**Fabiane Gallucci 1,\*, Ronaldo A. Christofoletti 1, Gustavo Fonseca 1 and Gustavo M. Dias 2**

Received: 30 November 2019; Accepted: 15 January 2020; Published: 20 January 2020

**Abstract:** For marine benthic communities, environmental heterogeneity at small spatial scales are mostly due to biologically produced habitat heterogeneity and biotic interactions, while at larger spatial scales environmental factors may prevails over biotic features. In this study, we investigated how community structure and β-diversity of hard-bottom-associated meio- and macrofauna varied in relation to small-scale (cm–m) changes in biological substrate (an algae "turf" dominated by the macroalgae *Gelidium* sp., the macroalgae *Caulerpa racemosa* and the sponge *Hymeniacidon heliophile*) in a rocky shore and in relation to larger-scale (10's m) changes in environmental conditions of the same biological substrate (the macroalgae *Bostrychia* sp) in different habitats (rocky shore vs. mangrove roots). Results showed that both substrate identity and the surrounding environment were important in structuring the smaller-sized meiofauna, particularly the nematode assemblages, whereas the larger and more motile macrofauna was influenced only by larger-scale changes in the surrounding ecosystem. This implies that the macrofauna explores the environment in a larger spatial scale compared to the meiofauna, suggesting that effects of spatial heterogeneity on communities are dependent on organism size and mobility. Changes in taxa composition between environments and substrates highlight the importance of habitat diversity at different scales for maintaining the diversity of the associated fauna.

**Keywords:** meiofauna; macrofauna; associated fauna; biological substrate; species diversity; community ecology; benthic ecology


**4. Main subsystems of gyrotron**

In a gyrotron, a hollow electron beam gyrating at cyclotron frequency under the influence of a strong axial magnetic field interacts with the transverse electric field excited inside the cavity. If the cyclotron frequency is synchronized with the frequency of millimeter wave supported by the cavity (cut-off frequency of the cavity) for the selected higher order mode, millimeter wave gets generated. This phenomenon is known as cyclotron resonance maser (CRM) interaction. The hollow gyrating electron-beam is generated with the help of magnetron injection gun (MIG)**.** The gyrating electron-beam is passed through a beam-tunnel and fed into an open ended interaction-cavity**.** The millimeter wave generated in the cavity region diffracts out with the help of a non-linear taper (NLT). The waveguide mode of electromagnetic-wave is covered to Gaussian mode with the help of a quasioptical launcher (QOL) and mirror units. The millimeter wave is taken out of the gyrotron with the help of a high power millimeter-wave window. The spent electron-beam is collected in a collector. The required axial magnetic field throughout the gyrotron, starting from the MIG to collector is provided by a magneticsystem consisting of a main superconducting-magnet along with a number of non-superconducting solenoid magnets. Out of all these subsystems, MIG and interaction-cavity are the most important subsystems of gyrotron. The following section describes some of these main subsystems of gyrotron.

### **4.1 Magnetron injection gun (MIG)**

Most high power gyrotrons use magnetron injection guns (MIGs), which produce annular electron-beams in which electrons gyrates in cyclotron frequency. The gyrating frequency is so chosen that the beam-wave interaction at desired mode can take place. For good interaction-efficiency, the transverse velocity component of electron should be as large as possible. A spread in transverse velocity results in a spread in axial velocity, and eventually reduces the efficiency of the gyrotron. Hence, the electron velocity spread should be kept as small as possible [8]. The cutsection view of a typical MIG with anode is shown in **Figure 8** indicating various parts of MIG.

The electrons are emitted from a annular cathode operating in temperature limiting regime of thermionic emission [2, 3]. MM-type dispenser cathode is used as emitter. The electron motion is taking place in crossed electric and magnetic fields so that the electrons follow helical trajectories around the magnetic flux lines with the electrons gyrating in cyclotron frequency. The accelerating potential of 20– 70 kV is applied between the cathode and the anode. The MIG can have a diode or a triode configuration. In the triode configuration, there are two anodes, namely

**Figure 8.** *Cut-section view of a typical MIG.*

modulating anode and accelerating anode. In triode configuration, second anode provides the main accelerating potential. Whereas, the first anode (which is closer to cathode) is used to fine-tune the velocity pitch-factor of the beam (ratio of transverse to axial beam-velocity) as well as for pulsing the beam (i.e., for switching the beam ON and OFF). Gyro-TWT's usually incorporate triode MIG. In diode configuration, there is only one anode. Diode MIG needs much simpler powersupply for providing the necessary voltages. However, on the flipside, gyrotrons with diode MIG have lesser control over the beam.


**1. Introduction**

Development trends point to the fact that the global population is urbanising [1] and that more than 50% of the world's population is now urban [1, 2]. Whilst this is the global reality, in Africa, more people are leaving in the rural areas [3]. These people are exposed to several challenges, most of which are a result of the urbanisation process. For example, they compete for food, land, water and other resources [4]. The rural areas are also the recipients of waste that is produced by the cities. Climate change, on the other hand, is making farming which is the major rural economic activity, difficult as yields are continuously reducing owing to extreme weather patterns. This exposes the rural communities to high levels of poverty and distress and also to exploit the natural resources in an unsustainable manner. In light of the aforementioned issues, and the need to meet global goals of eradicating hunger and poverty, achieve food security, make human settlements safe, inclusive, resilient and sustainable [5], there is need to rethink approaches to rural

development; a paradigm shift from the old paradigm guided by modernisation to a new paradigm that is driven by the quality of life and sustainability [6]. The United Nations Development Programme (UNDP) [7] recommends that such an approach requires the integration of the various actors and stakeholders who are involved in the development of rural places; planning is one tool that can integrate multiple actors and stakeholders towards the attainment of sustainable rural development [8].

