*Edited by Alba Scerrati and Giorgio Mantovani*

As diagnostic and functional neuroimaging advances, the choice of the best patienttailored treatment for cerebral aneurysm becomes far more difficult. New technologies that can help identify the most suitable therapy include machine learning algorithms to process big data, robotic applications for interventional procedures, and dynamic vascular flow models. Different biological and epidemiological parameters have been delineated as prognostic factors that add a fundamental piece of information to the decision of whether to proceed with surgery, endovascular treatment, or a combination of both. With technical improvement and prolonged patient life expectancy, recurrent cerebral aneurysm is becoming more common. To deal with the complex issue of aneurysm re-intervention, a clear definition of the clinical and radiological outcomes is essential. This book provides a comprehensive overview of the currently emerging innovations in the treatment of cerebral aneurysms, from their pre-operative holistic assessment to long-term follow-up, focusing on the opportunities provided by the newest technologies.

Published in London, UK © 2023 IntechOpen © semnic / iStock

Advances in Cerebral Aneurysm Treatment

Advances in Cerebral

Aneurysm Treatment

*Edited by Alba Scerrati and Giorgio Mantovani*


**4. Deep learning for human target analysis**

With the rapid emergence of new deep learning algorithms and architectures, the development of many domains such as speech recognition, visual object recognition, object detection, and even drug discovery and genomics has been accelerated. Deep learning is composed of multiple processing layers to learn high-level representations with multiple levels of abstraction, thus automating the process of feature extraction. Hence, deep models do not need heavy feature engineering and domain knowledge compared with traditional machine learning techniques. What is more, with so many complicated deep-level transformations, very complex functions can be learned, and more classification and recognition problems can be solved. As a result, deep learning has made great contributions in overcoming difficulties in artificial intelligence and advancing the development of artificial intelligence.

Next, we will mainly describe several deep learning models, which are used mostly in human target analysis field.

#### **4.1. Convolutional neural network**

Convolutional neural network (CNN) is inspired by the visual cortex structure which is composed of simple cells and complex cells. It adopts four key ideas: local connections, parameter sharing, pooling, and multilayers. In this way, CNN is able to fully explore the property of raw signals that there are compositional hierarchies, namely, extracting higherlevel features from the lower-level ones. As a result, convolutional neural networks, as one of the representative algorithms of deep learning, have made a remarkable progress in object detection and recognition, natural language processing (NLP), speech recognition, and medical image analysis in the past few years. In human activity recognition field, CNN is one of the most used deep learning models. For instance, Zhenyuan Zhang et al. have adopted this network to realize continuous dynamic gesture recognition using a radar sensor [30], while Youngwook Kim et al. detected and classified human activities using deep convolutional neural networks [32].

#### **4.2. Recurrent neural network**

In **Figure 7**, the RDS of the two-people scenario shows that the backscattering of the human targets is automatically separated in the 3D range-Doppler-time space. This indicates that RDS is not only able to show the range-Doppler signatures of a single extended target but also able to separate (or even track) multiple targets in the range-Doppler video sequence. Additional processing to separate multi-target reflection (e.g., the separating method pro-

As an example, the RDS has been demonstrated for human target analysis using an S-/C-band UWB radar, but RDS itself is in fact a generic tool. It can be used in various applications, such

posed in [19]) is not required anymore.

68 UWB Technology and its Applications

**Figure 7.** Range-Doppler surface of two human targets (threshold = −23 dB).

With the successful application in NLP, recurrent neural network (RNN) has caught researchers' attention. RNNs have shone light on modeling temporal sequences such as texts and speeches because they can mine timing and semantic information in them. From the perspective of network structure, RNN can remember the previous information and use it to influence the output of the following nodes. However, conventional RNN has its own limit: long-term dependencies. To overcome this problem, long short-term memory (LSTM) came into being and performed better in many tasks. LSTM owns three special gates: input gate, output gate, and forget gate. By using these memory units especially the forget gate, LSTM can access a long-range context of the sequential data. Due to these advantages above, many human activity recognition systems adopted RNN and its variants. Zhi Zhou et al. adopted multimodal signals, including HRRPs and Doppler profiles, which are acquired by the terahertz radar system to recognize dynamic gestures, and the recognition rate reaches more than 91% [22].

**5.2. Notice phase information**

**5.3. Take orientation sensitivity into consideration**

**5.4. Focus more on 1D and 3D domain radar echoes**

be paid to this part of human target analysis field.

ing and supporting and for their advisable opinions.

\*Address all correspondence to<EMAIL_ADDRESS>
Beijing University of Posts and Telecommunications, Beijing, China

future studies.

research topics.

**Acknowledgements**

**Author details**

Yuan He\*, Xinyu Li and Xiaojun Jing

Common energy-based power spectrograms after FT or STFT always abandon the phase information in backscatter echoes. But phase is an important attribute of any signal and contains a wealth of information such as transmission duration and distance. Pavlo Molchanov et al. investigated frequency and phase coupling phenomena for radar backscattered signals and proposed novel bicoherence-based information features [31]. We think phrase information in radar backscattering signals should be considered more in

Toward Deep Learning-Based Human Target Analysis http://dx.doi.org/10.5772/intechopen.81592 71

Doppler shift is caused by the radial velocity of the moving target. The radial velocity changes with the position of the target and the radar because it is the component of the object's velocity. In other words, when the radar is above the pedestrian, the Doppler is partly induced by the motion vertical component such as arm and leg vertical motions. In this case, negative Doppler will appear. As a result, if the relative position is different, radar backscattered signals produced by one subject performing a specified activity will differ a lot. How to overcome the orientation sensitivity of radar-based HAR is one of the future

Through the investigation of the current research status, compared with the researches in 2D domain, there are few research results on 1D and 3D domains of human echo signals, but through the discussion in previous chapters, we have reason to believe that the two forms of echoes have enough development potential and explore space. Thus, more attention should

I would like to express my gratitude to Pavlo Molchanov, Takuya Sakamoto, Pascal Aubry, Francois Le Chevalier, and Alexander Yarovoy for their contribution. I thank them for assist-

#### **4.3. Auto-encoder**

Auto-encoder is a high-performance deep learning network suitable for dealing with one-dimensional data by extracting optimized deep features. It learns a deep feature representation of raw input via several rounds of encoding-decoding procedures. Auto-encoder applies the layer-wise greedy unsupervised pre-training principle so as to quickly obtain an efficient deep network.

The commonly used variants of auto-encoder are mainly the following kinds: (1) sparse autoencoder, which is able to rebuild the input data well, and (2) de-noising auto-encoder and contractive auto-encoder which can make the models more generic by adding noise or a wellchosen penalty term.

