Values of material parameters \n
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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The recent enhancement of remote sensing capabilities, in terms of a variety of measured parameters and accuracy of the corresponding estimates, has been contributing to an effective improvement of the skill of forecasting models and, generally speaking, to the whole domain of operational oceanography which also benefits in real-time measurements provided by equipped buoys, moorings, and mobile platforms (floats, AUV, glider, etc.) that represent the principal resources to acquire in-situ ocean measurements.
While methodologies for the monitoring of the physical properties of the ocean are fully assessed and guarantee a good spatial and temporal coverage, techniques to monitor biochemical processes as well as meteorological phenomena over the ocean are still open issues [1].
Underwater acoustic systems, both active and passive, could contribute to fill this gap by listening to the ocean noise or by transmitting pulses and interpreting the received echoes to improve the knowledge on biological activities or meteorological phenomena at sea such as rain and wind and the potential harmful impact of human activities on the ecosystem.
The feasibility of using underwater acoustics to propagate signals date back to 1918 but only during the Second World War there was a massive exploitation of devices to detect submarines through sound navigation and ranging system (sonar). The first civilian experiments to measure the sea bottom and to detect schools of fishes were carried out in the first half of the twentieth century [2]. Since then, the benefits of underwater acoustics were proportional to the technological developments in both hardware and software components, especially for oceanographic applications.
Several aspects have to be taken into account when planning an underwater acoustics measurement program. The most important factor is the type of noise being measured and, accordingly, its expected features in terms of amplitude, frequency, duration, and so on, which drive the choice of measurement equipment. Indeed, the sound in the ocean is characterized by speed of propagation, attenuation, and presence of obstacles along the path and by the way in which the sound is scattered, backscattered, and refracted by both the bottom and the surface.
Ocean stratification is the main responsible event for the generation of beams (convergent and/or divergent) and grey areas, depending on the change in the speed due to the depth. In turn, sea temperature profiles are influenced by diurnal cycle, season, and weather conditions. During winter months, the surface water that is colder and saltier tends to sink, and it is replaced by warmer and deeper water masses. This mixing could originate a layer of isotherm water characterized by a homogeneous sound velocity defined as “mixed layer.” Below, the thermocline, that is the area in which temperature rapidly decreases with depth, dwells. The mixing of the water column implies an enlargement of the mixed layer and an erosion of the thermocline. The layer below the thermocline is characterized by a quite constant temperature and presents a minimum in the sound speed profile.
In the ocean, sound pressure levels (SPL) are retrieved using the sonar equation (Eq. (1)) as the difference between the transmitted power (SL) and the power loss (TL) through the path [3].
Equation (1) allows the quantification of SPL acquired by passive devices that simply listen to the numerous and heterogeneous ocean sounds like, among others, those produced by mammals, marine organisms, volcanoes, submarines, human activities, wind, waves, and rain.
Since active instruments are able to transmit a pulse and listen to its echo, it is necessary to consider also the intensity of the echo one meter from the target, that is, relative to the part of the sound that hits the target, the so-called target strength (TS). Thus, Eq. (1) is modified in Eq. (2)
Among the others types of applications, active instruments are commonly used to detect schools of fish, mines, and currents.
In the design of an acoustic experiment also, the availability, or the construction, of adequate infrastructure for carrying out the measurements for a desired duration is a key requirement. The two most common approaches consist of using mobile and fixed platforms.
In vessel-based surveys, hydrophones (either individually or in arrays) are deployed from the ship, and the analysis and recording equipment remain on the vessel, which may be either anchored or drifting. This solution is relatively easy to implement, the deployments can be quick, a relatively large area may be covered, the risk of losing instrumentation is low, the configuration of hardware devices can be adjusted online, and data can be monitored in real time. Nonetheless, the main disadvantage consists of the pre-defined and limited (usually short) period of time during which the measurements can take place. Also, autonomous moving platforms such as gliders can be equipped with hydrophones to explore the soundscape of relatively large areas of the ocean.
When continuous time monitoring is of interest or when the objective is to observe episodic and non-predictable phenomena (i.e., biological and geological events), a Eulerian approach is preferable. This consists of the use of fixed observatories that can be based on sea bottom stations cabled to the shore [4] or on instruments deployed on oceanic sub-surface moorings [5] or surface buoys. Several large initiatives are currently operational all over the world: Ocean Observatories Initiative (OOI) in the USA, Neptune in Canada, European Seas Observatory NETwork (ESONET), and the neutrino telescope sites in Europe [6]. Cabled observatories allow data to be streamed directly to the shore base and checked in real time [7]. Unless the goal is to measure air-sea surface interactions through acoustics or characterize the acoustic signature of ships, bottom-mounted deployments offer the advantage of minimizing both the influence of surface wave action and the disturbance by surface vessels, reducing the risk to keeping the hydrophone away from the pressure-release water-air surface and the risk of damage to the equipment.
The characterization of the ambient sound all over the world oceans, through the variety of approaches mentioned above, has become more common as interest in the trends in anthropogenic sound in the ocean grows. The European Commission endorsed this issue considering the introduction of energy, including underwater noise, into the ocean as a pollutant [8] and requesting to monitor it with the same operative methodologies like other physical, biological, and chemical parameters.
