**Meet the editors**

Aditi Singh is a research enthusiast, working as an application scientist in a biotechnology company. She has a PhD degree in molecular-cell biology from the University of Heidelberg in Germany and worked as a postdoctoral scholar at the University of California San Diego, USA. Aditi has more than nine years of experience in the field of molecular and cell biology, where she has been involved in various R&D projects and technical support. Her research interests are cell biology, RNA biology, gene editing, and infectious diseases.

Mohammad W. Khan is a research enthusiast, working as an application scientist in a biotechnology company. He has a PhD degree from the University of Heidelberg in Germany and a DVM degree from Maharashtra Animal and Fishery Sciences University, Nagpur, India. He also worked as a postdoctoral scholar at San Diego State University, USA. Dr Khan has more than 15 years of experience in the field of molecular and cell biology, where he has been involved in various R&D projects and technical support. He has authored and reviewed several research articles and has also served as guest editor for several high-impact journals. His research interests are protein biology, RNA biology, cancer research, immunology, genomics, gene editing, and infectious diseases.

Contents

**Preface VII**

Aditi Singh

**Section 2 Gene Silencing Applications 5**

Sanchez-Martin

Amal Qattan

and Moises Zotti

**Nucleic Acids 61**

**Knock-In System 83**

Lin

Chapter 1 **Introductory Chapter: Modulating Gene Expression - Abridging**

Chapter 4 **Nontransformative Strategies for RNAi in Crop Protection 41**

Chapter 5 **Strand Displacement Amplification for Multiplex Detection of**

Chapter 6 **MultiSite Gateway Technology Is Useful for Donor DNA Plasmid Construction in CRISPR/Cas9-Mediated**

Takeshi Yasuda and Katsushi Tajima

Ignacio García-Tuñon, Elena Vuelta, Sandra Pérez-Ramos, Jesús M Hernández-Rivas, Lucía Méndez, María Herrero and Manuel

Deise Cagliari, Ericmar Avila dos Santos, Naymã Dias, Guy Smagghe

Lingwen Zeng, Omar Mukama, Xuewen Lu, Shilin Cao and Donghai

**the RNAi and CRISPR-Cas9 Technologies 3**

Chapter 2 **CRISPR-ERA for Switching Off (Onco) Genes 7**

Chapter 3 **Gene Silencing Agents in Breast Cancer 27**

**Section 3 Novel Gene Silencing Methodologies 59**

**Section 1 Introduction 1**


**1. Introduction**

#### *1.1. Scope and Significance*

The future of connected and automated vehicles (CAVs) and the development of intelligent transportation systems (ITSs) are actively researched topics which open up a multitude of possibilities. With the progress in computation and communication technology in the last decade, some formerly unrealistic constructs are becoming more practically viable and demand proof-of-concept implementations. We envision a future where traffic participants and observers like CAVs and ITSs share their information resources in realtime for a safer and more efficient transportation and traveling experience. Herein, we outline a so-called *Central System* architecture that enables such information sharing and integration. We use the term *central* in a strictly logical sense to denote the emergence of a single, fully integrated and logically consistent environment and decision model in the cloud (the *digital twin*), while the physical implementation itself remains highly *distributed*, i.e., computation and communication loads are delegated to a spatially localized edge processing nodes hierarchy, as well as a network of third-party partners such as trusted data and algorithm providers. Understanding the wide ranging general applicability of building a well-integrated smart road and vehicle IoT, we made a dedicated effort to design the *Central System* as an easily extensible integration framework using industry standard interfaces. In the coming months we expect to be able to connect a part of the fast-developing Hungarian ITS facilities into the *Central System* and to provide non-stop

**Citation:** Tihanyi, V.; Rövid, A.; Remeli, V.; Vincze, Z.; Csonthó, M.; Peth˝o, Z.; Szalai, M.; Varga, B.; Khalil, A.; Szalay, Z. Towards Cooperative Perception Services for ITS: Digital Twin in the Automotive Edge Cloud. *Energies* **2021**, *14*, 5930. https://doi.org/10.3390/en14185930

Academic Editor: Marco Pau

Received: 13 July 2021 Accepted: 4 September 2021 Published: 18 September 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

real-time integration of real traffic data and thus demonstrate the first large-scale industrial application. In the current paper, we report the positive results of small-scale experiments conducted in late 2020.

Arguably, the most useful kind of information for traffic participants might very well be the establishment of a so-called *digital twin*: a dynamic and real-time model of the environment, which is the focus of our current work and provides the major information source to build other services upon. The functional prototype we built and present in this paper therefore realizes only the fundamental real-time environment perception function of the *Central System*, which for clarity we will call *Central Perception*—distinguishing it from an envisioned suite of other dependent functionalities like traffic analytics and planning, road infrastructure monitoring and management, cloud-based traffic control and vehicle control, specific applications on CAV testing grounds and logistic grounds, etc. Of course, such derivative services must be introduced in at least some detail to highlight the expected practical significance of our initial efforts. A schematic overview of the planned *Central System*, its participants and functionalities is represented in Figure 1.

