Difference between regular programming and visual programming.
\r\n\tIn the maintenance and conservation of our planet's biodiversity, knowledge of current biogeographical patterns, speciation or invasion processes, flora, fauna, natural history, and ecology have outstanding importance. Many areas of biodiversity are still completely undiscovered nowadays. The ecological impacts of global climate change, urbanization, overpopulation, environmental pollution, deforestation, land cover and land-use changes also have a significant impact on biodiversity, flora, and fauna, through biogeographical patterns.
\r\n\tThe management tools, methods and processes (as applied ecological aspects) of Protected Areas and National Parks are a very important part of conservation policy. I am sure that this book will be very useful for everybody who would like to get some insight into the recent problems of biodiversity research, ecology and conservation biology also from practical or theoretical viewpoints.
In this modern era, there has been a lot of development and upgradation in the field of Internet of things (IoT). The development has been actively growing in various sectors, for instance, industries, healthcare, education, agriculture, just to name a few. Consequently, it creates more employment opportunities. Thus, IoT should come into intensive usages with very careful awareness. The development of IoT programs can be done in various ways into the society. Although there have been a plenty of IoT development and applications, only very few academic institutions throughout the world are fully IoT equipped in their laboratories.
Since 2019, the COVID-19 pandemic has resulted in shutdown of educational institutions across the world. Globally, over 1 billion children are out of the classroom. As a result, education has changed dramatically with the distinctive rise of e-learning, whereby teaching is undertaken remotely and on digital platforms. While lecture sessions have been quite well organized, the laboratory sessions might be still challenging and dependent on the types of laboratories. To overcome this problem for an IoT design class, a virtual laboratory can be implemented. With a virtual laboratory, a laboratory session can be launched with flexibility of time. Self-learning process can be achieved by becoming more self-determining. It also supports collaborative group works and helps us develop critical thinking skills while doing individual assignments. The most important advantage is that it can be accessed from any location through Internet. Thus, any materials from the Internet can also be used. Nevertheless, there are some challenges in the virtual laboratory, which includes setting up the virtual laboratory environment, lack of social interaction between students and instructors, accessibility to various laboratory required technologies, software requirement and software inconsistency, student’s issues while teaching simultaneously, etc. A visual programming language (VPL) is any programming language that lets users create programs by manipulating program elements graphically rather than by specifying them textually. A VPL allows programming with visual expressions, spatial arrangements of text and graphic symbols used either as elements of syntax or secondary notation. Many VPLs known as dataflow programming are based on the idea of boxes and arrows, where boxes or screen objects are treated as entities connected by arrows, lines, or arcs representing relations. Different prototyping boards have its own programming language, such as C, Python, Java, and similar. To develop an IoT system, at least one of these programming languages is required. However, it is fortunate that during these days, several VPLs have been developed to help us start programming without knowing the programming language. These IoT visual programming tools have a user-friendly approach of programming. It has a graphical user interface (GUI), where the user can just drag and drop moving code blocks and execute a simple piece of logic.
Node-RED VPL is an open-source software, which is a flow-based development tool originally developed by International Business Machines (IBM) for wiring hardware devices together with application programming interface (API) and few online services as part of the IoT. Node-RED can be used flexibly under the Apache2 license. Some developed their own services based on Node-RED, while others changed to their own user interface (UI) and deployed it as built-in. It can be established as a platform where we can publish our own developed node so that anyone can use it. The open-source software is an alternative to interact with the class. Students can practice, study, and understand with basic engineering skills or even develop an IoT-based system with artificial intelligence (AI) capabilities, e.g., prediction, regression, clustering, and classification. It can be installed locally on a personal computer (PC) or a laptop. With its simplicity for learning and using as well as several built-in entities, it can be used in evolving innovative platforms providing ability to assign code to all interfaces with less coding complexity. In addition, it supports several IoT prototyping board such as Arduino, Raspberry, and Android as well as cloud-based platforms.
The objectives of the book chapter are as follows.
To describe the environment and features of Node-RED software with its abilities of numerous functions.
To deliver a complete outlook on each function of Node-RED that can be applied for an IoT-based virtual laboratory.
To talk over the suitability and rightness of machine learning and deep learning centered explanations in numerous practical fields.
To arrange for a broad opinion on machine learning algorithms, which can be put to build-up the abilities of a data-driven approach.
To highlight and summarize the possible study for smart systems including cloud technology.
To bring out the virtual awareness/importance of IoT including laboratory sessions.
The rest of the book chapter is organized as follows. Section 2 presents the past related existing works on VPL and NodeRED. In Section 3, the step-by-step methodology of the work is described in detail, which includes getting started, configuration, utilization, and machine learning (ML) package for Node-RED. The experimentational examples are provided along with the corresponding results in Section 4. Section 5 discusses about the results. Lastly, the conclusion is drawn in Section 6.