Sea surface shows ripples of different dimensions and shapes, depending on the force of the wind speed, and on the basis of their characteristics, they can be subdivided in two categories: capillary and gravity waves. Ocean capillary waves are strictly connected to surface tension and show short wavelength, whereas ocean gravity waves are due to the force of the air-sea interface conditions and their wavelength can reach several meters, especially in open oceans during storms. The characteristics of the waves, induced by winds, are identifiable by wind speed intensity and distance and by the duration of the event [9, 10].
When energy loss due to the air-sea friction is negligible, waves can propagate until one of these events occur: wind forcing persists, waves are hindered by the presence of dams or consume their energy on the coastline. Dissipations of energy reduce inversely proportional to wavelength; thus, large wavelengths, generally faster, smooth slowly and propagate over long distance even where wind is absent.
Waves induced by wind force can be modeled by Eq. (3) as
where
Wind blowing for an extended period of time over a long distance induces a rapid increase of both wave steepness and height. The upper limit of the height is reached when wave breaking generates a dissipation of energy able to balance the energy supplied by wind and, in this case, wave motion can be considered as fully developed. Each component of fully developed waves is a random ergodic process characterized by a variance equal to the mean quadratic value
Through the 0th and 1st spectral moments of
Several methodologies have been developed to estimate wave characteristics for open sea and coastal studies. In both environments, the difficulties to obtain measurements also with rough sea increased the use of data provided by satellite and, in the meantime, gave rise to a growing interest in the autonomous system capable to measure meaningful parameters in a continuous way and in all meteo-marine conditions.
In-situ technologies such as wave buoys [12], pressure and acoustic water level sensors [13], and upward-looking acoustic Doppler current profilers (ADCPs) [14] are generally employed to monitor and estimate ocean waves. Nonetheless, the use of a wave buoy is quite prohibitive in real open ocean environment with sea bottom deeper than 1000 m. In this case, the only possibility to collect wave estimate on long-term basis is to employ vertically oriented sonar installed on spar buoys [15, 16] that do not follow the surface but are designed to allow for negligible sensitivity to sea heave and height.
Acoustic wave meter systems are commonly based on a directional array of high frequency precision, and acoustic altimeters are installed in an upward-looking configuration. The echosounder transmits a short pulse, and the acoustic returns are amplified and subjected to compensation through a time-varying-gain circuit, which corrects for acoustic losses associated with beam spreading and attenuation in sea water. After digitization, the amplitudes of the echo are scanned to select a single target for each ping. The selection procedure chooses the target with the longest persistence from all targets having amplitudes above a user-specified threshold level.
Under the hypothesis of a constant sound speed, each altimeter emits a single beam toward the sea surface and measures the time between the emission and the received echo. Under stationary conditions of the sea state, wave height process can be considered as a stationary and ergodic stochastic process with zero mean. However, a truthful statistical description of sea waves is achieved only if the wave height process is supposed to be Gaussian [17].
In a real environment, not all samples satisfy the properties of the Gaussian distribution, and the measured echoes of the array of altimeters could be disturbed by reverberation of bubbles, dishomogeneity close to sea surface, and the presence of fishes lying between the altimeter and the sea surface. To overcome these issues, an ad-hoc processing algorithm has taken into account the correction for the motion of the platform hosting the acoustic array.
An acoustic wave meter system was installed at a depth of 10 m on the spar buoy part of the W1M3A observatory moored in the open Ligurian Sea (Northwestern Mediterranean Sea) [18].
The array was constituted of three brackets, which were 2.5 m long, equally spaced at 120°, hosting three high-frequency (500 kHz) altimeters that emit a narrow conical beam (6.0° width at −3 dB) which results in a small area being insonified at the surface (about 1.04 m). A Transistor-Transistor Logic (TTL) signal triggered the emission of the pulse by each altimeter. In order to avoid interferences, the acquisition system, which controlled and collected the output signals from the altimeters, synchronized and slightly shifted in time with the three TTL signals so that each ping (and consequently each sample acquisition) was delayed with respect to the others of few milliseconds.
The slow motion of the spar buoy, especially in the presence of strong winds and currents, can influence the acoustic measurements, thus, the wave mater package was inclusive of vertical accelerometers and a couple of two axis orthogonal inclinometers installed along the horizontal axis to correct the acquired data for the buoy motion.
The acquisition system simultaneously collected the time series provided by the three echosounders at a frequency of 2 Hz and buoy motion data (inclination and acceleration). First, the time series of the three echosounders was quality controlled in order to identify spikes, outliers, and samples not satisfying the Gaussian condition. The detected samples were then interpolated by means of spline functions. In order to preserve the phase-shift information between the three time series in all the cases in which the reconstruction of part of the overall time series was not possible due to the elevated number of bad samples, all waveforms (provided by the altimeters, the inclinometer, and the accelerometer) were adjusted homogeneously.
The obtained time series was then filtered to compensate for platform motion [19], and standard statistical parameters (i.e., wave height, period) were computed on the basis of the spectral density features of the acoustic profiles [9, 20]. The wave meter system was designed to create an equilateral-triangular array (Figure 1), allowing the estimates of the prevalent direction of the wave by means of the theory of the direction of arrival [21] valid under the assumption of the incoming planar wave.
A sketch of the array of acoustic altimeters.