**Figure 1.** *Central System* overview.

The premise of *Central Perception* is the following: assuming smart roads and vehicles in the near future, the same physical traffic environment will typically be perceived through many sensors and platforms of highly differing setups and capabilities at the same time, and these platforms may or may not be affiliated. Depending on application requirements like expected response time and reliability, the integration of environment perception information from various sources will pose certain challenges including communication, compatibility, synchronization, calibration, fusion, tracking, and end-to-end latency. Such an ad-hoc collective and collaborative perception system also requires certain sophistication in its architecture which must allow for scaling in a dynamically changing environment, especially considering CAVs that constantly change their location and whose sensor data must therefore be integrated with different stationary platforms at different points in time.

#### *1.2. Prior Work*

In the last two years, several connected automotive information system prototypes were realized, all of them demonstrating some degree of cooperative perception. Krämmer et al. from TU Munich and Fortiss built "Providentia" [1], an intelligent infrastructure system consisting of several gantries equipped with cameras and radars, serving as a support system for intelligent vehicles and aiding them in perceiving blind spots and objects behind cover. They process and fuse the sensory data both locally at the individual measurement stations and also centrally at designated edge computing nodes before communicating it to the consumer vehicles via 5G. Gabb et al. from Bosch and Karlsruhe showed theoretical guidelines (supported by experiments) for developing a similar system around the same time, at the Intelligent Vehicles conference in Paris [2]. The University of Tokyo released open source software for cooperative perception [3] while the University of California together with Toyota demonstrated a use-case of actually backpropagating the already integrated data from the cloud for driving assistance purposes [4]. Toyota also pursues similar topics with other Universities and independently, focusing on various settings like V2V communication [5] or camera and digital twin integration for visual guidance systems [6]. In Australia, researchers implemented the ETSI CPM messaging standard in an I2V setting, allowing sensor-less vehicles to perceive and autonomously react to pedestrians [7]. Recently, Chinese authorities announced to launch "world's first high-level cloud-controlled autonomous driving demonstration zone" (http://m. news.cctv.com/2020/09/11/ARTIeJEug9svYwuLazxQFzO3200911.shtml (accessed on 14 September 2021)) to be constructed in Beijing with similar long term targets as our *Central System* project.

As prior work we have carried out a measurement campaign (together with international industrial and academic partners) on a real-world motorway section in Hungary, which resulted sensory data useful for future automotive R&D activities due to the available ground truth for static as well as for dynamic content [8].

A possible first industrial application of *Central System* technology is likely to occur where experimental CAVs and ITSs are introduced earliest: on automotive testing grounds. In particular, development of *Central System* based Scenario-in-the-Loop (SciL) [9] control is already underway on the ZalaZONE [10] CAV proving ground in Hungary.

#### *1.3. Primary Contribution*

According to the best of our knowledge, we are the first to demonstrate a real-time cooperative perception platform that has both stationary and moving multi-sensor data sources and that combines several levels of data integration such as inter-sensor raw fusion, on-platform tracking and inter-platform local area fusion to finally create and visualize a simultaneous, centrally consistent model (*digital twin*) of all objects of interest in the area covered by the sensors' field of views.

We emphasize that our primary contribution lies in the demonstration of system-level possibilities as they were not demonstrated beforehand, i.e., the maintenance of a *digital twin* in a complex and heterogeneous environment. In this paper we do not claim any scientific novelty wrt. our individual subsystems, nor are they uniquely necessary for the development of our technological demonstration (we could have chosen alternative technological approaches to demonstrate the same concept).

To clarify, we define the following:

	- **–** *Cooperative perception services:* more reliable centralized perception via sensor fusion in the cloud (*Central Perception*).

#### **2. Problem Definition**

Arguably, the most crucial element in providing centrally maintained *digital twin* services is achieving a centrally integrated, real-time perception of the traffic environment. This paper presents a solution to the *Central Perception* problem that we specify as follows.

The overall system must acquire an integral and dynamic object-level view of the real-time traffic situation with at most 100 ms latency. The logical core of the system should be responsible for data integration while the peripheral measurement platforms will act as data sources. The overall architecture and the established interfaces must enable the simultaneous participation of connected intelligent equipment including static road-side infrastructure, mobile (vehicle-borne) and third-party measurement systems. Various sensor types and vendors must be supported, as well the precise and reliable detection of traffic participants including pedestrians, vehicles, obstacles, etc. As additional data sources, the system may use static HD maps and various traffic data (e.g., road meteorology) providers.