Auto-encoder is able to provide a powerful feature extraction approach for many tasks, which saves a lot of labor. In this way, auto-encoder can combine with whether conventional machine learning algorithm or other deep learning models and becomes a more robust one. Mehmet Saygin Seyfiolu et al. [33] used a convolutional auto-encoder architecture to discriminate 12 indoor activity classes involving aided and unaided human motions by recognizing different 2D Doppler maps, and Branka Jokanovic et al. [34] applied three stacked auto-encoders to extract deep features, respectively, and fuse the result together with a voting principle to classify activities.


**4. Conclusions**

The results of the present study showed that the same trends and similar particle size values were obtained by microscopy-assisted digital image analysis and laser diffraction particle size analysis for the determination of particle size in emulsions. Considering the installation costs of devices for measuring laser diffraction, microscopy-assisted digital image analysis seems to be a very useful and effective alternative. On the other hand, if sample characterization is to be performed, it should be noted that there are certain differences in the measured values, and slightly larger droplet sizes are calculated with

the microscopy-assisted digital image analysis technique. In addition, while the results can be obtained in about 5 min when taking measurements with laser diffraction devices (total time for analysis), the analysis and calculation process may take one or several days per sample with the procedure developed in the present study, unless working with highcapacity workstations. It was observed that comparable results were obtained for D(90), D[3,2], and D[4,3], which were correlated with the physical properties of the emulsions.

In summary, the microscopy-assisted digital image analysis procedure developed in this study can provide very reasonable results when it is impossible to obtain/use laser diffraction particle size analyzers. However, it should be noted that performing this method is labor- and time-intensive.

**Author Contributions:** P.S.: formal analysis, validation, investigation, data curation, and writing original draft. O.G.: conceptualization, methodology, and data curation. L.Y.A.: writing—review and editing and supervision. Z.E.: conceptualization, methodology, writing—original draft, and project administration. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) (Grant No. 120O763). The authors are also grateful to Croda for providing the emulsifiers.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.


*Optical Nonlinearities in Glasses DOI: http://dx.doi.org/10.5772/intechopen.101774*

short-range order. Therefore, glasses are typically brittle and optically transparent because they lack internal structure. The silica-based glass was undoubtedly the most studied given its multiple applications. Glasses that do not include silica as a main constituent exhibit other properties that make them useful for various applications, for example in optical fibers that work in different frequency domains than SiO2 fibers. These include fluoride glasses, tellurite glasses, aluminosilicates, phosphate glasses, borate glasses, and chalcogenide glasses. Common glasses are transparent materials in the spectral range of the visible and near-infrared region, although opaque in the far IR and UV region. The visible transparency threshold ends, for high wavelengths (λ), with UV absorption, due to electronic transitions between valence band levels and unfilled conduction band levels. For applications in photonics, there are two main categories of special glasses: chalcogenide glasses (CGs) and heavy metal oxide glasses. Chalcogenide glasses are based on the chalcogen elements S, Se, and Te. These glasses are formed by the addition of other elements such as Ge, As, Sb, Ga, etc. Heavy metal oxide and chalcogenide glasses offer the largest nonlinear response.

Most of the glasses are prepared by the melt of precursors. In solid form, glass is a non-crystalline (or amorphous) material. The deposition from a liquid solution (sol–gel method) is an alternative approach to obtain glass, especially in films form. Some compositions may otherwise be rather difficult to prepare by melt and that's why in practice this method is limited to a relatively small number of compositions. Therefore, the sol–gel processes allow the synthesis of glasses of extended composition ranges, allowing the fabrication of multiple oxide composition, but also nonoxide glasses, with a high degree of homogeneity, because reagents are mixed at the molecular level at temperatures lower than those required for conventional melting. However, the OH content of the sol–gel glasses is high and OH absorptions usually limit transmission at 1.4 μm.

Optical glasses are optically homogeneous glass that are applied in several optical functionalities. The first optical quality (flint) glasses were created at the end of the 19 century by Otto Schott, who also invented Ba crown glass, allowing the production of adjusted lenses for chromatic aberration [21]. X-ray diffraction (XRD) allows distinguishing a glass from a crystalline material. The pattern of SiO2 glass contains only a few, very broad peaks, which cannot be correlated by the Bragg law with planar distances (as in the case of crystals). SiO2 consists of a matrix of SiO4 tetrahedra (**Figure 5**) [22].

The presence of a glass modifier together with the glass formers (SiO2 or P2O5) breaks up the oxide network M–O–M (M = Si, P) and drives the transformation of the bridging oxygens (BO) into nonbridging oxygens (NBOs). The structural unit of SiO2 has Si-O atomic bonds whose electronic transitions occur in the UV range. For high λ, the transparency threshold ends due to the vibrations of the ions in the network (in resonance with the incident radiation). The amorphous character of the glass explains the absence of grain boundaries in its structure and, therefore, the absence of internal dispersion and reflection phenomena, which are always present in crystalline materials. Glasses are dielectric materials and therefore exhibit a large energy gap between the valence band and the conduction band in accordance with the band theory of solids. Their optical transmission is limited by electronic transitions (Urbach tail) for low wavelength, and multiphonon absorption at high wavelength, in the IR spectrum. The multiphonon absorption process is related to the fundamental vibration frequencies of the glass.

The transmittance spectrum varies from glass to glass, but the main differences are observed outside the transparency range (**Figure 6**). The glass has an optical transparent window which strongly depends on the compositions. Glasses made for use in the visible region have high transmittance across the entire wavelength range

#### **Figure 5.**

*A schematic representation of the structure of vitreous silica. The tetrahedral SiO4 units in silica are represented by triangular units [22].*

of 400 nm–800 nm. However, the structure of silicate glasses limits its transmission in the infrared region to above 3 μm. They have strongly bound electrons but non-bridging oxygens, with their weakly bound electrons, reduce transmission. Chalcogenide glasses, heavy metal fluoride glasses, and heavy metal oxide glasses extend this transmission to higher wavelengths. The telluride glasses have larger atoms and weaker bonds than oxide glasses and so its vibrational resonance occurs at a lower frequency, shifting the fundamental absorption cut-off to longer wavelengths (**Figure 6**).

The interest in chalcogenide glasses backs from 1950s when was reported high infrared transparency of the As2S glass, up to 12 μm [24]. The structure of chalcogenide glasses such as Ge-Sb-Se consists of covalently bonded atoms, like amorphous SiO2, with lacking periodicity. They include sulfide, selenide, and telluridebased glasses. As dielectric materials, their optical transparent window is dependent on electronic absorption at low wavelengths and multiphonon absorption at high

wavelengths. They have a band gap (Eg) that is dependent on the composition and decreases according to Sulfides < Selenides < Tellurides. A specificity of tellurides that differentiates it from the sulfides and selenides in its crystalline structure and physical properties is the large atomic number of Te. The energy gap may be taken from the glass absorption spectrum α(*ħ*ω) by extrapolating the linearized Tauc equation:

$$
\hbar \alpha \hbar \alpha \propto \left(\hbar \alpha - \mathbf{E}\_{\text{g}}\right)^{1/2} \tag{16}
$$

The absorption coefficient, α, varies exponentially with the photon energy, *ħ*ω in the Urbach tail.