Let us consider to divide the three altimeters into pairs (i, j), (j, l), and (i, l): the time delay between the sensors of each pair when the planar wave passes through can be expressed in matrix form through Eq. (5):
The assessment of the method was carried out comparing simultaneous wave estimates obtained by using the acoustic wave meter and acquired by a Datawell Waverider (DWR) directional buoy, a spherical one with 0.9 m of diameter, specifically designed to monitor wave characteristics. Wave data acquired by DWR buoys are basically displacement signals: one (the heave signal) for the non-directional wave rider and three (heave, north, and west displacement) for the directional wave rider. The mean, variance, skewness, and kurtosis of these signals are also computed. In the wave-statistical processing, zero-upcross waves are constructed from the heave signals, which are sorted by wave height and averaged in several fashions. This is the classic method of wave analysis that generates the significant wave height
The two buoys were moored at a nominal distance of 4 km for safety reasons, since the main buoy of the W1M3A observatory can span a circular area of 2 km by means of its slack mooring. These systems were continuously operational within the time of the validation which lasted 2 months from June to August, and all available estimates were used for the validation. During the period of the assessment, significant wave heights spanned from a minimum of 0.14 m to a maximum of 3.20 m and two storms occurred with the corresponding rough sea and strong wind speed. Thus, the acoustic wave meter was tested for several sea-state conditions. Although the majority of samples regard a smooth sea-state condition, a statistically significant number of samples refer to slight, and a moderate sea-state class was observed.
The validation of the acoustic wave meter system in terms of
(a) Time series of significant wave height as estimated trough the acoustic wave meter system and measured by the Datawell Waverider buoy. (b) Scatter plot of the wave direction estimated by the acoustic wave meter versus the one measured by the Datawell Waverider buoy.
Results demonstrate the feasibility to use an acoustic wave meter array as an affordable tool to measure waves on the long term and also in an open ocean where it is difficult to deploy discus buoys on deep sea bottom for an extended period of time. The system is still deployed on the W1M3A observatory and the collected data were used to indirectly assess the performance of the Dust Regional Atmospheric Model (DREAM) model to predict sea salt aerosol concentrations [22].
The first prototypes of acoustic Doppler current profilers were developed at the end of the 1980s with the aim of a continuous monitoring of ocean currents along the water column. Initially, these instruments transmitted a single narrowband pulse and through the auto-correlation technique and provided measurements of the first spectral order. Ten years later, the second generation of ADCP was put on market. It was characterized by a wider band and an enhanced data processing, exploiting the principle of Doppler effects. Nowadays, ADCP instruments transmit a pulse with known frequency and listen to the return echo that is backscattered from water drop, sediments, planktonic organisms, and all particles that are freely transported by ocean currents.
Part of the transmitted sound is backscattered in all directions, part is dissolved in the ocean, and another part comes back to the instrument. This signal is twice phase shifted because of the Doppler effect: when the scattering elements present in the ocean move away from the transducer, the sound is phase shifted of a quantity proportional to their relative speed (
Mathematically, a phase displacement corresponds to dilation in the time domain. The sound produced by a single particle and also its backscatter echo remains unchanged until the particle doesn\'t move, but in case of a small displacement from the source, the echo will need more time to reach the transducer and thus the return signal will be phase shifted. ADCP devices measure the phase of the signal to obtain the time dilation exploiting the principle that the speed of the particles can be calculated if the interval of time between two pulses is known. The only ambiguity is represented by the fact that the phase is measured in the interval 0–360° and when the phase exceeds 360°, it starts again at 0°. The easy solution consists of transmitting a train of pulses with very short time delay for each pulse in order to avoid changes in the phase of the particles of more than 360°.
Generally, ADCP instruments are constituted of two couples of transducers to measure north, east, and vertical components of the ocean current, and the profile is obtained subdividing the water column in several segments called bins. The main outputs of the ADCP devices are current speed and direction, but several ancillary parameters, used to calculate current characteristics, are also available.
The diel vertical migration (DVM) can be defined as the cyclic vertical displacement performed by most zooplankton species. Different DWM patterns have been observed, but the most common behavior is the swimming upward at sunset and downward at sunrise. Several environment causes such as light, temperature, food, and predation pressure, as well as endogenous origins like sex and age, influence DVM characteristics. Generally, the vertical distribution of the zooplankton is determined by net tows or pump samples that allow one to identify with the different species, but these samples are sparse in time and space and do not provide detailed information on the temporal variability, especially in the long-term period.
ADCP instruments are a powerful tool to overcome this issue, guaranteeing a quite continuous monitoring, also in extreme environments such as the Polar regions [23, 24] or highly productive basin, such as the Mediterranean Sea [25–27], at the expense of a specific taxonomic analysis.
Patterns of DVM can be detected through the analysis of the backscatter strength data (
Equation (8) was applied to high-resolution acoustic ADCP backscatter data acquired during winter 2009–2010 in the Ligurian Sea. The used backscatter data were provided by an upward looking 300 kHz ADCP (by RDI) deployed at about 150 m depth on a deep sea bed of 1200 m from November 2009 to April 2010. The device was set to sample every 15 min with a bin length of 2 m in order to obtain high resolution data both in time and in space.
Backscatter strength values show a seasonal variability with low values in winter from 70 m depth down to 127 m and a gradual increase till 100 m in early spring in correspondence with the increment of the net primary productivity that, in turn, implies more availability of food in the water column and less need for the zooplankton population to reach the surface to feed themselves. The analysis of the
(a) Daily averaged backscatter strength with, superimposed, the mean values and (b) daily averaged vertical velocity with, superimposed, the mean values, the hours of sunrise and sunset.