We specified and built a proof of concept system covering a substantial subset of above requirements, demonstrating the viability of the approach. Our functional sample provides following capabilities:

#### **3. Overview of the Central Perception System Architecture**

In order to support the requirements mentioned in Section 2, numerous design decisions had to be made regarding the prototype development of the measurement systems, the central server, and the communication between them. The following sections will discuss the overall design in some detail, the current section giving only a brief overview.

The realized system consists of one central server in the cloud and three wirelessly connected measurement systems, two of which are stationary and one that is mounted on a vehicle. At present we focus on fusion-based perception since we want to utilize the strengths of different sensor types on a single platform. Each measurement system has a sensory setup consisting of a camera-LiDAR pair, with the exception of the vehicle which has one camera and two LiDARs. For precise distance measurements and large-scale 3D reconstruction in automotive applications, stereo vision is becoming a less and less viable choice simply due to the precision loss at distances that are relevant to driving (compared to the high precision and falling prices of LiDAR technology) [11].

The measurement systems use the RTMaps software framework for data acquisition, synchronization, and data-flow processing. Detection and tracking is performed locally on the GPU-s and CPU-s of the measurement systems. The 3D pedestrian detection is done using low-level (raw) data fusion on the local system, i.e., the camera image and the corresponding LiDAR pointcloud are both necessary and are considered together in

calculating the 3D position of the detected pedestrian (in contrast to object-level fusion where each sensor arrives at a detection estimate separately, and fusion occurs only afterwards). The fused detections of each sensor cluster are then tracked locally to smooth out any remaining uncertainties or missing datapoints. The tracks are then communicated using standard SENSORIS message formats over 5G or DSRC to the central server, where the inter-systems track fusion occurs. Finally the resulting locally and globally fused tracks are displayed real-time in a digital twin simulation developed in Unity. The overview of the system components and connections is shown in Figure 2.

**Figure 2.** *Central Perception* prototype main components and protocols.

#### **4. Perception Module**

#### *4.1. System Calibration*

The proposed *Central System* integrates numerous different type of sensors (each having an assigned local coordinate system) such as LiDARs, cameras deployed in the infrastructure or attached to a vehicle. In addition to these sensors—in case of vehicles—the IMU/dGPS stand for an additional key element. For the system to work properly the calibration parameters have to be estimated first, i.e., all the intrinsics and extrinsics. Here, the intrinsics cover the internal parameters of cameras (such as focal length, principal point coordinates, skew, radial and tangential lens distortion) and the extrinsics stand for the transformations between the local coordinate systems of attached sensors as well as transformations from and to the Universal Transverse Mercator (UTM) frame which was selected to represent the global reference frame. For camera calibration the method published in [12] has been applied. The simplified calibration setup is illustrated by Figure 3. Let us briefly introduce the calibration approaches used to calibrate the proposed *Central System*.

**Figure 3.** Illustration of the simplified setup of calibration containing two infrastructure stations and a vehicle each equipped with a camera and a LiDAR. In the vehicle there is an IMU/dGPS system, as well.

#### 4.1.1. Chessboard Based Camera-LiDAR Calibration

The estimation of the rotation and translation between the camera-LiDAR pairs is more challenging than that of the camera-camera pairs, since we have to identify 3D points in the LiDAR point cloud and their corresponding image points in camera images. The estimation of LiDAR-camera extrinsics was based on the method proposed by authors in [13], which is a fully automatic extrinsic calibration approach aimed for LiDAR-camera extrinsics calibration by using a printed chessboard attached to a rigid planar surface. The key element of the method is to determine the 3D locations of chessboard corners in LiDAR's coordinate system. A full-scale model of the chessboard (A0 sized) is fitted to the segmented 3D points corresponding to the chessboard in the LiDAR point cloud. The intensities of light rays reflected form black and white patches of the chessboard are different and well distinguishable, thus the model is fitted to a 4D point cloud where the last dimension corresponds to the intensity of the given LiDAR point). The corners of the fitted model are considered to be the 3D corners of the chessboard.

The extrinsic calibration of the camera and LiDAR is performed by minimizing the re-projection error (given the estimated corners **M***i* in the LiDAR frame, their measured projections in the camera image **m***i* as well as the intrinsics of the camera (camera matrix **K**, radial and tangential distortion coefficients *p*1, *p*2, *p*3, *q*1, *q*2). *N* stands for the number of corner points considered.

$$\min\_{\mathbf{R}, \mathbf{t}} \sum\_{i=1}^{N} \left|| \mathbf{m}\_i - \hat{\mathbf{m}}\_i (\mathbf{M}\_i, \mathbf{K}, \mathbf{R}, \mathbf{t}, p\_1, p\_2, p\_3, q\_1, q\_2) \right||^2 \tag{1}$$

Figure 4 shows the process of data acquisition. The blue and yellow colors correspond to different LiDAR point intensities. Figure 5 shows the LiDAR points projected onto the camera image. In the chessboard image we can see both the detected corners and the 3D corners identified in the LiDAR point cloud and projected onto the camera image. The 3D viewer shows the detected corners together with the point cloud of the chessboard colored based upon the black and white patches of the fitted chessboard model. The achieved RMSE in case of five different poses of the chessboard can be followed in Table 1.

