Studies on architects’ attitudes towards the use of EWPs for construction.
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Studies indicate that increasing the use of EWPs in the construction sector has environmental benefits, as wood is a renewable and lightweight material [3, 4], where utilization of EWPs instead of conventional building materials e.g. concrete or steel, the total fossil fuel footprint of building construction can be diminished to a considerable extent through environmental-friendly-material replacement [5, 6]. Also, based on some estimates, substituting concrete with wood could lessen the energy used by construction processes by 40%, while greenhouse gas emissions could be reduced by 35% [7, 8]. Extensive use of EWPs may have helped make the transition towards more carbon-free production of building materials [9, 10].
A construction project involves a large number of participants with various roles, goals, and concerns [11] as in the case of high-rise timber buildings e.g. Mjøstårnet (Norway, 2019) (Figure 1), HoHo (Austria, 2020) (Figure 2). Besides, the building material selection process includes many parameters, e.g. cost, structural performance, environmental friendliness, fire performance, availability, and speed of construction [12, 13]. Moreover, the material selection procedure is a complex non-linear process involving various actors such as contractors, structural designers, developers, and architects [14, 15, 16].
Mjøstårnet (Norway, 2019) (Source: Wikipedia).
HoHo (Austria, 2020) (Source: Wikipedia).
Among these parties involved, architects play a critical role in material selection [17, 18], their perceptions influence their choice of material in the structural frame [13, 15, 19, 20]. Perhaps more importantly, architects’ perceptions could lead to an increase in the use of EWP for construction [19, 21].
In the literature, many studies have been carried out on the ecological, technological, economic, and social aspects of engineered wood products and various technical solutions in buildings [22]; while there is a relatively limited number of studies concentrating on the EWPs for construction from the stakeholders’ perspective (e.g. [11, 16, 20, 21, 22, 23, 24, 25]).
This chapter presented a comprehensive overview of the perceptions, attitudes, and interests of architects in the use of EWPs together with their perceived benefits and barriers for construction. This study gathered, mapped out, and systematized scattered and multifaceted knowledge on architects’ attitudes towards EWPs employment in buildings, and chronologically presented them in an accessible and manageable discourse. Notably, the chapter also revealed how this perception has changed over time.
An increasing emphasis on the climate impact of building materials in the construction sector should therefore increase the likelihood that future EWPs will be favored to a greater extent. In this sense, attempts to increase the awareness of architects about EWPs will have a positive effect, e.g. regarding the economic aspects. In this study, wood or timber refers to engineered timber products such as cross-laminated timber, laminated veneer lumber, glue-laminated timber/glulam.
In the literature, several studies from different countries such as Australia, America, Sweden are concentrating on wood as a structural material in the buildings from architects’ perspective through questionnaires and/or interviews (e.g. [13, 20, 23, 24, 26, 27]).
Among the studies, Truskett undertook surveys and interviews with architects in Victoria (Australia) to explore factors influencing the specification of wood products [28]. The findings showed that while the vast majority (90%) of those surveyed ‘always or mostly’ used structural timber for residential purposes, only 20% frequently used structural timber for non-residential applications. According to the architects, timber as a structural and finishing material has strong aesthetic appeal, but the factors such as maintenance and durability, professional networks, industry practice, information, and environmental issues hamper its general use.
Kozak and Cohen focused on the construction material selection of architects (n = 594 out of 3,986) and structural engineers (n = 384 out of 1,822) for non-residential buildings through an online survey in the United States and Canada [26]. The results showed that steel and concrete continue to be the most common material in non-residential applications, whereas wood governs the construction market for elderly housing and is also frequently employed in religious buildings, restaurants, and commercial/residential combinations. It was also pointed out that as the building height increases, the use of wood decreases. However, the attitudes of architects were encouraging towards the use of timber-frame if they had previously specified it.
In 2004, Wagner and Hansen scrutinized material preferences among architects and engineers in America and Chile by a cross-cultural comparison to establish a procedure for choosing a customer group of a company and then classifying its demands and needs [27]. They stated that American architects did not give much weight to issues concerning cost and ecological properties of the building material when deciding on wood construction, whereas other considerations e.g. dimensional stability were thought to have more potential for development when compared with competing materials like steel. Similarly, surveyed architects from Chile were not interested in the environmental features of the wood. Besides this, both sample groups of architects had a positive attitude towards the aesthetical properties of wood, and they perceived uniform quality as an essential asset. It was also noticed that fire-related issues received less concern among the architects than the relevant literature suggests [26].
O’Connor et al. studied architects’ and engineers’ perceptions regarding the use of wood in the North American non-residential construction sector through an extensive mail survey applied to a series of specifier’s focus groups [15]. This research identified several perceived challenges for wood utilization in construction: building code concerning fire safety; cost-competitiveness with steel especially in terms of complicated structures or structures with a longer span; design performance related to strength, durability, stiffness, and lack of established practices. They also proposed short- and long-term recommendations for addressing these obstacles.
The research of Bayne and Taylor also examined the barriers to the use of wood in Australian non-residential buildings to understand the reasons behind the common use of non-wood products e.g., concrete and steel [29]. They aimed to identify the reasons for the lack of confidence in specifying wood as a structural material in these buildings. As a result of interviews with 34 architects, engineers, building designers, and project managers, a range of strengths and weaknesses regarding the structural application of wood to non-residential use were underlined. Findings suggested that aesthetics, easy construction, and adaptability of design and fire performance were evaluated as advantages, while cost and speed of erection were taken as the most common obstacles by the specifiers. It was also concluded that the use of wood was found more suitable and promising by the architects for smaller building types such as housing for the elderly, schools, public buildings, and churches.
In 2008, Roos et al. presented perceptions of 23 Swedish architects and structural engineers about the material selection process, which also included a comparison of wood with other materials, the effects of main stakeholders, and the relation of wood construction with professional roles and knowledge [30]. Both architects and engineers were interested in using wood but perceived it as complicated. This study as a prospect highlighted the issues such as clear demonstration of business-sound wood compared with concrete and steel; functional information flows from the construction industry to wood sector; expression of smart solutions enabling flexibility and appropriate span lengths from an architectural standpoint; putting emphasis on aesthetic and visual aspects of timber-frame, and importance of providing more information about environmental benefits of wood by suppliers.
Bysheim and Nyrud investigated Norwegian architects’ perceptions regarding the use of wood as a structural material in urban construction via a questionnaire to measure attitudes towards the physical, mechanical, and fire-related properties of wood [31]. They found that many architects show a tendency to use structural timber, but few are planning to do so. They were also positive; fire and aesthetically related properties, costs of using structural timber compared to substitutes, and the energy-related properties of the material, the physical and mechanical properties of timber-frame. They felt qualified to specify timber-frame in buildings but did not perceive the choice of using structural timber as being entirely up to them. The architects had positive attitudes towards timber-frame in residential buildings but were negative towards use in other building types. Additionally, they were positive to utilize in buildings up to 3-story. The architects had little experience with timber-frame in other building types other than residential ones. Most of the respondents strongly agreed that other architects would have a positive tendency to them employing timber-frame, whereas contractors and real estate developers were perceived as negative towards the use of timber-frame.
Robichaud et al. explored the challenges to the use of wood in the North American non-residential construction sector [32]. This study aimed to examine the possible role of communication in this emerging market among architects through a survey (n = 165 out of 5,000). The results showed that generally, the architects assessed wood to be a ‘sincere’ but ‘unexciting’ structural material. When compared to concrete and steel, wood was perceived as the most environmentally friendly material. However, wood was rated with the lowest score in terms of durability, fire-resistant, structural performance, and contribution to the high building value. In the recommendation part of the study, the issue of better communication on the part of wood producers and product information was highlighted.
The research of Roos et al. analyzed architects’ and structural engineers’ attitudes and perceived factors that hinder or facilitate the specification of wood construction in Sweden [20]. The main finding of this research was that the material preference of architects and structural engineers is affected by attitudes concerning the properties of wood and beliefs about the control and ease of building in wood. Wood was generally perceived as a suitable building material. Issues about decay, instability, and sound transmission were assessed as negative aspects, while the features of strength, environmental friendliness, simple handling, and suitability for use along with industrial methods were taken as advantages of wood. Besides this, developers and contractors were perceived as the most influential parties by both Swedish architects and structural engineers in the material selection process.
The result suggests that if the following measures are taken into consideration, perceived obstacles could be lessened: (i) developing clearer business concepts for timber-based transparent and affordable construction approaches that decrease the uncertainty, (ii) creating prefabrication methods for wood to reduce the risk factor in the construction, (iii) developing education and training in building design and construction in wood, (iv) providing information about the environmental performance of wood as a building material, (v) improving the ‘professional status’ of wood via interesting design, (vi) supporting architects and engineers in pursuing wood construction and developing a dialogue among all the related professions.