Furthermore, in December and January, the DVM was influenced by moonlight: during full moon nights and clear skies ( December 2 and 31, 2009), the backscatter strength decreased in the surface layers while greater values extended in the water column down to the maximum analyzed depth, making the values acquired at different depths quite homogenous along the water column (Figures 4a and b). This behavior, in contrast with the common nocturnal DVM of new moon periods (Figures 4c and d), is a characteristic of macrozooplankton/micronekton species and can be interpreted as a way to escape from visual predators [30]. Indeed, corresponding vertical velocities show more variability in the surface layers and a marked downward movement at midnight that is not present during the other moon phases (Figures 4e and f).
Temporal series of backscatter strength profiles during the full moon on (a) 2, December 2009 and (b) 31, December 2009 and (e) the corresponding average vertical velocities. Temporal series of backscatter strength profiles during the new moon on (c) 16, November 2009 and (d) 16, December 2009 and (f) the corresponding average of vertical velocities.
Obtained results demonstrate the feasibility to use non-calibrated ADCP data to infer zooplankton behavior with respect to daily seasonal and inter-annual variability as well as to astronomic phenomena. In fact, the observed intense DVM signal can be an indication of the presence of the
Within the framework of the “Marine Strategy Directive to save Europe’s seas and oceans” edited in June 2008, one of the main challenges of the Europe member state is to adopt mitigation actions and policy plans aiming at an effective protection of the overall marine environment by 2020. The increase of the maritime traffic and of anthropogenic activities at sea, such as the extensive use of sonar and oil drilling activities, has contributed to modify the natural ocean environmental noise so much that in some basins, it is the main cause of changes in the behavior of marine mammals.
Underwater environmental noise plays a fundamental role in biodiversity conservation, and the first studies date back to the Second World War when acoustic experiments established that environmental noise is the sum of several factors including ship traffic, breaking waves, wind, rain, mammals\' vocalizations, and sound produced by marine organisms. In 1962, Wenz [31] demonstrated that ships generate noise at low frequencies and proposed curves that describe the spectrum level at different frequencies for noise generated by ships and wind that were at the base, and still are, of forecasting systems. The National Research Council in Ref. [32] introduced a new definition of environmental noise as the “
Wind is the major physical producer of noise over sea surface, and its spectral characteristics span a broadband frequency band, from less than 1 up to 50 kHz. The spectral curves show an increment for frequencies below 1 Hz, followed by a decrease as frequencies increase. As wind speed increases, the spectral curves maintain the same shape but show greater pressure levels. For wind speed > 10 ms−1, the sound produced on the sea surface can be undistinguished by the sound due to the passage of a distant ship. Moreover, it is often associated with a high wave that is responsible for the generation of small bubbles that, in turn, produce sound and make the detection, and especially the quantification, quite difficult.
Also, precipitation contributes to the ocean noise in the frequency band from hundreds of Hz to more than 20 kHz, and the corresponding spectra show different characteristics depending on the type of precipitation. In the case of drizzle, a clear peak originated at the acoustic resonance of small drops splashing on the sea surface is observable around 15 kHz. This peak tends to disappear with the increase of the drops\' dimension that produce sound at a frequency lower than 10 kHz and another peak at about 1–2 kHz in case of convective rain.
Tectonic processes, earthquakes, volcanic, and hydrothermal activities are the major geological sources contributing to the ocean environmental noise. Their spectra range from 1 to 100 Hz, show an initial burst, and the same noise persists for several minutes.
Biological sources are strictly related to marine organisms and mammals living in the ocean that produce signals spanning from 10 up to 200 kHz, depending on the species. In very productive basins, the biological sources are prevalent on the physical and geological components, whereas in high anthropological areas, the main responsible events of the noise are human activities.
Noise generated by ship passages is characterized by low frequencies (5–500 Hz) and propagates over long distances affecting wide areas. Each type of vessel (research vessels, leisure or fishing boats, tankers, commercial ferries, etc.) and also each single vessel are characterized by an own acoustic signature depending on cavitation phenomena, on the modulation of blade propeller, and on the on-board engines. Furthermore, noise produced by ships is variable and could be affected by environmental conditions especially for the interaction with the sea bottom.
Measurements of ocean environmental noise are related to the power of the propagating signal and to the characteristics of the acoustic path between source and receiver that can be modified by oceanographic dynamics, sound velocity propagation, and bathymetry. These components cause fluctuations in the pressure levels of the environmental noise depending on depth, time instants, and areas; thus, it is necessary to perform further experiments and to continuously monitor environmental noise to deepen the knowledge of its dynamic and the impact of the human activities.
Passive acoustic data of ocean ambient noise consists of measurements of sound pressure as a result of the superimposition of sounds generated by several types of events (i.e., rainfall, ship passages, or mammals\' vocalizations) to background noise, which is the natural noise in the absence of any sources, whose level is closely related to the intensity of blowing wind. Although background noise levels can be different from basin to basin, each source shows unique spectral characteristics that can be used to classify its type (physical, biological, anthropogenic) and, in some cases, also to obtain an estimate of atmospheric parameters over the sea surface (i.e., wind, rain). Indeed, multivariate analysis techniques can be applied to a combination of spectral levels, acquired at specific frequencies and least-square fit in different spectral bands to provide insights about the different sources forming the environmental noise.
Figure 5 shows the results of the multivariate analysis applied to acoustic data that was acquired in the open Ligurian Sea from March to November 2015 by means of a hydrophone installed on the W1M3A observatory. The output signal of the hydrophone was band-pass filtered and then digitalized at 16 bit with a sampling frequency of 100 kHz. Acoustic data were collected for few seconds, every tens of minutes, due to the large amount of data that such a sampling rate produces.