**Figure 4.** Data acquisition for chessboard-based camera-LiDAR calibration.

**Figure 5.** chessboard-based camera-LiDAR calibration results.

**Table 1.** The achieved RMSE in case of five poses.

#### 4.1.2. Box Based Calibration

In this second approach, the Camera-LiDAR extrinsics calibration relies on box corners, instead of a chessboard. The calibration box is placed at different locations (with known UTM coordinates measured in advance by a portable dGPS modul) of the working

area. For each pose of the box the point cloud and the corresponding camera image is acquired. In the LiDAR pointcloud, the box corners were determined by segmenting the points which correspond to the box in the point cloud followed by fitting the box model. Since the box corners are obtained with respect to the LiDAR coordinate frame and the corresponding UTM coordinates are known, the LiDAR pose wrt. the UTM frame can be estimated. Similarly, the camera pose can be determined by minimizing the re-projection error (see Equation (1)) associated with the selected corners with known UTM coordinates. Since the UTM coordinates of box corners as well as the IMU pose wrt. UTM are known, the transformation between the LiDAR and IMU coordinate systems can also be determined. Another well known approach to estimate LiDAR-UTM extrinsics is the hand-eye calibration which requires at least two motions (with non-parallel rotation axes) of the sensors (LiDAR and IMU) [14].

#### *4.2. Data Synchronization*

In order to keep the data streams synchronized among infrastructural and vehicular sensors, a common time source as well as a time protocol is needed. As the most commonly used time protocols, the Network Time Protocol (NTP) and the Precision Time Protocol (PTP) might be emphasized. During our experiments the NTP was utilized and the GPS time was used as time source. Each station (including two infrastructural stations and one measurement vehicle) was equipped with an on-board unit having an integrated GPS time source and running the NTP service (see Section 6.1). Each computing node's (PCs, DrivePX2 Tegra A and Tegra B) system clock has been synchronized with the GPS time by relying on the NTP protocol. Prior to testing the *Central Perception* system, the synchronicity of data streams from different sensors have been verified by experiments. To each data frame (independently on what type of sensor it originates from), a timestamp is assigned as it enters the computing framework. In the computing framework the data frames (from different streams) being closest in time are associated and processed afterwards as depicted by Figure 6.

**Figure 6.** Illustration of the data flow and timestamp based assignment of data frames.

As another alternative for time synchronization the Precision Time Protocol might be used, which instead of millisecond-level synchronization, aims to achieve nanosecond- or even picosecond-level synchronization. In case of the PTP, switches with PTP support are required for each station. For most commercial and industrial applications, NTP is more than accurate enough [15].

#### *4.3. 3D Object Detection*

3D object detection plays crucial role in environment perception and understanding. During the development of the *Central System* we payed much attention on the development of robust 3D object detection methods which are considered to be essential from the overall system performance point of view. We have considered two type of approaches:

#### 4.3.1. Yolo and Point Cloud Based Approach

Camera-based systems perform outstandingly well in case of recognition tasks, but when it comes to position estimation they are less accurate than LiDAR-based systems. Depending on the resolution and the number of used cameras, the baseline length, the accuracy of calibration as well as the accuracy of the pixel coordinates of points of interests, the position estimation might be improved; however, by including one or more LiDARs the location estimation of object's might significantly be improved.

The method presented below combines the advantages of the two sensors (i.e., the high resolution of cameras and the localization capabilities of LiDARs). In order to fuse camera images with LiDAR point clouds the sensors have to be calibrated (see Figure 3). Another crucial point here is to guarantee real-time processing which puts additional constraint (depending on the used hardware) on the complexity of applied algorithms. Nevertheless, the data streams of different sensors must be kept synchronized to ensure that data frames closest to each other in time are associated and processed accordingly (see Section 4.2).

As first step the detector receives images on its input and 2D object detection is performed by the YOLOv4 object detector [16]. The speed and accuracy of the algorithm are in line with the requirements defined, which means that the frame rate of the overall system was set to be at least 20 FPS (which currently stands for the upper limit for LiDARs). During the experiment pedestrians and cars have been considered as primary objects of interest, however the algorithm can easily be extended to detect additional classes such as motorcycles, buses, etc. The 2D detection may take several milliseconds even on the most powerful hardware (∼30 ms).