Hemström et al. assessed Swedish architects’ perceptions, attitudes, and interest towards steel-, concrete-, and timber-frames in multi-story buildings through a web-based questionnaire (n = 412 out of 3,600) [11]. The results indicated that concrete was found the most favorable frame material for multi-story construction mainly because of its performance of engineering-based issues e.g., stability and fire safety that was considered critical for the selection of frame material. The general attitude towards, and interest in, timber-frame utilization was positive and related to its perceived environmental features. Differing from findings in North America [15], this study showed that costs and time to construct a building are not perceived as major barriers to the use of wood among architects in Sweden. Contradictory to the perception of wood being a less suitable multi-story frame material than concrete and steel, the interest in the use of wood frames was large. Contrary to Norwegian findings [16], the overall attitude towards the use of wood frames in residential buildings presented here was not different from the attitude towards the wood in non-residential buildings. Besides this, the results showed that contractors, structural engineers, and building commissioners have a great influence on the choice of frame material.
In 2014, Xia investigated the reasons as perceived obstacles for comparatively limited use of the timber-frame in multi-story non-residential buildings (compared to low-rise housing) among industry professionals - also including architects - in Australia by a questionnaire survey (n = 74 out of 176) [13]. The results indicated five main groups of identified obstacles: (1) lack of support in official regulations, (2) lack of interest in the industry, (3) lack of experience in professionals, (4) perception of drawbacks, and inadequate knowledge about merits of timber-frame utilization. This study also made several recommendations concerning more supportive legislation by the governmental side to stimulate the use of wood in multi-story building construction, industry training to raise the awareness and knowledge of the technological improvements regarding EWPs, the attitudes of developers and investors as the most influential decision-makers towards increasing the awareness of timber-frame advantages.
Viluma and Bratuškins conducted research among architects and other stakeholders in Latvia to find out the main barriers to using wood for buildings through 38 interviews and questionnaires [33]. There were 73 answers from 85 registered persons of which 36 were architects, 25 were students and lecturers, as well as representatives from timber production and media. In this study, the main motivating factors and seven main barriers to the selection of wood constructions were identified. Research results showed that architects’ attitude towards timber-frame, in general, is positive, but they thought that due to the Latvian Fire Safety Regulation, it is not easy to find solutions for wood construction. Additionally, the architects emphasized the
Conroy et al. investigated familiarity, use, and perceptions of EWPs among the AIA-certified architects across Washington, Oregon, and California through an online questionnaire (n = 533 out of 3,469) [18]. The results indicated that durability, fire resistance, and strength were assessed as weaknesses of engineered wood products, unlike other studies such as [16, 29] that found architects saw wood fire performance as a strength. The architects from Washington and Oregon projected the use of wood in the construction industry to develop more in the next five years compared to steel and concrete. To boost the use of wood as a construction material for the structure and building enclosure in non-residential buildings, it was recommended that the forest products industry enhances its internet presence, developing interdisciplinary communication strategies.
As one of the most recent studies, Kuzman et al. attempted to better understand the specification process of EWPs and to provide an updated overview of the perceived identity of these materials among architects in Slovenia, Austria, France, Sweden, Croatia, and Bosnia, and Herzegovina [34]. The results indicated that generally, participating architects have a positive attitude towards wood utilization in all countries. Thermally modified wood was perceived as positive, whereas the architects were unfamiliar with more recently introduced wood modification methods e.g., acetylation, furfurylation (which are not well known). Their findings suggested that the opportunities for wood to gain a greater market share will grow.
Markström et al. probed Swedish architects’ perception of the use of EWPs in buildings and the parameters which positively influence the preference of these products via a survey questionnaire [24]. Findings highlighted that in general, and as per the more recent study by [34], the perception of EWPs is positive among Swedish architects, and most of them think that their use will increase in the future. They also added that other decision-makers with greater influence over the material selection, such as contractors, developers choose other materials. A lack of knowledge, as well as uncertainties about the quality over time, were other common reasons for not preferring EWPs. The results also indicated that environmental concerns and aesthetic appearance are the main reasons to select these materials for the architects involved in building projects. It was stated that knowledge, familiarity, and architects’ attitude play a role in increasing the use of EWPs.
Therefore, it can be said that perceived positive aspects of EWPs have markedly changed from earlier studies in 1997. By that year, in one of the studies by [28] entitled
[24] | architects in Sweden |
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[18] | architects in the US West Coast |
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[33] | stakeholders (including architects) in Latvia |
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[13] | industry professionals (including architects) in Australia |
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[11] | architects in Sweden |
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[20] | architects and structural engineers in Sweden |
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[32] | architects in North America |
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[31] | architects in Norway |
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[30] | architects and building engineers in Sweden |
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[29] | architects, engineers, building designers, and project managers in Australia |
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[27] | architects and engineers in the US and Chile |
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[26] | architects and structural engineers in the US and Canada |
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Studies on architects’ attitudes towards the use of EWPs for construction.
In terms of the major benefits and barriers to using EWPS for construction, similar findings were identified in different studies such as [11, 24, 32]. Architects mostly had a positive attitude towards aesthetic quality (e.g. [24, 31]) and environmental performance (e.g. [18, 26]) of EWPs.
Also, in Bayne and Taylor’s study on the barriers to the use of EWPs, it was found that the use of timber is more suitable for smaller buildings such as housing development [29]. Similarly, Bysheim and Nyrud found that architects took a positive attitude towards the use of timber as frame material in three-story houses [31].
Some other studies among architects such as [11, 18, 24, 32] highlighted
In studies conducted among architects in the USA [18, 32] and those in Sweden [30],
As many studies (e.g. [13, 33]) reported,
Besides, according to the participants in the study by Xia et al.,
Overall, architects’ perceptions of EWPs’ engineering performance can deter them from employing EWPs for construction. Such a change can be driven by an increase in examples of promising timber building applications. e.g. high-rise buildings (over 8-story), and so the general attitude of architects towards EWPs will be more positive in terms of engineering-based features such as sound insulation, fire safety, durability, and structural performance.
Future scenarios for wooden buildings could improve if there is a new trend towards greater importance of environmental factors in the choice of structural material facilitated by policies, which can make a difference in the demands of customers and the tendencies of contractors.
Moreover, architects can play an important role as prime marketers in increasing EWPs for construction, but it seems that more initiatives are required to enhance their familiarity since the lack of experience and level of knowledge may prevent architects from proposing timber in their projects.
The aim of this chapter was to understand the architects’ perceptions, attitudes, and interests in the use of EWPs for construction. In doing so, this research attempted to identify perceived major benefits and barriers to EWPs utilization.
Overall, architects mostly had a positive attitude towards the use of EWPs. Among EWPs’ positive aspects, aesthetic quality, environmental performance, energy efficiency, and speed of erection were the highlights. Durability, fire resistance, and strength were assessed as the most critical barriers to the common use of EWPs. These were followed by a lack of cost competitiveness, knowledge gaps or lack of expertise, and lack of developer interest.
In this sense, the following recommendations may help to overcome identified barriers by improving overall attitudes towards EWPs for construction:
(1) providing architecture students with more education and inspiration at university, more information about wood-based products, better design aids, and more design examples (2) supporting architects in timber construction by creating a sharing environment with members of these professions (3) developing more active participation in EWP-based problem solving and better interdisciplinary communication strategies among timber suppliers, the timber construction industry and the architectural community (4) developing business-oriented approaches for timber compared to traditional materials e.g. concrete in construction (5) developing effective timber prefabrication methods to reduce the risk factor in construction (6) Enhance the collaboration of different stakeholders such as government, client, designer, contractor, and supplier by issuing more supportive regulations and guidelines to increase the use of EWPs for construction.
It is believed that this chapter will help to deepen the understanding of various considerations shaping the decision-making process in the use of EWPs for construction.
Array processing research has flourished and raked-in much attention in the past five to six decades. It has been an evergreen topic that has fancied many researchers due to the sheer variety of its applications. Array Processing is a scientific field of study which involves the processing of information-bearing signals received by an array of sensors operating in an environment of interest [1], for example, on the ground, above ground or under water. An array has two or more sensors which are arranged in a specific geometrical layout. An array has better directional properties than an individual sensor.
Sensor arrays find application in diverse fields such as radar (radio detection and ranging), space exploration, sonar (sound navigation and ranging), seismology, chemical sensing, medical imaging, wireless communications, navigation, source localization etc. Depending on the application, the sensors could be antennas, microphones, hydrophones, geophones, ultrasonic probes etc. For example, hydrophone arrays are used in sonar; acoustic arrays are used for audio source localization; piezoelectric sensors are used in medical ultrasound; geophone arrays are used in seismology etc. More specifically, antenna arrays are used for electromagnetic applications such as radar, radio astronomy, remote sensing, wireless communications, positioning and navigation [2]. Exhaustive reading for the topic of array processing can be found in [3, 4, 5]. A mention of the multidisciplinary nature of this topic is given in the introductory chapter of [4] and final chapters of [3, 5]. A thorough coverage of phased array theory and array processing applications in the modern era is provided in [6]. Arrays can have different geometries – such as linear, planar, circular, hexagonal, spherical etc. An array spanning across more dimensions can extract more details about the scene of interest.