IoT has been playing an important role throughout the globe. It is a combination of both software and hardware tools. With the support of VPL, IoT can be applied into our day-to-day essential things. According to the survey by Ray in 2017, there were 13 VPLs for the usage and for the upgradation of IoT [1]. The VPL is classified into two types, namely open-source and proprietary-source with the four following main features, i.e., programming atmosphere, license, project source, and platform support [1]. Students can use open-source VPL such as Node-RED to learn and implement an IoT system or device with basic engineering skills and low-code programming. It helped many programmers to develop new software [2, 3]. Node-RED was initiated and was developed by IBM for connecting hardware devices with web-based editors [2, 3]. It was then applied further in the field of IoT. Rajalakshmi and Shahnasser came with a problem while making a cloud-system for IoT devices [4]. This is difficult to update the firmware by reinstalling the devices. To solve this issue, Node-RED was used without reinstalling the device and changing the programming code with quick setup [4]. In 2018, a model based on LoPy, which is a MicroPython triple-network development platform doubling up as a long-range (LoRa) Nano gateway, was connected to a system that uses Node-RED for interfacing with a local actuator and the external data with protocols [5, 6]. Their IoT development could be done in fog/clouds. The introduction of the IoT in education allows Internet-based communications to occur between things, sensors, and actuators. This has improved educational institutions [7, 8, 9, 10, 11, 12, 13, 14]. In 2015, Giang et al. implemented a distributed Node-RED (D-NR) framework for building various types of IoT applications, which can work efficiently with simple designing process and less time [15]. Abdel-Basset et al. could make an efficient framework by new ideas with lower cost and greater security [16]. The advantages in maximizing IoT became more for the institutions, i.e., bringing out an affluent knowledge, better quality of working efficiency, and gaining real-time experiences [17]. In addition, they could keep track record of all the university resources with secured data accessibility. Using an open-source platform such as Node-RED, it helps for the creation of new ideas, i.e., linking up with many other specialized courses, for instance, information and communication technologies (ICTs), embedded system design, humanities, agricultural, just to name a few, yielding a good IoT program of study for students to learn and make research [16]. Another model was proposed by Marquez et al. as an IoT educational platform for virtual academic communities [17]. In 2020, Torres et al. used VPL with Node-RED to improve their IoT system by reducing the time taken for the development, i.e., reducing the number of failed attempts while deploying an IoT system [18]. Home automation consisting of water heater, cooling systems, electrical outlets was raised all over the IoT using cloud-platform where Node-RED is used to make fast setup for remote monitoring and control of data with a mutual communication [19]. In 2021, David et al. worked on indoor crop agriculture by monitoring parameters such as humidity, temperature, and light intensity with the help of IBM-Bluemix, which is the IBM open cloud platform providing mobile and web developers access to IBM software for integration security transaction and other key functions [20]. The data was transformed into Node-RED platform through a mobile application for tracking purpose of the farmers. Thuluva et al. made a solution for interoperability problem for IoT semantic web technologies in industrial field using semantic Node-RED models with feasibility and scalability approach [21]. A rapid and low-cost IoT prototype was developed by Ferencz and Domokos within less time on Node-RED using the combination of various cycle power plant dataset [22]. A low-power wide area network (LPWAN) technology called LoRa with its medium access control (MAC) layer protocol called LoRaWAN was deployed by Fox et al. in 2019, and these end devices interact with a gateway using Node-RED connection to an IBM-IoT platform [23]. Node-RED was applied and analyzed by Olsson and Eric in terms of modeling and security with misuse of API and providing security guarantees [24]. Clerissi et al. made a model for testing and developing IoT platforms using Node-RED with the functional behavior and the static view of the system. The class diagrams were used by testing, defining, and generating in java script using a Mocha test framework [25]. Proper regulations and rules for all the Node-RED developers were given by Clerissi et al. [26]. They are about the comprehensibility issues, which can be used to increase efficiency by reducing errors and time to complete tasks.
VPL is any programming language that lets users create program by manipulating program elements graphically rather than by specifying them textually [27, 28]. A VPL allows programming with visual expressions, spatial arrangements of text and graphic symbols used either as elements of syntax or secondary notation. For instance, many VPLs known as dataflow or diagrammatic programming are based on the idea of “boxes and arrows,” where boxes or other screen objects are treated as entities, connected by arrows, lines, or arcs representing relations [29, 30, 31]. There are several different VPLs, which can be divided into different fields of applications, such as education (23 languages), multimedia (26 languages), video games (18 languages), systems (34 languages), automation (4 languages), data warehousing (8 languages), legacy (5 languages), and miscellaneous (10 languages) [29, 30, 31]. The difference between a regular programming and visual programming, in terms of type, nature, flexibility, speed, efficiency, interface, learning complication, space usage, and example, is shown in Table 1.
No | Context | Regular programming | Visual programming |
---|---|---|---|
1 | Type | Use only text | Use only graphics |
2 | Nature | Not user-friendly | User-friendly |
3 | Customizable | Very high | Moderate |
4 | Flexibility | High | Low |
5 | Speed | Very high | Low |
6 | Efficiency | Very high | Moderate |
7 | Interface | Not good | Great |
8 | Learning Complication | Take time | Easy to learn |
9 | Space Usage | Less | High |
10 | Example | Python, Java, etc. | Drakon, Helix, etc. |
Difference between regular programming and visual programming.
Node-RED is preinstalled in many devices, such as Raspberry Pi, Intel, Fujitsu, just to name a few. Moreover, there are several different cloud services including Cisco, Nokia, IBM, Hitachi, etc., using Node-RED as a VPL and a dataflow programming.
This section describes the complete scenario of methodology process. As can be seen in Figure 1, the first stage is installation, which involves the three following types, i.e., local machine, Raspberry Pi, and cloud services. After the correct installation, the Node-RED is configured involving all the basic nodes required to make a workflow with deployment activities. Next, the utilization is the main stage in which the development of the flows, handling errors of all the core nodes are explained. Accordingly, when the utilization process is completed, any type of experiments can be implemented with the expected and accurate outcomes.
Block diagram representing methodology workflow.
Getting started by installing Node-RED can be done on various criteria, which are explained as follows.
Running on a local machine for Windows 7 and above
Windows 7 and above versions have the capability of installing Node.js, Node-RED, and node package manager (npm) using command prompt (cmd) or Powershell. The procedure is described as follows.
Node.js (version 14.x long term support (LTS) or above) is installed.
All the local administrators are given their rights.
Cmd or Powershell is opened and the following code is run to check if it is installed correctly or not installed.