It is interesting to note that *n* and *n*2 and are usually directly correlated, such that low index (*n*) glasses, like certain fluorides and phosphates, have also low *n*2. On the other hand, a relationship between the material band gap and the *n*2 was also established. For example, the *n*2 value obtained for pure As2S3 was about 2.9 x 1018 m2 /W while for fused SiO2 was about 2.8 x 1019 m2 /W [25], which is comparatively about 10 times lower. So, materials with lower band gap seam to exhibit an increase in the nonlinear optical behavior; SiO2 has a gap of about 9 eV while that of As2S3 is 2.3 eV [19].

The increase of the nonlinear absorption coefficient (β), third-order nonlinear optical susceptibility (χ(3)), and nonlinear refractive index (n2) and decreasing the optical band gap (Eg) can be attributed to the formation of BO bonds and ions of higher polarizability in the glass matrix. It has been recognized the effect of the glass composition on the dependency of χ(3). In most multicomponent oxide glasses, there are both BO and NBO oxygens in the glass network (e.g. for a silicate glass, Si-O+ Na). The NBO bonds possess larger n2 than the BO of the more covalent Si-O-Si bonds [26]. It was also established that third-order nonlinear optical susceptibility of the glasses increases with increasing optical basicity and tendency for metallization of the glasses. This fact is associated with the polarizability of the anions (F < O2 < S2 < Se2) and the small optical band gap [19], which is related to the increasing metallicity of the oxides [27]. The theory of metallization of the condensed matter says that in the Lorentz–Lorenz equation, the refractive index becomes infinite when metallization of covalent solid materials occurs [27]. SiO2, B2O3, and GeO2 based glasses exhibit low refractive index and have low polarizability, large metallization tendency, and small χ(3). Tellurite and TiO2 based glasses, as well as B2O3 glasses containing a large amount of Sb2O3 and Bi2O3 with high refractive index, show large polarizability, small metallization tendency, and large χ(3) (**Figure 7**). Consequently, under the point of view of polarizability, highrefractive-index glasses with an increased tendency for metallization are promising materials for application as components of nonlinear optical devices.

Glass materials are excellent non-linear optical materials, being isotropic and transparent in a wide spectral range, combining low cost of fabrication with high optical quality, manufacturable not only as bulk shapes, or fibers, but also as thin films (e.g. nonlinear planar waveguides). Furthermore, when compared to polymers, glass is more stable and has the advantage over crystals since its atomic composition is easily tailored: a nonlinear optical glass can be obtained with any refractive index in a wide range [28]. Its properties can be adjusted through doping and compositional changes to fit the specified requests of each application. Its disordered structure allows light propagation inside that medium like no other material. They also exhibit good compatibility with silica-based systems and waveguide production in which high optical intensities and long interaction lengths can be achieved [28], giving rise to nonlinear structures in integrated optical devices [29].

#### **Figure 7.**

*Line-up of the Kerr effect among various glass compositions [19].*

For the fabrication of all-optical systems in information technology and integrated photonics, the chosen materials should exhibit high nonlinearities. Rather, low nonlinearities are essential for fibers in optical communications to avoid phenomena of self-focusing, self-phase modulation, Raman and Brillouin scatterings. NLO was considered the threshold to the total of information that can be transmitted in a single optical fiber. As laser power levels increase, NLO limits data rates, transmission lengths, and the number of wavelengths that can be transmitted simultaneously. Optical nonlinearities give rise to many "secondary" effects in optical fibers. These effects can be damaging in optical communications, but they find other applications, especially for the integration of all-optical functionalities in optical networks. The optical nonlinearities can give rise to gain or amplification, the conversion between wavelengths, the generation of new wavelengths or frequencies, the control of the temporal and spectral shape of pulses, and switching [6]. Thus, they can be distinguished in two types: that from scattering (stimulated Brillouin and stimulated Raman) and that from optically induced changes in the refractive index, resulting either in phase modulation or in the mixing of several waves and the generation of new frequencies (modulation instability and parametric processes, such as four-wave mixing). So, the nonlinear refractive index, also referred optical Kerr nonlinearity (n2), offers a means to achieve switching and amplifying functions in photonic devices and produces nonlinear effects, namely self-phase modulation, and four-wave mixing. Self-phase modulation implies changes in the phase and rising frequency of a pulse, which can cause spectral broadening. Four-wave mixing is a kind of nonlinear frequency conversion generated by the Kerr nonlinearity which enables, for example, high-speed communications, frequency conversion, sensing, and quantum photonics. The effect of ultrafast response time (10 s15 s) provides broad bandwidths, that can pull actual GHz electronic computing forward to PHz (1015) rates using all-optical signal processing [30]. In addition, spectral broadening, produced by changes in phase from the nonlinear refractive index, can enable the production of short-pulsed sources [30]. Four-wave mixing, on the other hand, can be used to generate optical frequency combs [30], which can measure precise frequencies of light and span spectral ranges useful for spectroscopic investigations.

Although these applications are of great practical interest, the Kerr effect (n2) is often small for common optical glasses (1020 to 1019 m2 /W) [30], leading to high thresholds for nonlinear effects and requiring special sources of high-power excitation.

## *Optical Nonlinearities in Glasses DOI: http://dx.doi.org/10.5772/intechopen.101774*

Transparent optical glasses exhibiting nonlinearities, e. g. large nonlinear refractive index and nonlinear absorption coefficient are good candidates for fiber telecommunication and for nonlinear optical devices such as optical switches, selffocusing, and white-light continuum generation. Glasses that exhibit significant nonlinearity are good candidates as Raman gains media to provide enhanced Raman gain over an extended wavelength range. Chalcogenide (As–Se) glasses and fibers are examples of good candidates as well tellurite fibers because of the high refractive index of TeO2 (2.3–2.4) [6] compared to the SiO2 (1.46). An As2S3 fiber exhibit a Raman coefficient is 300 times greater than that of silica fiber [6]. However, chalcogenide fibers have lesser chemical stability. In spite of that, chalcogenide glass has wide transparency transmission from 0.5 to 25 μm [31], enhancing their potential applications on the mid-IR. As shown in **Figure 8**, the long-wavelength cut-off edges of chalcogenide glasses depend on the mass of anionic elements and are extended between 12 and 20 μm. Their nonlinearity (Kerr effect) is 200–1000 times larger than that of the silica glass at a wavelength of 1.55 μm [32].