Average sound spectra for (a) different types of precipitation and (b) wind speed greater than 2 ms-1 subdivided in Beaufort classes compared to the average spectrum of the background noise.
During the analyzed period, wind was continuously blowing over the sea surface and generated a sound that increased proportionally to the reinforcement of its speed, and, similarly, spectral levels tended to increase monotonically from 0.5 to about 25 kHz. Beyond this threshold, the sound produced by strong wind resulted comparable and even lower than the one generated by moderate breeze because of the contemporary arising of large waves and, in turn, the generation of small bubbles that absorbed the emitted sound. Wind spectra were very different to the one obtained during episodes of convective rain but could mask events of stratiform and light rain since the spectral levels from 20 up to 30 kHz were very similar for wind speed greater than 8 ms−1. Furthermore, the resonance frequency of bubbles splashing on the sea surface is inversely proportional to their size and for this reason, large drops associated with heavy rain showed loud sound and, instead, small drops, typical of light rain events, presented a peak in the 10–15 kHz frequency band.
Several studies were carried out to quantify wind speed and rainfall amounts through the analysis of acoustic data. In Ref. [33], a logarithmic relation based on the sound pressure levels acquired at 8 kHz was proposed, and, recently, new parameterizations has been introduced for the Mediterranean region based on the results achieved during the Ionian Sea rainfall experiment and Ligurian sea acoustic experiment [34, 35].
The equation proposed in Ref. [35] was applied to the acquired acoustic data and compared to the in-situ wind speed observations provided by the W1M3A observatory for wind speed greater than 2 ms−1 (Figure 6a). Results show a good agreement between wind speed measurements provided by the anemometer and the estimates obtained using acoustic data, with a correlation of 87.5% and a root mean square error of 1.294 ms−1 taking into account that 2 ms−1 can be considered as the minimum wind speed that is acoustically detectable.
Time series of (a) wind speed and (b) rainfall as measured by the anemometer and the rain-gauge installed on the W1M3A marine observatory, and the estimates obtained from acoustic samples.
Rainfall rate and sound intensity are related by a logarithmic expression based on the sound pressure level at 5 kHz, whose coefficients can vary depending on the area of deployment [36]. Available acoustic data acquired in 2015 were processed following the algorithm proposed in Ref. [35] and compared to rainfall observations simultaneously acquired by a rain gauge installed on the W1M3A offshore observing system (Figure 6b). Results evidence the feasibility to use passive acoustic data to detect rainfall episodes, especially in case of intense events and the capability of quantity rainfall amounts with good accuracy, independently from rain types and the presence of wind speed.
Passive acoustic observations provide powerful support to complement visual surveys for the monitoring of marine mammals due to the fact that acoustic waves propagate for long distance. Visual observations are weather dependent, not available in remote or inaccessible areas, often limited in their use due to the short times animals may spend at the surface, and are sparse in time, whereas passive acoustic devices can be successfully employed for an extended period of time and can monitor a wide area providing information about both the presence and the species of the animals.
Vocalizations are the principal sounds generated by marine mammals that use them to communicate, to echolocate, and also for predatory or mating purposes [37]. Every species, and even each individual, can be recognized by its acoustic signatures, and for this reason, time-frequency analysis of time series of passive acoustic data is useful for the marine mammals\' monitoring. This is particularly true for basins where human activities are scarce and the environmental noise is dominated by mammals\' vocalizations like Thetys Bay in Antarctica or in a very productive area such as the “Cetacean Sanctuary” in the Ligurian Sea.
During the 29thItalian Antarctic expedition, a hydrophone was installed under the sea ice in Thetys Bay to study sound propagation. The basin is a natural habitat of different pinnipeds species (i.e., crabeater seal (
Spectrograms of vocalizations by Weddel and crabeater seals acquired during the 29th Italian Antarctic expedition.
The experiment took place in November, during the Weddel seals mating period [38], and this explains the reason why the prevalent types of detected calls from Weddel seals are trills and whistles, both ascending or descending as defined in Ref. [39]. Trill calls show a descending pattern, are emitted once, last for 15 s and cover a wide frequency range from 6 kHz down to few hundreds of Hz. Whistles ascending, although being single pulses, last few seconds maximum, and their patterns are characterized by a sharp increase from about 4 up to 5 kHz, followed by a smooth rise up to 6 kHz maximum. Whistles descending are a series of pulses initially emitted at about 1-s intervals, progressively reducing the interval and dropping from 10 to 2 kHz. The typical vocalization produced by crabeater seal is known as moan; its spectrum has power content lower than trills and whistles in the whole range of frequencies and the signal spans from 700 up to 6 kHz.
A similar experiment took place in the Ligurian Sea during 2015, allowing one to identify the presence of sperm whales (
Spectrograms of vocalization by (a) sperm whales, (b) Cuvier’s baked whales, and (c) striped dolphins acquired in the Ligurian Sea.
Sperm whales are the most common mysticete species in the Ligurian-Corsican-Provençal basin due to the high productivity that characterized the area supported by the permanent frontal structure of rich large biomass of krill, especially of
Odontocetes calls are much different from mysticete’s vocalization, presenting a wide variety of patterns of whistles ranging from few Hz up to more than 20 kHz and clicks used for echolocation that can extend between 50 and 150 kHz. Using the spectrograms, it is possible to distinguish the different species of the odontocetes and, in some cases, the sound emitted by the same individual.
