Information of the datasets used in the research.
\r\n\tAn update on clinical manifestations, their assessment, monitoring, and imagiology, including peripheral arthritis, enthesopathy, and extra-articular findings, and, the differential diagnosis with other diseases which evolves with axial and peripheral calcifications will be provided.
\r\n\r\n\t
\r\n\tAn important component of this book must be dedicated to the more recent treatments namely with biologic therapies but focusing also on new small molecule inhibitors and experimental therapies.
Fiber lasers have been studied almost from the onset of laser demonstration. Research on the development of innovative laser systems has been of constant interest in optics and photonics, having fast growth and becoming a central research area in scientific and industrial implementations. Fiber lasers have been widely studied because their unique characteristics of high power confinement, high beam quality, low insertion loss, compactness, and ruggedness. In general, they are attractive for different application areas such as medicine, telecommunications, optical sensing, and industrial material processing.
Fiber lasers make use of stimulated emission to generate light by using an active medium for gain supply. The use of fibers doped with rare-earth elements provides a gain medium with great thermal and optical properties for fiber laser development, in contrast to solid state lasers. Erbium-doped fibers (EDF) have been widely used for fiber laser implementations; however, in the last decade, the constant search of efficiency improvement in terms of very high gain with low pumping thresholds has significantly increased the use of Ytterbium doped fibers because they offer an efficiency above 80% [1].
Moreover, high-power fiber lasers are also of high interest for different applications such as spectroscopy, pump sources, and the study of nonlinear phenomena. In contrast with solid state lasers, a fiber laser requires longer interaction lengths favoring the occurrence of nonlinear effects when high power is achieved, making them desirable for optical switching, nonlinear frequency conversion, solitons, and supercontinuum generation, among other applications.
As it is known, pump diodes provide pump power limited to a few watts. This restriction also limits the fiber laser output power when conventional-doped (single-clad) fibers are used. With the development of double-clad fibers (DCF), high-power fiber lasers experienced significant advances since DCF makes an output power increase attainable. In conjunction with clad-pumping techniques, the DCF feature of large surface area-to-gain volume ratio, in addition to high doping concentration, offer high output power with an improved spatial beam confinement, in contrast with the use of single-clad doped fibers [2].
However, achieving high power continuous-wave (CW) operation of a fiber laser without output power fluctuations is not as straightforward. Taking into account this fact, the development of fiber lasers in pulsed regime provides a feasible alternative. In comparison with CW fiber lasers, pulsed fiber lasers provide high peak power that can be used in the generated wavelength or shifted to other wavelength range by nonlinear frequency conversion.
The most important pulsed regimes are Q-switching and mode-locking. In contrast with CW operation, in pulsed regimes the output is time dependent. In pulsed lasers by the Q-switched technique, stable and regular short pulses are obtained with pulse durations in the nanoseconds range; in contrast to ultrashort pulses obtained by mode-locked techniques, in this case the pulse duration corresponds to several cavity round trips. Q-switching can be developed by passive and active techniques.
Passive Q-switching is performed by using a saturable absorber element placed inside the cavity including graphene [3–5], carbon nanotubes (CNT) [6–8], transition metal-doped crystals [9–11], and semiconductor saturable absorber mirrors (SESAM) [12, 13]. On the other hand, the active Q-switching technique is based on the use of a modulator driven by an external electrical generator. Cavity loss modulation is typically performed by electro-optic modulators (EOM) [14, 15], and acousto-optic modulators (AOM) [16–18]. The EOM and the AOM are based on completely different principles of operation. While the EOM is based on the Pockels effect, the AOM modulates the refractive index of sound waves that generate a periodic grating as it propagates through the medium. In terms of operation, the main difference is the modulation bandwidth. Typically, the modulation bandwidth of an EOM is 500 kHz to 1 MHz, while for AOM is in the range of 50 to 100 MHz.
The use of the active Q-switching technique for pulsed laser operation allows higher energy pulses and stability. These advantages are increased in lasers based on integrated optics (all-optic) or all-fiber setups.
Otherwise, from the onset of the fiber Bragg gratings (FBG), the incorporation into the design of optical fiber lasers has been almost immediate, contributing significantly to the progress in this particular area. FBGs have been widely used as narrow band reflectors for generated laser wavelength selection. FBGs have unique advantages as optical devices including easy manufacture, fiber compatibility, low cost, and wavelength selection, among others. Moreover, the FBG central wavelength can be displaced or modified by mechanical strain or temperature application [19, 20]. This feature makes them capable devices for fiber lasers wavelength tuning [21] and for all-fiber modulation techniques [22, 23].
Moreover, dual wavelength fiber lasers (DWFL) have been studied previously [24–26]. Obtaining two wavelengths by using a single laser cavity has been attractive for potential application in areas such as optical sources, optical communications, optical instrumentation and others. The phenomenon of obtaining two wavelengths simultaneously with equal powers has been studied in terms of the competition between generated laser lines to improve the stability and DWFL emission control methods.
The use of variable optical attenuators (VOA) and high birefringence fiber loop Sagnac interferometer (
Furthermore, wavelength tuning in pulsed DWFL development suggests its possible application in microwave and mainly terahertz generation. For DWFL improvement, different techniques for tuning and setting the separation between generated laser lines have been developed. The main goal in DWFL wavelength tuning is to obtain wide separation and continuous wavelength tuning. A reliable approach for wavelength tuning in DWFLs is the use of FBGs where the Bragg wavelength is shifted by temperature changes [27] or by mechanical strain application [28, 29].
In this chapter, a brief explanation on Q-switched fiber laser operating principle for active technique is presented. Also, a description of operation characteristics of Q-switched lasers, mainly in active Q-switching technique, is presented. Additionally, the current state of the art (to our knowledge) regarding DWFL in actively Q-switched pulsed regime is reviewed.
Furthermore, experimental setups for a dual wavelength actively Q-switched fiber laser and an actively Q-switched fiber laser with single and dual wavelength operations are experimentally demonstrated and analyzed. The experimental results of the lasers are discussed and compared in terms of operation characteristics, including repetition rate, pulse duration, pulse energy, average power, and peak power.
Q-switching is a significantly employed technique in fiber lasers improvement. Q-switching is a suitable technique to obtain powerful pulses at low repetition rates from a few kHz to 100 kHz, typically; it can obtain short pulses in nanoseconds range, corresponding to several cavity round trips. This is in contrast with the mode-locking technique in which ultrashort pulses are obtained.
In recent years, Q-switched fiber lasers have been attractive due to their applications in medicine, optical time-domain reflectometry, terahertz generation, optical instrumentation, remote sensing, and materials processing in the industry.
Q-switching is performed by cavity losses modulation. The intracavity losses are maintained on a high level until the gain medium accumulates a considerable amount of energy supplied by the pumping source. Then the losses are abruptly minimized to build up the laser radiation within the cavity. As a result, a pulse with energy in a range of micro-Joules (even milli-Joules) is generated. Thus, the variation of the intracavity losses corresponds to a laser resonator Q-factor modulation. In general, Q-switched fiber lasers can be obtained with continuous or pulsed pump power. In the case of continuous pump power application, an important condition must be considered: a longer gain medium upper-state lifetime is required to avoid energy loss by fluorescence emission to reach stored high energy as a consequence. Particularly, in fiber lasers the saturation energy has to be high to prevent excessive gain that can lead to an early inception of laser generation. The pulse energy is commonly higher than the gain medium saturation energy.
Although Q-switched lasers based on bulk optics are regularly preferred over fiber lasers because of their larger mode areas to store more energy, the incorporation of bulk components in fiber lasers leads to a detriment of simplicity, robustness, and alignment of the laser. Also, the use of bulk elements in fiber lasers produces a beam quality degradation and the addition of high cavity losses, resulting in a decrease of laser performance and efficiency. Thus, in bulk fiber lasers approaches, the use of higher pump powers is required to increase the laser output power [23].
The Q-switching technique can be performed by passive and active methods:
Passive Q-switching is performed by using a saturable absorber element placed inside the cavity, which modulates automatically the losses within the laser cavity. As already mentioned, the variety of saturable absorber elements in passively Q-switched fiber lasers usually includes the use of graphene, CNT, metal-doped crystals, and SESAM [3–13]. The pulse repetition rate is determined and varied by the applied pump power, while the pulse duration and pulse energy are affected by the cavity and the Q-switching element parameters and commonly remain fixed. Thus, the repetition frequency cannot be modified with independence of other operation parameters [30]. To reach an efficient performance, the saturable absorber recovery time, commonly, has to be higher than the pulse duration and lower than the gain medium upper-state lifetime. Laser pulses generated with passive Q-switching typically present a low repetition rate range because of the applied pump power range. The main advantages of passively Q-switched lasers are their simple designs and low cost due to the fact that the use of external modulators and their electronics are not required.
On the other hand, active Q-switching is based on the Q-factor modulation using a modulator included in the fiber laser design. The modulation element is driven by an external electrical generator. In the active Q-switching technique, the stored energy, when cavity loss is high, generates a pulse soon after an external electrical signal arrives on the modulator driving the intracavity losses to a low level. In this case, the pulse duration and the pulse energy depends on the energy stored by the gain medium. Hence, the pump power and the repetition rate variations modify the achieved pulse duration and pulse energy. For the active technique, the modulating switching time does not have to be similar to the pulse duration, the pulse duration is in any case of the order of the laser resonator round-trip time. As has been mentioned previously, active cavity losses modulation is typically performed by EOMs and AOMs [14–18]. According to the technological progress, the used modulators have been experiencing important changes. In early actively Q-switched lasers approaches, modulators were mainly using bulk components. Then, they were designed by using integrated optics coupled to optical fibers. Recently, all-fiber modulators have been included in fiber laser designs to increase the overall performance of the laser. The acousto-optic Q-switching is performed by a radio frequency (RF) power controlling a transducer. The generated acoustic wave provides a sinusoidal optical modulation of the gain medium density resulting in an intracavity loss modulation. AOMs can rapidly modulate the cavity losses that allows the Q-switched pulses generation with pulse durations of a hundred of nanoseconds. The shortest pulse durations and the highest pulse energies are obtained at the lowest allowed repetition rate, however, at the cost of obtaining low output average power. The use of the active Q-switching technique for pulsed laser operation allows higher energy pulses and stability. These advantages increase with laser designs based on integrated optics or all-fiber laser designs.