The nonlinear optical properties of glasses have been considered of great interest for photonic devices to be used in several technological applications with a broad spectrum of phenomena, such as optical frequency conversion, optical solitons, phase conjugation, and Raman dispersion. Most of the previous investigations were devoted to crystalline materials such as Quartz, LiNbO3, KTiOPO4, and α-BaB2O4 [19]. Nevertheless, recently the development of special glass compositions exhibiting NLO properties have extended the research into practical applications of glass transparent materials for a wide range of effects, such as fast intensitydependent index, third-harmonic generation (THG), stimulated emission (or stimulated Raman scattering), second harmonic generation (SHG) and the multiphoton absorption [29]. Nonlinear phenomena in glasses, such as nonlinear refractive index, multiphoton absorption, and Raman and Brillouin scattering, depend on the glass itself, its nature (composition and structure), which is responsible for the nonlinearity. On the other hand, in glasses doped with RE ions or semiconductor nanoparticles, in which the glass assumes the role of host, the nonlinearity is produced by interactions between dopant ions, domains, and different phases (such as in glass-ceramics).

The first nonlinear effect in history is often associated with the beginning of the NLO [33], had occurred in 1875, when J. Kerr observed changes in the refractive index of a liquid (CS2) in the presence of an electric field. The Kerr effect or quadratic electro-optic effect is directly related to the third-order nonlinearity, χ(3). Pockels, 20 years later, observed another phenomena, the linear electro-optic effect [34], through the modification of the index of refraction of light in a

**Figure 8.** *Typical infrared (IR) transmission spectra of S-, Se-, and Te-based chalcogenide (ChG) glass [32].*

non-centrosymmetric crystal (Quartz) placed by an electric field. For a long time thereafter, these phenomena were little studied and found of non-practical applications. However, the decisive prerequisite for work out such effects demands high laser pump intensities and suitable phase-matching conditions. Significant effects of NLO (e.g., frequency conversion by taking advantage of second and third harmonic generation) only began to be observed experimentally in the early 60s, after laser invention, due to the fact that such NLO effects require high electromagnetic field intensities to manifest, which was only possible using high-power lasers. P. Franken reported the first observation of the SHG in 1961 after focusing a pulsed ruby laser (λ = 694 nm) into a Quartz crystal; the red incident beam generated an emitted blue light (λ = 347 nm) [35]. THG was soon experimentally reported in 1965 [36]. Since the late of the 80s the interest in NLO properties in glass began to increase [19]. As already mentioned, the nonlinear optical response of glasses is closely related to their anionic polarizability [29, 37] which is described as the deformation of electron clouds (dipoles) when the electromagnetic field is applied. The selection of suitable glass structure and composition can contribute to efficiently optical Kerr effect, self-focusing, intensity-dependent refractive index, and other χ(3) -related effects. In the literature, several reports have shown that the Kerr effect of non-conventional glass compositions is a viable option for self-phase modulation and broadband light generation in the near-infrared [29]. The χ(3) in resonant mode is an additional possibility. Due to the bandwidth requirements for transmitting information for both long-haul and local area networks, Raman amplification is considered a good option to face out the recent developments in the telecommunications fiber industry and diode laser technology. Compared, for instance, with Er3+-doped silica fiber amplifiers, in which the wavelength is fixed at 1550 nm, Raman gain bandwidths are larger, and the operational range only varies with the pump wavelength and the bandwidth of the Raman active medium (the glass nature) [29]. It is well known that the Kerr effect and Raman gain follow the polarity of the glass medium and are deeply impacted by the structure of some specific glasses, such as TeO2 glass, which have large electronic polarizability. Additionality the small length of Te–O bond (2.01 Å) [37, 38] is considered responsible for the large third-order nonlinear optical susceptibility of these kinds of glass [38]. It χ(3) value was as high as 1.4 1012 esu about 50 times as large as that of SiO2 glass [38].

The field of nonlinear optics of glasses has been mainly focused on two main groups: resonant and non-resonant [28]. Non-resonant interactions occur when the light excitation falls in the transparent wavelengths range of the glass longer than its electronic absorption edge. As no electronic transitions take place, the process can be seen as lossless and so an ultrafast glass response due to third-order electronic polarization is assured. Examples are, in general, high refractive index and high dispersion glasses like heavy flint optical glasses, or heavy metal oxide glasses, or chalcogenide glasses.

The resonant ones include semiconductor (quantum dots), or metallic nanoparticles doped glasses [10, 28] and the interaction occurs when the optical field's frequencies are near the electronic absorption edge so that its high resonant nonlinearity can be exploited. However, the isotropic structure glass and its amorphous state have inversion symmetry and do not exhibit second-order nonlinearity, χ(2), or Pockels effect which is necessary for applications such as electro-optic switching and modulation or wavelength conversion in photonic technology. Indeed, glass is a good example of optically isotropic material (as well cubic crystals) that does not exhibit (in principle) any behavior that arises from that condition (e.g. optical birefringence). However, this is not always the case because secondorder nonlinearity can be achieved in glass upon appropriate modification. For


**2.2 Bone Marrow and megakaryocytes in T1DM**

Changes in platelet indices in DM correlate with mekaryocyte DNA content or ploidy [11] reflecting direct influence on bone marrow function even prior to systemic circulation observations. The thrombopoiesis associated with T1DM releases an enormous amount of hyperactive platelets from the bone marrow into circulation [12]. Moreover, the newly released large but immature platelets from bone marrow have increased synthetic capacity for thromboxane and thrombospondin, have enhanced membrane surface expression of receptors such as GPIIbIIIa, CD61, and CD63, [13, 14], and have enhanced aggregation [14, 15]. In DM, the hyperglycemia acts through neutrophils to stimulate thrombopoietin (TPO) production in the liver and enhance megakaryocyte thrombopoiesis [16]. In T1DM, increased circulating TPO has been shown to be linked to elevated blood sugar levels and HbA1c HbA1C [17] providing evidence linking bone marrow megakaryopoiesis and metabolic state.


**5. What is 'publishable'?**

Interestingly, the idea that students should be creating publishable work is one that is deeply held in the field. However, exactly what publishable means is contested, as can be observed in Munden's [27] study. A key term of assessment used by educators in the field is that the work created by a student should be of publishable quality. In the US context, the aim of the MFA is to produce students who can create a publishable book-length submission for their final academic assessment [31]. This view is not unique to the United States. In 2000, the AAWP initiated a programme of state-based seminars, the first of which were held in Adelaide and Melbourne. These seminars involved many of the writing teachers in each state, from the TAFE and University sectors. Topics discussed included 'publishability' and 'publishable standard' [32]. In the United Kingdom in 2013, Cusk [33] reported that work should be 'of publishable standard'. As the 'Subject Association for Creative Writing' in the United Kingdom, the NAWE [34], rather than providing an overall guideline like the Association of Writers & Writing Programs (AWP), presents an outline of what various programmes offer. In this, some courses do state that they aim for publishing outcomes. Others do not indicate whether this is a focus.