“node –version; npm –version” (Powershell),
“node –version && npm –version” (cmd)
Node-RED is installed by the command “npm install -g –unsafe-perm node-red.”
As soon as installed, the cmd and type “node-red” is opened. As an output, a terminal log of Node-RED is displayed on the screen.
Running on a Raspberry Pi or any other IoT module
Buster is the supported version for Node-RED at present for all the Raspberry Pi Operating Systems (OS).
The subsequent command is used to install node.js, npm and node-red on any Raspberry Pi.
“bash<(curl-sLhttps://raw.githubusercontent.com/node-red/linuxinstallers/ master/deb/update-nodejs-and-nodered)”.
(This command is used on any Debian-based OS like Ubuntu, DietPI.)
“bash<(curl-sLhttps://raw.githubusercontent.com/node-red/linux-installers/master/rpm/update-nodejs-and-nodered)”.
(This command is used on any Red-Hat packet management (RPM) based OS like Red Hat, Fedora, CentOS, Oracle Linux.)
Running on different cloud services (IBM cloud, Amazon web services, Microsoft Azure)
Node-RED is used on different cloud-based services with many features depending on the user or the client requirements.
For International Business Marketing (IBM) cloud: It is highly virtualized with high power, storage, networking, security, data management, analytics, developer tools, IoT, and integration and migration of virtual servers.
For Microsoft Azure: It is also having the same abilities like IBM cloud with some extra added features such as replacing as an supplement for many other on-premise virtual servers with best recovery support.
For Amazon web services (AWS): It has various new features when compared with many other cloud-services for developing new innovative smart devices, where it includes all the required API with less cost for the third-party usage.
This section describes about various types of configurations that are needed for the deployment of each different application with default parameters in the default file directory.
Normal application: Entire structure is loaded by default settings file, which is a built-in source.
Embedded application: It is passed into property called RED.init(), where embedded.
Run-time configuration: It defines the time-value of each node during the deployment.
Logging configuration: Only console logging is supported in Node-RED.
Node configuration: It is in the hierarchical format used for the application deployment.
External module configuration: It defines about the run-time handling external npm modules and decides whether the editor allows new node modules to be installed such as the function node to have their own dynamic configured dependencies.
Editor configuration: It is a set of files with different coding styles with various text editor plugins.
adminAuth: It permits security for user in the editor and admin API.
paletteCategories: It describes the sequence of types in the palette. By default, the pattern is subflow, common, function, network, sequence, parser, storage.
The run-time configuration is explained as follows.
Flow file: It is used to store the flows.
Userdir: It is used to store user data, credentials, and library data.
Nodesdir: It is used to search additional installed nodes.
Uihost: It is an interface to listen all connection on IPv4.
Uiport: It is a port to serve ui editor.
Httpadminroot: It is the root url, which contains both API and editor UI.
httpAdminAuth: It allows HTTP validation on the editor UI.
httpAdminMiddleware: It is an array of all functions, which is added to all admin routes.
httpNodeRoot: It is the node root for all the urls that run HTTP at all endpoints.
httpNodeAuth: It enables HTTP Basic Authentication
httpRoot: It enables the root url to run on both admin and node endpoints through overriding httpAdminRoot and httpNodeRoot value.
https: It permits https.
httpStaticAuth: It supports basic confirmation and validation of HTTP with the static content.
httpNodeCors: It is source distribution for the nodes, which are responsible for HTTP endpoints with cross-origin authentication.
httpNodeMiddleware: It permits custom processing. For example, validation is required for the node.
Various stages of logging configuration used in Node-RED are described as follows.
Fatal: Errors that make application unworkable are tracked.
Error: Tracked errors for requests and fatal errors.
Warn: Record of the problems about non-fatal and fatal errors.
Info: Tracked information of application, warnings, error, and fatal errors.
Debugging (Debug): Tracked information, which is more verbose than information, warnings, error, and fatal errors.
Tracing (Trace): Tracks about all complete logging, debugging, info, warnings, error, and fatal errors.
The node configuration is explained as follows.
Function Node: It is for gathering bits and pieces to attach into universal functions.
functionExternalModules: It allows adding additional modules that are available to the function.
Debug Node: Any message directed to the debug sidebar tab with maximum size and characters.
MQTT Nodes: If the link is misplaced, how much time to pause in milliseconds before trying to connect.
Serial Nodes: How much time to pause in milliseconds before making an effort to revive into serial port
socketReconnectTime: How much time to pause in milliseconds before trying to connect again.
socketTimeout: How much time to pause in milliseconds before scheduling out any port.
The important nodes used for the basic functioning of the Node-RED are called “core nodes.” The main six types of core nodes are explained as follows.
Inject: It starts any flow manually by clicking the inject button. It can be at required intervals or any time within the editor.
Debug: It is used to show messages in the Debug sidebar in the editor, and the control on the node can be used to permit or restrict its outcome.
Function: It permits JavaScript code to run beside the messages delivered from it.
Change: It is used to transform message properties and fix context properties without changing a Function node with multiple operations such as set, change, move, delete.
Switch: It permits messages to be routed into different divisions of a flow by means of calculating set of instructions compared with each message with rules values, sequence, expression, otherwise property.
Template: It is used to produce text by message properties to fill the template.
It is the section in which all the necessary placement and arrangement of the nodes are done for the successful execution on the Node-RED.
Flow structure: It helps us organize flows, approaches for splitting into smaller, reusable components, and how to modify them to make use in different platforms.
Message design: It helps how to design messages to create nodes and flows, which can work together with any number of nodes and are easier to maintain.
Documenting flows: It helps about making or providing documentation on what tools and techniques Node-RED provides.