In many of the above applications, it is of primary interest to sense the directions from which the source signals impinge the array. The signal of interest may be an electromagnetic wave, a sound wave, an underwater acoustic wave, a gas diffused into the surroundings, the location of a tumor etc. The use of multiple sensors bestows the array with a sense of direction. Individual sensors cannot sense direction.
A uniform linear array (array in which the antenna elements are arranged along a straight line) with an inter-element spacing of
A uniform linear array (ULA) with
Direction of Arrival (DOA) estimation involves determining the directions/angles at which electromagnetic sources are located with reference to the receiver array or the directions from which electromagnetic signals strike the array [7]. DOA estimation methods have been classified into three broad approaches, namely, classical methods, subspace methods and maximum-likelihood (ML) methods [8]. A new class of DOA estimation algorithms based on compressed sensing (CS) and sparse recovery have received much attention in the recent past [9, 10, 11, 12]. Dealing with coherent arrivals is a main problem in DOA estimation of practical sources. Two signals are said to be coherent if one is a scaled and shifted version of the other. Multipath interference and intentional jamming are the main causes for sources being coherent to each other [13]. When coherent sources arrive at the array, the array covariance matrix becomes non-diagonal, singular and rank-deficient. That is, its rank would be less than the number of incoming signals [14]. Hence, when subspace methods are used for DOA estimation, an additional step of spatial smoothing would be needed to restore the rank of the covariance matrix. Also, a major drawback of subspace methods is that they need prior information about the number of source angles to be detected, which is often impossible in practical scenarios (as the number of sources is usually unknown, a priori). Akaike information criteria (AIC) test and/or minimum description length (MDL) test and their variants are generally used to estimate the number of sources beforehand. However, these methods are also susceptible to failure when the signals are coherent [15].
In a passive array signal processing system, the array of sensors just listens to the environment, as in passive sonar, radio astronomy and wireless communication. Contrarily, in an active array signal processing system, a transmitter is used to illuminate the environment and the array listens to the signal reflected by the environment and/or objects of interest, as in radar, active sonar and medical imaging [1]. Both passive and active DOA estimation are quite famous in array processing literature.
Antenna arrays offer better gain and directivity than single antennas. High directivity enables the array to confine its radiation or reception to certain directions. As the array size increases (i.e., as the number of array elements increase), so does its aperture. Arrays with wider apertures provide narrower beam-widths and finer angular resolutions than those with smaller apertures [16]. The spatial response or radiation pattern of the array indicates the directions in which the array radiates its energy or receives energy from. Due to its directional properties, an array is often regarded as a spatial filter [17].
An antenna array can serve two purposes. It can help (i) determine the directions from which source signals impinge the receiver (direction of arrival (DOA) estimation), and (ii) in focusing the radiation pattern towards certain directions based on the knowledge of desired and undesired signal directions (beamforming). Additionally, antenna arrays also offer electronic beam-steering, whereby the array’s main beam can be pointed towards a desired direction just by controlling the element phases, without the need for any mechanical movement of the antenna platform.
A static array is one whose response does not change over time. The performance of such an array may be degraded severely under situations such as interfering signals, clutter returns, deliberate electronic countermeasures, non-hostile interference and multipath propagation. An adaptive array has the ability to control its response based on changing conditions of the signal environment coupled with the knowledge of desired and undesired signal directions [2].
Beamforming is the process of obtaining sharply focused beams in a given direction by applying a suitable set of complex weights (element currents and phases) to each of the sensors. Adaptive beamforming (ABF) involves the continuous adjustment of these weights to track the changes in the signal environment using adaptive algorithms that are based on certain specific optimization criteria. Generally, the optimization criteria optimize some measure of system performance such as mean square error, variance or likelihood [7].
Element spacing in an array is of high importance as it influences the occurrence of grating lobes. Grating lobes are large undesired side lobes (that are similar to the main beam in stature) that appear in the radiation pattern and radiate the energy in unwanted directions. Ideally, an inter-element spacing of half wavelength is followed in order to avoid spatial aliasing and to prevent the formation of grating lobes. In applications that require a limited field-of-view (FoV), presence of grating lobes does not hinder the array operation if the scanning region is limited to the grating-lobe-free area. An example of this is found in automotive radar systems, where the FoV is limited to −45° to +45°. Hence, an inter-element spacing of 0.59λ is generally used. Even though grating lobes exist beyond
Beamforming is one of the key enabling technologies for 5G systems that operate in the millimeter wave frequency band [18, 19]. It is known that millimeter frequencies are more sensitive to blockages and path loss. Huge array gains and sharp directional beams obtained from antenna arrays housing hundreds of antennas can counteract the propagation effects of the millimeter channel. DOA estimation also assumes prominence in 5G as DOA-based beamforming is one of the main requirements for smart antennas [20].
Though there are many definitions of sparse arrays in the array processing literature, the focus of this book chapter would be only on grid-based sparse arrays.
In grid-based sparse arrays, the sensors are assumed to be located on the grid points defined by an integer multiple of the basic inter-element spacing. That is, the sensors are assumed to lie at
Different types of linear sparse arrays have been studied in the past. Minimum Redundancy Arrays (MRAs) have numerous useful properties and had been primarily studied in the past in relation to radio astronomy [21, 22]. MRAs have also been applied in digital communications [23, 24, 25]. Numerous properties and modern applications of MRAs can be found in [26, 27, 28].
The use of sparse or non-uniform arrays in close future communication systems such as fifth generation (5G) telecom and the Internet of Things (IoT) is appealing as sparse arrays need fewer active elements than ULAs and operate with little or no degradation in performance [29].
The area of sparse arrays is continuously evolving and there are at least 50 types of linear sparse arrays in the current literature. A comprehensive understanding of various sparse array configurations is essential in the current scenario as more and more sparse arrays are being rapidly introduced into the literature. To the best of our knowledge, a comprehensive review on the properties of 1D sparse arrays has not been taken up in the past (barring the works by Liu and Vaidyanathan, Cohen and Eldar). This is a sincere attempt to bring a few well-known sparse arrays under one roof so that their characteristics could be compared.
The rest of the chapter is organized as follows. Section 2 gives an introduction to linear sparse arrays. Section 3 explains some terminology related to sparse arrays and discusses the characteristics of a few well-known sparse arrays. Section 4 gives an overview of sparse arrays with special properties and also discusses the effect of sensor failures on array performance. Section 5 provides a few future directions and Section 6 concludes the chapter.
Sparse arrays have aperture widths equal to regular/filled/full arrays but are sparsely populated. They consist of voids that arise due to missing/inactive sensors. The vacancies are deliberately created and are woven into the design of the sparse array to achieve certain desired characteristics. The span of the array is called its aperture. It is the sum of all the inter-element spacings. A ULA with
Another advantage of sparse arrays is that they are less affected by mutual coupling compared to ULAs. Sparse arrays are generally analyzed in the co-array domain. A difference co-array (DCA) is formed from the physical sparse array by considering all the spatial lags (differences) that can be generated using the available sensors. A missing spatial lag forms a hole. The DCA should be hole-free as the presence of holes introduces ambiguity in the estimation of spatial correlation and hence spatial angles.
There are several types of linear sparse arrays such as minimum hole arrays (MHAs) or Golomb Arrays, minimum redundancy arrays (MRAs), co-prime arrays, nested arrays, super-nested arrays and so on. MRAs and MHAs have been widely used for interferometry in radio astronomy [6, 21, 22, 30, 31]. While MRAs and MHAs existed for more than five decades, other sparse arrays such as co-prime arrays, nested arrays and super-nested arrays have been introduced in the past decade [32, 33, 34]. A good review on the properties of these sparse arrays can be found in the initial sections of [27, 35]. The introduction of co-prime array in the past decade can be considered as a watershed moment which has opened doors for modern applications of linear sparse arrays. Following that, nested arrays were introduced. These arrays offer hole-free co-arrays and also have closed-form expressions to determine the sensor locations. Many variants of the co-prime array [36, 37, 38] and the nested array [39, 40] have been proposed in the recent past. These arrays either improve the aperture or reduce the number of sensors needed to obtain a given aperture or make the array more immune to mutual coupling or they increase the hole-free region in the DCA.
Following are the desirable characteristics of sparse arrays:
The chosen array should provide the largest aperture for a given number of sensors. It is well-known that arrays with larger apertures provide better resolution and DOA estimation accuracy.
The array should be least affected by mutual coupling.
The array should have a hole-free co-array to facilitate unambiguous parameter estimation using the entire span of the co-array.
It is desirable that the array has closed-form expressions for sensor positions. Otherwise, there should be a provision to obtain the sensor positions using a look-up table (LUT) or through tabulated entries.