Several studies have been conducted on rural development and planning in Zimbabwe [9–11]. These studies are critical in the literature of rural planning and development. However, they are silent on the relationship between sustainable rural development and planning. For example, PlanAfric [11] is more evaluative on the rural development approaches in Zimbabwe. Davies [9] analyses the trends in rural planning pre and post-independence in Zimbabwe whilst Hahlani [10] looks into factors that hinder integrated rural planning. This chapter emphasises the instrumentality of planning in sustainable rural development and argues that sustainability in rural planning is a product of deliberate planning. It further argues that without planning, rural areas will not be able to cope with the prevalent issues of climate change particularly considering that most rural areas in Zimbabwe are agro-based. The chapter has eight sections. Following this section is the background and overview. This is followed by a section on the theories that inform the study, literature review and methodology. The results are presented in the proceeding section from which a discussion and recommendations section follows. The last section is the conclusion of the chapter.

### **2. Background and overview**

Rural development has been at the centre of development for most countries across the globe. However, the assumed model of developing rural places was inspired or guided by the modernist philosophy whose focus was mainly hinged on, among other processes, capital investment, the application of science to production and urbanisation [6]. This approach was also materialistic in its conceptualisation of development; hence, little consideration was paid to qualitative aspects of development such as improved quality of life and access to basic services. The modernist approach to the development of rural places resulted in the bias towards urban areas as it was generally assumed that urbanisation of rural places was an indicator of development, thus, as put forward by Rao [12], more attention was put to industrialisation than the agricultural sector, hence the rural suffered. Development was also top-down in approach [6]. The resultant effect of this approach to development was increased poverty in rural places [13, 14], unsustainable exploitation of raw materials from the rural areas as inputs for industrialisation in urban areas, environmental degradation and the consequential effects such as climate change.

After observing that the modernist approach to development was associated with several problems, there was a paradigm shift to a sustainable centred approach to development. According to [6], this approach paid more attention to agricultural development and a participatory approach to development programmes. Following a series of global goals for sustainable development, for example, the World Commission on Environment and Development [15], the Millennium Development Goals and the Sustainable Development Goals, the need to use resources within the regenerative capacity of the environment was put at the centre of developmental goals. In addition, the need to combat the effects of climate change and eradicate poverty and hunger has made it necessary to revisit the approaches to development

#### *No Sustainability without Planning for It: Scope and Dimensions for Sustainable Rural Planning… DOI: http://dx.doi.org/10.5772/intechopen.101811*

planning, rural areas included. The UNDP [7] has observed and placed planning at the centre of sustainable development because of its coordinative and integrative capacity.

In Zimbabwe, rural development has been the centre of development policy since independence. According to Davies [9], it has been politically motivated; the aim being that of redressing disparities caused by colonial policies. Several policies, for example, the Growth with Equity and the Growth Point Policy, were put in place to ensure that rural development was realised. The model to rural development, however, reflected the modernist approach as key among the policies, was the development of growth points that were to lead the industrialisation of rural places. However, the effectiveness of the policies in addressing the problems of disparities is debatable as little progress has been made [16]. Like most rural places across the globe, Zimbabwe's rural places are experiencing a myriad of challenges that range from social, economic and environmental [11, 17]. Food insecurity, poverty, income inequalities and the lack of employment opportunities reinforce each other in a vicious cycle by eroding human capital and decreasing labour productivity, thereby perpetuating poverty and social inequalities [18].

Despite having multiple interested stakeholders in rural planning, the approaches that have been adopted to guide and encourage rural development are disintegrated and are characterised by gaps and overlaps [10, 11], necessitating an approach that integrates the various activities and programmes of the multiple actors in rural planning and development [6, 16]. Planning is important in achieving sustainability as it helps to cover the social, political, environmental and physical issues that affect the prosperity of rural areas [7, 19]. Against this background, the chapter examines the instrumentality of planning in enhancing sustainability in rural development and planning in Zimbabwe and shows that without planning for it, sustainability in rural development will not be achieved.

### **3. Theories informing the study**

This study is informed by the sustainability theory and the style/models of planning theory. These models seek to highlight how planning is done and its procedures. This section also seeks to highlight the multi-dimensions of planning as well as the importance of planning in different scenarios and in promoting sustainable development.

### **4. Sustainability theory**

The theory of sustainability is derived from the concept of sustainable development which is premised on the notion of maintaining or improving the state and availability of resources or materials over the long term [20]. It entails meeting fundamental human needs while conserving the life support systems of the earth. This implies that chasing the sustainability goal should aim at maintaining, improving or sustaining conditions that are beneficial to people, their communities and the environment [21]. Sustainability is preoccupied with the long-term treatment of the biophysical environment and the resources therein, social systems and people in ways that are consistent with human wellbeing and the stability of a dynamic system [20]. However, Bossel [22] outlined that the sustainability of human society is now at stake because the change of the environment is so fast that there is inadequate time for adaptive response. This overwhelming rate of change affects the ability of the ecological system to respond,

consequently making the system to lose viability and sustainability. In this study, the theory of sustainability plays a critical role in justifying the need to plan for sustainability in rural areas. The reasons, according to the sustainability, include the desire to improve maintain and improve the social, economic and environmental conditions of the rural areas within the development process of rural places. The rational is that, by planning for sustainable rural development, both rural and urban areas become sustainable because of the rural-urban relations [1, 2, 14].