Moreover, most of the Q-switched fiber laser approaches are focused on the use of single-clad fibers as a gain medium. In comparison with single-clad Er or Yb doped fibers, Er/Yb double clad co-doped fibers (EYDCF) can be used to suppress the self-pulsing of Er ion-pair [4]. This effect, in addition to cladding pump techniques, can be used to increase the pump power efficiency, minimizing the gain degradation by using EYDCF as a gain medium.
Regarding passively Q-switched fiber lasers, different approaches using EYDCF have been reported [30–33]. Laroche et al. [30] in 2002 presented a pioneer experimental setup of pulsed lasers for passive Q-switched technique using EYDCF as gain medium. They presented an open cavity laser using Cr2+:ZnSe as a saturable absorber. V. Philippov et al. [33] reported a similar configuration by using Cr2+:ZnSe and Co2+:MgAl2O4 as saturable absorbers. The maximum average power of 1.4 W was achieved in pulses with durations from 370 to 700 ns for a repetition rate between 20 kHz and 85 kHz.
In the case of actively Q-switched lasers using EYDCF, to our knowledge, a small number of approaches have been reported [34, 35]. Recently, González-García et al. [34] reported a linear cavity EYDCF laser Q-switched by an acousto-optic modulator. The pump power is introduced to the EYDCF by a free space subsystem carefully optimized by using a lenses design.
Nowadays, the development of DWFL has been of interest because of their ability to obtain two laser wavelengths simultaneously by the use of a single cavity. DWFL\'s advantages are low cost, simple design, fiber compatibility, and low loss insertion devices, making feasible more complex optical devices design. From its first approaches in CW fiber lasers, DWFL research has increased because of their potential applications in different areas such as optical communication systems, optical instrumentation, optical sources, and spectral analysis.
In recent years, the experience in the study of DWFL in CW regime has been incorporated into the implementation of DWFLs in pulsed regime. With this advancement, it has opened the possibility of new applications where high output power is required such as research of nonlinear phenomenon study and remote sensing. The main issue in DWFL operation is centered on the difficulty of obtaining two stable wavelengths simultaneously because doped fibers behave as a homogeneous gain medium at room temperature, causing a competition between the generated laser lines that leads to a generated laser line\'s instability.
Commonly used methods to balance the generated wavelengths include the use of polarization controllers (PC) and variable optical attenuators (VOA), among others; however, most of them are arbitrary methods in the absence of a measurable physical variable for wavelength competition analysis and characterization, affecting the repeatability in the laser performance. The methods are focused on adjusting laser intracavity losses to balance the simultaneously generated wavelengths. In previously reported investigations, the Sagnac interferometer with high birefringence (Hi-Bi) fiber loop capability has been theoretically and experimentally demonstrated as a trustworthy alternative for the adjustment of losses within the cavity [21, 36–38], since the Hi-Bi FOLM periodical spectrum can be finely displaced in wavelength by temperature variations applied on the Hi-Bi fiber loop [37].
Different experimental setups of DWFL by passive Q-switching technique have been reported [39–41]. However, to our knowledge, there have not been reported approaches using EYDCF as a gain medium. Concerning cavity losses adjustment for dual wavelength laser operation, the most frequent method is the use of PC in ring cavity fiber lasers. H. Ahmad et al. [40] reported a ring cavity passive Q-switched DWFL operating at 1557.8 nm and 1559 nm by using PC for dual wavelength generation. A nonlinear optical loop mirror (NOLM) with a dispersion-decreasing taper fiber (DDTF) in the fiber loop is used as a passive Q-switching element.
Regarding actively Q-switched fiber lasers, only few attempts have been reported in which dual wavelength emission is obtained. In 2013, G. Shayeganrad [42] reported a compact linear cavity actively Q-switched DWFL. The Q-switching is performed by an AOM. The gain medium is a c-cut Nd:YVO4 crystal with a feature of dual-wavelength generation in Q-switched regime without adjustment elements. An undoped YVO4 crystal is used for stimulated Raman scattering (SRS) effect enhancement. The SRS simultaneous wavelengths at 1066.7 and 1083 nm are shifted at 1178 and 1199.9 nm.
S.-T. Lin et al. [43] reported a selectable dual-wavelength actively Q-switched laser. By using two electro-optic periodically poled Lithium niobate (PPLN) integrated crystals, the output wavelengths between 1063 and 1342 nm are selected with voltage variations on PPLN Bragg modulators (EPBM) sections. It is worth mentioning that both cited experimental setups are designed with bulk optic elements with high pump power application, around 20 W. As has been said above, such designs require fine alignment, so typically, efficiency and instability problems are presented.
The all-fiber or optical fiber coupled laser systems promise to be an option for solving alignment issues minimizing losses within the laser cavity. Pump-to-signal efficiency can be increased and, consequently, highly increased pump power is not required to obtain more energetic pulses. However, for such designs, the output power is typically limited by the maximum signal power handled by the employed components.
Therefore, the use of double-clad doped fibers provides a stable and straightforward method to generate high energy nanosecond pulses in actively Q-switched dual-wavelength fiber lasers. From reported investigations, EYDCF offers high conversion efficiency for high-energy pulses generation [44, 45].
Regarding EYDCF use, in 2014, an actively Q-switched wavelength tunable DWFL using EYDCF as a gain medium has been reported [44]. The linear cavity laser incorporates the use of bulk components to introduce pump power in the EYDCF. The laser wavelengths are generated and simultaneously tuned by using a polarization maintaining fiber Bragg grating (PM-FBG). The maximal separation between generated wavelengths of 0.4 nm is adjusted by polarization axis adjustment performed by a PC. The simultaneous wavelength tuning range of ~11.8 nm is obtained by axial strain applied on the PM-FBG. The maximal average power of 22 mW is obtained with a repetition rate of 120 kHz with a pump power of 1.5 W.
Recently, a self-Q-switched (SQS) EYDCF laser with tunable single operation and dual wavelength operation using a Hi-Bi FOLM as a spectral filter was experimentally demonstrated [45]. The wavelength tuning in single wavelength operation and the cavity loss adjustment for dual wavelength operation is performed by temperature variations applied in the FOLM Hi-Bi fiber loop, allowing Hi-Bi FOLM spectrum wavelength shifting. Stable SQS pulses with duration of 4.1 µJ and repetition rate of 25 kHz are obtained with a pump power of 575 mW. The single wavelength tuning range over 8.4 nm is obtained with FOLM Hi-Bi fiber loop temperature variation in a range of ~7.2 °C. Separation between generated simultaneous dual wavelengths is 10.3 nm.
Then, we propose the use EYDCF fiber as gain medium for the design of actively Q-switched lasers with operation in single and dual wavelength. Also, we propose the use of FBGs and Hi-Bi FOLM as cavity elements that allow modifying the characteristics of laser operation and improve its performance by straightforward methods.
In this section, an experimental analysis of a ring cavity dual-wavelength actively Q-switched fiber laser with an EYDCF as a gain medium is presented. A pair of FBGs is used for separately generated laser lines tuning by mechanical compression/stretch applied on the FBGs. Simultaneously generated dual-wavelength laser lines tuning are presented with wavelengths separation from 1 nm to the maximal separation of 4 nm (without the need of cavity loss adjustment).
The experimental setup is presented in Figure 1. The fiber ring cavity laser is based on the use of 3 m of EYDCF as a gain medium. The EYDCF is pumped with a laser source at 978 nm through a beam combiner. The pump power of 5 W is limited by the maximal AOM signal power of 1 W. An optical isolator with maximal output power of 5 W is used to ensure unidirectional operation. An optical subsystem formed with a 50/50 optical coupler with output ports connected to FBG1 and FBG2 with central wavelengths at 1543 and 1548 nm respectively, allows dual wavelength emission at FBG reflected wavelengths; it is also used for separate laser wavelength emission monitoring at outputs 1 and 2. The FBGs with approximately 99% of maximum reflectance are placed on mechanical devices for generated laser wavelength tuning by applying axial strain on the gratings. The simultaneously generated laser wavelengths are measured at 90/10 coupler output 3. A fiber-pigtailed AOM driven by a RF signal is placed for active Q-switching pulsed laser operation. The output spectra monitored at output ports (1, 2, and 3) are measured with an OSA and also the Q-switched pulses are detected and observed with a photodetector and an oscilloscope, respectively.
Experimental setup for actively Q-switched dual wavelength ring cavity fiber laser.
Figure 2 shows the experimental results for the dual-wavelength fiber laser spectrum measurements with fixed pump power of 5 W. The measurements were obtained at output 3 with an OSA with attenuation. Output power results are presented in linear scale to support the achieving of two simultaneous laser wavelengths with equal powers. Two simultaneous wavelengths are obtained without requiring cavity losses adjustment in the presented wavelength separation tuning range, however, we noticed the requirement of cavity losses adjustment for wavelength separations above 4 nm. Results for dual wavelength operation with cavity losses adjustment (wavelengths separation above 4 nm) are not presented since it was performed introducing curvature losses by fiber bending applied between 50/50 output ports and FBGs connections; an arbitrary method in which it is not possible to characterize the competition between the generated laser lines.
Figure 2(a) shows the generated laser lines spectrum measurements for dual wavelength Q-switched laser operation with different wavelength separations. The separation tuning from 1 to 4 nm is achieved by mechanical compression/stretch applied on the FBGs. The repetition rate remained fixed at 70 kHz. As it is shown, dual wavelength laser operation is generated simultaneously with approximately equal laser lines output powers without an adjustment of losses within the cavity. As it can be seen, for the repetition rate and pump power settings, a preference exists to generate the longer wavelength during the competition between the laser lines.