The availability of passive acoustic recordings covering a long period of time could really improve the knowledge of mammals\' vocalizations in their natural environment, especially in winter months where it is difficult to carry out visual surveys due to potential bad weather conditions.
The combination of active and passive underwater acoustic methods could significantly contribute to the monitoring of the oceanic environment and to a better characterization of the ocean status. Analysis of acoustic observations in the time domain allows the detection of seasonal trends or inter-annual variability helpful for the identification of climate change’s causes and/or impacts, as well as for the definition of mitigation actions and strategic plans devoted to the protection of the marine environment. Otherwise, analysis of acoustic data in the frequency domain makes possible to distinguish geophysical phenomena, such as wind and rain, and biological sources, such as vocalizations of marine mammals and anthropogenic noise by means of their own acoustic signatures. Specifically, the application of Fast Fourier Transform (FFT), wavelet, and autocorrelation techniques could provide insights about wave fields and give evidence of the presence of several marine mammals or different patterns referable to migratory processes, typical of zooplankton and micronekton species.
Indeed, in-situ acoustic measurements provided by a directional array of upward looking echosounders, installed on a spar buoy, have been used to obtain estimates of wave height, period, and direction in the open Ligurian Sea. Results show the feasibility to use acoustics to obtain reliable observations of wave field using a fixed platform not specifically designed to follow the slope of the waves. Measurements provided by active devices have been also successfully employed to monitor the behavior of zooplankton in relation to daily cycle and moon illumination for a long period of time that cannot be obtained using sporadic cruises or net samples sparse in time.
Experiments based on the installation of hydrophones carried out in different basins demonstrated the potentiality of passive acoustic data used to identify a variety of processes. Known as the mean noise level of the basin in which the hydrophones are deployed, it was possible to apply algorithms to automatically quantify rain and wind by means of the noise produced on the sea surface. Furthermore, the application of time-frequency analysis allowed the creation of spectrograms from which the types of mammals living in a different basin were easily detected.
The research leading to these results has received part of the funding from the European Community’s Seventh Framework Programme FP7/2007–2013 under the grant agreement n° 312463 (FixO3) and the Flagship Project RITMARE funded by the Italian Ministry of Education, University, and Research.
Computational imaging with a single-pixel camera (SPC), or single-pixel imaging (SPI), is a remarkable alternative to conventional imaging [1]. SPCs are based on sampling the scene with a sequence of microstructured light patterns codified onto a programmable spatial light modulator (SLM), while the intensity of the light coming from the object is measured by a detector without spatial resolution. The image is computationally recovered from the fluctuations of the electric signal provided by the detector. Thus, the quality of this temporal signal is a key factor in order to recover a high quality image.
\nThe simplicity of the detection stage in SPI is one of the main advantages of the technique. It can be exploited to use very sensitive light sensors in low light level applications [2, 3]. It is also useful in order to measure the spatial distribution of different parameters such as the spectral content [2, 4] or the polarization state [5] of the light coming from the objects. Besides, SPI has shown robustness to the presence of scattering media [6, 7]. Moreover, the SPC can be an interesting choice for imaging using light with a spectrum beyond the visible region, such as in the infrared (IR) and terahertz spectral regions. Furthermore, SPI techniques are very well suited for the application of compressive sampling (CS, also referred to as compressive sensing) methods, which noticeably reduce the measurement time, the bottleneck of this technique [8, 9].
\nAmong the different possible detectors, photodiodes are the most common sensors in general single-pixel imaging applications. In this chapter, we develop a numerical model of a single-pixel camera based on a photodiode, which considers the characteristics of the incident light, as well as the photodiode specifications [10]. Our model takes into account the photocurrent, the dark current, the photocurrent shot noise, the dark-current shot noise, and the Johnson-Nyquist (thermal) noise. In particular, we study the signal-to-noise ratio (SNR) as a function of the optical power level and the wavelength of the incident light, as well as the photodiode temperature. We restrict our study to silicon (Si) and indium-gallium-arsenide (InGaAs) photodiodes.
\nIn the following sections, first, we describe in detail the single-pixel cameras. Second, we review the properties of the electrical signal provided by photodiodes based on Si and InGaAs materials. Third, we present a numerical model of the single-pixel camera. Next, we apply this model to study the SNR of a single-pixel camera in different contexts. After that, we compare some of these numerical results with those experimentally obtained in the laboratory. Finally, we emphasize the main conclusions.
\nThe SPC is able to provide images of a scene with a bucket detector, such as a photodiode, by using light-structured illumination. A schematic representation of the optical system is shown in Figure 1. A set of microstructured light patterns is codified onto a programmable spatial light modulator (SLM) and sequentially projected onto the scene to be analyzed. The light reflected (or transmitted) by the scene is collected by a lens and focused onto a photodiode. The photodiode provides us with an electrical current proportional to the integrated light intensity, which is digitized by a data acquisition system (DAQ). The photodiode signal represents the dot product between each microstructured light pattern and the scene. The image is retrieved from the photodiode signal and the microstructured light patterns using computational algorithms.
\nSchematic representation of a single-pixel camera.