### **5. Literature review**

This section reviews the literature on the key aspects of rural sustainability, planning for rural sustainability, the actors in the sustainability of rural places as well as their role. It also looks at the factors that enhance or hinder planning for sustainability in rural areas. Rural sustainability is defined by Bryant and Granjon [23] as the development strategies that maintain and produce healthy rural communities. Such communities will have compatible economic, political, socio-cultural and environmental values that respond to any requirements in these dimensions in the long run. Rural sustainability is dynamic as information, conceptualisation and values about the different dimensions of sustainability change with time [23, 24]. As such Bryant and Granjon [23] recommend that in planning for sustainability there is a need to take account of the fact that what is appropriate at a given place in time maybe inappropriate in the same place as changes occur. For example, the nature and extent of ruralurban links have changed [25, 26]. This significantly affects the approaches to rural sustainability planning. Similarly, what is appropriate in a place at a given time may not be appropriate in another place at the same time. Rural places that are closer to cities have a different set of needs compared to rural places that are further away from cities.

Sustainability, whether urban or rural, maintains the same characteristics, goals and dimensions. Bryant and Granjon [23] highlight those previous conceptions about sustainability in rural places focused on the ecological imperative, giving less attention to socio-cultural, economic and political issues. However, with a continued evolution in the concept of sustainability, and changes in social and economic aspects in both rural and urban places, sustainability is now fundamentally the same, with differences only seen in the environments, population density and activities between the rural and urban places. This entails that certain characteristics of the rural places influence sustainability planning in rural contexts. Some of these characteristics are linked to the biophysical environment which is considerably important for rural communities who depend largely on its resources for livelihood activities. Thus, for most people in the rural places who are mostly small holder peasant farmers, availability of arable land, fertile soils, sufficient water, good climatic conditions and the presence of flora and fauna are important. Land availability and favourable climatic conditions for example, are significant biophysical factors for successful farming [27].

The natural resources that are entrenched in the biophysical environment are an important source of many livelihoods in rural places. Bryant and Granjon [23] observe that besides farming-related activities, there are other livelihood activities such as sand mining and gravel extraction as well as forestry and woodland management. Rural communities are also characterised by small communities that can be concentrated into small villages, dispersed or both. However, rural places that are close to cities and towns have certain characteristics that differentiate them from those that are distant from the cities. Resources in such places are critical for the well-being of the population in cities as well.

#### *No Sustainability without Planning for It: Scope and Dimensions for Sustainable Rural Planning… DOI: http://dx.doi.org/10.5772/intechopen.101811*

Planning for sustainability should therefore take account of the characteristics of the rural places and the relationship between the rural places and the urban areas since these are interlinked systems [8, 23, 25]. However, focusing on rural development and sustainability tend to treat the rural places as homogeneous places [28] and as independent from urban areas [29]. A comprehensive approach is required for the realisation of sustainability as both a process and a goal, hence, the United Nations, [30] and Marsden [24] argue that planning is key if sustainability is to be attained.

Scholarly and policy works have identified several factors that affect planning for sustainability [9, 20, 23, 24, 31]. One of the factors that impact on planning for sustainability and its subsequent realisation is state policy [31]. His argument is informed by Lipton's [14] urban bias theory which, among other issues, argue that states act to protect the interests of cities and to discriminate against the interests of the rural places. In this instance, financial, human and productive resources are deliberately moved out of the rural places to benefit urban dwellers. Douglass [32] argues that, on top of providing food and natural resources to cities, rural places have pronounced poverty and hunger with relatively low income; they continue to experience out-migration and environmental degradation. State policies in this instance are discriminatory and lead to the extraction of resources from the rural places. This incentivise urban areas resulting in a reduced supply of labour in rural places. This in turn, endangers the sustainability of rural places [31, 32].

The continued separation of rural and urban places in terms of policy and planning is another factor that affects rural sustainability planning [33]. This continued separation of focus between the rural and urban places in the face of changing rural and urban relations [26] has a negative influence in the planning and realisation of sustainability [23]. To address the problems associated with the separation, [33] recommends that plans that transcend rural and urban places whilst protecting the natural and urban activities are required.

Planning for sustainability involves multiple actors which range from the public institutions, private organisations, civic organisations, non-governmental organisations and the local people who participate in the planning processes and activities. The public sector usually provides a framework for sustainable development through the various arms and tiers of the government. The private sector is usually involved in the implementation of sustainability programmes through investments while the civic organisations and nongovernmental organisations play a variety of activities that ranges from advocacy to supporting and mobilising the local people. With such a range of actors, [33] recommends that there is a need for strong institutional frameworks that coordinates the plans and activities of the various actors that are involved in the planning of sustainability. Failure to coordinate the plans and activities of the various actors and the rural and urban places results in chaotic and complex spaces that make sustainability difficult to plan for and achieve [34]. Thus, without planning for it, sustainability will remain a pipe line dream.

Several approaches have been put in place to address sustainability challenges across the globe. At the global level, ratifications have been put in place and international goals set to ensure that sustainability. The Brundtland Commission of 1987 marks the dedicated commitment towards sustainable development. The Millennium Development Goals and the current Sustainable Development Goals are some of the international efforts towards sustainability. These frameworks have been adopted by different countries in a localised manner to plan for sustainability. The adopted approaches and strategies are not uniform across the globe [35]. The approaches are linked to the main challenges to sustainable development that places face, hence, priority also differs among and between nations. The approaches include strategies earmarked to deal with poverty, environmental

deterioration, marginalisation, water and sanitation, safe, resilient and sustainable human settlements as well as climate change issues among others [5, 28, 30, 35]. To address the challenges, some countries have developed sector-specific programmes and projects. States have developed policies that aim to trigger planning towards sustainability in different contexts, for example, climate change policies, housing policies and economic blueprints. However, scholarly work shows that most of these approaches are planned and implemented in silos, making planning for sustainability insufficient [11, 29, 34].

In summary, scholarly work shows that the sustainability of rural places is key to the attainment of sustainability in both rural and urban areas. There is also clear evidence from scholarly work that the approaches to sustainability planning generally lack comprehensiveness, making the efforts disintegrated and less effective. The question that remains to be answered is, what should planning-for-sustainability, particularly in the rural context, take?.