It is described as tracing out the bug or an error, which helps to reduce the developing time and correct the errors easily. The different types of errors on Node-RED are explained as follows.
Logging error: It displays the error with the date and time of the error and the node, which is noted as an error.
Catchable error: It will not be logged, but it informs about run-time error. Then, the Catch node will be used to produce a flow which can handle it.
Sub-flow error: It will not be logged but it informs about run-time error. Then, the Catch node will be used to produce a flow, which can handle it.
Uncatchable errors: If the error is written in the log, then a message is seen in the Debug sidebar and log outcome. But creation of a flow is not possible to handle it.
Uncaught errors: It causes the Node-RED run-time to shut down and cannot be controlled in the flow as they are produced by bugs in nodes.
Status changing errors: It is used to control modifications in node position by including the position property which provides the data about the position with the node that caused the incident.
This node-red-contrib-machine learning module for Node-RED contains a set of nodes offering machine learning functionalities. Such nodes have a python core that takes advantage of common ML libraries such as SciKit-Learn and Tenserflow. Classification and outlier detection can be performed using this package.
These flows create a dataset, train a model, and then evaluate it. Models, after training, can be used in real scenarios to make predictions.
Flows and test datasets are available in the “test” folder. We need to make sure that the paths specified inside nodes’ configurations are correct before trying to execute the program. “node-red” can be run from the folder “.node-red/node-modules/node-red-contrib-machine-learning” and the paths will be automatically correct. The flow shown in Figure 2 loads a csv file, shuffles it, and creates a training and a test partition.
Flow for loading a csv file, shuffling it, and creating a training and a test partition.
The flow shown in Figure 3 loads a training partition and trains a “decision tree classifier” and then saves the model locally.
Flow for loading a training partition and training a “decision tree classifier”.
The flow shown in Figure 4 loads a test partition and evaluates a previously trained model.
Flow for loading a test partition and evaluating a trained model.
Figure 5 shows the flow of how to use a trained model during deployment. Data is received via mqtt, predictions are made and then sent back.
Flow for showing how to use a trained model during deployment.
In this section, various types of experiments related to IoT are provided. ESP32, which is a microcontroller chip manufactured by Espressif Systems, is applied. It consists of a low-cost and a low-power chip with features such as Wi-Fi (IEEE802. 11 b/g/n), Bluetooth, and built-in antenna. The distributed hash table (DHT11) is a basic, ultralow-cost digital temperature and humidity sensor. It uses a capacitive humidity sensor and a thermistor to measure the surrounding air and spits out a digital signal (using an 8-bit microcontroller unit (MCU)) on the data pin. An Arduino integrated development environment (IDE) is used to write and upload programs to the Arduino compatible boards supporting the languages C and C++. Message queuing telemetry transport (MQTT), which is a lightweight messaging protocol, is used on such a small microcontroller that allows messaging between device to cloud and cloud to device with supporting several IoT devices. By using MQTT commands can be sent to control outputs, data can be read and published from sensors. Therefore, communications between multiple devices can be established. We can send a command with a client to control outputs, or we can read data from a sensor and publish it to a client.
The procedure of this task is described as follows.
The ESP32 is connected to the DHT11 sensor as shown in Figure 6 (GND TO GND, 3.3 V TO VCC, GPIO4 TO DATA PIN).
If the connections are correct, then a light present on the DHT11 sensor is turned on.
The DHT11 sensor is programmed into the Arduino integrated development environment (IDE) and uploaded into the ESP32 to show the corresponding temperature and humidity.
If the required packages are absent, then the Arduino IDE shows an error while uploading the code.
After the code is successfully uploaded, we can click on the serial monitor to see the corresponding temperature and humidity readings of the DHT11 sensor.
The Node-RED software is installed into the system. Then, the local hosting address of the Node-RED is open. This can be run on any browsers.
The required nodes, i.e., dashboard and serial port, are installed.
A flow is created as shown in Figure 7.
A serial is inserted into the port node, one function node for temperature and another function node for humidity, which display the DHT11 sensor data.
To get the representation, two-gauge nodes for temperature and humidity are inserted and connected to their respective function nodes.
Finally, the overall workflow is deployed.
ESP32 connection with DHT11 temperature sensor (serial port).
Node-RED serial port connection for temperature and humidity.
The procedure of this task is described as follows.
The ESP32 is connected to the DHT11 sensor as shown in Figure 8 (GND TO GND, 3.3 V TO VCC, GPIO4 TO DATA PIN).
If the connections are correct, then a light present on the DHT11 sensor will be turned on.
The DHT11 sensor is programmed into the Arduino (IDE) and uploaded into the ESP32 to show the temperature and humidity.
If the required packages are absent, then the Arduino IDE shows an error while uploading the code.
After the code is successfully uploaded, click on the serial monitor to see the temperature and humidity readings of the DHT11 sensor.
The Node-RED software is installed into the system. Then, the local hosting address of the Node-RED is open. This can be run on any browsers.
The required nodes, i.e., dashboard, MQTT-in, and MQTT-out nodes, are installed.
A flow is created as shown in Figure 9.
Two MQTT-in nodes, one for temperature and another for humidity, are inserted.
This extracts the sensor data from the ESP32 through the MQTT broker service and gets into the Node-RED platform with the help of the local-host ip address.
To display this data, the one gauge is connected to the temperature node and the other is connected to the humidity node.
Finally, the overall workflow is deployed.
ESP32 and DHT11 temperature sensor (MQTT broker service).
MQTT broker service for temperature and humidity with node-RED.
In this example, a user interface (UI) has a major role for making an interface between the user and the Node-RED dashboard by the means of a system camera and a UI-table. Figure 10 shows the procedure of this task, which is described as follows.