In general, sparse arrays such as MRAs and MHAs are represented in the
Another common representation is in the form of a binary string of 1 s and 0 s which represent the presence or absence of a sensor element on the respective grid point. For example, in the representation
For a sparse array with sensors at
The distinct entries (
In simple terms, the difference set is obtained by subtracting all possible sensor positions in the given sparse array. This gives rise to spatial lags. The DCA is then formed by considering only the non-repeating (distinct) spatial lags. For example, a sparse array with sensors at {0, 1, 4, 6} can generate all spatial lags (differences) between 0 and 6, resulting in a difference set of {0, −1, −4, −6, 1, 0, −3, −5, 4, 3, 0, −2, 6, 5, 2, 0}. The DCA {−6, −5, −4, −3, −2, −1, 0, 1, 2, 3, 4, 5, 6} is obtained by sorting the numbers and retaining only the distinct elements. Note that the repeating lag of zero is considered only once.
The number of unique lags in the DCA of a sparse array gives the number of source angles that can be detected during DOA estimation (often known as the degrees of freedom (DOFs) offered by the sparse array). Unique lags are often used in the analysis of sparse arrays with holes in the DCA. The usefulness of arrays that have holes in the DCA is limited by the span of the central continuous portion of the DCA. Though there are methods [43, 44] that can extend the continuous portion of the DCA, they are computationally intense. A more useful parameter, namely, the uniform degrees of freedom (UDOF), which indicates the number of continuous entries in the DCA; is often used to avoid ambiguity (as it clearly specifies the hole-free span of the coarray) [43].
Defining
A unit spacing corresponds to a spatial lag of one and can be generated by any pair of sensors that are adjacent to each other. In other words, any two sensors that are half-wavelength apart from each other are said to have unit spacing. The concept of unit spacing is important in determining the role of mutual coupling on the array’s performance. Empirically, arrays that have a large number of adjacent sensors are more susceptible to the effects of mutual coupling than those with fewer unit spacings.
It is necessary to understand the structure of a ULA and the normalized sensor positions in order to comprehend the properties of grid-based sparse arrays.
As per definition, there would be no missing sensors in the ULA. Hence, the sensor positions are given by the set
MRAs are synthesized from a ULA by eliminating selected sensors such that the sensors thus retained are capable of generating all possible spacings between zero and a specified number [41]. A ULA has many sensor combinations that provide a given spatial lag (e.g., considering the sensor positions from 0 to 9, a spatial lag of 4 can be obtained using any of the redundant sensor pairs {9, 5}, {8, 4}, {7, 3}, {6, 2}, {5, 1} and {4, 0}). MRAs minimize this redundancy by carefully removing select sensors. An optimum MRA has sensors at positions that are just enough to provide all the spatial lags from 0 up to a maximum number
For arrays with more than four elements, it is not possible to get rid of the redundancy completely if one were to ensure all possible spatial lags. For example, in a 5-element MRA whose sensors are located at {0, 2, 5, 8, 9}, it can be seen that the available sensor pairs can generate all the spacings between 0 to 9. But in particular, a spacing of 3 can be obtained using the sensor pairs {5, 2} and {8, 5}. This leads to redundancy but is inevitable for large arrays.
Consider an
A value of
MRAs do not have closed-form expressions to determine the optimum sensor positions and have to be synthesized using exhaustive search mechanisms. The optimization problem to find sensor positions in MRAs is given by
where
MHAs are obtained by optimizing the sensor positions such that a given spatial lag is obtained at most once. In other words, the sensors should be placed such that the spatial lags generated by them are unique. No two sensor pairs shall generate the same lag. Additionally, it is not bothersome in MHAs even if the available sensor pairs cannot generate all the spacings between 0 and
Coincidentally, the definitions of a zero redundancy MRA and a perfect Golomb array (MHA) bear the same physical meaning. Arrays with fewer than four elements qualify both as zero redundancy MRAs as well as perfect MHAs (Eg: A 4-element array with sensors at {0, 1, 4, 6}). However, for arrays with more than four sensors, neither zero redundancy MRAs exist nor do perfect MHAs. MRAs and MHAs mean different things for arrays with more than four sensors.
The objective function to synthesize MHAs is described below. A Golomb ruler consists of a set of integers
The coprime array consists of two ULAs. It was one of the first sparse arrays introduced with closed-form expressions (CFE) for sensor positions. That is, the sensor locations or the sparse array configuration can be immediately obtained once the number of sensors is given without the need for any exhaustive search mechanisms. One ULA has
The overall co-prime array is
Nested arrays (NA) can provide hole-free DCAs and were introduced as an alternative to MRAs and as an improvement over co-prime arrays. Nested arrays are better than co-prime arrays as they provide hole-free co-arrays. They too provide CFEs for element positions when the number of sensors is known. Two ULAs are needed to obtain a nested array. The first ULA has
For example, in a 10-element NA,
Though nested arrays are better than co-prime arrays in terms of the ability to provide hole-free co-arrays, they are severely affected by mutual coupling. This is due to the dense ULA portion at the beginning (level 1). Super-nested arrays were introduced to overcome this drawback of nested arrays [34]. In super-nested arrays, the level 1 elements of the NA are re-arranged (interleaved) to different positions within the span of the array so that the number of sensors with unit spacing gets reduced, thereby making the array less susceptible to mutual coupling. A 10-element super-nested array has sensors at {0, 2, 4, 7, 9, 11, 17, 23, 28, 29}. It can be observed that the level 1 elements of the NA are interleaved to different positions. Super-nested arrays too provide the same aperture as nested arrays for a given number of sensors and have CFEs for element positions. The formulation of super-nested arrays is slightly complicated and is, therefore, not explained here.
As a continuation, augmented nested arrays (ANAs) [49] were formulated. In ANAs, the level 1 dense sub-array of NA is split into several parts and is re-arranged to the left and right of the level 2 sparse array. The design of ANAs is elegant as they provide larger apertures, higher DOFs, and are less susceptible to mutual coupling than nested and super-nested arrays.
An improved nested array (INA) that provides larger aperture than the nested array for the same number of sensors has been proposed [50]. The improved nested array has a hole-free co-array. However, like the original nested array, the Yang’s nested array is also vulnerable to the effects of mutual coupling as it too has a dense ULA portion at the beginning. This array has a total of
The values of
It follows that a 10-element Yang’s nested array has
An extended nested array was also proposed in 2016 [51]. However, it does not offer apertures as large as the Yang’s INA described here.
A nested array configuration that provides larger aperture than the two-level nested array has been recently proposed [52]. This array provides larger aperture than MRAs (obviously than nested, super-nested, improved nested, and co-prime arrays) for a given number of sensors. However, Huang’s nested array suffers from holes in the co-array. The construction is similar to that of the Yang’s nested array in that there is a level 1 ULA, level 2 ULA with increased spacing and a separate sensor at the end. However, the number of sensors at each level and the element spacing in level 2 determine the sensor locations and the overall behavior of the array.
The triply primed array (TPA) is the union of three ULAs with different inter-element spacings. Three mutually prime numbers
Table 1 lists out the optimum sensor positions for different 10-element linear sparse arrays. The optimum MHA configuration for 10 sensors has been obtained through table look-up [46]. The sensor positions are shown in Figure 2. The continuous part of the DCAs of these sparse arrays are shown in Table 2.
Type of sparse array | Sensor positions | Aperture |
---|---|---|
ULA | [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] | 9 |
MRA | [0, 1, 3, 6, 13, 20, 27, 31, 35, 36] | 36 |
MHA | [0, 1, 6, 10, 23, 26, 34, 41, 53, 55] | 55 |
Co-prime array | [0, 3, 5, 6, 9, 10, 12, 15, 20, 25] | 25 |
Nested array | [0, 1, 2, 3, 4, 5, 11, 17, 23, 29] | 29 |
Super-nested array | [0, 2, 4, 7, 9, 11, 17, 23, 28, 29] | 29 |
Yang’s Nested array | [0, 1, 2, 3, 4, 10, 16, 22, 28, 33] | 33 |
Huang’s Nested array | [0, 1, 2, 3, 7, 15, 23, 31, 39, 48] | 48 |
Triply Primed array | [0, 12, 15, 20, 24, 30, 36, 40, 45, 48] | 48 |
Sparse array configurations for 10 physical sensors.
Sensor positions for various sparse arrays (arrays are considered in the same order as listed in
Type of sparse array | Continuous part of the DCA without holes | Uniform DOFs |
---|---|---|
ULA | 19 | |
MRA | 73 | |
MHA | 71 | |
Co-prime array | 35 | |
Nested array | 59 | |
Super-nested array | 59 | |
Yang’s Nested array | 67 | |
Huang’s Nested array | 19 | |
Triple Primed array | [0] | 01 |
Continuous portion of the difference co-array of the sparse arrays listed in Table 1.