*3.3.1 High-temperature effects on surface properties of concrete*

As seen in **Figure 2**, some color changes happen on the surface of concrete samples due to high temperature effects. As a result of these color changes the range of temperature values can be certainly appraised. It can be seen that cracking, rupture, and color changes happened much more in the concrete samples exposed to 900°C than the ones with 400 and 600°C.


*Proceeding Paper* **Developing a Simulation Model for Autonomous Driving Education in the Robobo SmartCity Framework †**

**Daniel Juanatey, Martin Naya, Tamara Baamonde and Francisco Bellas \***

GII, CITIC Research Center, Campus de Elviña, 15008 A Coruña, Spain<EMAIL_ADDRESS>(D.J<EMAIL_ADDRESS>(M.N<EMAIL_ADDRESS>(T.B.)

**\*** Correspondence<EMAIL_ADDRESS>
† Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.

**Abstract:** This paper focuses on long-term education in Artificial Intelligence (AI) applied to robotics. Specifically, it presents the Robobo SmartCity educational framework. It is based on two main elements: the smartphone-based robot Robobo and a real model of a smart city. We describe the development of a simulation model of Robobo SmartCity in the CoppeliaSim 3D simulator, implementing both the real mock-up and the model of Robobo. In addition, a set of Python libraries that allow teachers and students to use state-of-the-art algorithms in their education projects is described too.

**Keywords:** intelligent robotics; educational robots; self-driving cars; robotic simulation; computer vision

### **1. Introduction**

This work is focused on an educational framework for teaching intelligent robotics in secondary school or university, developed at the University of Coruña: the Robobo SmartCity. This framework is based on two main elements: (1) the smartphone-based robot Robobo [1], and a (2) model of a smart city, considering both simulation-based and real formats. In this framework, many different challenges and lessons on intelligent robotics can be carried out, mainly focused on the field of self-driving vehicles. It allows teachers to propose challenges dealing with basic problems in robotics, such as control navigation and obstacle avoidance, but also more complex ones that require computer vision, such as traffic sign detection and object identification. The idea of using autonomous driving and smart cities as environments for robotics teaching is becoming quite popular. For example, the authors of [2] proposed a modular and integrated approach towards teaching autonomous driving. Another relevant approach is the autoauto platform [3], which utilizes the concept of self-driving cars for teaching robotics and AI to young students. Costa et al. [4] presented an autonomous driving simulator to gain the attention of and prepare the students to compete in the Portuguese National Robotic Festival (PNRT), especially in the Autonomous Driving Competition (ADC). A very similar approach to the one proposed here is Duckietown [5], an online MOOC for teaching AI and robotics based on self-driving cars, with which the authors have created a whole educational environment, with different city layouts, traffic signals, etc.


**3. Results**

#### *3.1. uSZU Band Selection*

After uSZU of the 177 original bands, 20 were retained in the analysis. Selected bands ranged across the spectrum with a preference for the red edge (five bands were selected between 670 and 735 nm). uSZU also appears to have selected several bands that are at the edges of wavelengths removed due to atmospheric noise, suggesting the sensitivity of the technique to spectral artifacts. Figure 4 shows the final selection of bands using the uSZU algorithm.

**Figure 4.** Spectral profile of the average spectra in each endmember class (solid line), plus or minus one standard deviation of the spectral average (dotted line). Green vegetation is represented by dark green, non-photosynthetic vegetation is represented by yellow, soil is represented by blue, and char is represented by black. Spectral bands selected by the uncorrelated Stable Zone Unmixing (uSZU) algorithm are represented by red vertical lines.

#### *3.2. Endmember Selection and Processing Times*

The number of endmembers selected by each technique varied considerably (Table 2). EMC has an inherent limit to the number of endmembers that may be selected; in EMC no more than the number of endmember classes multiplied by three endmembers are kept (the maximum possible number of endmembers in an EMC reduced library is therefore 12 in this study). uSZU generally had the effect of increasing the number of endmembers selected for all techniques except EMC. The increase was generally moderate, resulting in a 10–25% increase in the number of endmembers. Despite the larger number of endmembers selected, the processing time for the MESMA algorithm was dramatically reduced for all band reduction techniques, with time reductions ranging between 30% and 55%.

**Table 2.** The number of endmembers and modeling times of various combinations of endmember and band selection techniques. Modeling time was assessed on a 640 × 530 pixel Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) scene within the Rim Fire. Models were run using a computer with an Intel Xeon central processing unit E7-4850v2 at 2.3 GHz with 48 cores and 512 gigabytes of random-access memory. Models were run in a manner to minimize run time with computer resources. Uncorrelated Stable Zone Unmixing (uSZU) represents a feature reduction technique. EMC, In-COB, and IES are all endmember reduction technique for Multiple Endmember Spectral Mixture Analysis (MESMA). Total models represents the number of models need to run four endmember MESMA.

#### *3.3. Unmixed Images and Overall Model Comparison*

The eight endmember libraries produced broadly similar patterns (Figure 5). The EMC method without band selection stands out as modeling more NPV than the others, notably modeling NPV in areas that other approaches modeled as GV. Unmixed images generated from libraries without uSZU band selection appear to have slightly more NPV than their uSZU counterparts, but otherwise give similar results.

While the models were qualitatively similar, there were quantifiable differences between them (Table 3). The number of pixels successfully modeled, defined by the number of pixels where any endmember combination tested by the MESMA algorithm resulted in an RMSE below the threshold of 0.025, ranged from 79.1% of the fire area (uSZU In-CoB) to 93.0% of the fire area (IES). Soil was the most variable endmember class between the models, with one scene containing as few as 7.5% of pixels with any modeled soil cover (uSZU EMC) to 41.8% of pixels (IES). Char and GV were the most commonly modeled endmember classes with between 36.9% and 53.1% of pixels containing char and between 35.3% and 54.1% of pixels containing GV.