The Node-RED software is installed into the system. The local hosting address of the Node-RED is opened. This can be run on any browser.
The required nodes, i.e., random, dashboard, UI-table, UI-webcam, tfjs-coco-ssd, tf-model, tfjs-node (tf = tensorflow), are installed.
We connect the nodes by inserting button node = 2 (capture and clear), change node = 4 (set msg.capture, set msg.filename, set msg.image, set msg.payload), UI-webcam node = 1, UI-table node = 1, tfjs-coco-ssd = 1, debug node = 2 (msg.payload), file node = 1.
We check whether the camera of the PC/laptop is working or not.
After deploying successfully, we go to the hosting ip address followed by /ui, which opens another new webpage showing the output of the live web camera.
Node-RED flow for live camera capture and object detection using machine learning (ML).
The object detection flow recognizes objects in an image and annotates objects with bounding boxes. An image can be loaded from a built-in camera, the file system, or by injecting the default image. We need to make sure that we have the node-red-contrib-browser-utils package installed for all these input nodes to work. This flow uses three of the custom nodes mentioned above (tf-function, tf-model, and post-object-detection). The loaded image is passed into the preprocessing node as msg.payload. The msg object is a JavaScript object that is used to carry messages between nodes. By convention, it has a payload property containing the output of the previous node. The preprocessing function node is an example of tf-function that directly calls the tf.node.decodeImage method with the predefined tf variable. The node produces a Tensor4D image representation as the payload and then passes it to the COCO SSD lite node, which is an instance of the tf-model custom node. This loads the COCO-SSD lite model.json from an external URL and runs inference on the model.
The result of the model goes through the postprocess node that returns an object array containing bbox, className, and score properties. The objects node combines an additional property, complete that is set to true, to the msg with the image object. Then, the bounding-box node draws bounding boxes on the input image and displays it in the browser.
Once after the successful deployment, we go to the hosting ip address followed by:1880/ui, which opens another new webpage representing the gauge readings of the temperature and the humidity of the DHT11 sensor. The temperature and humidity readings are directly sent from the serial port to the Node-RED directly. Figure 11 shows an example of the dashboard showing temperature and humidity from the DHT11 sensor (serial port).
Dashboard of temperature and humidity from DHT11 sensor (serial port).
Once after the successful deployment, go to the hosting ip address followed by:1880/ui, which opens another new webpage representing the gauge readings of the temperature and the humidity of the DHT11 sensor. Temperature and humidity readings are sent from the sensor to MQTT broker (as a cloud), then finally sent to the Node-RED as a third-party service. Figure 12 shows an example of the dashboard showing temperature and humidity from the DHT11 sensor (MQTT broker service).
Dashboard of temperature and humidity from DHT11 sensor (MQTT broker service).
When the deployment is successful, go to the hosting ip address followed by:1880/ui, which opens another new webpage asking for the permission to allow camera (Press allow). Then, we can see the live camera working and press the capture button to capture. It detects the object with the help of TensorFlow analysis node as earlier describe and the output can be seen on the debug panel with the accuracy score of its correctness. The examples of the single object and the dual object detection are shown in Figures 13 and 14, respectively.
Debug message showing single object class and accuracy score.
Debug message showing dual object class and accuracy score.
From the given examples, they can also be made visible on a mobile phone for both iOS and Android. It is done through remote access just by installing the application named “RemoteRED” from the mobile app store. The steps are explained as follows.
Install the Remote-RED node in the Node-Red.
Open the Remote-RED settings and configure it to get the QR code.
Open the Remote-RED application from the mobile phone and scan the QR code.
Wait for a minute as it is asynchronous to get updated on the mobile phone.
Now we can see the same output of the Node-RED on the mobile phone Remote-RED application as shown in Figure 15.
This process is the same and it works for any Node-RED workflow.
Connecting to remote-RED application (iOS/android).
The results show that simulated systems developed using the Node-RED can be deployed in the real world without changing the system parameters. Moreover, the simulated system can be connected with hardware platforms easily. VPL-based hands-on tools are effectively smoothing the beginner’s learning curve on IoT. The stranded network protocols and IoT protocols are easy to understand using hands-on experience and deploy in the Node-RED environment. Node-RED system models are easy to debug compared with the traditional programming debug methods. The platform works well in both Internet and Intranet mode as all the components are virtually presented in Node-RED. After all, the laboratory instructors can effectively convey the basics of network and IoT concepts to the students through a VPL language including the Node-RED.
The book chapter described a complete outlook of Node-RED that can be applied for an IoT based virtual laboratory. The configuration, the flow development, the requirements, and the usage of the Node-RED were explained with respect to handling all the various types of errors. We modeled, implemented, and tested the IoT virtual laboratory using Node-RED. The implemented virtual laboratory system is currently serving for flexible postgraduate programs and broadcasting completely online. Students obtain great encouragement and motivation toward virtual IoT hands-on practices as they can manage their own and convenient time and a location. The virtual laboratory concept utilizes the available hardware (Wi-Fi IEEE802. 11 b/g/n routers, classical Bluetooth hardware, and system-on-chip (SoC) like MCU) at the student location. The virtual IoT laboratory concept was proved to help students to learn faster than a classical theory class or a video-recorded lesson. With the simplicity of Node-RED and its built-in entities a few examples, which can be used for an IoT-based virtual laboratory, were done to evolve innovative platforms with less coding complexity. It provides flexibility such that the remote laboratory can run on several operating systems or on a mobile application. The proposed solution is platform-independent, and therefore, it can be implemented on low-cost hardware for smaller systems, and the clients can run on mobile devices. The first and second examples are typical scenarios in IoT on Node-RED platform including Arduino and third-party cloud service, whereas the third example is live camera capture with object detection capability. Finally, it was also shown about how Node-RED can be used as a mobile application remotely. After the completion of the book chapter, the readers are supposed to be able to develop IoT systems using VPL-Node-RED, integrate IoT with Node-RED, to design various workflows for IoT on Node-RED, develop real-time IoT applications, and apply security features for IoT and Node-RED while using cloud-based services. Hence, IoT with Node-RED has the capability to change the entire education system, which makes better learning with good interaction and flexibility.