Discussion about the term publishable often does not provide clarity as to whether the work will actually be published, and this is a concern to some in the field. In 1999, Dawson [35] argued that a creative component of publishable quality is a work that will withstand the same critical assessment applied to the cannon of literature. In 2005, Dawson [36] contended that the postgraduate student's submission must hold to this standard. This approach does not indicate whether the work will be published. Bourke and Neilsen [37] expressed this concern about unpublished work being assessed and go on to demonstrate that few students at the time of submitting their final projects have achieved this standard or publication. Some seek to address the issue, but there is no one approach. Kroll [38] wrestled with what the term publishable means and challenges the resistance to marketplace preparation. She wants more transparency of the term and proposes either including an assessor from the industry (such as an editor or agent) on an examination committee or having a non-academic who looks for what sells to supply a report to examiners, particularly when the examiner is not well published. Harper [39] considers publishability an out-of-date standard for assessment.

The term continues to be the standard of evaluation, as Boyd [40] reveals when examining the issue of what publishable quality is in creative writing doctorate outcomes from 1993 to 2008 in Australia. Boyd concludes that publishable is still the main method used for evaluation. Her findings also reveal that universities focus on literary creative work that is not as publishable. This further demonstrates a resistance to commercial marketplace outcomes. Boyd seeks to negotiate an alternative in proposing that more popular genres should be given recognition within higher education, and this can be done by reframing the terms used. Krauth [41] notes that Boyd's study demonstrates the publishable nature of creative work developed in programmes because nearly half of the creative works that Boyd assessed in her research did reach the marketplace in some fashion. This analysis indicates that publishing does matter.

Publishing is not just a measure of what students should aim for; publication is valued and is used as a measurement of programme success. Edmonds [42] considers a shelf of published books displayed at the University of Adelaide as a sign of a high standard of success. However, he is careful to point out that publishing is not the only

#### *Creative Writing in Higher Education: A Literature Review of the Marketplace Relationship DOI: http://dx.doi.org/10.5772/intechopen.109427*

outcome from the training and programme. Further, he advises, the current situation is very different to the 1970s when validation came from a few readers. Edmonds argues that in the modern marketplace validation may still be small and localised within academic publishing discourse. He does not want to see a shift to commercialisation for validation. Edmonds [43] also argues the importance of a certain type of narrow market print journal that can provide marketplace outcomes, but does not believe that outcome is necessary for every student. Negotiation of the marketplace can and does come in the form of developing and supporting outlets for publication such as small presses and literary journals and through efforts to recognise and value these publications in the field. Now that many journals are digitally published, this raises questions about the effect on a student's relationship to the marketplace.

Some researchers demonstrate concern about focusing on the idea of publishing and valuing any commodity outcome over the creative act itself. Harper [44] proposes that whether the work is published or publishable is not more valuable than other undertakings in creative practice. Harper is not alone in this view. Others consider that despite the 'publishable' issue, creative writing education is about something else. There are more ways that the field demonstrates resistance to a focus on the marketplace, and these are found in other terms that are used.


**Author details**

Christine Challen1 \* † and Kay Sidebottom2 \* †

1 Sunderland University, Sunderland, United Kingdom

2 University of Stirling, Stirling, Scotland, UK

\*Address all correspondence to<EMAIL_ADDRESS>and<EMAIL_ADDRESS>
† The authors are equal contributing.

© 2024 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Thinking, Alternatively: The Application of Art, Philosophy and Holistic Practice in the Teaching… DOI: http://dx.doi.org/10.5772/intechopen.114039*


**4. Model-based controller design**

In a typical AO system, the requirement of changing the mirror shape within a predefined time *T* from an initial deformation △*w*∗ 1 to a final deformation △*w*∗ 2 is derived from the higher-ranking AO control loop (see Figure 6). This control loop runs at a fixed cycle time and sends mirror deformations △*w*∗(*t*) to the shape controller in order to correct for measured optical disturbances in the AO system (not shown in Figure 6). The step inputs △*w*∗(*t*) for the model-based shape controller in Figure 6 are in general ill-suited for model-based feedforward control and need to be filtered beforehand. The transition time *T* for changing the mirror shape from its initial shape △*w*1 to its new desired shape △*w*2 is assumed to be smaller than the cycle time of the higher-ranking AO control loop. This requirement assures that the closed loop mirror dynamics can be neglected with respect to the higher-ranking AO control loop when designing the AO loop controller.

For model-based shape control of deformable mirrors, the control scheme shown in Figure 6 can be used. It consists of a deformable mirror with multiple inputs *u* = *u*1 *u*2 ... *uM*

3 In Equation (20) and for the following analysis, it is assumed that the eigenfunctions *Wk* (*r*, *θ*) are normalized with respect to the *L*2 scalar product �*Wk* , *Wk* � = 1.

(force actuators), multiple outputs *y* = *y*1 *y*2 ... *yM* (position sensors), and an underlying shape controller in a two-degree-of-freedom control structure. The control structure contains an online trajectory generator Σ∗ *traj*, a static and a dynamic model-based feedforward controller Σ−1 *stat* and Σ−1 *dyn*, and a model-based feedback controller Σ*crtl*.

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Modeling and Control of Deformable Membrane Mirrors

*traj*,

*traj*

**Figure 6.** Two-degree-of-freedom structure for shape control of deformable mirrors modeled as a multi-input multi-ouput (MIMO) system with co-located non-contacting force actuators and position sensors consisting of a trajectory generator Σ∗

**Figure 7.** Illustration of a general input signal △*w*∗(*t*) and the resulting output signal *yd* (*t*) of the trajectory generator Σ∗

There are several ways to generate the output signal *yd* based on the step input △*w*∗, e.g. finite impuls response (FIR) filters, sigmoid basis functions, or segmented polynomials. In [20] and [21] a detailed introduction to this topic is given with respect to control of deformable mirrors. A more general and comprehensive discussion on trajectory generation can be found in [45]. Here, a polynomial ansatz function is used to generate the output signal

Depending on the required smoothness of *yd*, the reference trajectory *yd* is described by

2 − △*w*∗

1 + (△*w*∗

*stat* and Σ−1

*dyn*, and a feedback controller Σ*ctrl* .

1 )*κ*(*t* − *t*0), *t* ∈ [*t*0, *t*1] , *t*1 = *t*0 + *T*, (21)

a static and a dynamic model-based feedforward controller Σ−1

within a fixed transition time *T* = *t*2 − *t*1.

*yd* for simplicity.