The proliferation of linear sparse arrays in the past decade has led to the development of coarray-based DOA estimation methods. Coarray methods are based on the concept of difference co-array (DCA) and are well-suited for angle estimation in sparse arrays. As the physical array has missing sensors due to the sparseness, the array correlation matrix does not represent a Toeplitz structure and is not suitable for estimation of spatial correlation. Therefore, the analysis is shifted to the co-array domain. Due to the continuity of the DCA, the co-array correlation matrix represents a complete Hermitian Toeplitz structure and can be used to estimate spatial angles. Co-array MUSIC algorithm is widely used for DOA estimation in sparse arrays [32, 33]. More recently, other algorithms such as the co-array root-MUSIC [54, 55] and the co-array ESPRIT [56] have been introduced. The Khatri Rao (KR-MUSIC) algorithm which is applicable only to quasi-stationary sources (i.e., the sources which can be assumed to be stationary for short time durations) was introduced prior to the co-array MUSIC [57]. Recently, many algorithms based on compressed sensing have been introduced for DOA estimation in sparse arrays [58, 59]. In summary, DOA estimation algorithms that operate (i) when the number of sources is unknown, (ii) in the presence of coherent arrivals, (iii) under unknown mutual coupling, (iv) under low signal-to-noise ratio (low SNR) conditions, (v) under low snapshot conditions, (vi) in the presence of non-uniform or random noise, and (vii) in a short computational time; are largely sought-after for practical applications [60, 61].
According to the principle of pattern multiplication, the overall array response is the product of the array factor and the element pattern. The element pattern for an isotropic antenna is unity. Hence, in an array of isotropic antenna elements, the far-field pattern depends only on the array factor. The array factor of sparse arrays is conveniently evaluated using the element positions
where,
To get further idea on the characteristics of sparse arrays, the array factors of a few prominent sparse arrays given in Table 1 were evaluated using Eq. (6) and plotted using MATLAB. Table 3 lists the sparse arrays considered and their 3-dB beamwidths. Figure 3 shows the plot of array factors of the considered sparse arrays. It can be observed that MRAs provide the narrowest main beam characteristics for a given number of sensors as they possess the largest aperture among all the sparse arrays considered here. It has to be noted that the MHA can offer even finer beamwidth than MRAs.
Type of Sparse Array | Observed HPBW |
---|---|
ULA | 10.3° |
MRA | 2.2° |
Nested | 3.1° |
Co-prime | 4.1° |
Super-nested | 2.9° |
Beamwidths for the 10-element sparse arrays listed in Table 1.
Array factors of sparse arrays listed in
It would be good to have a few more figures representing the weight functions of the above sparse arrays and their DOA estimation performance when used with co-array MUSIC. However, they are omitted from this chapter for lack of space.
Another important aspect that a designer must be aware of, is that there can exist many sensor configurations for a given aperture in the case of MRAs and MHAs. Each of these configurations may possess different radiation characteristics or sensitivity. For example, the first column in Table 4 shows a few MRA configurations M1 – M4, each of which has seven sensors and is capable of offering an aperture of
MRA configuration | Beamwidth ( | First Side lobe level (dB) |
---|---|---|
M1 - [0, 1, 2, 3, 7, 11, 15] | 5.73° | −6.24 |
M2 - [0, 1, 3, 6, 10, 14, 15] | 5.16° | −6.50 |
M3 - [0, 1, 4, 8, 13, 14, 15] | 4.93° | −5.26 |
M4 - [0, 2, 4, 5, 8, 14, 15] | 5.50° | −6.58 |
Beamwidths and PSLs for different MRAs with same aperture and same number of sensors.
Array factors of MRAs M1-M4 mentioned in
Hence, care must be exercised before choosing a given MRA configuration.
Many modifications to coprime arrays have been proposed in the Literature. For example, the thinned coprime array, the coprime array with compressed inter-element spacing (CACIS), coprime array with displaced subarrays (CADiS), coprime array with reduced sensors (CARS) etc. [37, 38, 62]. Most recent is the generalized thinned coprime array (GTCA) [63], of which, the above ones can be considered as special cases. A multi-level prime array which extends the concept of coprime arrays has also been proposed [36]. The three-level prime array (3LPA) is a special case of the multi-level prime array and should not be confused with the triply primed array described in Section 3.2.9.
Recently, a unified array geometry in the form of a generalized nested subarray (GNSA) was proposed [55]. The geometry has a nested structure with two prototype arrays A and B. Specifically, if the prototype arrays A and B are MRAs, then the geometry leads to the nested MRA (NMRA) thereby providing the largest sparse array and associated hole-free coarray. On the other hand, when A and B are ULAs, the design provides a sparse array with the least aperture and DOFs. It can be said that the GNSA has revolutionized the development of sparse arrays in recent times. The prototype arrays could be nested arrays or super-nested arrays, or for that matter, any sparse array that has a hole-free coarray [55].
Active sensing applications need hole-free sum co-arrays. Symmetric sparse arrays are also useful for certain applications. The concatenated nested array (CNA) is one such array which is obtained by appending the level 1 elements of the nested array just after the level 2 elements such that the overall array is symmetric [40]. However, this array heavily suffers from the effects of mutual coupling owing to the closely spaced elements at both the ends. The Interleaved Wichmann Array (IWA) was proposed to overcome the mutual coupling problem of the CNA by re-arranging the sensors such that the number of sensor pairs with unit spacing is reduced [39]. On similar lines, a nested structure using two CNAs, namely, the Kløve array has been introduced with hole-free sum and difference coarray and is suitable for active as well as passive sensing [64]. More recently, low redundancy arrays with nested arrays and Kløve-Mossige as basis were proposed with hole-free sum coarray [65]. The sum coarray is defined as
where all the cross summations between sensor positions are considered.
While the difference coarray approach is well suited for the DOA estimation of circular sources, many non-circular source signals exist in practice. For example, binary phase shift keying (BPSK), minimum shift keying (MSK), unbalanced quadrature phase shift keying (UQPSK) etc. Non-circular sources have non-zero pseudo covariances (non-zero ellipse covariance matrix) which can be used to enhance the aperture of the virtual array to further exploit the received information for parameter estimation [66, 67]. To fully leverage the special properties of non-circular sources, the DOA estimation is performed using the sum difference co-array (SDCA) which is defined as
where
The use of difference co-array along with the sum co-array increases the virtual array span and leads to increased DOFs than possible by using the DCA alone. In SDCA-based designs, the vectorized conjugate augmented MUSIC (VCAM) algorithm is generally used for DOA estimation [68].
One of the prominent designs for sparse arrays that are suitable for non-circular sources is the nested array with displaced subarray (NADiS) as it provides CFEs for element positions, virtual apertures, and DOFs. The NADiS array has a large central continuous portion in the SDCA thereby providing large uniform DOFs. However, the SDCA is not completely hole-free. As an improvement, sparse array for non-circular sources (SANC) array was proposed with a hole-free SDCA [69]. A drawback of the SANC array is that it has no CFE for element positions. Therefore, the sensor positions in SANC have to be determined through exhaustive searching. Though an improved nested array with SDCA (INAwSDCA) [68] was proposed recently, there is no comparison with the NADiS and SANC arrays. An ultimate design of the nested array for non-circular signals is the translated nested array [67] which has CFEs for sensor positions and provides larger apertures than the NADis and the SANC. In addition, the SDCA of translated nested array is hole-free.
In recent years, moving array platforms or array motion are being exploited to obtain higher DOFs in sparse arrays. The synthetic coprime array [70], dilated nested array [71] and the multi-level dilated nested array [72] are few examples of sparse array designs that leverage platform motion to fill the holes in the DCAs. This topic is pretty new and is widely being explored.
In an array of identical elements, the overall pattern depends on parameters such as the array geometry, inter-element spacing, amplitudes and phases of the individual elements and the inherent pattern of each element [16]. In most cases, the array geometry and the type of array elements is fixed. For example, assume a linear array with patch antennas. In such cases, only the spacing of elements, their relative amplitudes and phases can be altered to modify the array pattern.
Perturbations in any of these parameters can distort the array’s response. Worst of all is the partial or complete failure of one or more sensors in the array. Element failures in sensor arrays can cause distortions in the main beam, side lobe levels and null placements, thereby disrupting the normal functioning of the array. Fault diagnosis and fault compensation are needed to ensure smooth operation of arrays. Several methods have been reported in literature that can (i) identify the location of the faulty element(s) and (ii) compensate or restore the array response through suitable weighting of the remaining healthy antennas in the array. Consider a 10-element ULA with uniform feeding. Imagine that the seventh sensor fails. In this case, the algorithm should be able to identify the position of the failed element and also determine suitable weights to be applied to the remaining nine sensors so that the compensated pattern closely resembles the response of the healthy array. As the weights are no more uniform, the use of digital beamforming is called for. Many bio-inspired algorithms or compressed sensing techniques have been used in the past either to detect sensor failures or to compensate the pattern of a faulty array or for both [73, 74, 75, 76, 77, 78, 79, 80]. An extreme case and new perspective is presented in [81], where a sparse array is said to be formed when one or more elements of an ULA fail at random.
The notions of robustness, fragility, essentialness etc., in relation to sparse sensor arrays have been introduced in recent years by Liu and Vaidyanathan [82, 83, 84]. The fragility of a sparse array gives a measure of how vulnerable the array is to its’ sensor failures. Fragility is defined as the number of essential sensors to the total number of sensors in the sparse array. A sensor is said to be essential if its failure/absence alters the difference coarray or introduces holes into the coarray. Arrays in which all sensors are essential are known as maximally economic sparse arrays (MESA). MRAs, nested and super nested arrays are maximally economic as all their sensors are essential. For this reason, these arrays are highly fragile with a fragility of
It is well-known in MRA theory that, in an array of
As an example, a 10-element RMRA has sensors at {0, 1, 2, 6, 7, 11, 15, 16, 18, 19} [85]. The weight function of the RMRA is plotted in Figure 5. It can be seen that all the spatial lags from
Weight function of the 10-element RMRA described above.