The variety of IoT devices in the future is expected to dramatically grow. Our Node-RED IoT platform was designed to be extendible. To model these future scenarios, we expect to support new IoT device types by using the generic framework for creating new devices. An example of such devices could be wearables that generate movement data. This type of data can be emitted at high volumes but small size. Another device type, which is expected, is an IoT device that is configurable at runtime. This would give us the ability to change the behavior of the device according to an operation plan. An example of this type of device would be a smart home thermostat. Furthermore, we also want to improve the intelligence of our smart testing framework providing machine learning libraries that can continuously learn from past data to improve prediction accuracy. Finally, empirical research can be done collecting a large amount of data in order to know the impact of virtual IoT laboratories on the students with their involvement as it helps the instructors to improve their laboratory sessions further.
The authors declare no conflict of interest.
All publications on this website are published under the Open Access model, without any subscription, registration, or access fees required from the user or his/her institution. In accordance with the Budapest Open Access Initiative's (BOAI) definition of Open Access, users are allowed to read, download, copy, distribute, print, search, and link to the full text versions of all Chapters. To read more about our Open Access Statement click here.
\n\nFor Editorial Policies for journals please consult individual journal pages.
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\\n\\n\\n\\nThe Internet has changed the dynamics of scholarly communication and publishing which is why we find it necessary to clearly indicate our stance on what we consider to be a published scientific work. A significant number of working papers, early drafts, and similar works in progress are shared openly online between members of the scientific community. It has become common practice for researchers to announce their work on a personal website or a blog in order to gather comments and suggestions from other researchers. Such works and online postings are ‘published’ in the sense that they are made publicly available, but this does not mean that if submitted for publication by IntechOpen they are not original works. We differentiate between reviewed and non-reviewed works when determining whether a work is original and has been published in a scholarly sense or not.
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All published Book Chapters are licensed under a Creative Commons Attribution 3.0 Unported License. Monographs are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license granted to all others. Our Copyright Policy aims to guarantee that original material is published while at the same time giving significant freedom to our Authors. IntechOpen upholds a flexible Copyright Policy meaning that there is no copyright transfer to the publisher and Authors hold exclusive copyright to their work.
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\n\nIn line with publication ethics practices recommended by COPE, ICMJE, and other similar organizations, IntechOpen's contributing Authors, Academic Editors, and Peer Reviewers are required to declare fully all possible conflicts of interest.
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\n\n\n\nTo identify instances of fraud and misconduct during the publishing process, IntechOpen implements a robust policy governing such occurrences. In line with our general commitment to openness, and in order to maintain the highest scientific standards, we are committed to transparency about our editorial policy regarding retractions and corrections.
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\n\n\n\nIn line with the Principles of Transparency and Best Practice in Scholarly Publishing, you can access a more detailed description of IntechOpen's Advertising Policy.
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\n\nYou are invited to download, use, reproduce, make derivative works of, display, distribute and cite the Online First works. You can find "How to Cite and Reference" by following the link at the end of each online book chapter. Please be aware that it is possible that further editing and changes might be made before the final release of the book.
\n\nIf there are supplemental materials to the chapter, these will be published at the time the final book is published online.
\n\nReaders and Authors can notify us if they find any errors in the works published under Online First. All major errors will be accompanied by a separate correction notice, erratum or corrigendum (Retraction and Correction Policy.)
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The chapter describes the design, implementation and integration of a ground penetrating radar (GPR) using a software defined radio (SDR) platform into the aerial drone. The chapter?s goal is first to tackle in detail the development of a custom-designed lightweight GPR by approaching interplay between hardware and software radio on an SDR platform. The SDR-based GPR system results on a much lighter sensing device compared against the conventional GPR systems found in the literature and with the capability of re-configuration in real-time for different landmines and terrains, with the capability of detecting landmines under terrains with different dielectric characteristics. Secondly, the chapter introduce the integration of the SDR-based GPR into an autonomous drone by describing the mechanical integration, communication system, the graphical user interface (GUI) together with the landmine detection and geo-mapping. This chapter approach completely the hardware and software implementation topics of the on-board GPR system given first a comprehensive background of the software-defined radar technology and second presenting the main features of the Tx and Rx modules. Additional details are presented related with the mechanical and functional integration of the GPR into the UAV system.",book:{id:"5905",slug:"robots-operating-in-hazardous-environments",title:"Robots Operating in Hazardous Environments",fullTitle:"Robots Operating in Hazardous Environments"},signatures:"Manuel Ricardo Pérez Cerquera, Julian David Colorado Montaño\nand Iván Mondragón",authors:[{id:"177422",title:"Dr.",name:"Julian",middleName:null,surname:"Colorado",slug:"julian-colorado",fullName:"Julian