Spectrum measurements for dual-wavelength actively Q-switched laser operation, (a) different generated wavelengths separation tuning with fixed repetition rate, (b) generated laser lines with wavelength separation of 1 nm and repetition rate variations.
Figure 2(b) shows the generated dual wavelength laser lines with fixed wavelength separation of 1 nm (
Figure 3 shows the output power ratio for the two simultaneously generated laser lines measured
Dual wavelength operation generated laser wavelengths power ratio P(λ2)/P(λ1) for repetition rate variations and different wavelengths separations.
In Figure 3, it can be clearly observed that with increasing repetition rate, the competition between laser lines has an imbalance in which the longest wavelength has a preference to be generated. Strong competition allowing dual wavelength laser operation with almost equal output powers from 20 kHz to about 60 kHz of repetition rate can be observed. With repetition rate variations from 60 kHz to 100 kHz, the longer wavelength output power increases significantly, at the expense of the shorter wavelength output power. As it can be also observed, the range of repetition values over which the longer wavelength starts growing significantly is shortened when increasing the separation between the generated laser wavelengths. As it is shown, for a wavelength separation of 1 nm, the maximum power ratio is about 2 times, with a repetition rate of 100 kHz. However, for a wavelength separation of 4 nm and a repetition rate of 70 kHz, an output powers ratio in which
Figure 4 is a group of experimental results for actively Q-switched dual wavelength laser pulses. The results also show pulse profiles for different repetition rate variations, comparison between pulses measured at different outputs, and experimental analysis of pulses time shift by repetition rate variations.
Figure 4(a) shows the optical pulse measurements for actively Q-switched dual wavelength laser operation. The wavelength separation remains fixed at 4 nm between simultaneously generated laser lines. With the use of a photodetector and an oscilloscope, the pulse traces together with the leading pulse of the signal applied to the AOM were obtained at output 3 where both generated wavelengths are simultaneously measured. The resulting pulses were obtained for different repetition rate variations from 50 to 100 kHz. For actively Q-switched operation, with the increase of repetition rate, the pulse duration typically increases as pulse amplitude decreases. As it can be seen, with a repetition rate of 50 kHz, there is a time shift of 93.7 ns between the leading edge of the electrical pulse applied to the AOM and the generated laser pulse. As we can observe, the time shift depends on the repetition rate.
The dependence of the temporal pulse shift on the repetition rate variations is shown in Figure 4(b). As it is shown, the pulse time shift increases as the repetition rate increases. Thus, it can be observed that for a repetition of 100 kHz, the pulse temporal shift between the electrical modulation signal leading edge and the generated pulse increase to ~2.3 µs.
Figure 4(c) shows the pulse traces that correspond to the same dual wavelength generation with wavelength separation of 4 nm and repetition rate of 50 kHz. Since the FBGs have a reflection close to 100% at the central wavelength, it is possible to obtain independently single laser concerning each of the generated laser wavelengths at the outputs 1 and 2 as a result of the signal transmitted by each FBG. Thereby, the pulses generated by the laser wavelength
Optical pulses for Q-switched dual-wavelength operation with wavelength separation of 4 nm, (a)pulse profiles with repetition rate variations, (b) pulse time shifts on repetition rate variations, (c) separatepulse profiles for
Figure 5 shows the output power in dual wavelength operation for generated laser wavelength separations
Average power on repetition rate variations of Q-switched dual wavelength operation with wavelength separations of 1 nm and 4 nm.
Figure 6 shows the measured pulse duration and the estimated pulse energy on repetition rate variations and the estimated pulse peak power for dual-wavelength laser operation. Results are obtained for wavelength separation between generated laser lines of 1 nm and 4 nm. Pulse profiles for Q-switched dual wavelength operation with both generated wavelength separations were performed with a photodetector and monitored by an oscilloscope. From pulse shape measurements, pulse duration was obtained. The pulse energy for each wavelength separations is estimated with the repetition rate and the average power results shown in Figure 5. Estimation of pulse peak power is obtained with the pulse energy and the pulse duration achieved results.
Typically for actively Q-switched lasers, with the increase of repetition rate, the obtained pulses widens increasing the pulse duration. Thus, although the pulses train average power increases with the repetition rate increase (see Figure 5), the optical pulses are less energetic by the widening and the pulse peak power reduction experienced (see Figure 4(a)).
Figure 6(a) shows the pulse duration and pulse energy on repetition rate variations for dual wavelength laser operation. The pulse duration and pulse energy show a typical behavior of actively Q-switched lasers. With the repetition rate increase, pulse duration increases and pulse energy decreases are observed. For simultaneously generated laser wavelength separation of 1 nm, maximal pulse energy (blue line, circled symbol) of ~5.86 µJ and minimal pulse duration (black line, circled symbol) of ~220 ns are obtained with the minimal repetition rate of 50 kHz. Similarly, for a wavelength separation of 4 nm at the same repetition rate, the maximal pulse energy (blue line, squared symbol) of 5.98 µJ and the minimal pulse duration of 295 ns is obtained.
The estimated pulse peak power on repetition rate variations is shown in Figure 6(b). As it can be observed, the pulse peak power (pulse amplitude) for the wavelength separation of 1 nm is higher compared with what is observed for the wavelength separation of 4 nm. This result is essentially attributed to a smaller increase in pulse duration for the measurements of
Q-switched dual wavelength operation with wavelength separations of 1 nm and 4 nm on repetition rate variations, (a) pulse duration and pulse energy, (b) pulses peak power.
In this section, an experimental analysis of an actively Q-switched ring cavity fiber laser has been presented. Through experimental and estimated results of laser spectra emission and generated laser pulses, the behavior of the dual wavelength laser operation of competitions between the simultaneously generated laser lines and the evolution of generated laser pulses has been analyzed. Actively Q-switched pulsed laser parameters as repetition rate, pulse duration, pulse energy, average power of the laser emission, and peak pulse power has been also experimentally studied in terms of different tuned separations for two simultaneously generated wavelengths and variations on rate repetition of the electrical signal applied to the AOM. Results have been shown that are generalized to any actively Q-switched laser and particularly for lasers with dual wavelength operation. It is worth mentioning that for the proposed experimental setup, it is not necessary to implement a cavity losses adjustment method for the shown operation tuning range (wavelengths separations from 1 to 4 nm), however, a cavity losses adjusting method is required when during the competition between generated laser wavelengths there exists a wavelength preferred for the laser emission.
In this section, a linear cavity actively Q-switched fiber laser is proposed for experimental analysis. In contrast to the laser experimental setup demonstrated in the previous section (in which explaining the parameters of active Q-switched fiber lasers was intended), the experimental laser setup is a linear cavity configuration in which a method to adjust the losses within the cavity (when required) for simultaneous dual wavelength laser operation is presented. The proposed configuration includes the use of a Sagnac interferometer with high birefringence fiber in the loop (Hi-Bi FOLM) used as a spectral mirror and mainly for cavity loss adjustment during the laser lines competition in two simultaneous laser lines generation. The use of Hi-Bi FOLM as a reliable method of cavity losses adjustment for lasers operating in dual wavelength application has been extensively studied by our research group [21, 37, 38, 45]. The main objective of this section is to illustrate through a proposed experimental setup that the Hi-Bi FOLM can also be used to implement dual wavelength fiber lasers in pulsed regime for the actively Q-switched technique as well as the experimental analysis of dual wavelength laser operating parameters.
The proposed actively Q-switched fiber laser experimental setup is shown in Figure 7. The linear cavity laser is bound by two FBGs at one end and a Hi-Bi FOLM at the other end. A 3-m length of EYDCF used as a gain medium is pumped by a laser source at 978 nm through a beam combiner. The pump power was fixed to 1.5 W. An AOM driven by an RF signal generator is used for application of the active Q-switching technique. FBG1 and FBG2 with reflections of 99% at central wavelength tuned to 1542.7 nm and 1552.7 nm, respectively, are used as narrow band mirrors for generated laser wavelengths selection. With the selected FBGs central wavelengths, the separation between generated laser lines is ~10 nm. The Hi-Bi FOLM is formed by a 50/50 coupler with output ports interconnected through a Hi-Bi fiber segment of ~56 cm. The Hi-Bi FOLM is acting as a wide band mirror with a periodical spectrum. With the selected Hi-Bi fiber segment, the spectrum period is ~10.3 nm [35]. The Peltier device used for Hi-Bi fiber temperature control is used to shift the Hi-Bi FOLM spectrum in wavelength. This Hi-Bi FOLM spectrum displacement is the method for cavity losses adjustment for dual wavelength laser operation [35]. The splices between Hi-Bi fiber ends and 50/50 output ports are placed in mechanical rotation stages for Hi-Bi FOLM transmission spectrum amplitude adjustment [35]. The Hi-Bi FOLM amplitude was adjusted near maximal contrast. The unconnected 50/50 coupler port (output port) is used for Hi-Bi FOLM transmission spectrum measurement (with pump power below the laser generation threshold) and for laser spectrum measurement with an OSA. The output port is also used for pulses detection by a photodetector and observed on an oscilloscope.
Experimental setup for actively Q-switched linear cavity dual wavelength fiber laser.
Figure 8 shows the cavity losses adjustment performance for single and dual wavelength laser operations. The adjustment is performed by temperature changes in the Hi-Bi FOLM fiber loop. The temperature meter and control has a resolution of 0.06 °C. The repetition rate was set to 60 kHz.
Figure 8(a) shows the three Hi-Bi FOLM transmission spectrum for the Hi-Bi fiber loop temperatures in which single wavelength operations for
Cavity losses adjustment for laser operation, (a) Hi-Bi FOLM transmission spectrum wavelength displacement for single and dual wavelength operation Hi-Bi fiber temperatures, (b) generated laser spectrums for single and dual wavelength operations.
In Figure 9, pulsed regime measurements for actively Q-switched dual wavelength laser operation are presented. Pulse train profile and average power on repetition rate variations are shown.