Consider a situation where a particular element in the above RMRA fails (say the element at position 11). The weight function of the RMRA with failed sensor is shown in Figure 6. It can be observed that the weights of a few spatial lags fall down to one but none of them becomes zero. As long as there is just a single-element failure in RMRAs, the weight of any given spatial lag never becomes zero, meaning that holes would never occur in the DCA. This justifies the robustness of RMRAs.
Weight function of the RMRA with failed sensor at position 11.
In recent years, sparse arrays based on fractal geometries have been proposed. Such arrays use a small sparse array as a base (called generator) to obtain larger sparse arrays through recursive formulations. Examples include the Cantor arrays proposed by Liu and Vaidyanathan and the generalized fractal sparse arrays proposed by Cohen and Eldar [35, 87].
Any review of linear sparse arrays would be incomplete without a mention of their usage in the design of sparse MIMO radar arrays. MIMO radar arrays make use of co-located transmit and receive arrays. These transmit and receive arrays work in tandem and produce the effect of a large virtual array. The transmitting array consists of
Following the introduction of the minimum redundancy MIMO radar [45] in 2008, several other sparse MIMO configurations have been proposed in the literature [89, 90, 91, 92, 93]. However, a thorough review of the properties of sparse MIMO arrays is beyond the scope of this article. However, it is easy to foresee that if the transmit and receive arrays are maximally sparse with hole-free co-arrays (like MRAs), the resulting MIMO radar array would also be highly sparse and, therefore, capable of providing the largest virtual array aperture for a given number of sensors and hence the highest DOFs as reported recently [94]. Several novel designs of sparse MIMO radar arrays are being proposed. Another research area which has gained traction in recent years is the co-existent MIMO radar and MIMO communications.
Evolutionary and swarm-based algorithms have been extensively applied for array pattern optimization. The No-free-lunch (NFL) theorem in optimization theory says that there is no single optimization algorithm that works well against all the optimization problems (objective functions). Going by the above fact, it might so turn out that there might exist a few algorithms which could be more suitable over others for synthesizing MRAs and MHAs. As many powerful nature inspired optimization algorithms (grey wolf optimizer, moth flame optimizer, whale optimization, sparrow search algorithm and other hybrid meta-heuristic approaches etc. [95, 96, 97, 98]) have been introduced in the recent past, it would be a worthwhile effort to try synthesizing MRAs and MHAs using such algorithms. One such attempt to determine large MRAs using parallel processing has been recently reported [99]. In recent times, deep learning methods are being employed to synthesize sparse arrays for joint requirements such as hole-free coarrays, low peak side lobes, and optimum far-field performance [100, 101].
In the future, research could be done to determine large RMRAs such that tabulated entries on the optimum RMRA configurations for a given number of sensors could be widely made available to the scientific community. Similarly, efforts could be made to find robust nested arrays with closed-form expressions for sensor positions as an alternative to RMRAs [33, 85].
In sparse arrays, beamforming is usually performed in the co-array domain [102, 103, 104]. The weights of the virtual sensors in the coarray are adjusted to obtain the desired beam pattern. It would be interesting to see how the failure of one or more sensors in the physical array affects these beamforming weights. Detection of failed sensors in the physical array and subsequent compensation of the beam pattern in the coarray are a few open research challenges for sparse array analysis.
In this chapter, the properties of various linear sparse arrays have been compared. It is found that different sparse arrays have different design criteria and trade-offs. It remains as a future scope to synthesize new MRAs and RMRAs using the latest bio-inspired and/or deep learning algorithms. Antenna array processing techniques will be in the limelight for many years to come as many future wireless communication systems (infrastructure-based and ad hoc) heavily rely on them. DOA estimation algorithms and adaptive beamforming methods using sparse/full arrays in the presence of array miscalibrations/failures are a trending research topic at the moment. It is believed that this chapter serves as a comprehensive guide to new researchers in the field of sparse array signal processing.
This work was not funded by any agency or grant.
There are no conflicts of interest.
My thanks to Dr. Chun-Lin Liu, Assistant Professor, Department of Electrical Engineering, National Taiwan University for providing wide open access to his MATLAB codes on co-array MUSIC and super-nested arrays.
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\n\nSljedeća terminologija odnosi se na Odredbe i uvjete, te na sve naše ugovore:
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\n\nStranke, strane odnosi se na klijenta i na nas, ili samo na klijenta ili nas.
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by"}}]},subject:{topic:{id:"1384",title:"Theriogenology",slug:"veterinary-medicine-and-science-pathology-theriogenology",parent:{id:"302",title:"Pathology",slug:"veterinary-medicine-and-science-pathology"},numberOfBooks:1,numberOfSeries:0,numberOfAuthorsAndEditors:30,numberOfWosCitations:42,numberOfCrossrefCitations:23,numberOfDimensionsCitations:62,videoUrl:null,fallbackUrl:null,description:null},booksByTopicFilter:{topicId:"1384",sort:"-publishedDate",limit:12,offset:0},booksByTopicCollection:[{type:"book",id:"5861",title:"Theriogenology",subtitle:null,isOpenForSubmission:!1,hash:"b5aae519c030c5492d65c181d9c0ea57",slug:"theriogenology",bookSignature:"Rita Payan Carreira",coverURL:"https://cdn.intechopen.com/books/images_new/5861.jpg",editedByType:"Edited by",editors:[{id:"38652",title:"Dr.",name:"Rita",middleName:null,surname:"Payan-Carreira",slug:"rita-payan-carreira",fullName:"Rita 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Heat stress decreases the secretion of luteinizing hormone and estradiol resulting in reduced length and intensity of estrus expression, increased incidence of anoestrus and silent heat in farm animals. Oocytes exposed to thermal stress lose its competence for fertilization and development into the blastocyst stage, which results in decreased fertility because of the production of poor quality oocytes and embryos. Furthermore, low progesterone secretion limits the endometrial functions, and subsequently embryo development. In addition, the increased secretion of endometrial prostaglandin F2 alpha during heat stress threatens the maintenance of pregnancy. In general, the percentage of conception rate was found to be reduced by 4.6% for each unit increase in temperature humidity index (THI) above 70, and heat stress during pregnancy further slows down the growth of the foetus and results in lower birth weight. In tropical and subtropical regions, during hot days, the testicular temperature may increase and impair both the spermatogenic cycle and semen quality, which culminates in decreased bull fertility. The effects of heat stress on livestock can be minimized via adapting suitable scientific strategies comprising physical modifications of the environment, nutritional management and genetic development of breeds that are less sensitive to heat stress. In addition, the summer infertility may be countered through advanced reproductive technologies involving hormonal treatments, timed artificial insemination and embryo transfer, which may enhance the chances for establishing pregnancy in farm animals.",book:{id:"5861",slug:"theriogenology",title:"Theriogenology",fullTitle:"Theriogenology"},signatures:"Govindan Krishnan, Madiajagan Bagath, Prathap Pragna,\nMallenahally Kusha Vidya, Joy Aleena, Payyanakkal Ravindranathan\nArchana, Veerasamy Sejian and Raghavendra Bhatta",authors:[{id:"89780",title:"Dr.",name:"Veerasamy",middleName:null,surname:"Sejian",slug:"veerasamy-sejian",fullName:"Veerasamy Sejian"},{id:"177210",title:"Dr.",name:"Raghavendra",middleName:null,surname:"Bhatta",slug:"raghavendra-bhatta",fullName:"Raghavendra Bhatta"},{id:"177220",title:"Dr.",name:"M",middleName:null,surname:"Bagath",slug:"m-bagath",fullName:"M Bagath"},{id:"201967",title:"Dr.",name:"Govindan",middleName:null,surname:"Krishnan",slug:"govindan-krishnan",fullName:"Govindan Krishnan"},{id:"201968",title:"Ms.",name:"Archana",middleName:null,surname:"Pr",slug:"archana-pr",fullName:"Archana Pr"},{id:"201969",title:"Ms.",name:"Pragna",middleName:null,surname:"Prathap",slug:"pragna-prathap",fullName:"Pragna Prathap"},{id:"201970",title:"Ms.",name:"Aleena",middleName:null,surname:"Joy",slug:"aleena-joy",fullName:"Aleena Joy"},{id:"201971",title:"Dr.",name:"Vidya",middleName:null,surname:"Mk",slug:"vidya-mk",fullName:"Vidya