Colorado"},{id:"197884",title:"Prof.",name:"Ivan",middleName:null,surname:"Mondragon",slug:"ivan-mondragon",fullName:"Ivan Mondragon"},{id:"199958",title:"Prof.",name:"Manuel",middleName:null,surname:"Perez",slug:"manuel-perez",fullName:"Manuel Perez"}]},{id:"15855",title:"Kinematics of AdeptThree Robot Arm",slug:"kinematics-of-adeptthree-robot-arm",totalDownloads:14625,totalCrossrefCites:1,totalDimensionsCites:2,abstract:null,book:{id:"152",slug:"robot-arms",title:"Robot Arms",fullTitle:"Robot Arms"},signatures:"Adelhard Beni Rehiara",authors:[{id:"29287",title:"Dr.",name:"Adelhard",middleName:"Beni",surname:"Rehiara",slug:"adelhard-rehiara",fullName:"Adelhard Rehiara"}]},{id:"62978",title:"Intelligent Robotic Perception Systems",slug:"intelligent-robotic-perception-systems",totalDownloads:2395,totalCrossrefCites:5,totalDimensionsCites:11,abstract:"Robotic perception is related to many applications in robotics where sensory data and artificial intelligence/machine learning (AI/ML) techniques are involved. Examples of such applications are object detection, environment representation, scene understanding, human/pedestrian detection, activity recognition, semantic place classification, object modeling, among others. Robotic perception, in the scope of this chapter, encompasses the ML algorithms and techniques that empower robots to learn from sensory data and, based on learned models, to react and take decisions accordingly. The recent developments in machine learning, namely deep-learning approaches, are evident and, consequently, robotic perception systems are evolving in a way that new applications and tasks are becoming a reality. Recent advances in human-robot interaction, complex robotic tasks, intelligent reasoning, and decision-making are, at some extent, the results of the notorious evolution and success of ML algorithms. This chapter will cover recent and emerging topics and use-cases related to intelligent perception systems in robotics.",book:{id:"7227",slug:"applications-of-mobile-robots",title:"Applications of Mobile Robots",fullTitle:"Applications of Mobile Robots"},signatures:"Cristiano Premebida, Rares Ambrus and Zoltan-Csaba Marton",authors:[{id:"203409",title:"Ph.D.",name:"Cristiano",middleName:null,surname:"Premebida",slug:"cristiano-premebida",fullName:"Cristiano Premebida"},{id:"254880",title:"Dr.",name:"Rares",middleName:null,surname:"Ambrus",slug:"rares-ambrus",fullName:"Rares Ambrus"},{id:"254881",title:"Dr.",name:"Zoltan-Csaba",middleName:null,surname:"Marton",slug:"zoltan-csaba-marton",fullName:"Zoltan-Csaba Marton"}]},{id:"67705",title:"Advanced UAVs Nonlinear Control Systems and Applications",slug:"advanced-uavs-nonlinear-control-systems-and-applications",totalDownloads:1943,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Recent development of different control systems for UAVs has caught the attention of academic and industry, due to the wide range of their applications such as in surveillance, delivery, work assistant, and photography. In addition, arms, grippers, or tethers could be installed to UAVs so that they can assist in constructing, transporting, and carrying payloads. In this book chapter, the control laws of the attitude and position of a quadcopter UAV have been derived basically utilizing three methods including backstepping, sliding mode control, and feedback linearization incorporated with LQI optimal controller. The main contribution of this book chapter would be concluded in the strategy of deriving the control laws of the translational positions of a quadcopter UAV. The control laws for trajectory tracking using the proposed strategies have been validated by simulation using MATLAB®/Simulink and experimental results obtained from a quadcopter test bench. Simulation results show a comparison between the performances of each of the proposed techniques depending on the nonlinear model of the quadcopter system under investigation; the trajectory tracking has been achieved properly for different types of trajectories, i.e., spiral trajectory, in the presence of unknown disturbances. Moreover, the practical results coincided with the results of the simulation results.",book:{id:"7792",slug:"unmanned-robotic-systems-and-applications",title:"Unmanned Robotic Systems and Applications",fullTitle:"Unmanned Robotic Systems and Applications"},signatures:"Abdulkader Joukhadar, Mohammad Alchehabi and Adnan Jejeh",authors:null}],onlineFirstChaptersFilter:{topicId:"22",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82223",title:"Biomechanical Design Principles Underpinning Anthropomorphic Manipulators",slug:"biomechanical-design-principles-underpinning-anthropomorphic-manipulators",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.105434",abstract:"The biomechanical design of an artificial anthropomorphic manipulator is the focus of many researchers in diverse fields. Current electromechanical artificial hands are either in the research stage, expensive, have patents, lack severely in function, and/or are driven by robotic/mechanical principles, which tend to ignore the biological requirements of such designs. In response to the challenges addressed above this chapter discusses the potential of current technology and methods used in design to bridge the chasm that exists between robot manipulators and the human hand. This chapter elucidates artificial anthropomorphic manipulator design by outlining biomechanical concepts that contribute to the function, esthetics and performance of artificial manipulators. This chapter addresses joint stabilization, tendon structures and tendon excursion in artificial anthropomorphic manipulators.",book:{id:"11455",title:"Recent Advances in Robot Manipulators",coverURL:"https://cdn.intechopen.com/books/images_new/11455.jpg"},signatures:"Mahonri William Owen and Chikit Au"},{id:"82056",title:"Learning Robotic Ultrasound Skills from Human Demonstrations",slug:"learning-robotic-ultrasound-skills-from-human-demonstrations",totalDownloads:12,totalDimensionsCites:0,doi:"10.5772/intechopen.105069",abstract:"Robotic ultrasound system plays a vital role in assisting or even replacing sonographers in some cases. However, modeling and learning ultrasound skills from professional sonographers are still challenging tasks that hinder the development of ultrasound systems’ autonomy. To solve these problems, we propose a learning-based framework to acquire ultrasound scanning skills from human demonstrations1. First, ultrasound scanning skills are encapsulated into a high-dimensional multi-modal model, which takes ultrasound images, probe pose, and contact force into account. The model’s parameters can be learned from