Figure 9(a) shows the pulse train in time domain for dual wavelength laser operation with repetition rate of 60 kHz measured at the output port. The Hi-Bi fiber loop temperature was set to 25.9 °C for dual wavelength operation with equal powers as it is shown in Figure 8(b). For repetition rates below 35 kHz and above 75 kHz, unstable pulses are generated since the laser pulses displace outer the modulating AOM electrical signal time window. The inset on Figure 9(a) shows a sample pulse from the pulses train. The estimated pulse duration is ~448 ns.
In Figure 9(b), the average power on repetition rate variations for dual wavelength operation is shown. Measurements obtained with pump power of 1.5 W and repetition rate from 35 to 75 kHz, were performed at the output port with a power meter. As it can be seen, the average output power increases with the repetition rate from 58.3 to 84.9 mW.
Actively Q-switched dual wavelength laser operation measurements, (a) pulse train with repetition rate of 60 kHz, (b) average power on repetition rate variations.
Figure 10 shows the experimental results of pulse parameters for the actively Q-switched laser on dual wavelength operation. Measured pulse duration and estimated pulse energy and pulse peak power dependences on repetition rate variations are shown.
Actively Q-switched dual wavelength laser operation pulse parameters, (a) pulse duration and pulse energy on repetition rate variations, (b) pulse peak power on repetition rate variations.
In Figure 10(a), results for pulse duration and pulse energy on repetition rate variations from 35 to 75 kHz are presented. As it can be observed, pulse duration and pulse energy present a behavior typically obtained in actively Q-switched lasers. Pulse duration increases as pulse energy decreases with the repetition rate increase. The pulse duration shows a widening in a range of 213 to 586 ns. The pulse energy decreases as the pulse widens from 1.67 to 1.13 µJ.
Figure 10(b) shows the pulse peak power dependence on repetition rate variations. As it is shown, the pulses undergo a peak power decrease as repetition rate increases. With the lower repetition rate, the pulses have less pulse duration, are more energetic, and with a higher peak power.
In this chapter, actively Q-switched fiber lasers for single and dual wavelength operation have been experimentally investigated. The investigation is based on single and dual wavelength operation of actively Q-switched fiber lasers. The documental investigation is focused on reported approaches on Q-switched fiber lasers taking into account cavity elements, configurations, experimental results, and new fiber technologies incorporation.
A review from the operating principle of pulsed lasers in the Q-switched technique to single and dual wavelength operation, mainly those lasers that use an active Q-switching method was presented. The research was led to reach the point where double clad fibers (specifically EYDCF) are used as the gain medium and the application of the active Q-switching technique by using AOM.
An analysis of the main parameters of actively Q-switched fiber lasers, including the repetition rate, pulse duration, pulse energy, average power, and peak power characteristics of the technique was experimentally discussed. This experimental study was presented in terms of a couple of proposed actively Q-switched fiber laser experimental setups.
The actively Q-switched parameters\' typical behavior was mainly discussed in the first experimental setup proposed, a ring cavity dual wavelength actively Q-switched fiber laser based on the use of fiber Bragg gratings for wavelengths selection. The second experimental setup is a linear cavity actively Q-switched fiber laser with single and dual wavelength operations with a Hi-Bi FOLM. The use of the Hi-Bi FOLM as a method to adjust the losses within the cavity (when required) for simultaneous dual wavelength laser operation was discussed.
Cátedras-CONACyT project 2728. CONACyT Postdoctoral felow 160248. This work was supported by CONACyT grants 237855 and 255284.
Rapid population growth and urbanization have caused many problems in the implementation of developmental projects in cities. Haphazard infrastructural project execution that includes disregard in prioritizing city (or cities) selection is also a factor hampering sustainable development practices. Development projects that rely on selected organizations, which in turn rely on human judgment, can lead to unrealistic criteria evaluation, causing delays in project execution [1]. However, the fact remains that continued infrastructure development is unavoidable, especially since urban cities constantly need to evolve and grow to keep up with the times [2].
The selection of a city (or a group of cities) is one of the most important steps for sustainable development. The selection criteria must ensure that the city (cities) has high priority for development and is (are) in line with the needs of the local citizens. Moreover, timely selection requires effective planning and analysis and must consider multiple conflicting and disproportionate factors (such as those that have critical socioeconomic and environmental implications to different stakeholders). Urban planning application using remote sensing (RS) and geographical information systems (GIS) is one of the many areas that can be explored for city selection. Such applications would not only eliminate human bias but would also be able to make more objective decisions based on data.
Remote sensing can be applied in different aspects of urban planning such as (but not limited to) urban traffic analysis, urban environment analysis (air and water pollutions), and urban expansion. With recent developments in remote sensing technologies, remote sensing data can be exploited for urban studies. One example is the classification of land use based on high spatial and spectral resolution data such as orthomosaic and elevation images. Multidimensional spatiotemporal data can now be reliably obtained by sensors in different scale ranges and with flexible repetition rates [3].
Medium- to high-resolution satellite imagery can be used by urban planners and land managers to monitor land conditions to support decision-making for sustainable urban development. Remote sensors are able to provide voluminous amounts of data, which can be exploited to produce/update GIS maps or for detection changes in urban land covers. High-resolution satellite sensors available on IKONOS, for example, can collect diverse geospatial data for studying vegetation. The sensors can sense 4 m resolution multispectral and 1 m resolution panchromatic, Quickbird imageries with 2.4 m resolution multispectral and 61 cm resolution panchromatic, and Worldview-4 imageries with 1.24 m resolution multispectral and 31 cm panchromatic. Medium-resolution satellite sensors, available on Landsat-8, Sentinel-2, and SPOT, are also valuable data sources for urban and vegetation change detection from various time periods during the same season, which further supports analyzing any past changes. Analysis of such data can then be used for decision-making and planning for further development of a particular urban area [4].
Remote sensing data can be integrated with other spatial data to perform various types of full-fledged assessments. GIS techniques can be utilized to integrate the required spatial data and analytic data from various sources, such as field survey data, topographic maps, aerial photographs, and also archived data. The data can be represented as location (i.e., latitude and longitudes) or even as tabular attributes. GIS techniques play a substantial role in the data integration process of multilayer spatial information along with statistical information in various developmental scenarios [5].
Multi-criteria decision-making (MCDM) is concerned with making a decision by evaluating multiple conflicting criteria. It embodies various methods and procedures where the gist is the formal incorporation of multiple conflicting criteria in the analytical process [6]. In the context of GIS, this refers to the spatial decision-making process based on GIS data with geolocation tags. Spatial decision-making techniques have been used to solve many GIS problems such as locating solar plants, urban planning, and project construction optimization [7]. Advanced MCDM methods include simple additive weighting (SAW) [8], analytic hierarchy process (AHP) [9], and TOPSIS [10]. Fuzzy set theory and random set theory are also MCDM techniques that incorporate sophisticated algorithms to resolve uncertainty in data [11, 12, 13, 14].
TOPSIS is a MCDM technique that deals with real-world problems. It basically ranks criteria on the basis of the shortest distance from the positive ideal solution (PIS) and the farthest distance from the negative ideal solution (NIS) [15]. The work in [2] illustrates the application of a GIS-based MCDM tool for urban infrastructural planning. Awasthi et al. [16] presented a fuzzy TOPSIS method for selecting the best location for an urban distribution center in Canada. Uysal and Tosun [17] proposed a fuzzy TOPSIS-based maintenance management system using 17 criteria categorized under 5 contending parameters. The criteria were deduced from questionnaire feedbacks and interviews administered to company maintenance managers. In addition, Momeni et al. [18] presented a fuzzy TOPSIS-based method for maintenance strategy selection. Baysal et al. [19] developed a two-stage fuzzy method to determine the best sub-municipal projects among a set of proposed projects. The method simplifies the selection process and provides an objective decision outcome for stakeholders. Shelton and Medina [20] presented an integrated method to prioritize transportation projects in Wilmington Area, USA, based on multi-criteria decision support systems, AHP and TOPSIS methods. The process optimally selects the important routes that best serve the interest of the general public.
Based on the literature, TOPSIS has been successfully applied in many fields, producing reasonably accurate results. This study proposes an automated TOPSIS-based solution for prioritizing urban projects based on criteria that meet sustainable development. Specifically, this work addresses the following questions on the value of remote sensing (and GIS) to urban planning:
Which remotely sensed dataset(s) is (are) useful for urban planning?
Which criteria can be derived from remotely sensed data?
What are the major factors that need to be considered in urban developments?
From these questions, this study further looks at the automated prioritization of urban projects based on criteria that meet sustainable development practices. The specific objectives are (i) to identify factors that play major roles in urban development and (ii) to develop a geospatial solution based on TOPSIS for prioritizing projects for urban development.
This study focuses on Libya, a country in the Maghreb region of North Africa (Figure 1). Libya borders the Mediterranean Sea to the north and Egypt to the east. Along the southeast of Libya is Sudan, Chad. To the south is Niger. Algeria and Tunisia constitute the western border. Libya is the 17th largest nation in the world with a landmass of over 1,759,540 km2. The study area in the northern part of Libya covers six districts, namely, Darnah, Al Jabal Al Akhdar, Benghazi, Al Marj, Al Qubbah, and Al Hizam Al Akhdar (Figure 1). Libya is geographically bounded between 20°00′00″ E and 23°30′00″ E and 31°00′00″ N and 33°00′00″ N. The climate in Libya is categorized by hot and dry summers with high temperatures. The mean annual temperature in the coastal region ranges from 14.2°C (Shahat) to 21.0°C (Tripoli Airport) and at stations in the interior region (inland) between 21.3°C (Al Qaryat) and (Ghat) 23.4°C (1945–2009). Libya is one of the driest countries in the world with mean annual rainfall along the Libyan coast ranging between 140 and 550 mm and rarely exceeding 50 mm in the interior regions (1945–2010). December and January are the wettest months with 6 months (October–March) receiving 87.1% of the total annual precipitation. The majority of rainfall occurs in the winter season with the rainy season beginning in September-October and ends in March-April [21].