The ideas on which SPCs are based were proposed by Golay in 1949, for spectroscopy applications [11], and by Decker in 1970, for imaging [12]. However, the first efficient SPC was created in 2006 [13], by using a programmable SLM. The most common types of SLMs are the liquid crystal spatial light modulator (LC-SLM) and the digital micromirror device (DMD) [14]. In general, DMDs are more used than LC displays, in SPI applications, except when phase modulation is required. A DMD is a microelectromechanical system device that contains a pixelated display composed by millions of tiny switchable mirrors. Each mirror is able to switch to either \n
As a result, the SPCs have been successfully applied in many different imaging areas during the last decade. Among them, we can mention infrared imaging [17, 18], terahertz imaging [19], ultrasonic imaging [20], 3D computational imaging [21, 22], imaging through scattering media [23, 24, 25], 3D and photon counting light detection and ranging (LIDAR) imaging systems [3, 26, 27], stereoscopic imaging [28], microscopy [29, 30], holography [31, 32], and ophthalmoscope imaging [33].
\nThe set of microstructured light patterns is also an important element of the illumination stage in SPI. The light patterns commonly used for illumination are speckle patterns, binary random distributions, and functions of different basis such as noiselets [34], wavelets [35], Fourier, and Walsh-Hadamard (WH) functions. In fact, the measurement time and the resolution of the image are directly related with the properties of the light patterns. Particularly, the WH functions are easily coded on the DMD display due to the binary modulation nature of the DMD.
\nIn SPCs, the image is computationally reconstructed from the electric signal provided by the photodensor; in our case, a photodiode. In this context, the photodiode signal represents the inner product of the set of microstructured light patterns with the scene. Therefore, to analyze the quality of the image, it is convenient to study the properties of the electrical signal provided by the photodiode and its noise sources.
\nBy definition, a photodiode is a semiconductor device that converts the optical signal into a current signal by electronic processes [36]. The electrical current of the photodiode is composed by two terms, the photocurrent \n
where P is the optical power level of the light source and \n
where \n
Assuming that the temperature is lower or close to room temperature, the first term in Eq. (3) can be considered negligible [36]. Taking this into account and substituting this expression in Eq. (2), the dark current is given by
\nwhere \n
and by the Sajal Paul relation for the In\n
where \n
\n | \n\n | \n\n\n | \n\n\n | \n
---|---|---|---|
Si | \n\n\n | \n\n\n | \n\n\n | \n
GaAs | \n\n\n | \n\n\n | \n\n\n | \n
InAs | \n\n\n | \n\n\n | \n\n\n | \n
Values of material parameters \n
In the single-pixel camera process, the photocurrent and the dark current signals have an associated error, due to the discrete nature of the electrical charge [45]. The noise of the former one is known as the photocurrent shot noise \n
where \n
where \n
For the sake of completeness, we will consider the Johnson-Nyquist (or thermal) noise \n
where \n
Consequently, the signal-to-noise ratio (SNR) of the electrical current signal in units of decibels is defined as [37].
\nIn this section, a numerical model of the SPC is described. The camera model takes into account the optical power level \n
In this model, the microstructured light patterns are 2-D functions \n
In absence of noise, the mathematical properties of \n
The numerical process developed to simulate the SPC, from the moment in which the light source illuminates the DMD up to the image reconstruction (see Figure 1), is as follows:
\nwhere \n
where \n
The numerical model described in the previous section was used to analyze the performance of a SPC formed by photodiode detectors under different circumstances. Three different studies were developed analyzing the image quality when: (1) the optical power level of the light source changes; (2) we use light sources with different wavelengths; and (3) the photodiode temperature varies. The simulations were performed for two commercial photodiodes, DET36A Thorlabs and DET10C Thorlabs, whose specifications are shown in Table 2. Moreover, the dark current \n
Parameter | \nSymbol | \nSilicon biased detector | \nIn\n | \n
---|---|---|---|
Detector name | \n\n | DET36A Thorlabs | \nDET10C Thorlabs | \n
Photodiode active area | \n\n\n | \n\n\n | \n\n\n | \n
Wavelength range | \n\n | 350–1100 nm | \n900–1700 nm | \n
Band gap energy at 298 K | \n\n\n | \n1.1114 eV | \n0.7379 eV | \n
Rise time response | \n\n\n | \n14.0 ns | \n10.0 ns | \n
Noise bandwidth | \n\n\n | \n0.025 nHz | \n0.035 nHz | \n
Bias voltage | \n\n\n | \n10.0 V | \n5.0 V | \n
Saturation current at 298 K | \n\n\n | \n0.35 nA | \n1.0 nA | \n
Shunt resistance | \n\n\n | \n1.0 G\n | \n10.0 G\n | \n
NEP at \n | \n\n | \n\n | \n\n\n | \n
Photodiode parameters data.
Simulated dark current, dark-current shot noise and thermal noise as a function of the temperature for (a) Si biased detector (DET36A Thorlabs) and (b) InGaAs biased detector (DET10C Thorlabs).
Firstly, we analyzed the image quality as a function of the optical power level of the light source. We fixed the wavelength of the light source to 520 nm and the photodiode temperature to 298 K. Figure 3 shows the photocurrent, the dark current, and the total current associated to the single-pixel camera for two different optical power levels; (a) \n
Photocurrent, dark current, and total current with their associated noise values as a function of the WH pattern index. Two different optical power values were considered (a)
On the other hand, we numerically evaluated the image quality using the SNR metric defined by [48]. The reference image is obtained by SPI techniques but using only the photocurrent values without considering the noise sources. Afterward, we plot in Figure 4 the SNR as a function of the optical power of the light source. As expected, the image quality obtained by the SPC improves when the optical power increases. In the same figure, we also plot the curve of the SNR of the photodiode signal as a function of the optical power level. The reference signal is again the photocurrent signal without noise values. Of course, the SNR is the same in both cases. Therefore, we will use the SNR applied to the images from now onwards.