Mk"}]},{id:"55006",doi:"10.5772/intechopen.68650",title:"Immunocastration as Alternative to Surgical Castration in Pigs",slug:"immunocastration-as-alternative-to-surgical-castration-in-pigs",totalDownloads:1876,totalCrossrefCites:9,totalDimensionsCites:19,abstract:"Surgical castration of piglets is a routine practice in pig production used to prevent the incidence of boar taint of pig meat, which may develop in entire male pigs as they reach puberty. This practice is being presently questioned in the European Union, and there is a strong initiative to end it. The initiative is presently voluntary; however, key stakeholders of European pig production sector have signed a declaration, and the actions undertaken by them already affect the business. Before such new concepts in pig production can be implemented, alternative solutions are needed, one of them being immunocastration. The present chapter will thus focus on the presentation of immunocastration as one of the promising alternatives to surgical castration. Theoretical and practical aspects of immunocastration in pig production will be described, and the advantages and disadvantages of this alternative will be summarised. Physiological principles of immunocastration and impacts on metabolism, growth performance, body composition and meat quality will be described and aspects of public acceptability reviewed.",book:{id:"5861",slug:"theriogenology",title:"Theriogenology",fullTitle:"Theriogenology"},signatures:"Marjeta Čandek-Potokar, Martin Škrlep and Galia Zamaratskaia",authors:[{id:"23161",title:"Dr.",name:"Marjeta",middleName:null,surname:"Čandek-Potokar",slug:"marjeta-candek-potokar",fullName:"Marjeta Čandek-Potokar"},{id:"198220",title:"Dr.",name:"Martin",middleName:null,surname:"Škrlep",slug:"martin-skrlep",fullName:"Martin Škrlep"},{id:"198221",title:"Prof.",name:"Galia",middleName:null,surname:"Zamaratskaia",slug:"galia-zamaratskaia",fullName:"Galia Zamaratskaia"}]},{id:"55696",doi:"10.5772/intechopen.69444",title:"Estrus Cycle Monitoring in Wild Mammals: Challenges and Perspectives",slug:"estrus-cycle-monitoring-in-wild-mammals-challenges-and-perspectives",totalDownloads:1852,totalCrossrefCites:0,totalDimensionsCites:6,abstract:"The knowledge of reproductive physiology is of paramount importance to guide reproductive management and to make possible future application of assisted reproduction techniques (ARTs) aiming ex situ conservation of wild mammals. Nevertheless, information on the basic reproductive aspects of wild mammals remain scarce, and appropriate management practices have not yet been developed for all the species. This chapter discusses the methods most currently used for reproductive monitoring in wild females. Additionally, the difficulties regarding their use in different species and the possibilities of these procedures in captivity or in free-living mammals are addressed.",book:{id:"5861",slug:"theriogenology",title:"Theriogenology",fullTitle:"Theriogenology"},signatures:"Alexandre R. Silva, Nei Moreira, Alexsandra F. Pereira, Gislayne C.X.\nPeixoto, Keilla M. Maia, Lívia B. Campos and Alana A. Borges",authors:[{id:"90066",title:"Dr.",name:"Alexandre",middleName:"Rodrigues",surname:"Silva",slug:"alexandre-silva",fullName:"Alexandre Silva"},{id:"177090",title:"Dr.",name:"Alexsandra Fernandes",middleName:null,surname:"Pereira",slug:"alexsandra-fernandes-pereira",fullName:"Alexsandra Fernandes Pereira"},{id:"177093",title:"MSc.",name:"Gislayne Christianne Xavier",middleName:null,surname:"Peixoto",slug:"gislayne-christianne-xavier-peixoto",fullName:"Gislayne Christianne Xavier Peixoto"},{id:"198314",title:"Prof.",name:"Nei",middleName:null,surname:"Moreira",slug:"nei-moreira",fullName:"Nei Moreira"},{id:"198315",title:"MSc.",name:"Keilla Moreira",middleName:null,surname:"Maia",slug:"keilla-moreira-maia",fullName:"Keilla Moreira Maia"},{id:"198316",title:"MSc.",name:"Lívia Batista",middleName:null,surname:"Campos",slug:"livia-batista-campos",fullName:"Lívia Batista Campos"},{id:"198317",title:"MSc.",name:"Alana Azevedo",middleName:null,surname:"Borges",slug:"alana-azevedo-borges",fullName:"Alana Azevedo Borges"}]},{id:"56522",doi:"10.5772/intechopen.69549",title:"Role of Melatonin in Reproductive Seasonality in Buffaloes",slug:"role-of-melatonin-in-reproductive-seasonality-in-buffaloes",totalDownloads:1725,totalCrossrefCites:2,totalDimensionsCites:5,abstract:"Buffaloes are characterized by seasonal reproductive activity. Anestrus buffalo heifers and lactating buffaloes were used to study the effect of melatonin treatment on the resumption of ovarian activity during out-of-breeding season. Buffaloes of treated group were injected or implanted with melatonin (18 mg melatonin/50 kg body weight). Using CIDR-eCG protocol preceded with melatonin successfully achieved estrus behavior and induced conception rate during out-of-breeding season. Furthermore, the reproductive performance of buffaloes during out-of-breeding season was clearly improved by melatonin implantation in conjunction with CIDR-eCG protocol due to the luteotrophic effect of melatonin expressed as increasing diameter of CL (corpus luteum) and progesterone concentration. This improvement resulted in greater values of conception rate, in melatonin implanted compared to not implanted buffaloes. Melatonin implantation in anestrus buffalo heifers increased the diameter of largest follicles and melatonin concentration but progesterone and luteinizing hormone (LH) concentrations were decreased. In addition, melatonin implantation in anestrus lactating buffaloes increased the SOD (superoxide dismutase) enzyme activity. Sustained release of exogenous melatonin significantly protects against oxidative stress while increasing beneficial total antioxidant capacity (TAC) concentration in summer-stressed anestrus buffaloes. Melatonin implantation in conjunction with CIDR-eCG protocol successfully improved some blood metabolites, in anestrus buffalo heifers during out-of-breeding season under tropical conditions.",book:{id:"5861",slug:"theriogenology",title:"Theriogenology",fullTitle:"Theriogenology"},signatures:"Tamer Awad Ramadan",authors:[{id:"197651",title:"Dr.",name:"Tamer",middleName:"Awad",surname:"Ramadan",slug:"tamer-ramadan",fullName:"Tamer Ramadan"}]},{id:"54974",doi:"10.5772/intechopen.68651",title:"Markers for Sperm Freezability and Relevance of Transcriptome Studies in Semen Cryopreservation: A Review",slug:"markers-for-sperm-freezability-and-relevance-of-transcriptome-studies-in-semen-cryopreservation-a-re",totalDownloads:1586,totalCrossrefCites:0,totalDimensionsCites:4,abstract:"Advances in sperm assessment techniques have offered new perspectives to improve the technology of semen cryopreservation. This review addresses some recent achievements in the proteomics of seminal plasma and spermatozoa and exemplifies its importance as markers for sperm fertility following cryopreservation. Recent advances in transcriptome studies on sperm RNA-Seq data have generated new information aimed to unravel the physiological roles of RNAs in the sperm-egg fertilization processes and their associations with male fertility. The relevance of the sperm freezability markers and the potential associations of RNA-profiling sequences with the sperm biological functions have been discussed.",book:{id:"5861",slug:"theriogenology",title:"Theriogenology",fullTitle:"Theriogenology"},signatures:"Leyland Fraser",authors:[{id:"199650",title:"Dr.",name:"Leyland",middleName:null,surname:"Fraser",slug:"leyland-fraser",fullName:"Leyland Fraser"}]}],mostDownloadedChaptersLast30Days:[{id:"55696",title:"Estrus Cycle Monitoring in Wild Mammals: Challenges and Perspectives",slug:"estrus-cycle-monitoring-in-wild-mammals-challenges-and-perspectives",totalDownloads:1852,totalCrossrefCites:0,totalDimensionsCites:6,abstract:"The knowledge of reproductive physiology is of paramount importance to guide reproductive management and to make possible future application of assisted reproduction techniques (ARTs) aiming ex situ conservation of wild mammals. Nevertheless, information on the basic reproductive aspects of wild mammals remain scarce, and appropriate management practices have not yet been developed for all the species. This chapter discusses the methods most currently used for reproductive monitoring in wild females. Additionally, the difficulties regarding their use in different species and the possibilities of these procedures in captivity or in free-living mammals are addressed.",book:{id:"5861",slug:"theriogenology",title:"Theriogenology",fullTitle:"Theriogenology"},signatures:"Alexandre R. Silva, Nei Moreira, Alexsandra F. Pereira, Gislayne C.X.