The data used in this study include Landsat satellite imagery acquired in the year 2017 with 15 m resolution panchromatic and 30 m resolution multispectral, Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) downloaded from USGS data archive with 30 m resolution, population density map obtained from GHSL with 250 m resolution, road network map from Diva-GIS, and MODIS satellite imagery from where the land surface temperature with 0.25° resolution was derived. Other data include rainfall data at 0.25°, net primary productivity (NPP) at 0.1°, NDVI at 0.1°, and air quality (CO, NO2) at 0.25° resolution (Table 1). Details of the Landsat data are presented in Table 2. Seven set of images with overlapping areas were acquired between 4 February 2017 and 1 March 2017. In addition, the highest cloud cover was 1.03%, which does not pose a problem for land use information extraction from the study area. Since the images have overlapping areas, they were preprocessed and mosaicked to create one seamless image of the area for effective and efficient processing (Figure 1d).
Data | Source | Resolution |
---|---|---|
Landsat satellite imagery | USGS | 30 m |
DEM | USGS | 30 m |
Population density | GHSL | 250 m |
Road network | Diva GIS | / |
Land surface temperature | MODIS | 0.25° |
Rainfall | MODIS | 0.25° |
Net primary productivity | MODIS | 0.1° |
NDVI | MODIS | 0.1° |
Air quality (CO, NO2) | MODIS | 0.25° |
Information of the datasets used in the research.
Image ID | Acquisition date | Raw | Path | Cloud cover (%) |
---|---|---|---|---|
Landsat 8 OLI 1 | 1 March 2017 | 38 | 182 | 0.77 |
Landsat 8 OLI 2 | 13 February 2017 | 39 | 182 | 1.03 |
Landsat 8 OLI 3 | 4 February 2017 | 38 | 183 | 0.00 |
Landsat 8 OLI 4 | 4 February 2017 | 37 | 183 | 0.11 |
Landsat 8 OLI 5 | 4 February 2017 | 39 | 183 | 0.13 |
Landsat 8 OLI 6 | 19 February 2017 | 37 | 184 | 1.00 |
Landsat 8 OLI 7 | 19 February 2017 | 38 | 184 | 0.00 |
Information of the Landsat images.
(a) Map of Libya with northern part highlighted, (b) the location of the urban area, (c) the road networks in the study area, and (d) mosaicked Landsat images.
Four preprocessing steps were performed on the Landsat satellite images: (i) Pan-sharpening using a fusion of the panchromatic and multispectral bands for the enhancement of the spatial resolution of multispectral band; (ii) atmospheric correction, which is applied to correct the atmospheric distortion by retrieving surface reflectance and engage topographic correction as well as adjacency effect correction; (iii) radiometric correction, which converts radiance values to the pure surface reflectance to enhance image capability and contrast; and (iv) mosaicking to create one seamless image coverage of the area for effective and efficient processing [22]. The MODIS source data was preprocessed using MODIS Conversion Tool Kit (MCTK). Note that the spatial resolution of the MODIS dataset varied according to the source. However, during the preprocessing, they were resampled to 30 m to match the DEM and Landsat resolutions.
In this study, we considered 17 critical urban conditioning factors for selecting the most suitable city or cities for sustainable urban development. The factors are grouped into five main categories: (i) topography, (ii) land use and infrastructure, (iii) demography and climate, (iv) vegetation, and (v) air quality.
Topography is a very important consideration for urban development projects [23]. For this study, altitude and slope are the two main factors related to topography. Altitude is important for citing facility because it affects the living conditions as well as breathing behavior. The collected DEM shows that the study area is between −4 and 865 m above mean sea level (Figure 2a). The slope factor, which ranges from 0 to 14° (can be classified as almost flat), was also generated (Figure 2b). Such data is important when estimating cost. For example, any increase in slope will increase the cost of facility installation and maintenance since moving workers, transport vehicles, and machineries will be more difficult (i.e., up and down a slope). Low slope areas also may incur undesirable cost, in the instance of weather anomalies such as dust/sand storms.
Elevation criteria: (a) altitude, (b) slope, (c) land use, (d) distance to the primary routes, (e) distance to the secondary routes, (f) distance to the trails, (g) distance to the Benghazi city, (h) percent of urban areas, (i) population density, (j) LST, (k) rainfall, (l) NDVI, (m) NPP product, (n) CO concentrations, (o) NO2 concentrations.
Land use and infrastructure are also important considerations for urban projects. Land use information can show human activity patterns, whereas infrastructure can indicate development status in a particular city. The land use of the study area was derived from Landsat images and refined based on Google maps (Figure 2c). In this study we applied the SVM classifier, which was based on object-based image analysis (OBIA) using the ENVI 5.3 software. Training sites for the SVM were collected form all land use classes by stratified random method (i.e., at least 80 sample points for each class) [24]. The area contains five main land use types: (i) irrigated crops, (ii) vegetation, (iii) artificial areas, (iv) bare lands, and (v) waterbodies. Most parts of the study area were bare land (desert), which were predominantly located in the middle and southern parts of the study area. The northern part mostly comprised of irrigated crops and artificial areas, specifically man-made features and urban areas.
Road networks play an important role in the country’s economy, serving the people by linking main cities to industrial and commercial sites. In this study, three types of roads, namely, main routes, secondary routes, and trail routes (Figure 2d–f), were considered as factors for city selection. The northern part of the city is supported by main routes (Figure 2d). These roads mainly link other cities to Benghazi, which support Benghazi city itself. Main routes span a significant number of kilometers within the study area. The study area also contains several kilometers of secondary routes (Figure 2e). Unlike main routes, secondary routes are found in most parts of the study area and in different cities including Benghazi. Secondary routes mainly support transportation of goods and are used for civil construction projects. Finally, trail routes support the rural areas, mainly for transportation of agricultural produce to the markets. The class of roads plays a vital role in selecting a city for development according to the available budget. Cities that can support more people will normally be prioritized for development projects.
Another important factor related to infrastructure is the percentage of built-up areas in a particular city. This is important because cities with many built-up areas indicate little or no space for new projects. On the contrary, cities with fewer built-up areas mean that they are more suitable for new developmental projects. The Normalized Difference Built-up Index (NDBI), which is a quantity of the intensity of urban area from satellite images, was used in this study [25]. The NDBI was initially established regarding the ratio of bands 4 and 5 of TM sensor. However, the NDBI can be adopted on Landsat-8 data or even any multispectral sensor data [26]. It basically extracts the urban areas where there is an upper reflectance in the short-wave infrared band associated to the near-infrared band. The accuracy of built-up areas extracted using NDBI is reported to be around 93% [27, 28]. We calculate the NDBI (Eq. (1)) based on the work in [27]:
Besides infrastructure factors, distance to the city is also a critical factor. Preferably, a city’s location should be as near as possible to the capital or large cities such as Benghazi. This is because it facilitates ease of access to better business opportunities and education. Therefore, in this work, the distance to Benghazi city is one of the important parameters. Specifically, the desired distance to the city should range from 0 to 600 km (Figure 2g) so that cities such as Darnah and Al Qubbah (which are as far as 400 km away from Benghazi) are also covered.
An increase in a city’s population often leads to an increase in urbanization. Moreover, if the population increase is rapid, urbanization often happens randomly. This can be a major problem for most cities in a developing country. However, proper planning and effective decision-making can mitigate this problem. In this study, the population density (Figure 2i) was analyzed. The analysis results indicate that the northern part (mostly around Benghazi city) is most populated with a density of 669 people per 250 m cell of raster data.
Climate is a factor that also influences the selection of cities for developmental projects. Land surface temperature (LST) and rainfall are two factors considered in this work (Figures 2j and 3k). The LST map shows that the southern part of the study area (mostly desert with no vegetation) has higher surface temperature compared to the northern parts. Another observation from the map reveals that Benghazi has slightly higher temperature than other urbanized areas. In arid regions, people often prefer to settle in areas with low temperature. The average day-night temperature ranges from 11° to 20° Centigrade. High temperatures are also observed in the west-southern part, whereas the lowest temperature is found in the northern part of Al Jabal Al Akhdar cities.
Flowchart of the methodology implemented in this study.
Rainfall, which is another climate factor, is also considered in deciding the location of settlement and development. This is because rainfall frequency and intensity affect the dryness of the cities, the local climate system, as well as agriculture activities. Figure 2k presents the rainfall intensity of the study area for year 2016 where minimum and maximum rainfall intensities were 48 and 1119 mm per month, respectively. The central part of the area has less amount of rainfall compared to other areas.
Normalized difference vegetation index (NDVI) is an indicator derived from remote sensing satellite data. It is mostly used to monitor vegetation cover over any area on the planet. It serves as a good indicator for vegetation cover of the study area. The presence of abundant vegetation is able to lower the local temperature as well as reduces the negative effects of noise and air pollutants. In the study area, the northern part has higher NDVI compared to the south (Figure 2l).
Larger amounts of vegetation can indicate higher vegetation productivity. Having higher vegetation productivity helps asses the net primary productivity (Figure 2m). Plant productivity plays a major role in the global carbon cycle by absorbing some of the carbon dioxide released through coal, oil, and other fossil-fuel burning. Large NPP values are found in the southern part of the study area.
Air quality directly affects the environment and consequently people’s health. In this work, we have considered the CO and NO2 (Figure 2n and o) air quality indicators. In 2016, higher CO and NO2 levels were measured in the southern part of Benghazi. Benghazi city also recorded high levels of these gasses for the year under investigation. The air quality data was extracted from the MODIS source with a resolution of 0.25°. We utilized the ENVI 5.3 software to process the MODIS imagery. However, in order to prepare unprocessed MODIS satellite images for analysis, they must firstly be converted into ENVI format. This was done using the MODIS Conversion Tool Kit.