In the next stage, the point cloud is projected onto the camera image and each estimated 2D bounding box gets associated with the LiDAR points which projections are bounded by the given box. As result a set of frustums is obtained (one for each 2D bounding box) containing the 3D points of the objects of interest. Let us denote the set of these frustums by F*i*. Given F*i* the 3D bounding box corresponding to the given object might either be estimated on neural basis by a convolutional neural network trained to perform detection in frustums or the position of the object might be determined based on a simple reasoning.

By the reasoning based approach first the false points (foreground, background points) from F*i* are eliminated and a small 2D window inside each bounding box is defined. The size and position of the window is proportional to the size of the original box. The scaling factor and position were set empirically based on the type of object. Since these windows generate significantly narrower frustums, the points falling inside it are more likely to belong to the object of interest. Let us denote the set of these points as F*i* . The location of the object is determined as the mean of the points falling inside the volume bounded by F*i* and satisfying the constraint

$$d\_{\min} < ||\mathbf{p}\_j - \mathbf{c}|| < d\_{\min} + \delta, \quad \mathbf{p}\_j \in \mathcal{F}\_{\text{i}}, \quad j = 1..\text{N}\_{\text{i}}.\tag{2}$$

where **c** stands for the camera center and *Ni* represents the number of points in F*i*, *δ* = max{*objectwidth*, *objectheight*}.

$$d\_{\min} = \underset{\mathbf{q} \in \mathcal{F}\_i'}{\operatorname{argmin}} \|\mathbf{q} - \mathbf{c}\|. \tag{3}$$

This is an extremely simple and therefore very fast way to filter out unnecessary points and localize the object of interest Figure 7. The latency of the detection can be followed in Figure 8.

**Figure 7.** Detected objects.

4.3.2. Yolo and Homography Based Approach

The detector described above uses the lidar point cloud to estimate the 3D location of the target, the method introduced in this section focuses on a single camera based 3D localization of targets. Homography and its estimation is well known topics in the literature, but let us briefly summarize it: Let us denote a world point by **M** and its image coordinates by **m**. Let us consider the scenario when the world points of interest are lying on the XY plane, thus their Z coordinate is zero. These points are projected onto the image plane of the camera as follows:

$$\mathbf{m} = \mathbf{PM} = \mathbf{K}[\mathbf{R} \mid \mathbf{t}] \begin{bmatrix} X \\ Y \\ 0 \\ 1 \end{bmatrix} = \mathbf{K} \begin{bmatrix} \mathbf{r}\_1 & \mathbf{r}\_2 & \mathbf{r}\_3 & \mathbf{t} \end{bmatrix} \begin{bmatrix} X \\ Y \\ 0 \\ 1 \end{bmatrix} = \underbrace{\mathbf{K} \begin{bmatrix} \mathbf{r}\_1 & \mathbf{r}\_2 & \mathbf{t} \end{bmatrix}}\_{\mathbf{H}} \begin{bmatrix} X \\ Y \\ 1 \end{bmatrix} \tag{4}$$

where *ri* denote columns of the rotation matrix **R**, **t** stands for the translation and **K** denotes the camera matrix containing the camera intrinsics. In order to estimate the homography

**H** the following cost function is minimized (measurement error is considered in both the image and world plane):

$$\min\_{\mathbf{H}, \hat{\mathbf{m}}\_i', \hat{\mathbf{m}}\_i} \sum\_{i=1}^N \left\lVert \left| \mathbf{m}\_i - \hat{\mathbf{m}}\_i \right| \right\rVert^2 + \left\lVert \left| \mathbf{m}\_i' - \hat{\mathbf{m}}\_i' \right| \right\rVert^2,\\ \text{s.t. } \hat{\mathbf{m}}\_i' = \mathbf{H} \hat{\mathbf{m}}\_{l\prime} \; \forall \; i, \tag{5}$$

where **m***i* and **m** *i* stand for the measured point pairs while **m**<sup>ˆ</sup> *i* and **m**<sup>ˆ</sup> *i* stand for the estimated perfectly matched correspondences, i.e., **m**<sup>ˆ</sup> *i* = **H m**<sup>ˆ</sup> *i* [17].

We have used 18 markers **m** *i* with known UTM coordinates (measured by a mobile GNSS system in advance with an accuracy of ∼20 mm) and their image projections **m***i* to estimate the homography. **m***i* stand for the undistorted normalized image points. The detection part of the approach uses the YOLO4 [16] neural network to detect targets of various types in images (during our experiment pedestrians were the main objects of interest, however other object types are also supported by the proposed perception system). The point of interest for each detected pedestrian was set to be the center point of the bottom edge of its 2D bounding box. Let us denote these points by **m***i*. By applying the estimated homography **H**, the image points *mi* can be transformed to the *XY* plane of the UTM coordinate system as **m** *i* = **Hm***i*, ∀*i*. Here we omit the true altitude, thus it was set to zero for each point. Although this kind of 3D detection is very useful for static cameras (for example cameras installed in the infrastructure), in case of cameras attached to a vehicle, change in pitch or roll of the vehicle (caused for example when accelerating or making a hard turn, etc.), invalidates the estimated homography. In addition, the uncertainty of the measured image points **m***i* must also be considered. Given both the uncertainty of **H** and **m***i*, the covariance of the estimated points **m***i* is given by:

$$
\boldsymbol{\Sigma}\_{\mathbf{m}\_i'} = \mathbf{J}\_{\mathbf{h}} \,\,\boldsymbol{\Sigma}\_{\mathbf{h}} \,\,\mathbf{J}\_{\mathbf{h}}^T + \mathbf{J}\_{\mathbf{m}\_i} \,\,\boldsymbol{\Sigma}\_{\mathbf{m}\_i} \,\,\mathbf{J}\_{\mathbf{m}\_i'}^T \tag{6}
$$

where **Σ <sup>m</sup>***i* , **Σ <sup>m</sup>***i* and **Σh** stand for the covariance matrix of the estimated road point **mi** , the measured image point **m***i* and the estimated homography **h**, respectively (vector **h** is composed from the concatenated rows of **H**). Furthermore, **Jm***i* and **Jh** stand for the Jacobians of **m** *i* = **Hm***i* wrt. **m***i* and **h**, respectively [17].

Since the vehicle is moving, the detected objects have to be transformed according to the actual pose of the vehicle to a global coordinate system (e.g., UTM). Firstly, the detections should be estimated wrt. a selected reference coordinate system and then based on the actual pose of the vehicle transformed to the UTM frame. The reference coordinate system for the vehicle was set to be the coordinate system of the IMU shifted along the vertical axes to the ground level (road level). Let us refer to this coordinate system as *IMUgl*. In order to estimate the homography which transforms the image points directly to *IMUgl*, one needs to estimate the marker coordinates in *IMUgl*. Since the IMU modul (used during our experiments) includes a differential GPS with a dual antenna system, the UTM coordinates and the heading of the vehicle can be measured with an accuracy of ∼20 mm which might be considered to be sufficient for autonomous driving related applications. Based on the measured pose of the vehicle, the UTM to *IMUgl* rigid transformation can be determined, thus the markers in *IMUgl* can be calculated. By applying the homography (estimated based upon markers in the *IMUgl* and the corresponding image plane points) to points *mi*, the 3D position of each detected target is obtained directly in *IMUgl*. Since the pose of the vehicle is continuously measured with a sampling rate of 100 Hz, the detections can directly be transformed to the UTM frame in real-time.

#### *4.4. Object Tracking*


**Haidar Ali 1, Muhammad Ahsan Binyamin 1, Muhammad Kashif Shafiq 1 and Wei Gao 2,\***

Received: 23 May 2019; Accepted: 22 June 2019; Published: 10 July 2019

**Abstract:** There are numeric numbers that define chemical descriptors that represent the entire structure of a graph, which contain a basic chemical structure. Of these, the main factors of topological indices are such that they are related to different physical chemical properties of primary chemical compounds. The biological activity of chemical compounds can be constructed by the help of topological indices. In theoretical chemistry, numerous chemical indices have been invented, such as the Zagreb index, the Randi´c index, the Wiener index, and many more. Hex-derived networks have an assortment of valuable applications in drug store, hardware, and systems administration. In this analysis, we compute the Forgotten index and Balaban index, and reclassified the Zagreb indices, *ABC*4 index, and *GA*5 index for the third type of hex-derived networks theoretically.

**Keywords:** forgotten index; balaban index; reclassified the zagreb indices; *ABC*4 index; *GA*5 index; *HDN*3(*m*); *THDN*3(*m*); *RHDN*3(*m*)

### **1. Introduction**

Topological indices are very useful tools for chemists which are provided by Graph Theory. In a molecular graph, vertices denotes the atoms and edges are represented as chemical bonds in the terms of graph theory. To predict bioactivity of the chemical compounds, the topological indices such as ABC index, Wiener index, Randi*c*´ index, Szeged index and Zagreb indices are very useful.

A graph *ξ* is a tuple, which consists of the n-connected vertex set |*V*(*ξ*)| and the edge set |*E*(*ξ*)|. *τ*(*m*) denotes the degree of a vertex '*m*' in a graph *ξ*. A graph can be represented by the polynomials, numeric numbers, a sequence of numbers, or a matrix. Throughout this article, all graphs examined are simple, finite, and connected.

As a chemical descriptor, the topological index has an integer attached to the graph which features the graph, and there is no change under graph automorphism. Previously, interest in the computing chemistry domain has grown in terms of topological descriptors and is mainly associated with the use of unusual quantities, the relationship between the structure property, and the relationship of the structure quantity. The topological indices that are based on distance, degree, and polynomials are some of the main classes of these indices. In a number of these segments, degree-based displayers are widely important and chemical graphs play an integral part in theory and theoretical chemistry.