\nSNR of the signal and the recovered images as a function of the optical power
Second, we analyzed how the wavelength of the light source influences the performance of the SPC. The optical power of the light source was set to \n
(a) SNR as a function of the wavelength of the light source; (b) responsivity data of both photodiodes [
Finally, we analyzed the dependence of the image quality with the photodiode temperature. The wavelength of the light source was set to 520 and 1600 nm for the DET35A and the DET10C detectors, respectively. For each detector, three curves of the SNR as a function of the photodiode temperature are plotted for constant values of the optical power (\n
SNR dependence with the photodiode temperature for three optical power levels:
A scheme of the experimental setup by using transillumination is depicted in Figure 7. In this case, a DMD (DLP Discovery 4100, Texas Instrument) was illuminated with a collimated light beam generated with an incoherent white-light source. A narrow band pass filter (P10-515-S 93819, Corion) centered at a wavelength of 520 nm with a bandwidth of 20 nm was used to avoid spectral artifacts. In order to apply SPI techniques, microstructured light patterns corresponding to 2-D functions of the orthonormal WH basis with 64 \n
Experimental setup of the single-pixel camera.
Figure 8(a) shows numerically and experimentally recovered images with different levels of the optical power. We can see that, in both cases, the noise level decreases with the optical power. This is corroborated by the results in Figure 8(b), which show that the SNR curve corresponding to images obtained with the simulated and the experimental systems have a similar dependence with the optical power. This fact confirms the validity of our numerical model.
\n(a) Numerically and experimentally recovered images for different optical power levels
However, even though the model has been developed taking into account the most important noise factors during the imaging process, there is still a discrepancy in the values of the SNR for the experimental and the simulated images. This difference is produced by several other noise sources not included in the model. First, we have considered that both the DMD reflectance and the object transmittance are ideal binary functions, which is not true in practice. Second, we did not introduce background, or ambient, light into the numerical model, with the unavoidable associated noise. Finally, we did not consider the current-to-voltage and the analog-to-digital conversion processes, which produce certain amount of noise. A clear example in the last case is the quantization noise.
\nIn this chapter, a numerical model of a single-pixel camera (SPC) has been developed, considering the characteristics of the incident light and the physical properties, as well as the specifications of the photodiode. We have accomplished a careful and rigorous mathematical review of the electrical behavior of Si and InGaAs detectors. Our model takes into account the photocurrent, the dark current, the photocurrent shot noise, the dark-current shot noise, and the Johnson-Nyquist (thermal) noise of two commercial photodiodes, a Si and an InGaAs photodetector.
\nNumerical simulations with our model have allowed us to analyze the behavior of the single-pixel imaging (SPI) technique in different contexts. In particular, we have studied the quality of the final image as a function of the power of the light source. We have corroborated the reduction of the SNR for low light levels. We have also observed the clear link between the quality of the photocurrent signal and the quality of the reconstructed image. These results can be useful to predict the behavior of imaging systems working in low light level conditions.
\nWe have also studied the dependence of the SNR with the wavelength of the light source. In this case, we conclude that the influence of the wavelength arises from the variation of the quantum efficiency and the responsivity of the photodetector. Such analysis could be the first step in the application of SPI techniques to multispectral imaging.
\nFinally, we have analyzed the quality of the images provided by the SPC as a function of the photodiode temperature. The study is performed for both a Si biased and an InGaAs biased detector. The main conclusion in this case is that the SNR of the reconstructed images changes only slightly with the temperature for high values of the light power. However, the reduction is clearly significant for low light levels. Therefore, cooling the detector can play an important role in photon counting or low light level applications.
\nAn experimental SPC has been developed in order to validate the results provided by our model. The quality of the images obtained experimentally does not match perfectly with that predicted by the model. The discrepancy is due to several unaccounted sources of uncertainty such as nonuniformities in the mirrors of the DMD or in the object substrate, as well as noise introduced in the signal amplification process or the analog to digital conversion procedure. However, we have shown that the quality of the final image, in terms of the SNR, changes in a similar way with the light power. This allows us to confirm that the model can be useful to predict the behavior of SPI systems based on photodiodes under different circumstances.
\nWe acknowledge financial support from MINECO (FIS2016-75618-R and FIS2015-72872-EXP), Generalitat Valenciana (PROMETEO/2016/079), and Universitat Jaume I (P1-1B2015-35). Yessenia Jauregui-Sánchez acknowledges the Santiago Grisolía support from Generalitat Valenciana (GRISOLIA/2015/037).
"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges".
\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.
",metaTitle:"About Open Access",metaDescription:"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges.\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.",metaKeywords:null,canonicalURL:"about-open-access",contentRaw:'[{"type":"htmlEditorComponent","content":"The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\\n\\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\\n\\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nOAI-PMH
\\n\\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\\n\\nLicense
\\n\\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\\n\\nPeer Review Policies
\\n\\nAll scientific works are Peer Reviewed prior to publishing. Read more
\\n\\nOA Publishing Fees
\\n\\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\\n\\nDigital Archiving Policy
\\n\\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\\n"}]'},components:[{type:"htmlEditorComponent",content:'The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\nOAI-PMH
\n\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\n\nLicense
\n\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\n\nPeer Review Policies
\n\nAll scientific works are Peer Reviewed prior to publishing. Read more
\n\nOA Publishing Fees
\n\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\n\nDigital Archiving Policy
\n\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
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