This section describes the modeling process, specifically the application of TOPSIS for scheduling and prioritizing the cities for urban development (Figure 3). First, a medium-resolution Landsat-8 satellite image from the study area was acquired and preprocessed. Then, the image was segmented using a multiresolution segmentation algorithm and classified into several classes using object-based image classification. The multiresolution algorithm has three main parameters, namely, scale, shape, and compactness. Since these parameters are data and application dependent, in this study, we had to select them empirically via trial and error. This meant that the best values were determined via visual examination of the segmentation results. After the segmentation process, several attributes were selected and used as class predictors in the classification algorithm. From the spectral attributes, the five bands of the Landsat-8 image were selected. For spatial attributes, shape index, roundness, compactness, and density were used [29, 30]. In the classification step, the support vector machine (SVM) algorithm was used. Although the SVM is a relatively simple binary classifier, it has very good generalization capabilities if properly trained [31, 32].
Several other digital data such as DEM and population density were also obtained from various online sources. The factors used as described in the previous section are widely reported in the literature for selecting urban projects or relevant projects. Fuzzy overlay (FO) analysis was carried out to determine the importance of each parameter to achieving the goal of the study. The SVM classifier was further applied to refine the results obtained from the FO model. Finally, the cities were sorted according to their importance by applying the TOPSIS model on the results of the SVM.
Fuzzy overlay analysis is based on the fuzzy set theory that relies on membership relationship of events to define specific sets or classes [33]. Operationally, FO is similar to overlay analysis but differs in the reclassified values and results from the combination of multiple criteria. It involves problem definition, partitioning into sub-models and determining the significant layers. FO transforms the data to a common scale and defines the likelihood of the data belonging to a specific class, for example, slope values being transformed into the probability of fitting into the favorable suitability set based on a scale of 0 to 1, expressed in terms of membership [34]. Input raster are not weighted in FO since the transformed values indicate the possibility of membership rather than using ratio scale as with weighted overlay and weighted sum. The equation using fuzzy Gaussian function can be given as [35]
The inputs
As previously mentioned, the results of FO are refined using the SVM, which develops a linear regression between suitability status and criteria factors. SVM aims to determine an optimal separating hyperplane (maximizing the margin width) between two classes in feature space [36]. The training points near the hyperplane are called support vectors and are utilized for classification once the decision line/surface is obtained. The separating hyperplane is found as follows:
where
where
Developed in [38, 39, 40], TOPSIS is a multi-criteria decision tool based on the intuition that a selected alternative has the shortest possible geometric distance from the PIS. In other words, the alternative has the longest geometric distance from the NIS [41]. The analysis compares a set of alternatives by assigning weightage to each criterion to compute the geometric distance between possible alternatives to determine the ideal alternative based on the assumption that the criteria uniformly increases or decreases. TOPSIS allows trade-offs between criteria; a poor result in one criterion can be compensated by a good result in another criterion. TOPSIS provides a more realistic model than non-compensatory methods by including or excluding alternative solutions using hard cutoffs. Consider
Step 1: Construct the normalized decision matrix calculated using Eq. (6):
Step 2: Construct the weighted normalized decision matrix using Eq. (7):
Step 3: The positive and negative ideal solutions are determined by
Step 4: Calculation of separation (positive and negative) measurement using Euclidean distance. Eq. (9) is used to calculate the distance.
Step 5: Closeness to the ideal solution is calculated using Eq. (10):
Step 6: Ranking alternatives based on closeness to the ideal solution. TOPSIS has been used in different circumstances (e.g., individual and grouping). By applying the TOPSIS model using the results of the FO as input, the cities were sorted according to their importance for proposed urban development projects.
According to [43], the most contributing factors to urban suitability are topography, land use and infrastructure, vegetation, demography and climate, and air quality. Therefore, these factors should be thoroughly analyzed to discover the most (and the least) suitable area for urbanization. Hence, in this work, 17 detailed factors were analyzed in order to rank each’s importance (via weight assignment) for the selection of a city (or cities) for sustainable urban development. Subsequently, a suitability map was generated based on the FO (Figure 4a) method. A continuous scale was used for suitability weightage, which ranges from 0 (less suitable) to 1 (highly suitable). From the generated map, areas indicated as most suitable are located in the northern parts, especially the areas surrounding Benghazi and the northern parts of Al Marj and Al Jabal Al Akhdar. Sole reliance on the generated map, however, does not help much in deciding city development prioritization. As a result, the map was further refined to make it much more distinct for decision-makers. To do this, the map was firstly reclassified into three categorical classes: (i) not suitable, (ii) less suitable, and (iii) highly suitable. This was done using the natural break classification method (Figure 4b) where several samples were selected from the not suitable and highly suitable areas (results of FO) to generate training and testing data. These datasets were then used to train a SVM to classify between the two classes. Table 3 presents the estimated factors and their coefficients. The result indicates that land use, distance to primary route, distance to capital city, rainfall, NPP, and NO2 have negative effects on the suitability level of the selection process. The remaining factors have positive effects. Among the positive factors, population density has the highest effects on the selection process.
Results of fuzzy overlay in (a) continuous scale and (b) categorical format.
Based on the estimated coefficients, the suitability map in Figure 5 was produced. It can be seen that the map reflects the same thing as in the previous suitability map. However, it is clearly more informative for decision-makers. Based on this, the cities were ranked according to their importance using TOPSIS method.
FO after refinement with SVM.
Table 4 presents the positive ideal, negative ideal, closeness coefficient, and TOPSIS rank for each of the cities being analyzed. According to the closeness coefficients, the ranking order for the cities is as follows:
Benghazi
Al Jabal Al Akhdar
Al Marj
Darnah
Al Hizam Al Akhdar
Al Qubbah
Evaluation criteria | SVM weight | Criteria code |
---|---|---|
Land use | −0.36 | C12 |
Percent built-up area | 0.97 | C1 |
NDVI | 2.15 | C10 |
Altitude | 0.60 | C15 |
Slope | 0.96 | C7 |
Distance to primary route | −2.35 | C4 |
Distance to secondary route | 0.74 | C6 |
Distance to trail lines | 0.27 | C8 |
Distance to capital city | −0.62 | C13 |
Population density | 4.88 | C3 |
Rainfall | −1.17 | C5 |
LST | 0.23 | C11 |
NPP | −0.10 | C2 |
Carbon monoxide | 1.23 | C14 |
Nitrogen dioxide | −1.18 | C9 |
List of criteria, estimated coefficient, and their code.
City | A+ | A− | Closeness coefficient | TOPSIS rank |
---|---|---|---|---|
Darnah | 2.58 | 1.41 | 0.35 | 4 |
Al Jabal Al Akhdar | 2.05 | 1.74 | 0.46 | 2 |
Benghazi | 0.98 | 3.40 | 0.77 | 1 |
Al Marj | 2.23 | 1.57 | 0.41 | 3 |
Al Qubbah | 3.13 | 1.08 | 0.25 | 6 |
Al Hizam Al Akhdar | 2.69 | 1.29 | 0.32 | 5 |
Ranking of cities on the basis of importance for urban development.
The ranking results were then used to generate a map for final decision-making (Figure 6). Cities with green and light green colors are suggested to be prioritized first for development. More details about the TOPSIS analysis can be found in the Appendices.
The map showing the cities’ ranks based on TOPSIS.
Based on the results, the importance of each group of factors was evaluated (Figure 7). The bar chart shows the importance of the standardized factor weights in each group. Demography and vegetation are the two most influential factors with positive contribution, followed by vegetation and topography. The other factors have negative contribution.
Importance degree of factor groups.
Recall that the refined suitability map was recategorized into the three classes of “not suitable,” “less suitable,” and “highly suitable,” These classes reflect the degree of urban development suitability in the study area. The categorized suitability map can be validated accurately through each class. The continuous refined is suitability map ranging from 0 to 1. The most suitable areas that range from 0.751 to 1 fall into high suitable class, while the moderate suitable areas for urban development were extracted from 0.401 to 0.751 from the continuous refined suitability map. Consequently, the least feasible areas were classified out of 0.001 to 0.40. The categorized suitability map was then validated based on the same randomly selected samples (Table 5). The SVM model accurately classified 1178 samples, which is about 78.5% of the total samples tested, which produced kappa index of o.67. The kappa index is calculated using Eq. (11) [44]:
Accuracy metric | Value |
---|---|
Correctly classified instances | 1178 (78.5%) |
Incorrectly classified instances | 322 (21.4%) |
Kappa statistic | 0.67 |
Overall accuracy assessment of SVM modeling.
where
AUC for the SVM.
Class | ROC area | PRC area |
---|---|---|
Not suitable | 0.934 | 0.884 |
Less suitable | 0.799 | 0.60 |
High suitable | 0.852 | 0.673 |
Average | 0.861 | 0.719 |
Accuracy assessment of SVM modeling based on ROC.
An automated geospatial solution for selecting and ranking cities in Libya for urban development is proposed in this chapter. The suitability map showed that most areas indicated to be suitable are in the northern part of Libya. The results indicate that land use, distance to primary route, distance to large city, rainfall, NPP, and NO2 have negative effects on the level of suitability for the selection process, whereas the other factors have positive effects with population density taking the lead. It is revealed that SVM model accurately classifies 1178 samples, about 78.5% of the total samples tested which produced kappa statistic of 0.67. The high-priority city was selected as Benghazi that is followed by Al Jabal Al Akhdar. The results suggest that demography and vegetation are the two most influential factors contributing to the selection of city for development in Libya. This study is limited to analysis of six cities; the procedure developed through this study can be extended to other cities. It is of the opinion that evaluated criteria can be adjusted according to the environment and the current development of the cities.