**Table 3.** Percent of total pixels within the Rim Fire boundary that were successfully modeled and the percent of pixels containing each endmember class for various endmember selection techniques. In total, approximately 7,850,000 pixels within the Rim Fire boundary were considered. A single pixel was allowed to contain up to three endmember classes (not including shade).

**Figure 5.** MESMA cover fraction images within the Rim Fire burn scar, soil cover is not shown in the image series. The boundary of the Rim Fire is represented by a white line. Black indicates areas that were not classified or only had soil cover. Endmember selection and band selection techniques are as follows: (**i**) EMC; (**ii**) uSZU EMC; (**iii**) In-CoB; (**iv**) uSZU In-CoB; (**v**) IES; (**vi**) uSZU IES; (**vii**) Reduced IES, (**viii**) uSZU Reduced IES.

#### *3.4. Endmember Sources in Model Selection and the Image*

Most spectral libraries selected endmembers from all sources (Table 4). In the initial spectral library, a majority of spectra were derived from AVIRIS imagery, however, a disproportionally small amount of spectra from this source were selected by most endmember reduction techniques. This suggests that, by the metrics used by the endmember reduction techniques, most image derived spectra were redundant and were therefore eliminated. In contrast, spectra created in Wind River [51] were

disproportionally retained by most endmember selection techniques. This indicates spectra from that source were generally distinct from other spectra included in the initial spectral library.

**Table 4.** The source of endmembers for each endmember reduction technique.

When spectral libraries were used to model cover fractions across the Rim Fire, spectra generated from the AVIRIS images were disproportionally selected, given their abundance in the reduced spectral libraries (Table 5). All spectral sources were used to some degree. It should be noted that not all endmember classes modeled in this study were collected for each spectral source and there were large differences in the number of spectra collected for each source.

**Table 5.** Percentage of pixels modeled by source for each endmember reduction technique. A single pixel can be modeled by multiple different sources, resulting in totals that exceed 100%.


**4. Conclusion**

In this chapter, we have taken data of people affected by coronavirus in India till 20 January (1). Then we converted this data into three differential equations with the help of SIR model. We solved the equation made from SIR model with HPM. From the result of solving, we estimated the people who got infected with corona virus in the coming 5 days. We converted the result from HPM into a table and graph and from the result we saw that in the coming days, corona cases are increasing and recovering but the corona positive rate is very high, and the rate of recovery is very short. We saw that the information about Corona-positive cases being given by the Government of India was also that the rate of positive is increasing very fast, but the rate of recovery is very low. From all these, we can now say that by solving with HPM we get the result very close to the actual result. We have predicted cases of corona till Jan 31 using SIR model, risk factors for the coronavirus disease. The risk is especially high if two or three of the Cs come together.


**1. Introduction**

In 1990s, first telephony systems were introduced in vehicles to enable drivers to converse in hands-free phone calls through the vehicle's embedded microphones and loudspeakers while driving [1]. To assure the audio quality during the handsfree telecommunication, a number of speech signal processing techniques are widely used. Besides of hands-free telephony, speech dialog systems have been developed to enable drivers to communicate with their vehicle functions and media contents by means of voice communication [2, 3]. In the core of a speech dialog system, there is an acoustic model that performs the speech recognition task. Speech dialog systems require high quality audio input to assure the accuracy of the speech recognition.

In a vehicle audio system, the phone is connected to the infotainment head unit via a Bluetooth communication channel which allows the driver's speech (near end) to route from the microphones mounted inside the vehicle to the other side of the tele-communication network (far end). Vice versa, the speech signal received from the far end is played on the vehicle's loudspeakers.

A major problem that typically rises in this communication system is that the far end hears a replica of their own voice back from the vehicle with a certain delay (i.e. acoustic echo). The observed acoustic echo is due to the acoustic feedback from loudspeakers to the microphones in the vehicle [4–6]. Various acoustic echo cancelation (AEC) solutions have been developed to address this issue. Most of these AEC solutions use adaptive filters that aim to simulate the acoustic path between speakers and microphones and thereby estimate and subtract the echo from the received signal [4–6].

Another major problem is that the signals captured by the microphones are contaminated with ambient noise and reverberation. The ambient noise often consists of stationary noise sources (engine noise, road noise, windows vibrations), and non-stationary cross-talks from other car occupants. To address the stationary ambient noise issues, high-pass filters were used mainly to filter out the engine noise and structural vibrational components in the captured signal [1, 2]. To address non-stationary ambient noise, adaptive algorithms have been extensively developed (e.g. [7]).

Moreover, directional microphones have been previously used in vehicles to form a spatial focus toward the driver while attenuating signals arriving from other directions. These directional microphones were often Cardioid type which were usually mounted on the ceiling of the cabin, over the head of the driver, and directed toward the driver's mouth. Most common Cardioid microphones are electret condenser components and achieve this type of directivity by means of mechanical channels mounted in their membranes. In 2000s, a new generation of microphones, known as micro electromechanical system (MEMS), were introduced to the electronic industry providing superior performance in coding the sound while having a low-cost footprint [8]. Since the mobile phone industry started to extensively deploy MEMS in their products, this type of microphones has prevailed in most telecommunication applications e.g. in tablets, wearable devices, medical systems, and automobiles [8].

A major difference between MEMS and electret microphones is that MEMS microphones, due to their specific design and miniature structure, are omnidirectional i.e. are agnostic to directions since they treat sounds arriving from all directions equally. Therefore, the desirable directivity needs to be implemented by means of external post processing. To do so, a number of MEMS microphones are placed in a certain distance from each other forming an 'array' and specific array signal processing techniques are applied to exploit the time differences and relative phase shifts across the signals captured by the microphones in the array to amplify or attenuate sounds arriving from specific directions and thereby create the desired spatial directivity [9–11].