In this article, we consider some important topological indices and some important derived graphs. We examine their chemical behavior by the help of topological indices. These topological indices are of use to chemists.

Chen et al. [1] gleaned a hexagonal mesh which consists of triangles. Triangle graphs are called oxide graphs in terms of chemistry. We can construct a *hexagonal mesh* by joining these triangles, as shown in Figure 1. There does not exist any hexagonal mesh whose dimension equals 1. By the joining of six triangles, we make a hexagonal mesh of dimension 2, *HX*(2) (see Figure 1 (1)). By putting

the triangles around the all sides of *HX*(2), we obtain hexagonal mesh of dimension 3, *HX*(3) (see Figure 1 (2)). Furthermore, we assemble the nth hexagonal mesh by putting *n* triangles around the boundary of each hexagon.


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**Chapter 5**

**Abstract**

addressing these challenges.

**1. Introduction**

**69**

Very Low Voltage and High

Efficiency Motorisation for

This chapter details the design of a new innovative solid bar winding for electrical machines (either motors or generators) dedicated to the electric propulsion. The goal of this new winding technique is to enhance the performance by better utilizing the stator slot and increasing the copper fill factor to higher than 75%, and also to reduce the inactive copper at the end-windings. Accordingly, many advantages arise from the application of this solid bar winding: higher torque-to-weight ratio, better thermal behavior, lower rotor losses, higher efficiency, higher reliability and lower cogging torque. However, the solid bar has its inherent constraints, which should be considered with care when designing an electric motor: the AC copper losses and the manufacturing process. The suggested winding technique aims at

**Keywords:** solid bar winding, permanent magnet synchronous machine, high performance electric motor, high power-to-weight ratio, electric propulsion,

The need for a higher competitive electrical machines, mainly in terms of power

The opportunities for achieving a big improvement against the state of the art

The following expression provides the basic relationship of the sizing electromagnetic power of electric motors (rotational movement). This expression highlight some, but not all, obvious paths to follow in order to improve the performance.

are very limited and challenging due to the very small degree of freedom.

density and efficiency, is increasing especially in embedded applications (aerospace, Vertical Take-Off and Landing, Electric Vehicle, etc.); these performances are a key differentiator between competitors. As a rule of thumb, nowadays, a good power-to-weight ratio of PM electric motors is around 3 kW/kg (EMAG + mechanical packaging) [1]. Nevertheless, higher values have been proclaimed by many companies and star-ups, but for experimental prototypes where the maturity of the product is still questionable. The definition of the power to weight ratio is still versatile and controversial because, on one hand, the estimation of the total motor weight relies on many parts where some of them are not always considered in the calculation: EMAG, mechanics, coolant weight (in some cases is shared with the system), cables, power electronic, etc., on the other hand, the flexible definition of

AC copper loss, low voltage winding, high slot fill factor

the output power (continuous or transient).

Electric Vehicles

*Daniel Matt and Nadhem Boubaker*

## **Chapter 5**


**Author details**

deprived condition [97]. Meanwhile, brassinosteroid receptor mutants such as *det2* (*deettiolated 2*) and *bri1* (*brassinosteroid insensitive*) were more sensitive to the ABA. Consequently, synthesis of GAs was also deeply affected [98]. This result signifies the importance of brassinosteriods

Auxins are generally known for their roles in the root induction. Ogawa et al. reported upregulation of a number of auxin biosynthetic genes and genes encoding for auxin-carrying proteins in response to exogenous GA4 application [91]. The GA was well known to promote the auxin synthesis and the transportation of ethylene. Chiwocha et al. (2005) evidenced that the interaction of ethylene biosynthetic genes with the auxin signaling genes such as *axr1* and *axr2* was mediated by GA [99]. The BIG-gene, named due to its large size, encodes the calossin/ pushover protein involved in the efflux transportation of auxin [100]. Repressor of RGA proteins by the GA can be delayed by the attenuating auxin transportation or signaling [101]. Contrastingly, recent study in *Arabidopsis* by Lui et al. observed that the mutants of auxin receptors or biosynthesis genes showed the dramatic release of seed dormancy. This auxinmediated seed dormancy was coordinated with ABA signaling [102]. Both GA and ABA have strong influence on auxins during germination and dormancy, respectively. This kind of crosstalk between the hormones helps in the flexibility of the embryo/seeds in response to the

During the developmental stage of embryos into the vigorous photoautrotropic organisms, numerous metabolic processes are activated and they include oxidation of proteins, cellular structural changes, and synthesis of macromolecules. The cascade of metabolic process ceases with the development of the radicle governed by the well-directed ROS accumulation. Interlinked relation between the GA and ABA aids in the proper development of the embryo, seed filling, desiccation tolerance, imbibition, hydrolysis, temporal and spatial distribution of ROS, proteolysis, and radicle protrusion. The recent evidences suggest that ABA-GA crosstalk with other phytohormones, such as ethylene, brassinosteroids and auxin, could play a vital role in the development of the seed. The important components other than the free radicals

linked with the GA-ABA and their responses to the light and temperature could be one of the

Prabhakaran Soundararajan and Abinaya Manivannan were supported by a scholarship from

interesting areas getting more attention on the seed research.

the BK21 Plus Program, the Ministry of Education, Republic of Korea.