TOPSIS 1
City | Percent urban | NPP | Population | Primary route | Rainfall | Secondary route | Slope | Trail route | NO | NDVI | LST | land cover | Distance to Benghazi | CO | Altitude |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 | |
Darnah | 0.37 | 3.68 | 131333.00 | 142935.00 | 680.50 | 356539.00 | 0.86 | 0.00 | 118.32 | −0.42 | 14.46 | 200 | 139404.00 | 81.09 | 226.11 |
Al Jabal Al Akhdar | 0.49 | 4.20 | 224171.00 | 196505.00 | 422.65 | 696167.00 | 0.82 | 221496.00 | 117.11 | −0.34 | 15.11 | 200 | 162514.00 | 79.77 | 341.61 |
Benghazi | 44.77 | 2.18 | 571466.00 | 52346.60 | 370.37 | 41068.40 | 0.36 | 27.63 | 148.66 | −0.10 | 17.19 | 30 | 541.96 | 78.36 | 55.55 |
Al Marj | 0.11 | 4.12 | 202732.00 | 188407.00 | 409.90 | 1328530.00 | 0.62 | 395212.00 | 120.24 | −0.30 | 15.86 | 200 | 147881.00 | 80.26 | 261.23 |
Al Qubbah | 0.00 | 5.83 | 47112.30 | 75610.90 | 461.25 | 906233.00 | 0.36 | 555787.00 | 127.56 | −0.52 | 16.51 | 200 | 441023.00 | 82.69 | 209.68 |
Al Hizam Al Akhdar | 0.28 | 4.31 | 164855.00 | 170954.00 | 598.51 | 1449880.00 | 0.39 | 346141.00 | 127.79 | −0.41 | 17.47 | 200 | 110122.00 | 80.76 | 122.89 |
Criteria sign range | −1 | 1 | 1 | 1 | 1 | 1 | −1 | 1 | −1 | 1 | −1 | −1 | −1 | −1 | −1 |
W(Lambda) | −0.36 | 0.98 | 2.15 | 0.60 | 0.96 | −2.35 | 0.74 | 0.27 | −0.62 | 4.88 | −1.17 | 0.23 | −0.10 | 1.23 | −1.18 |
Ideal | 0.00 | 5.83 | 571466.00 | 196505.00 | 680.50 | 1449880.00 | 0.36 | 555787.00 | 117.11 | −0.10 | 14.46 | 30.00 | 541.96 | 78.36 | 55.55 |
The worst | 44.77 | 2.18 | 47112.30 | 52346.60 | 370.37 | 41068.40 | 0.86 | 0.00 | 148.66 | −0.52 | 17.47 | 200.00 | 441023.00 | 82.69 | 341.61 |
TOPSIS 2
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
44.40 | 3.68 | 131333.00 | 142935.00 | 680.50 | 356539.00 | 0.00 | 0.00 | 30.35 | −0.42 | 3.00 | 0.00 | 301619.00 | 1.60 | 115.49 | |
44.28 | 4.20 | 224171.00 | 196505.00 | 422.65 | 696167.00 | 0.04 | 221496.00 | 31.56 | −0.34 | 2.35 | 0.00 | 278509.00 | 2.92 | 0.00 | |
N= | 0.00 | 2.18 | 571466.00 | 52346.60 | 370.37 | 41068.40 | 0.50 | 27.63 | 0.00 | −0.10 | 0.28 | 170.00 | 440481.04 | 4.33 | 286.06 |
44.66 | 4.12 | 202732.00 | 188407.00 | 409.90 | 1328530.00 | 0.25 | 395212.00 | 28.42 | −0.30 | 1.60 | 0.00 | 293142.00 | 2.43 | 80.38 | |
44.77 | 5.83 | 47112.30 | 75610.90 | 461.25 | 906233.00 | 0.50 | 555787.00 | 21.11 | −0.52 | 0.95 | 0.00 | 0.00 | 0.00 | 131.93 | |
44.49 | 4.31 | 164855.00 | 170954.00 | 598.51 | 1449880.00 | 0.48 | 346141.00 | 20.87 | −0.41 | 0.00 | 0.00 | 330901.00 | 1.93 | 218.72 | |
Normal | 99.55 | 10.3 | 681594.73 | 363626.55 | 1232.02 | 2302576.25 | 0.88 | 796219.68 | 60.05 | 0.90 | 4.25 | 170 | 746988.97 | 6.28 | 408.49 |
TOPSIS 3
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.445 | 0.36 | 0.192 | 0.393 | 0.552 | 0.154 | 0 | 0 | 0.505 | −0.47 | 0.71 | 0 | 0.403 | 0.25 | 0.282 |
0.444 | 0.41 | 0.328 | 0.540 | 0.343 | 0.302 | 0.05 | 0.278 | 0.526 | −0.37 | 0.55 | 0 | 0.372 | 0.46 | 0 |
0 | 0.21 | 0.838 | 0.143 | 0.300 | 0.017 | 0.562 | 0 | 0 | −0.11 | 0.07 | 1 | 0.589 | 0.69 | 0.700 |
0.448 | 0.4 | 0.297 | 0.518 | 0.332 | 0.576 | 0.278 | 0.496 | 0.473 | −0.33 | 0.38 | 0 | 0.392 | 0.39 | 0.196 |
0.449 | 0.57 | 0.069 | 0.207 | 0.374 | 0.393 | 0.563 | 0.698 | 0.352 | −0.57 | 0.22 | 0 | 0 | 0 | 0.322 |
0.446 | 0.42 | 0.241 | 0.470 | 0.485 | 0.629 | 0.537 | 0.434 | 0.348 | −0.45 | 0 | 0 | 0.442 | 0.31 | 0.535 |
TOPSIS 4
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
−0.164 | 0.35 | 0.415 | 0.236 | 0.533 | −0.364 | 0 | 0 | −0.318 | −2.28 | −0.83 | 0 | −0.044 | 0.31 | −0.333 | |
−0.163 | 0.4 | 0.709 | 0.325 | 0.331 | −0.711 | 0.037 | 0.075 | −0.331 | −1.8 | −0.65 | 0 | −0.040 | 0.57 | 0 | |
0 | 0.21 | 1.808 | 0.086 | 0.290 | −0.041 | 0.417 | 0 | 0 | −0.52 | −0.08 | 0.234 | −0.064 | 0.85 | −0.826 | |
−0.165 | 0.39 | 0.641 | 0.311 | 0.321 | −1.356 | 0.206 | 0.134 | −0.298 | −1.62 | −0.44 | 0 | −0.043 | 0.48 | −0.232 | |
−0.165 | 0.56 | 0.149 | 0.125 | 0.361 | −0.925 | 0.418 | 0.189 | −0.221 | −2.78 | −0.26 | 0 | 0 | 0 | −0.381 | |
−0.164 | 0.41 | 0.521 | 0.283 | 0.468 | −1.480 | 0.398 | 0.118 | −0.219 | −2.18 | 0 | 0 | −0.048 | 0.38 | −0.632 | |
Ideal | 0.00 | 0.56 | 1.81 | 0.33 | 0.53 | −0.04 | 0.42 | 0.19 | 0.00 | −0.52 | 0.00 | 0.23 | 0.00 | 0.85 | 0.00 |
The worst | −0.17 | 0.21 | 0.15 | 0.09 | 0.29 | −1.48 | 0.00 | 0.00 | −0.33 | −2.78 | −0.83 | 0.00 | −0.06 | 0.00 | −0.83 |
TOPSIS 5
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.16 | 0.20 | 1.39 | 0.09 | 0.00 | 0.32 | 0.42 | 0.19 | 0.32 | 1.76 | 0.83 | 0.23 | 0.04 | 0.54 | 0.33 |
0.16 | 0.16 | 1.10 | 0.00 | 0.20 | 0.67 | 0.38 | 0.11 | 0.33 | 1.28 | 0.65 | 0.23 | 0.04 | 0.28 | 0.00 |
0.00 | 0.35 | 0.00 | 0.24 | 0.24 | 0.00 | 0.00 | 0.19 | 0.00 | 0.00 | 0.08 | 0.00 | 0.06 | 0.00 | 0.83 |
0.17 | 0.16 | 1.17 | 0.01 | 0.21 | 1.32 | 0.21 | 0.05 | 0.30 | 1.10 | 0.44 | 0.23 | 0.04 | 0.37 | 0.23 |
0.17 | 0.00 | 1.66 | 0.20 | 0.17 | 0.88 | 0.00 | 0.00 | 0.22 | 2.26 | 0.26 | 0.23 | 0.00 | 0.85 | 0.38 |
0.16 | 0.15 | 1.29 | 0.04 | 0.06 | 1.44 | 0.02 | 0.07 | 0.22 | 1.66 | 0.00 | 0.23 | 0.05 | 0.47 | 0.63 |
We believe financial barriers should not prevent researchers from publishing their findings. With the need to make scientific research more publicly available and support the benefits of Open Access, more and more institutions and funders are dedicating resources to assist faculty members and researchers cover Open Access Publishing Fees (OAPFs). In addition, IntechOpen provides several further options presented below, all of which are available to researchers, and could secure the financing of your Open Access publication.
",metaTitle:"Waiver Policy",metaDescription:"We feel that financial barriers should never prevent researchers from publishing their research. With the need to make scientific research more publically available and support the benefits of Open Access, more institutions and funders have dedicated funds to assist their faculty members and researchers cover the APCs associated with publishing in Open Access. Below we have outlined several options available to secure financing for your Open Access publication.",metaKeywords:null,canonicalURL:"/page/waiver-policy",contentRaw:'[{"type":"htmlEditorComponent","content":"At IntechOpen, the majority of OAPFs are paid by an Author’s institution or funding agency - Institutions (73%) vs. Authors (23%).
\\n\\nThe first step in obtaining funds for your Open Access publication begins with your institution or library. IntechOpen’s publishing standards align with most institutional funding programs. Our advice is to petition your institution for help in financing your Open Access publication.
\\n\\nHowever, as Open Access becomes a more commonly used publishing option for the dissemination of scientific and scholarly content, in addition to institutions, there are a growing number of funders who allow the use of grants for covering OA publication costs, or have established separate funds for the same purpose.
\\n\\nPlease consult our Open Access Funding page to explore some of these funding opportunities and learn more about how you could finance your IntechOpen publication. Keep in mind that this list is not definitive, and while we are constantly updating and informing our Authors of new funding opportunities, we recommend that you always check with your institution first.
\\n\\nFor Authors who are unable to obtain funding from their institution or research funding bodies and still need help in covering publication costs, IntechOpen offers the possibility of applying for a Waiver.
\\n\\nOur mission is to support Authors in publishing their research and making an impact within the scientific community. Currently, 14% of Authors receive full waivers and 6% receive partial waivers.