Σ∗

*traj* : *yd*(*t*) = △*w*∗

There are two control objectives adressed by the control structure shown in Figure 6. One is achieving good tracking performance along a spatio-temporal trajectory *yd* describing the evolution of the mirror shape from an initial shape △*w*∗ 1 to a new commanded mirror shape △*w*∗ 2 in a predefined time *T*. The second control objective is the stabilization of the deformable mirror shell by feedback control Σ*ctrl* along the generated trajectory *yd* and in its final position △*w*∗ 2 with respect to external disturbances and model uncertainties.

#### **4.1. Design of the trajectory generator** Σ∗ *traj*

The trajectory generator Σ∗ *traj* generates a continuously differentiable output signal *yd* that reaches its final value △*w*∗ 2 within a fixed transition time *T*. In Figure 7, the piecewise constant input △*w*∗ and filtered output signal *yd* are illustrated for a single set-point change. Since the deformable mirror consists of many inputs and outputs, the signals △*w*∗ and *yd* are vectorial variables containing deformation values either in modal or physical coordinates. The choice of units can be driven by the higher-ranking AO control loop and is only of minor importance for the following section.4

**Figure 5.** Bode diagram of the analytical local transfer functions of a deformable mirror at different co-located actuator/sensor positions (normalized).

4 A linear forward or backward transformation can be used to transform physical coordinates *w*(*r*, *θ*, *t*) into modal coordinates *fk* (*t*) using the orthonormal eigenfunctions *Wk* (*r*, *θ*) in 13.

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12 Adaptive Optics

controller Σ−1

shape △*w*∗

positions (normalized).

Phase [°]

0

Amplitude [dB]

final position △*w*∗

The trajectory generator Σ∗

reaches its final value △*w*∗

importance for the following section.4

Actuator/Sensor 21 Actuator/Sensor 110 Actuator/Sensor 537

coordinates *fk* (*t*) using the orthonormal eigenfunctions *Wk* (*r*, *θ*) in 13.

(force actuators), multiple outputs *y* =

**4.1. Design of the trajectory generator** Σ∗

the evolution of the mirror shape from an initial shape △*w*∗

an online trajectory generator Σ∗

*stat* and Σ−1

*y*1 *y*2 ... *yM*

shape controller in a two-degree-of-freedom control structure. The control structure contains

*dyn*, and a model-based feedback controller Σ*crtl*. There are two control objectives adressed by the control structure shown in Figure 6. One is achieving good tracking performance along a spatio-temporal trajectory *yd* describing

deformable mirror shell by feedback control Σ*ctrl* along the generated trajectory *yd* and in its

*traj*

constant input △*w*∗ and filtered output signal *yd* are illustrated for a single set-point change. Since the deformable mirror consists of many inputs and outputs, the signals △*w*∗ and *yd* are vectorial variables containing deformation values either in modal or physical coordinates. The choice of units can be driven by the higher-ranking AO control loop and is only of minor

100 101 102 103 -150

Frequency [Hz]

Frequency [Hz]

**Figure 5.** Bode diagram of the analytical local transfer functions of a deformable mirror at different co-located actuator/sensor

4 A linear forward or backward transformation can be used to transform physical coordinates *w*(*r*, *θ*, *t*) into modal

100 101 102 103

2 in a predefined time *T*. The second control objective is the stabilization of the

2 with respect to external disturbances and model uncertainties.

*traj*, a static and a dynamic model-based feedforward

*traj* generates a continuously differentiable output signal *yd* that

2 within a fixed transition time *T*. In Figure 7, the piecewise

(position sensors), and an underlying

1 to a new commanded mirror

**Figure 6.** Two-degree-of-freedom structure for shape control of deformable mirrors modeled as a multi-input multi-ouput (MIMO) system with co-located non-contacting force actuators and position sensors consisting of a trajectory generator Σ∗ *traj*, a static and a dynamic model-based feedforward controller Σ−1 *stat* and Σ−1 *dyn*, and a feedback controller Σ*ctrl* .

**Figure 7.** Illustration of a general input signal △*w*∗(*t*) and the resulting output signal *yd* (*t*) of the trajectory generator Σ∗ *traj* within a fixed transition time *T* = *t*2 − *t*1.

There are several ways to generate the output signal *yd* based on the step input △*w*∗, e.g. finite impuls response (FIR) filters, sigmoid basis functions, or segmented polynomials. In [20] and [21] a detailed introduction to this topic is given with respect to control of deformable mirrors. A more general and comprehensive discussion on trajectory generation can be found in [45]. Here, a polynomial ansatz function is used to generate the output signal *yd* for simplicity.

Depending on the required smoothness of *yd*, the reference trajectory *yd* is described by

$$\Delta^\*\_{\text{traj}}: \quad y\_d(t) = \triangle w\_1^\* + (\triangle w\_2^\* - \triangle w\_1^\*) \kappa(t - t\_0), \quad t \in [t\_0, t\_1], \ t\_1 = t\_0 + T,\tag{21}$$

$$\kappa(t) = \begin{cases} 0 & \text{if } t \le (t\_1 - t\_0) \\ \frac{(2n+1)!}{n!(t\_1 - t\_0)^{2n+1}} \sum\_{i=0}^{n} \frac{(-1)^{n-i}}{i!(n-i)!(2n-i+1)} (t\_1 - t\_0)^i t^{2n-i+1} \\ & \text{if } 0 \le t \le (t\_1 - t\_0) \\ 1 & \text{if } t \ge (t\_1 - t\_0). \end{cases} \tag{22}$$

static command *ustat*

system dynamics Σ−1

displacement [µm]

1 measured

0 5 10 15 20 time [ms]

without feedforward control showing a settling time above 5-10 ms

for a practical implementation in existing systems.

Σ−1

Afterwards, the final feedforward command *ud* can be computed as

is implemented at this stage and only the trajectory generator Σ∗

therein in case of zero dynamics in the differential equation (19).

*stat* : *ustat*

*m* (*t*) as

identified

displacement [µm]

**Figure 8.** Measured step responses of the first and second eigenfunctions of an Alpao DM88 deformable membrane mirror

consider only the static components of the inverse system dynamics (23) and compute a

*m* (*t*) = 1

Regarding the computational complexity, in [20] is shown that the computational demands of feedforward control scale quadratically with the number of system inputs and outputs. However, since the control signal only needs to be computed with the frequency of the outer AO-loop (e.g. 1 kHz in modern astronomical AO systems), there are currently no burdens

In Figure 8 and Figure 9 measurement results of a setpoint change without and with feedforward control for an Alpao deformable mirror with 88 voice coil actuators is shown. In the experiment, the first two eigenfunctions of the deformable mirror are excited by a modal step input (spatially distributed step command for all 88 actuators at the same time) and a model-based feedforward control input *ud* (spatially distributed time-varying signal for all 88 actuators) based on a polynomial of degree *n* = 3 and identified modal damping and eigenfrequencies in (24). Clearly, overshoot and settling time of the feedforward scheme show considerable improvements to a pure step command. Thereby, no feedback control

Feedforward control of deformable membrane mirrors has also been demonstrated at the P45 adaptive secondary prototype of the Large Binocular Telescope adaptive secondary mirror in [21] with similar results. In addition, a flatness based feedforward control is proposed

In order to further improve the system response to feedforward control and in order to add additional disturbance rejection capabilities to the DM control scheme, a model-based

*dyn* are used to generate the control command *u* = *ud* (see Figure 6).