, H2O2, and •OH pertaining to the seed potential is the NO. Tapping of the NO

in the GA synthesis for successful seed germination.

88 New Challenges in Seed Biology - Basic and Translational Research Driving Seed Technology

**5.3. Auxin**

environmental stimuli.

**6. Conclusions**

such as O2

**Acknowledgements**

Prabhakaran Soundararajan1 , Abinaya Manivannan1 and Byoung Ryong Jeong1,2,3\*

\*Address all correspondence to<EMAIL_ADDRESS>
1 Division of Applied Life Science (BK21 Plus), Graduate School, Gyeongsang National University, Jinju, Republic of Korea

2 Institute of Agriculture and Life Science, Gyeongsang National University, Jinju, Republic of Korea

3 Research Institute of Life Science, Gyeongsang National University, Jinju, Republic of Ko‐ rea


**1. Introduction**

Technology transforms education, changes how, when and where students learn, and empowers students and educators at every phase of their teaching and learning voyages. Technology is known for creating opportunities for students to learn online and cooperate through the exchange of ideas and experiences. Technology provides massive accessibility to an abundance of information without boundaries. The use of mobile devices or devices like laptops, tablets, smartphone, and computers are already a ubiquitous element of everyday life for students and educators to access unlimited digital learning materials. Conversely, teaching and learning using technology make students personalise their learning, technology gives power to students by giving them ownership of how they absorb information, making education applicable to their digital lives as digital natives (twenty-first-century students) for lifelong learning. Consequently, we cannot compare the students of the 16th century with the students of the 21st century, because technology has changed considerably [1]. With technology, students can easily access an endless supply of online learning resources outside of the classroom, inspiring them to be critical thinkers, problem solvers, innovators, and creators. With the help of technology, students can acquire the skills and competencies necessary to become 21st-century human resources. However, technology has its challenges, particularly when it comes to human resources, resources and implementations. The challenges facing technology in the education sector are budget constraints, lack of proper training of educators, weak network infrastructure and coverage, educators not willing to use technology and afraid of technology, load shedding, unreliable and incompetent mobile devices and software. Due to the use of portable devices and the inexperience of educators and educational institutions, students might encounter disruptions or diversions from their studies as well as ineffective teaching methods [2].

Teaching and learning technology tools like Virtual Reality (VR), Video conferencing platforms, Online textbooks, eBooks, Education technology, Asynchronous learning, Synchronous learning, Hybrid technology, Smart video cameras, television and others can make classroom and virtual classrooms more exhilarating. It is expected of educators to acquire cutting-edge knowledge and diverse approaches to use technology as a tool to improve the learning environment, support their instruction, and help students learn through it [3]. The use of technology in teaching and learning may additionally promote a more diverse classroom where students are encouraged to work together and be curious. With access to online pertinent content, learning applications, real-time student data, and more, technology could enable students to achieve new heights. With the use of technology, educators can develop blended learning platforms and environments, influence digital tools for formative and summative assessments, and introduce a new approach to teaching and learning in both traditional classroom settings and visually impaired classrooms. Television used in the classroom is a great help for young and old students as it is a great tool for online teaching and learning. It can help students find more interesting and fun in learning.

In his article, Borysiuk [4] listed the following advantages of using information technologies in the classroom: (1) stronger motivation and interest in learning; (2) individualization of instruction; (3) objectivity of control; (4) activation of learning through the use of visually striking and quickly evolving forms of information presentation; (5) development of skills and abilities for creative endeavours; (6) training of information culture; (7) developing the ability to make operational decisions in complex situations; (8) giving students access to information databases so they can find information fast; (9) a rise in the quantity of assignments finished; (10) reinforcement of independent study; (11) increased motivation and mental activity as a result of the variety of activities, which may include playing games; (12) increased information flow; and (13) new chances for students to participate more actively in the learning process thanks to online education. Additionally, according to Borysiuk's article [4], using information technology has the following benefits: (1) it fosters greater collaboration; (2) it offers limitless resources; (3) it aids in the development of future-ready skills; (4) it conserves valuable resources; (5) it updates information instantly; and (6) it takes up little space.