### **2. Spatial filtering: beamforming**

#### **2.1 Basic concepts**

A beamformer is a signal processing module that performs spatial filtering to separate signals that have overlapping temporal and frequency contents but originate from different spatial locations [9–12]. A conventional linear beamformer, as shown

*Spatial Audio Signal Processing for Speech Telecommunication inside Vehicles DOI: http://dx.doi.org/10.5772/intechopen.105002*

**Figure 1.**

*A linear beamformer that consists of an array of M microphones. The signal captured by jth microphone passes through the jth finite-impulse-response filter defined by its weights (W j). The direction of arrival (DOA) is shown by θ.*

in **Figure 1**, is a filter-and-sum system that consists of a number of filters that are applied on the input array signals and the results are thereafter summed. The task is to set the complex filter coefficients (*W j* in **Figure 1**) in a manner to amplify specific directions in the received array signals and suppress other directions.

From another perspective, a beamformer can be viewed as a multiple-input single-output system whose output *y[k]* is determined based on Eq. (1) below.

$$\mathcal{Y}[n] = \sum\_{j=1}^{M} \sum\_{p=1}^{L} W\_{j,p} \, \mathbf{x}\_j[n-p] \tag{1}$$

Eq. (1) can also be viewed as a summation of *M* finite impulse response (FIR) filters with *L* coefficients per each filter that are applied on input signals (*x j [n]*). Eq. (1) can be summarized into Eq. (2) where *T* denotes Hermitian (complex conjugate) transpose and *W* represents an *M* � *L* matrix of filter coefficients.

$$\mathbf{y}[n] = \mathbf{W}^T \ast \mathbf{x}[n] \tag{2}$$

#### **2.2 Conventional beamforming**

A conventional beamformer assumes that each described filter needs to apply a specific tap delay (*τ*) on the corresponding array signal to properly align the inputs to achieve the desired directivity in the output. In this sense, each FIR filter has the following frequency response. The first filter (*p* = 1) has no associated tap delay since the signal from the first microphone is considered the zero-phase reference.

$$r(\boldsymbol{\alpha}) = \sum\_{p=1}^{L} |\mathcal{W}\_p| \, e^{-j\boldsymbol{\alpha}\boldsymbol{\tau}(p-1)} \tag{3}$$

Assuming that the propagating sound pressure is a complex plane wave with the direction of arrival (DOA) θ and frequency ω, the tap delay at *pth* filter (*τp*) is a function of *θ*, and Eq. (3) can be re-written as below.

$$r(\alpha) = W\_p \, d(\alpha, \theta) \tag{4}$$

$$D(\boldsymbol{\alpha}, \theta) = \left[\mathbf{1}; \mathbf{e}^{j\alpha \tau\_2(\ \theta)}; \dots \mathbf{e}^{j\alpha \tau\_M(\ \theta)}\right]^T \tag{5}$$

The term *D(ω,θ)* is known as the array vector response. *D(ω,θ)* determines the spatial outcome of the beamformer and thus is also called steering vector or direction vector. The simplest solution is to apply a constant delay per array, a so called 'delayand-sum' algorithm. Accordingly, each array signal is delayed by *τp* ¼ ð Þ *p* � 1 *d c* sin ð Þ*θ* where *c* is the speed of sound, 343 m/s at 20C temperature, and *p* extends from 1 to *M*.

The distance between the array elements, *d*, is an essential geometric constraint that has a great effect on the performance of such a delay-and-sum configuration. An important limitation that is imposed on the performance of the beamforming due to the distance between microphones (*d*) is the 'spatial aliasing frequency' ( *f al*Þ that is calculated by *f al* ¼ *c* 2*d* which gives the upper frequency limit of the delayand-sum system. This is because, at this frequency ( *f al*Þ, the phase shift at the microphones equals half the wavelength (*λ*Þ of the signal (see Figure 3 and Figure 4 of [12]). Therefore, to avoid spatial aliasing, the distance between the microphones (*d*) should be chosen carefully for the delay-and-sum beamformer to push *f al* above the frequency range of interest.

In more sophisticated beamformers, the tap delay values (*τp*) in Eqs. (4)–(5) are set as a function of angular frequency (*ω*) in a *filter-and-sum* configuration. The aim is to control the behavior of the system at different frequency ranges and assure a consistent directivity across the entire frequency range of interest. A well-designed filter-and-sum beamformer with tailored frequency-dependent tap delays, *τp*ð Þ *ω s*, can overcome the upper frequency barrier ( *f al*Þ to some good degree.

If the angles at which the interfering signals arrive is known, it is possible to design the steering vector so that the beamformer minimizes sound intensities (represented by statistical variance in the data) arriving from these specific angles. In this configuration, called linearly constrained minimum variance (LCMV) beamforming, the steering vector is designed to multiply null in given interference directions while amplifying the desired DOA.

**Figure 1** presents a one-dimensional beamformer which operates in *xy* plane as DOA (*θ*Þ is in that plane. However, if necessary, it is possible to add microphones on *z* axis where similar equations, Eqs. (1)–(5), can be written in the *xz* plane with a DOA in that plane. Accordingly, a two-dimensional beamformer would be created which filters the *xyz* space with regard to one DOA in *xy* plane and another DOA in *xz* plane.

From another perspective, **Figure 1** depicts a 'broadside' beamformer which is designed to form a beam toward the target which is located in the broadside plane of the microphone array. However, if the target is located along with the axis of the array (therefore θ = �90), then the configuration is called 'end-fire' [9]. In an endfire configuration, the summation in **Figure 1** is replaced by subtraction. Consecutively, each filter output is subtracted in Eq. (1) instead. Thus, an end-fire beamformer is also called a 'filter-and-subtract' or a 'differential' beamformer. This type of beamformer, which can be viewed as a special case of the general beamforming shown in **Figure 1**, forms a beam toward either above the array axis (θ = 90) or below the array axis (θ = �90).