*bj ω*2 *j fj*(*t*)

0 5 10 15 20 time [ms]

Modeling and Control of Deformable Membrane Mirrors

*ud*(*t*) = *udyn*(*t*) + *ustat*(*t*). (26)

. (25)

*traj* and the inverse modal

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measured identified

The transition time *T* must be chosen shorter than the higher-ranking AO loop cycle time but long enough to comply with input constraints of the force actuators. The shorter the transition time *T* is chosen, the higher the required input forces *u* will be in order to drive the mirror from one deformation to another one. The required smoothness of the trajectory *yd* depends on the parameter *n* and should be chosen *n* ≥ 3 in order to be able to apply the model-based feedforward control scheme presented in the next section.

#### **4.2. Design of the model-based feedforward controller** Σ−1 *stat* **and** Σ−1 *dyn*

The basic idea of model-based feedforward control is the application of a desired input signal *ud*(*t*) such that the system response *y*(*t*) follows a desired output behavior *yd*(*t*) (see Figure 6). The transient deformation of the deformable mirror is described by the planned output *yd*(*t*) and leads to a new steady shape △*w*∗ 2 using an inverse model of the system dynamics of the DM. The design of the desired response *yd*(*t*) is performed in the on-line trajectory generator Σ∗ *traj* described in Section 4.1.

As shown in Figure 6, the feedforward control concept is seperated into a static and a dynamic component Σ−1 *stat* and Σ−1 *dyn*. The feedforward component Σ−1 *dyn* contains the dynamic inverse of the relevant mirror dynamics and is based on the modal system dynamics (19) reading

$$f\_{\vec{j}}(t) + \underbrace{\frac{\lambda\_d + \kappa\_d \mathfrak{f}\_{\vec{j}}^4}{\rho h}}\_{2d\_{\vec{j}}\omega\_{\vec{j}}} f\_{\vec{j}}(t) + \underbrace{\frac{D\mathfrak{f}\_{\vec{j}}^4}{\rho h}}\_{\omega\_{\vec{j}}^2} f\_{\vec{j}}(t) = \underbrace{\frac{1}{\rho h} \sum\_{m=1}^M W\_{\vec{j}}(r\_m, \theta\_m)}\_{b\_{\vec{j}}} u\_m(t) \,. \tag{23}$$

By transforming the desired output behavior *yd* into modal coordinates *fj*(*t*) with relation (13), the left hand side of Equation (23) is fully defined by the desired modal output behavior *fk*(*t*) and the first two time derivatives of *fk*(*t*). Thus, the dynamic part of the required input command *ud* achieving the planned output behavior *fk*(*t*) can be computed as

$$
\Sigma\_{dyn}^{-1}: \quad u\_m^{dyn}(t) = \frac{1}{b\_j} \left( \vec{f}\_j(t) + 2d\_j \omega\_j \dot{f}\_j(t) + \omega\_j^2 f\_j(t) \right) \tag{24}
$$

for a limited number of *j* = 1, . . . , *N* dynamic eigenmodes. Since viscous and Rayleigh damping typically increase for higher order eigenmodes, it is not necessary to consider all eigenmodes in the dynamic feedforward component Σ−1 *dyn*. Instead, it is sufficient to

with

**Figure 8.** Measured step responses of the first and second eigenfunctions of an Alpao DM88 deformable membrane mirror without feedforward control showing a settling time above 5-10 ms

consider only the static components of the inverse system dynamics (23) and compute a static command *ustat m* (*t*) as

$$
\Sigma\_{\rm stat}^{-1} : \quad \mu\_m^{\rm stat}(t) = \frac{1}{b\_j} \left( \omega\_j^2 f\_j(t) \right). \tag{25}
$$

Afterwards, the final feedforward command *ud* can be computed as

14 Adaptive Optics

generator Σ∗

reading

dynamic component Σ−1

¨ *fj*(*t*) +

*κ*(*t*) =

*yd*(*t*) and leads to a new steady shape △*w*∗

*traj* described in Section 4.1.

*stat* and Σ−1

*λd* + *κdβ*4 *j*

˙ *fj*(*t*) +

*ρh* � �� � 2*djωj*

Σ−1

*dyn* : *udyn*

all eigenmodes in the dynamic feedforward component Σ−1

  0 if *t* ≤ (*t*1 − *t*0) (2*n*+1)! *n*!(*t*1−*t*0)2*n*+1

1 if *t* ≥ (*t*1 − *t*0).

model-based feedforward control scheme presented in the next section.

**4.2. Design of the model-based feedforward controller** Σ−1

*n* ∑ *i*=0 (−1)*n*−*i*

The transition time *T* must be chosen shorter than the higher-ranking AO loop cycle time but long enough to comply with input constraints of the force actuators. The shorter the transition time *T* is chosen, the higher the required input forces *u* will be in order to drive the mirror from one deformation to another one. The required smoothness of the trajectory *yd* depends on the parameter *n* and should be chosen *n* ≥ 3 in order to be able to apply the

The basic idea of model-based feedforward control is the application of a desired input signal *ud*(*t*) such that the system response *y*(*t*) follows a desired output behavior *yd*(*t*) (see Figure 6). The transient deformation of the deformable mirror is described by the planned output

of the DM. The design of the desired response *yd*(*t*) is performed in the on-line trajectory

As shown in Figure 6, the feedforward control concept is seperated into a static and a

inverse of the relevant mirror dynamics and is based on the modal system dynamics (19)

By transforming the desired output behavior *yd* into modal coordinates *fj*(*t*) with relation (13), the left hand side of Equation (23) is fully defined by the desired modal output behavior *fk*(*t*) and the first two time derivatives of *fk*(*t*). Thus, the dynamic part of the required input

for a limited number of *j* = 1, . . . , *N* dynamic eigenmodes. Since viscous and Rayleigh damping typically increase for higher order eigenmodes, it is not necessary to consider

*Dβ*4 *j ρh* � �� � *ω*2 *j*

command *ud* achieving the planned output behavior *fk*(*t*) can be computed as

*bj* � ¨

*m* (*t*) = 1

*dyn*. The feedforward component Σ−1

*fj*(*t*) = 1

*fj*(*t*) + 2*djωj* ˙

*ρh*

*M* ∑ *m*=1

*i*!(*n*−*i*)!(2*n*−*i*+1)(*t*1 − *t*0)*i*

*t* 2*n*−*i*+1

(22)

if 0 ≤ *t* ≤ (*t*1 − *t*0)

*stat* **and** Σ−1

2 using an inverse model of the system dynamics

*Wj*(*rm*, *θm*)

� �� � *bj*

*fj*(*t*) + *ω*2

*j fj*(*t*) �

*dyn*. Instead, it is sufficient to

*dyn*

*dyn* contains the dynamic

*um*(*t*), (23)

(24)

with

$$
\mu\_d(t) = \mu^{dyn}(t) + \mu^{stat}(t). \tag{26}
$$

Regarding the computational complexity, in [20] is shown that the computational demands of feedforward control scale quadratically with the number of system inputs and outputs. However, since the control signal only needs to be computed with the frequency of the outer AO-loop (e.g. 1 kHz in modern astronomical AO systems), there are currently no burdens for a practical implementation in existing systems.

