typically, input signals are provided in the
length. As shown in Fig. 1A, it extends from the minor nuclease hypersensitive site (*) on the proximal side to the iab- 7 PRE (which corresponds HS3) (Hagstrom et al., 1997; Mishra et al., 2001) on the distal side and includes two major chromatin-specific nuclease hypersensitive regions, HS1 and HS2 (Karch et al., 1994). The largest hypersensitive region, HS1, contains six consensus GAGA factor binding sites arranged in three pairs, 1-2, 3-4 and 5-6. The ubiquitously expressed GAGA factor is encoded by the Trithorax-like (Trl) gene (Farkas et al., 1994), and it is thought to function in the formation and/or maintenance of the nucleosome free regions of chromatin associated with a variety of cis-acting elements in flies, including enhancers, promoters, Polycomb Response Elements (PRES) and boundaries (Lehmann, 2004). Chromatin immunoprecipitation experiments demonstrate that GAGA is associated with the Fab-7 boundary in vivo (Strutt et al., 1997). Moreover, the GAGA-binding sites in HS1 are important for boundary function. In previous studies, we found that the enhancer blocking activity of the minimal 1.2 kb boundary is compromised in both the embryo and adult when GAGA sites 1-5 are mutated (Schweinsberg et al., 2004). Although this finding indicates that GAGA (or another protein that recognizes the GAGA consensus) is required for Fab-7 boundary activity throughout development, the GAGA sites are not functionally equivalent. We found that when only the centromere proximal pair, 1-2, are mutated, blocking of the ftz UPS stripe enhancer in the ectoderm of early embryos by the minimal Fab-7 boundary is weakened, but there is no apparent effect on the blocking of either the ftz NE enhancer in the CNS of older embryos or the w enhancer in adults. By contrast, mutation of the central pair, 3-4, weakens blocking of the w enhancer in the eye, but has little effect on the blocking of the ftz enhancers in embryos.
3 .1 Endothelium-dependent hypoxic vasoconstriction is not mediated by endothelin. The hypoxic response o f pulmonary arteries have been studied in many laboratories. With a few exceptions (Holden and McCall 1984; Madden et al, 1985; Ogata et al. 1992; Yuan et al. 1990), it has been necessary to pretreat the vessels with vasoconstrictors in order to obtain responses o f sufficient magnitude and reproducibility to study (Archer et al. 1989; Bennie et al. 1991, Hoshino et al. 1988, Jin et al. 1992; Johns et al. 1989; Rodman et al. 1990). Concern that such pretreatment may not be physiologic has been tempered by the realization that in vivo vessels are bathed continuously by vasoactive substances and the observation that vasoconstrictors could prevent the loss o f hypoxic responsiveness otherwise seen in isolated lungs perfused with physiological salt solutions (Berkov 1974; McMurtry 1984). Precontraction could allow the artery to achieve some threshold state required for the expression o f direct hypoxic stimulation as change in vasomotor tone and/or provide a process for indirect hypoxic modulation. These effects could occur in vascular smooth muscle, endothelium or both.
In the Digital Confocal Microscopy Technology, the three-dimensional biological specimen are moved along the optical axis to obtain two-dimensional slice images of the sequence through the objective lens focal plane of bio-optical microscope, which are then processed through three-dimensional microscopic image restoration algorithm to obtain the clear two-dimensional image sequence and three-dimensional images . The space size of 3D-PSF (Three Dimensional PointSpreadFunction) used for biological image restoration processing determines the restoration effect and restoration time . Since most energy of 3D-PSF is concentrated in the area close to the cone top of double cone in the center, how much the space area in the center of 3D-PSF should be selected, and what kind of relationship exists between the restoration effect and restoration time of images are still the problems to be studied and solved.
Alessio et al. characterized the detector response using Monte Carlo simulations  and empirical measurements . The detector response in an empirical measurement that separated radial and axial components was modeled as a pointspreadfunction (PSF), and PSF information was incorporated into a three-dimensional (3D) iterative re- construction [4, 6]. A reconstruction algorithm using PSF correction has been reported to improve the spatial resolution in the scanner FOV. Such algorithms are provided from the vendors under different names (SharpIR from GE, TrueX HD·PET from Sie- mens and ×Sharp from Philips). However, PSF correction changes quantitative accuracy due to the Gibbs ringing overshoot at the edges [4, 9]. Furthermore, although few stud- ies have evaluated the effects of PSF correction using the National Electrical Manufac- turers Association (NEMA) body phantom located at the center of the FOV [10, 11], the relationship between FDG uptake at any location throughout the FOV and the ac- curacy of PSF correction remains unclear. The spatial resolution of the scanner can be measured by placing a point source within the scanner and acquiring scan data at vary- ing locations in both the radial and axial dimension [6, 8].
3D-PSF can be viewed as a three-dimensional matrix in discrete spatial. Its different cross-sections (x-y) along the central axis (optical axis or z-axis) of double funnel are corresponding to radial 2D-PSF (Two Dimensional PointSpreadFunction) with a series of different defocus amount, wherein the middle section at z=0 is the focal plane 2D-PSF. The space size of 3D-PSF includes the radial size (x-y) and axial size z.