#### *2.2.1 Fixed beamforming vs. adaptive beamforming*

In fixed beamforming, the DOA is known and time-invariant thus the steering vector, *D(ω,θ*), can be set for a known fixed geometry. A good example of fixed beamforming is in automotive industry where the target talker (driver) sits in a

### *Spatial Audio Signal Processing for Speech Telecommunication inside Vehicles DOI: http://dx.doi.org/10.5772/intechopen.105002*

fixed location and the DOA toward the microphones is predetermined. Fixed beamforming can be viewed as a 'data-independent' algorithm since the steering vector is designed solely based on the known geometry of the sound propagation and is independent of the received data. In contrast, in adaptive beamforming, DOA varies and the steering vector should adapt to the changes in DOA. For an example, an adaptive beamformer is needed in case the system is supposed to localize and adapt itself to capture signals from all car occupants (besides of the driver) who are sitting at different location inside the vehicle. In this case, the system should iteratively find the target talker first and then update its steering vector toward that target. Another example of an adaptive beamformer is in the 'cocktail party' problem wherein the target location can vary in the room and the system should constantly localize the target and the beamforming algorithm should adapt to the new DOA and other geometrical factors, accordingly. From this perspective, adaptive beamformers can be viewed as being 'data-dependent'systems since their parameters change according to variations in the received data. As a result, adaptive beamformers usually require substantial computational resources [10, 13, 14].

An adaptive beamformer is often accompanied by a pre-processing stage whose task is to localize the target and determine the new DOA. This 'localization'stage usually accomplishes its task by examining the data and finding optimum DOA that maximizes a specific metric such as signal strength, or speech intelligibility [10, 13– 15]. Alternatively, some localization algorithms are built on minimizing a specific cost function, such as noise and reverberation, in the signal. When the localization algorithms finds the DOA, the values in the steering vector (*D(ω,θ)*) should adapt to this new angle.

There are some relatively newer solutions that merge the 'localization' and 'beamforming'stages together. Warsitz and Haeb-Umbach [14] presented an algorithm that optimizes the FIR filter coefficients (denoted by *W* in Eqs. (1)–(2) above) by iteratively estimating and maximizing the cross power spectral density of the microphone signals. An important feature of this algorithm is that the filter coefficients are optimized directly without localizing the source. In other words, the DOA information is implicitly absorbed in the optimization problem although it is possible to extract the underlying DOA information from the results afterwards, if needed.

#### **2.3 Neural-based adaptive beamforming in speech recognition applications**

Speech signal enhancement (SSE) techniques, such as beamforming, have traditionally been performed as an independent pre-processing stage to speech recognition back ends [13, 15]. In this conventional setup, SSE algorithms are performed to improve the signal-to-noise ratio (SNR) by reducing ambient noise and reverberation in the captured signal. The output of the SSE stage is then fed into acoustic models, usually deep neural networks, which perform the automatic speech recognition (ASR) task.

In the last few years, adaptive beamforming algorithms have been designed that are tuned jointly together with the speech recognition backend [13, 15, 16]. To do so, the FIR coefficients (shown by *W* in **Figure 1** and also in Eqs. (1)–(2)) are jointly trained together with the parameters of the ASR model where the optimization is performed using a gradient learning algorithm. The goal of this optimization process is to find FIR coefficients that result in higher ASR accuracy.

Several neural-network approaches have been developed to address the ASR problem [15] but the most successful ASR models are currently built on the convolutional deep neural network (CL-DNN) concept [13, 15]. The input is filtered by a time-domain filterbank pre-processor, usually a Gammatone filterbank together with a nonlinearity function, which is supposed to loosely mimic the

human auditory periphery (cochlea) in terms of spectral feature extraction and compression [17]. The output is then fed into the CL-DNN model. The first stage in the CL-DNN model is the *fconv* layer that convolves the output signals across the filterbank channels and the results are pooled along the frequency axis. The next stage comprises a number of long short-term memory (LSTM) layers. LSTM network is a specific type of recurrent neural network that is tailored for recognizing sequential time-series data such as audio. The final stage is a single fully-connected DNN that consists of at least 1024 hidden units [13, 15, 16].

Sainath et al. [13, 16] presented a multi-microphone solution to incorporate the data captured by *M* microphone arrays into the CL-DNN model. They replaced each spectral channel of the Gammatone filterbank pre-processor with FIR filters that are connected to the microphones and are used for beamforming (identical to Eqs. (1) and (2) above). They essentially created a filter-and-sum beamformer per spectral channel. The difference is that the tap delays (*Tp*) and therefore DOA data are implicitly absorbed in the FIR coefficients similar to earlier works by Warsiz and Haeb-Umbach [10]. Sainath et al. [13, 16] trained the beamforming FIR coefficients together with the CL-DNN parameters using a gradient learning algorithm to maximize the ASR accuracy. Sainath et al. [16] showed that during the training, the FIR coefficients become optimized to extract both spectral and spatial features of the incoming speech signals. They showed that the multi-microphone ASR model with joint beamforming achieves an over 10% improvement in word error rate (WER) compared to its single-microphone counterpart.

Besides of excellent ASR accuracy, a major benefit of neural-network based beamforming is that the model is, to a great extent, independent of the array spacing whereas the conventional beamforming relies on the prior knowledge of the distance between microphones (*d*) to calculate the tap delays. Due to its remarkable success in ASR, neural-based beamforming is prevailing in all ASR systems that have access to multiple microphone input. A very good candidate for applying this technique is in automotive ASR systems wherein online voice assistants based on this technique are currently being designed and evaluated.