In Figure 8 and Figure 9 measurement results of a setpoint change without and with feedforward control for an Alpao deformable mirror with 88 voice coil actuators is shown. In the experiment, the first two eigenfunctions of the deformable mirror are excited by a modal step input (spatially distributed step command for all 88 actuators at the same time) and a model-based feedforward control input *ud* (spatially distributed time-varying signal for all 88 actuators) based on a polynomial of degree *n* = 3 and identified modal damping and eigenfrequencies in (24). Clearly, overshoot and settling time of the feedforward scheme show considerable improvements to a pure step command. Thereby, no feedback control is implemented at this stage and only the trajectory generator Σ∗ *traj* and the inverse modal

system dynamics Σ−1 *dyn* are used to generate the control command *u* = *ud* (see Figure 6).

Feedforward control of deformable membrane mirrors has also been demonstrated at the P45 adaptive secondary prototype of the Large Binocular Telescope adaptive secondary mirror in [21] with similar results. In addition, a flatness based feedforward control is proposed therein in case of zero dynamics in the differential equation (19).

In order to further improve the system response to feedforward control and in order to add additional disturbance rejection capabilities to the DM control scheme, a model-based

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, (28)

with *x*(*t*) = �

*A* =

*Dβ*4 1

> . . . . . . . . . ... . . . . . . . . . . .

*f*1(*t*) *f*2(*t*) ... *fN*(*t*) ˙

0 *Dβ*4 2

0 0 *Dβ*4

3

0 0 0 ... *Dβ*4

*B* = 1 *ρh*

*C* =

*J* = �∞

0

. .

system along the planned trajectory *xd*(*t*) using the optimization criterion

(*x*(*t*) − *xd*(*t*))

*f*1 ˙ *f*2 ... ˙

*ρh* 0 0 ... 0 *λd*+*κdβ*4

*fN*(*t*)

0 0 0 ... 0 1 0 0 ... 0

0 0 0 ... 0 0 1 0 ... 0

0 0 0 ... 0 0 0 1 ... 0

*ρh* ... 0 0 0 *λd*+*κdβ*4

0 0 0 ... 0 0 0 0 ... 1

*W*1(*r*1, *θ*1) ... *W*1(*rM*, *θM*) *W*2(*r*1, *θ*1) ... *W*2(*rM*, *θM*)

. ... .

*WN*(*r*1, *θ*1) ... *WN*(*rM*, *θM*) 0 ... 0

. ... .

0 ... 0

*W*1(*r*1, *θ*1) ... *WN*(*r*1, *θ*1) 0 ... 0 *W*1(*r*2, *θ*2) ... *WN*(*r*2, *θ*2) 0 ... 0

*W*1(*rM*, *θM*) ...  Assuming the pair (*A*, *Q*) is observable, a minimization of (31) can be achieved by the state feedback

. . . . . ... . . .

. ... .

*ρh* 0 ... 0 0 *λd*+*κdβ*4

*N*

. .

. .

  1

�*T* and system matrices

*ρh* 0 0 ... 0

*ρh* 0 ... 0

3 *ρh* ... 0

>

> >

*T Q* (*x*(*t*) − *xd*(*t*)) + *u*(*t*)*TRu*(*t*)d*t*, (31)

Σ*ctrl* : *u*(*t*) = −*K* (*x*(*t*) − *xd*(*t*)) (32)

. ... .

Modeling and Control of Deformable Membrane Mirrors

. .

*N ρh*

, (29)

. (30)

http://dx.doi.org/10.5772/52726

2

*ρh* 0 0 0 ... *λd*+*κdβ*4

. .

. .

**Figure 9.** Measured responses of the first and second eigenfunctions of an Alpao DM88 deformable membrane mirror with feedforward control showing a settling time of 2 ms

feedback controller is designed in the following section and its sparse structure for local implementation is discussed.


**6. Specific organs**

## **6.1 Liver**

The liver has vital functions including plasma protein synthesis, hormone production, detoxification, decomposition of red blood cells, and regulation of glycogen storage. Due to these important functions, liver failure poses a threat to life. Historically, liver transplantation is second to kidney transplantation. Liver transplantation is a definitive treatment modality for acute liver failure and endstage chronic liver disease. The shortage of organ supply has initiated the usage of new therapeutic systems to reduce mortality and bridge patients to transplantation.

Albumin dialysis, plasmapheresis, column perfusion, and hybrid devices are among the instruments that have been used to compensate for liver function.

Artificial liver support systems may replace the failing functions of the organ and therefore time is gained for liver regeneration or transplantation. Artificial liver support systems are expected to detoxify, replenish plasma proteins, and reverse the inflammatory process. Among the artificial liver support systems, molecular adsorbent recirculating system [MARS], therapeutic plasma exchange [TPE], single- pass albumin dialysis [SPAD], and Prometheus can be named. MARS uses albumin dialysis to replace the detoxification function of the liver while TPE improves survival in patients with acute liver failure. MARS and TPE improve systemic hemodynamics and the grade of hepatic encephalopathy [36, 37].

Extracorporeal liver support systems are either cell- based [biological] or noncell-based. Non- cell-based systems include high volume plasma exchange and albumin dialysis. Bioartificial liver systems improve neurologic function, reduce intracranial pressure and increase cerebral perfusion pressure Biological extracorporeal liver support systems aim to support the failing liver through detoxification [38].

Shen Yi and colleagues have performed a time-series-based meta-analysis of randomized clinical trials and observational studies that examined differences in mortality in acute-on-chronic liver failure patients treated with artificial liver support systems. The results revealed that an artificial liver support system had reduced the risk of short-term [1–3 months] mortality for patients with acute-onchronic liver failure by nearly 30%. As the results of the meta-analysis suggested, an artificial liver support system might reduce medium-term [6 month-1 year]