\\n\\nWhile providing support and advice to all our international Authors, waiver priority will be given to those Authors who reside in countries that are classified by the World Bank as low-income economies. In this way, we can help ensure that the scientific work being carried out can make an impact within the worldwide scientific community, no matter where an Author might live.
\\n\\nThe application process is open after your submitted manuscript has been accepted for publication. To apply, please fill out a Waiver Request Form and send it to your Author Service Manager. If you have an official letter from your university or institution showing that funds for your OA publication are unavailable, please attach that as well. The Waiver Request will normally be addressed within one week from the application date. All chapters that receive waivers or partial waivers will be designated as such online.
\\n\\nDownload Waiver Request Form
\\n\\nFeel free to contact us at funders@intechopen.com if you have any questions about Funding options or our Waiver program. If you have already begun the process and require further assistance, please contact your Author Service Manager, who is there to assist you!
\\n\\nNote: All data represented above was collected by IntechOpen from 2013 to 2017.
\\n"}]'},components:[{type:"htmlEditorComponent",content:'At IntechOpen, the majority of OAPFs are paid by an Author’s institution or funding agency - Institutions (73%) vs. Authors (23%).
\n\nThe first step in obtaining funds for your Open Access publication begins with your institution or library. IntechOpen’s publishing standards align with most institutional funding programs. Our advice is to petition your institution for help in financing your Open Access publication.
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\n\nFor Authors who are unable to obtain funding from their institution or research funding bodies and still need help in covering publication costs, IntechOpen offers the possibility of applying for a Waiver.
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\n\nThe application process is open after your submitted manuscript has been accepted for publication. To apply, please fill out a Waiver Request Form and send it to your Author Service Manager. If you have an official letter from your university or institution showing that funds for your OA publication are unavailable, please attach that as well. The Waiver Request will normally be addressed within one week from the application date. All chapters that receive waivers or partial waivers will be designated as such online.
\n\nDownload Waiver Request Form
\n\nFeel free to contact us at funders@intechopen.com if you have any questions about Funding options or our Waiver program. If you have already begun the process and require further assistance, please contact your Author Service Manager, who is there to assist you!
\n\nNote: All data represented above was collected by IntechOpen from 2013 to 2017.
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This introductory chapter explains how a new tool can be added to this toolkit: robots. The use of robotic assets in search and rescue operations is explained and an overview is given of the worldwide efforts to incorporate robotic tools in search and rescue operations. Furthermore, the European Union ICARUS project on this subject is introduced. The ICARUS project proposes to equip first responders with a comprehensive and integrated set of unmanned search and rescue tools, to increase the situational awareness of human crisis managers, such that more work can be done in a shorter amount of time. The ICARUS tools consist of assistive unmanned air, ground, and sea vehicles, equipped with victim-detection sensors. The unmanned vehicles collaborate as a coordinated team, communicating via ad hoc cognitive radio networking. To ensure optimal human-robot collaboration, these tools are seamlessly integrated into the command and control equipment of the human crisis managers and a set of training and support tools is provided to them to learn to use the ICARUS system.",book:{id:"6181",slug:"search-and-rescue-robotics-from-theory-to-practice",title:"Search and Rescue Robotics",fullTitle:"Search and Rescue Robotics - From Theory to Practice"},signatures:"Geert De Cubber, Daniela Doroftei, Konrad Rudin, Karsten Berns,\nAnibal Matos, Daniel Serrano, Jose Sanchez, Shashank Govindaraj,\nJanusz Bedkowski, Rui Roda, Eduardo Silva and Stephane Ourevitch",authors:[{id:"206420",title:"Dr.",name:"Geert",middleName:null,surname:"De Cubber",slug:"geert-de-cubber",fullName:"Geert De Cubber"}]},{id:"5974",doi:"10.5772/6475",title:"Autonomous Formation Flight: Design and Experiments",slug:"autonomous_formation_flight__design_and_experiments",totalDownloads:4474,totalCrossrefCites:14,totalDimensionsCites:16,abstract:null,book:{id:"3696",slug:"aerial_vehicles",title:"Aerial Vehicles",fullTitle:"Aerial Vehicles"},signatures:"Yu Gu, Giampiero Campa, Brad Seanor, Srikanth Gururajan and Marcello R. 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The SDR-based GPR system results on a much lighter sensing device compared against the conventional GPR systems found in the literature and with the capability of re-configuration in real-time for different landmines and terrains, with the capability of detecting landmines under terrains with different dielectric characteristics. Secondly, the chapter introduce the integration of the SDR-based GPR into an autonomous drone by describing the mechanical integration, communication system, the graphical user interface (GUI) together with the landmine detection and geo-mapping. This chapter approach completely the hardware and software implementation topics of the on-board GPR system given first a comprehensive background of the software-defined radar technology and second presenting the main features of the Tx and Rx modules. Additional details are presented related with the mechanical and functional integration of the GPR into the UAV system.",book:{id:"5905",slug:"robots-operating-in-hazardous-environments",title:"Robots Operating in Hazardous Environments",fullTitle:"Robots Operating in Hazardous Environments"},signatures:"Manuel Ricardo Pérez Cerquera, Julian David Colorado Montaño\nand Iván Mondragón",authors:[{id:"177422",title:"Dr.",name:"Julian",middleName:null,surname:"Colorado",slug:"julian-colorado",fullName:"Julian Colorado"},{id:"197884",title:"Prof.",name:"Ivan",middleName:null,surname:"Mondragon",slug:"ivan-mondragon",fullName:"Ivan Mondragon"},{id:"199958",title:"Prof.",name:"Manuel",middleName:null,surname:"Perez",slug:"manuel-perez",fullName:"Manuel Perez"}]},{id:"67705",title:"Advanced UAVs Nonlinear Control Systems and Applications",slug:"advanced-uavs-nonlinear-control-systems-and-applications",totalDownloads:1906,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Recent development of different control systems for UAVs has caught the attention of academic and industry, due to the wide range of their applications such as in surveillance, delivery, work assistant, and photography. In addition, arms, grippers, or tethers could be installed to UAVs so that they can assist in constructing, transporting, and carrying payloads. In this book chapter, the control laws of the attitude and position of a quadcopter UAV have been derived basically utilizing three methods including backstepping, sliding mode control, and feedback linearization incorporated with LQI optimal controller. The main contribution of this book chapter would be concluded in the strategy of deriving the control laws of the translational positions of a quadcopter UAV. The control laws for trajectory tracking using the proposed strategies have been validated by simulation using MATLAB®/Simulink and experimental results obtained from a quadcopter test bench. Simulation results show a comparison between the performances of each of the proposed techniques depending on the nonlinear model of the quadcopter system under investigation; the trajectory tracking has been achieved properly for different types of trajectories, i.e., spiral trajectory, in the presence of unknown disturbances. Moreover, the practical results coincided with the results of the simulation results.",book:{id:"7792",slug:"unmanned-robotic-systems-and-applications",title:"Unmanned Robotic Systems and Applications",fullTitle:"Unmanned Robotic Systems and Applications"},signatures:"Abdulkader Joukhadar, Mohammad Alchehabi and Adnan Jejeh",authors:null},{id:"60953",title:"Small to Medium UAVs for Civilian Applications in Indonesia",slug:"small-to-medium-uavs-for-civilian-applications-in-indonesia",totalDownloads:1319,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Indonesian government needs a well-built, easy to operate unmanned aircraft systems (UAS) to perform various civilian missions as UAS are a well-known platform for dirty, dull, and dangerous missions. Hence, the Indonesian government has an organization that performs research and development of UAS, named as Aeronautic Technology Center. This organization is placed underneath Indonesian National Institute of Aeronautics and Space. The UAS developments in this institute are primarily driven by civilian uses; therefore, the UAS size, sensor types, and mission payload are optimized for civilian missions. In order to produce the decent to the best quality of the aerial image, which is the essential product for various civilian missions, the UAS regularly flies under the cloud. For this reason, the Aeronautic Technology Center is only developing the LASE (low altitude, short-endurance) and the LALE (low altitude, long endurance) UAS type as of now. The UAS development was begun with LSU-01, followed by LSU-02, LSU-03, and LSU-05. The LSU-01, LSU-02, and LSU-03 are in the operational phase, while the LSU-05 is in the experimental Phase. In this chapter, the specification of the platforms and the sensor capabilities that are relevant with the demands of users in the civilian sector are described.",book:{id:"6465",slug:"drones-applications",title:"Drones",fullTitle:"Drones - Applications"},signatures:"Fuad Surastyo Pranoto, Ari Sugeng Budiyanta and Gunawan Setyo\nPrabowo",authors:[{id:"223333",title:"M.Sc.",name:"Fuad",middleName:"Surastyo",surname:"Pranoto",slug:"fuad-pranoto",fullName:"Fuad Pranoto"},{id:"223356",title:"MSc.",name:"Ari Sugeng",middleName:null,surname:"Budiyanta",slug:"ari-sugeng-budiyanta",fullName:"Ari Sugeng Budiyanta"},{id:"223357",title:"MSc.",name:"Gunawan Setyo",middleName:null,surname:"Prabowo",slug:"gunawan-setyo-prabowo",fullName:"Gunawan Setyo Prabowo"}]},{id:"67003",title:"Vision-Based Autonomous Control Schemes for Quadrotor Unmanned Aerial Vehicle",slug:"vision-based-autonomous-control-schemes-for-quadrotor-unmanned-aerial-vehicle",totalDownloads:952,totalCrossrefCites:0,totalDimensionsCites:3,abstract:"This chapter deals with the development of vision-based sliding mode control strategies for a quadrotor system that would enable it to perform autonomous tasks such as take-off, landing and visual inspection of structures. The aim of this work is to provide a basic understanding of the quadrotor dynamical model, key concepts in image processing and a detailed description of the sliding mode control, a widely used robust non-linear control scheme. Extensive MATLAB simulations are presented to enhance the understanding of the controller on the quadrotor system subjected to bounded disturbances and uncertainties. The vision algorithms developed in this chapter would provide the necessary reference trajectory to the controller enabling it to exercise control over the system. 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