A potential shortcoming of the neural-based beamforming is that the source localization information (i.e. DOA) is implicitly embedded in the model and might not be extractable and interpretable in terms of physical geometry. This could impose a limitation in applications which require an explicit knowledge of the source location. Besides, an important distinction is that neural-based beamforming parameters are tuned solely based on ASR objectives and might not necessarily improve the audio quality (e.g. SNR) with regard to the human psychoacoustics [13, 15, 16]. Therefore, neural-network based beamforming is currently considered more applicable to speech recognition tasks rather than to applications such as telephony wherein human listeners are involved. The feasibility of neural-network based beamforming for telephony applications and its relation to human psychoacoustics need further investigations.

#### **2.4 Beamforming applications in automotive industry**

Beamforming techniques introduced into the automotive industry almost at the same time that the first automotive hands-free telephony and speech dialog systems were being devised [1]. Although there have been some studies using multiple microphones [18], it is by far more common to only have dual microphones available in vehicles for beamforming. There are mainly two reasons for this, the first one is the production costs, and the second one complications in the vehicle's interior design and excess wiring if multiple microphones are used. Therefore, in

*Spatial Audio Signal Processing for Speech Telecommunication inside Vehicles DOI: http://dx.doi.org/10.5772/intechopen.105002*

the following sections regarding automotive applications, two-microphone solutions are in focus.

**Figure 2**(A) shows a car with two dedicated microphones (marked M1 and M2) about 4.5 centimeters apart (*d = 4.8* cm) that have been mounted in the car ceiling. The DOA is ideally around 90 degrees according to the illustrated coordinates. To provide a fixed beamforming solution for this particular geometry, an 'end-fire' differential beamformer should be used since the desired source is located along the axis of the array (θ = �90). The input signals are filtered according to Eqs. (1)-(5) and then subtracted. The frequency-dependent tap delays for microphone M2 (i.e., *τ*2ð Þ *ω* ) were chosen to enable the steering vector to enhance sounds that arrive from the driver side (θ = 90).

**Figure 2** shows the beam patterns resulted from an end-fire filter-and-sum beamformer where the tap delays for M2 microphone (i.e. *τ*2ð ÞÞ *ω* have been adjusted as a function of frequency at several frequency channels covering the frequency range from 0.1 to about 7 kHz. **Figure 2(B)** shows the beam pattern at 1 kHz. This beam pattern demonstrates that sounds from θ = 90 (driver side) have passed through the system whereas sounds from θ = �90 = 270 have been substantially attenuated. A very similar beam pattern is shown by **Figure 2(C)** at 2 kHz

#### **Figure 2.**

*A) a car cabin geometry with a dual microphone mounted in the car ceiling. B) the beampattern achieved by the described end-fire (differential) beamformer at 1 kHz, C) at 2 kHz, and D) at 4 kHz. (A) dual microphones in a personal car, (B) beampattern at 1KHz, (C) beampattern at 2KHz and (D) beampattern at 4KHz.*

although the beam pattern at 4 kHz, shown in **Figure 2(D)**, deviates somewhat with minimal effect on overall performance.

The presented beamformer was tested in-situ by placing a head-and-mouth simulator system at the typical location of the driver's head and playing standard hearing-in-noise test (HINT) sentences [19] while the engine was running in idle mode creating some stationary background noise. The raw signals captured by the M1 microphone were recorded. The test was repeated while applying the described beamforming on the raw signals. The beamforming results were compared to the raw signal. The results showed a signal-to-noise ratio improvement (SNRI) of 5.7 dB (A) across frequencies between 0.1 and 8 kHz.

**Figure 3** shows the beamforming geometry in a large truck cabin wherein a dual microphone array is installed on the overhead compartment. The distance between the array and the driver's mouth is about 0.4 m and the DOA is approximately 30 degrees (*θ* = 30 in *zy* plane) although these numbers vary depending on the height and other biometrics of the driver. The distance between the two microphones (*d*) is 23 mm which yields a higher spatial aliasing frequency upper limit compared to the system shown in **Figure 2**. In this case, an end-fire beamformer can be designed to form a beam downwards toward the cabin's floor (θ = 90). The drawback is that an amount of engine noise and AC fan noise will also leak into the beamformer since these noise signals originate from the dashboard which is also located below the overhead compartment.

Alternatively, a broadside beamformer can be used to direct the beam toward the DOA of θ = 30. As a well-known drawback, the broadside configuration also amplifies the angle that is 180 degrees behind the DOA (i.e. 30 + 180 = 210

#### **Figure 3.**

*A truck cabin geometry with a dual microphone installed in an overhead compartment forming DOA of approximately 30 degrees (θ = 30) toward the mouth of a 180-cm long male driver. The yellow arrow shows the first-wave propagation from the driver's mouth whereas the green arrow shows the noise signal propagation (engine noise and AC fan noise).*

#### *Spatial Audio Signal Processing for Speech Telecommunication inside Vehicles DOI: http://dx.doi.org/10.5772/intechopen.105002*

degrees in this case). This is because the broadside beamforming, characterized by Eqs. (1–4), is agnostic to the 180-degrees axis and any sound coming from θ + 180 is treated equally as θ. However, since the overhead console behaves as a mechanical damper for sounds and vibrations coming from the roof and the backward direction, broadside solution appears to be a better solution in this practical case.

A broadside filter-and-sum has been devised with tailored frequency-dependent tap delays to facilitate a consistent beamforming toward the driver at frequencies between 0.1 and 8 kHz. The in-situ measurements and beam patterns are not finalized yet. However, the preliminary analysis indicates that the system can achieve an SNRI of about 6 dB when the engine is running in idle mode. The described beamformer functions on *xz* plane. However, a third microphone can be added on *y* axis next to the existing pair of microphones to perform beamforming on *xy* plane as well. This new beamformer on *xy* plane can be tuned to attenuate sounds arriving from the co-driver's side although adding multiple microphones are currently uncommon in vehicles.