\nPeixoto, Keilla M. Maia, Lívia B. Campos and Alana A. Borges",authors:[{id:"90066",title:"Dr.",name:"Alexandre",middleName:"Rodrigues",surname:"Silva",slug:"alexandre-silva",fullName:"Alexandre Silva"},{id:"177090",title:"Dr.",name:"Alexsandra Fernandes",middleName:null,surname:"Pereira",slug:"alexsandra-fernandes-pereira",fullName:"Alexsandra Fernandes Pereira"},{id:"177093",title:"MSc.",name:"Gislayne Christianne Xavier",middleName:null,surname:"Peixoto",slug:"gislayne-christianne-xavier-peixoto",fullName:"Gislayne Christianne Xavier Peixoto"},{id:"198314",title:"Prof.",name:"Nei",middleName:null,surname:"Moreira",slug:"nei-moreira",fullName:"Nei Moreira"},{id:"198315",title:"MSc.",name:"Keilla Moreira",middleName:null,surname:"Maia",slug:"keilla-moreira-maia",fullName:"Keilla Moreira Maia"},{id:"198316",title:"MSc.",name:"Lívia Batista",middleName:null,surname:"Campos",slug:"livia-batista-campos",fullName:"Lívia Batista Campos"},{id:"198317",title:"MSc.",name:"Alana Azevedo",middleName:null,surname:"Borges",slug:"alana-azevedo-borges",fullName:"Alana Azevedo Borges"}]},{id:"55491",title:"Mitigation of the Heat Stress Impact in Livestock Reproduction",slug:"mitigation-of-the-heat-stress-impact-in-livestock-reproduction",totalDownloads:4243,totalCrossrefCites:9,totalDimensionsCites:23,abstract:"Heat stress affects the fertility and reproductive livestock performance by compromising the physiology reproductive tract, through hormonal imbalance, decreased oocyte quality and poor semen quality, and decreased embryo development and survival. Heat stress decreases the secretion of luteinizing hormone and estradiol resulting in reduced length and intensity of estrus expression, increased incidence of anoestrus and silent heat in farm animals. Oocytes exposed to thermal stress lose its competence for fertilization and development into the blastocyst stage, which results in decreased fertility because of the production of poor quality oocytes and embryos. Furthermore, low progesterone secretion limits the endometrial functions, and subsequently embryo development. In addition, the increased secretion of endometrial prostaglandin F2 alpha during heat stress threatens the maintenance of pregnancy. In general, the percentage of conception rate was found to be reduced by 4.6% for each unit increase in temperature humidity index (THI) above 70, and heat stress during pregnancy further slows down the growth of the foetus and results in lower birth weight. In tropical and subtropical regions, during hot days, the testicular temperature may increase and impair both the spermatogenic cycle and semen quality, which culminates in decreased bull fertility. The effects of heat stress on livestock can be minimized via adapting suitable scientific strategies comprising physical modifications of the environment, nutritional management and genetic development of breeds that are less sensitive to heat stress. In addition, the summer infertility may be countered through advanced reproductive technologies involving hormonal treatments, timed artificial insemination and embryo transfer, which may enhance the chances for establishing pregnancy in farm animals.",book:{id:"5861",slug:"theriogenology",title:"Theriogenology",fullTitle:"Theriogenology"},signatures:"Govindan Krishnan, Madiajagan Bagath, Prathap Pragna,\nMallenahally Kusha Vidya, Joy Aleena, Payyanakkal Ravindranathan\nArchana, Veerasamy Sejian and Raghavendra Bhatta",authors:[{id:"89780",title:"Dr.",name:"Veerasamy",middleName:null,surname:"Sejian",slug:"veerasamy-sejian",fullName:"Veerasamy Sejian"},{id:"177210",title:"Dr.",name:"Raghavendra",middleName:null,surname:"Bhatta",slug:"raghavendra-bhatta",fullName:"Raghavendra Bhatta"},{id:"177220",title:"Dr.",name:"M",middleName:null,surname:"Bagath",slug:"m-bagath",fullName:"M Bagath"},{id:"201967",title:"Dr.",name:"Govindan",middleName:null,surname:"Krishnan",slug:"govindan-krishnan",fullName:"Govindan Krishnan"},{id:"201968",title:"Ms.",name:"Archana",middleName:null,surname:"Pr",slug:"archana-pr",fullName:"Archana Pr"},{id:"201969",title:"Ms.",name:"Pragna",middleName:null,surname:"Prathap",slug:"pragna-prathap",fullName:"Pragna Prathap"},{id:"201970",title:"Ms.",name:"Aleena",middleName:null,surname:"Joy",slug:"aleena-joy",fullName:"Aleena Joy"},{id:"201971",title:"Dr.",name:"Vidya",middleName:null,surname:"Mk",slug:"vidya-mk",fullName:"Vidya Mk"}]},{id:"55324",title:"The Role of Androgens in Ovarian Follicular Development: From Fertility to Ovarian Cancer",slug:"the-role-of-androgens-in-ovarian-follicular-development-from-fertility-to-ovarian-cancer",totalDownloads:1755,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Androgens, steroid hormones produced by follicular cells, play a crucial role in the regulation of ovarian function. They affect folliculogenesis directly through androgen receptors (ARs) or indirectly through aromatization to estrogens. Androgens are thought to be primarily involved in preantral follicle growth and prevention of follicular atresia. It also seems possible that they are involved in the activation of primordial follicles. According to the World Health Organization, endocrine-disrupting chemicals (EDCs) are substances that alter hormonal signaling. EDCs comprise a wide variety of synthetic or natural chemicals arising from anthropogenic, industrial, agricultural, and domestic sources. EDCs interfere with natural regulation of the endocrine system by either mimicking or blocking the function of endogenous hormones as well as acting directly on gene expression or through epigenetic modifications. Disruptions in ovarian processes caused by EDCs may originate adverse outcomes such as anovulation, infertility, or premature ovarian failure. In this chapter, we aim to point out a possible involvement of androgen excess or deficiency in the regulation of ovarian function. We will summarize the effects of EDCs expressing antiandrogenic or androgenic activity on female physiology. Continuous exposition to even small concentration of such compounds can initiate oncogenesis within the ovary.",book:{id:"5861",slug:"theriogenology",title:"Theriogenology",fullTitle:"Theriogenology"},signatures:"Malgorzata Duda, Kamil Wartalski, Zbigniew Tabarowski and\nGabriela Gorczyca",authors:[{id:"177042",title:"Ph.D.",name:"Malgorzata",middleName:null,surname:"Duda",slug:"malgorzata-duda",fullName:"Malgorzata Duda"},{id:"177918",title:"Dr.",name:"Zbigniew",middleName:null,surname:"Tabarowski",slug:"zbigniew-tabarowski",fullName:"Zbigniew Tabarowski"},{id:"205391",title:"MSc.",name:"Kamil",middleName:null,surname:"Wartalski",slug:"kamil-wartalski",fullName:"Kamil Wartalski"},{id:"205392",title:"MSc.",name:"Gabriela",middleName:null,surname:"Gorczyca",slug:"gabriela-gorczyca",fullName:"Gabriela Gorczyca"}]},{id:"56522",title:"Role of Melatonin in Reproductive Seasonality in Buffaloes",slug:"role-of-melatonin-in-reproductive-seasonality-in-buffaloes",totalDownloads:1726,totalCrossrefCites:2,totalDimensionsCites:5,abstract:"Buffaloes are characterized by seasonal reproductive activity. Anestrus buffalo heifers and lactating buffaloes were used to study the effect of melatonin treatment on the resumption of ovarian activity during out-of-breeding season. Buffaloes of treated group were injected or implanted with melatonin (18 mg melatonin/50 kg body weight). Using CIDR-eCG protocol preceded with melatonin successfully achieved estrus behavior and induced conception rate during out-of-breeding season. Furthermore, the reproductive performance of buffaloes during out-of-breeding season was clearly improved by melatonin implantation in conjunction with CIDR-eCG protocol due to the luteotrophic effect of melatonin expressed as increasing diameter of CL (corpus luteum) and progesterone concentration. This improvement resulted in greater values of conception rate, in melatonin implanted compared to not implanted buffaloes. Melatonin implantation in anestrus buffalo heifers increased the diameter of largest follicles and melatonin concentration but progesterone and luteinizing hormone (LH) concentrations were decreased. In addition, melatonin implantation in anestrus lactating buffaloes increased the SOD (superoxide dismutase) enzyme activity. Sustained release of exogenous melatonin significantly protects against oxidative stress while increasing beneficial total antioxidant capacity (TAC) concentration in summer-stressed anestrus buffaloes. 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Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},subseries:[{id:"7",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:null,institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda",middleName:"R.",surname:"Gharieb",fullName:"Reda Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. Osma",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSDv7QAG/Profile_Picture_1626602531691",institutionString:null,institution:{name:"Universidad de Los Andes",institutionURL:null,country:{name:"Colombia"}}},{id:"69697",title:"Dr.",name:"Mani T.",middleName:null,surname:"Valarmathi",fullName:"Mani T. Valarmathi",profilePictureURL:"https://mts.intechopen.com/storage/users/69697/images/system/69697.jpg",institutionString:"Religen Inc. | A Life Science Company, United States of America",institution:null},{id:"205081",title:"Dr.",name:"Marco",middleName:"Vinícius",surname:"Chaud",fullName:"Marco Chaud",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSDGeQAO/Profile_Picture_1622624307737",institutionString:null,institution:{name:"Universidade de Sorocaba",institutionURL:null,country:{name:"Brazil"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/77190",hash:"",query:{},params:{id:"77190"},fullPath:"/chapters/77190",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()