clinical ultrasound data demonstrated by professional sonographers. Second, the target function of autonomous ultrasound examinations is proposed, which can be solved roughly by the sampling-based strategy. The sonographers’ ultrasound skills can be represented by approximating the limit of the target function. Finally, the robustness of the proposed framework is validated with the experiments on ground-true data from sonographers.",book:{id:"10823",title:"Cognitive Robotics",coverURL:"https://cdn.intechopen.com/books/images_new/10823.jpg"},signatures:"Miao Li and Xutian Deng"},{id:"82057",title:"An Episodic-Procedural Semantic Memory Model for Continuous Topological Sensorimotor Map Building",slug:"an-episodic-procedural-semantic-memory-model-for-continuous-topological-sensorimotor-map-building",totalDownloads:5,totalDimensionsCites:0,doi:"10.5772/intechopen.104818",abstract:"For humans to understand the world around them, learning and memory are two cognitive processes of the human brain that are deeply connected. Memory allows information to retain and forms an experiences reservoir. Computational models replicating those memory attributes can lead to the practical use of robots in everyday human living environments. However, constantly acquiring environmental information in real-world, dynamic environments has remained a challenge for many years. This article proposes an episodic-procedure semantic memory model to continuously generate topological sensorimotor maps for robot navigation. The proposed model consists of two memory networks: i) episodic-procedural memory network (EPMN) and ii) semantic memory network (SMN). The EPMN comprises an Incremental Recurrent Kernel Machines (I-RKM) that clusters incoming input vectors as nodes and learns the activation patterns of the nodes for spatiotemporal encoding. The SMN then takes neuronal activity trajectories from the EPMN and task-relevant signals to update the SMN and produce more compact representations of episodic experience. Thus, both memory networks prevent catastrophic forgetting by constantly generating nodes when the network meets new inputs or updating node weights when the incoming input is similar to previously learned knowledge. In addition, idle or outlier nodes will be removed to preserve memory space.",book:{id:"10823",title:"Cognitive Robotics",coverURL:"https://cdn.intechopen.com/books/images_new/10823.jpg"},signatures:"Wei Hong Chin, Naoyuki Kubota and Chu Kiong Loo"},{id:"81922",title:"Skill Acquisition for Resource-Constrained Mobile Robots through Continuous Exploration",slug:"skill-acquisition-for-resource-constrained-mobile-robots-through-continuous-exploration",totalDownloads:14,totalDimensionsCites:0,doi:"10.5772/intechopen.104996",abstract:"We present a cognitive mobile robot that acquires knowledge, and autonomously learns higher-level abstract capabilities based on play instincts, inspired by human behavior. To this end, we (i) model skills, (ii) model the robot’s sensor and actuator space based on elementary physical properties, and (iii) propose algorithms inspired by humans’ play instincts that allow the robot to autonomously learn the skills based on its sensor and actuator capabilities. We model general knowledge in the form of competencies (skills) of the mobile robot based on kinematic properties using physical quantities. Thus, by design, our approach has the potential to cover very generic application domains. To connect desired skills to the primitive capabilities of the robot’s sensors and actuators, it playfully explores the effects of its actions on its sensory input, thus autonomously learning relations and dependencies and eventually the desired skill. KnowRob is used for knowledge representation and reasoning, and the robot’s operation is based on ROS. In the experiments, we use a millirobot, sized 2 cm2, equipped with two wheels, motion, and distance sensors. We show that our cognitive mobile robot can successfully and autonomously learn elementary motion skills based on a playful exploration of its wheels and sensors.",book:{id:"10823",title:"Cognitive Robotics",coverURL:"https://cdn.intechopen.com/books/images_new/10823.jpg"},signatures:"Markus D. Kobelrausch and Axel Jantsch"},{id:"81693",title:"The Neo-Mechanistic Model of Human Cognitive Computation and Its Major Challenges",slug:"the-neo-mechanistic-model-of-human-cognitive-computation-and-its-major-challenges",totalDownloads:9,totalDimensionsCites:0,doi:"10.5772/intechopen.104995",abstract:"The neo-mechanistic theory of human cognition is currently one of the most accepted major theories in fields, such as cognitive science and cognitive neuroscience. This proposal offers an account of human cognitive computation, and it has been considered by its proponents as revolutionary and capable of integrating research concerning human cognition with new evidence provided by fields of biology and neuroscience. However, some complex cognitive capacities still present a challenge for explanations constructed by using this theoretical structure. In this chapter, I make a presentation of some of the central tenets of this framework and show in what dimensions it helps our understanding of human cognition concerning aspects of capacities, such as visual perception and memory consolidation. My central goal, however, is to show that to understand and explain some particular human cognitive capacities, such as self-consciousness and some conscious informal reasoning and decision making, the framework shows substantial limitations. I conclude the chapter by suggesting that to fully understand human cognition we will need much more than what the neo-mechanistic framework is actually able to provide.",book:{id:"10823",title:"Cognitive Robotics",coverURL:"https://cdn.intechopen.com/books/images_new/10823.jpg"},signatures:"Diego Azevedo Leite"},{id:"81719",title:"Service Robots in Healthcare Settings",slug:"service-robots-in-healthcare-settings",totalDownloads:10,totalDimensionsCites:0,doi:"10.5772/intechopen.104640",abstract:"Robots will play a part in all aspects of healthcare. The presence of service robots in healthcare demands special attention, whether it is in the automation of menial labour, prescription distribution, or offering comfort. In this chapter, we examine the several applications of healthcare-oriented robots in the acute, ambulatory and at-home settings. We discuss the role of robotics in reducing environmental dangers, as well as at the patient’s bedside and in the operating room, in the acute setting. We examine how robotics can protect and scale up healthcare services in the ambulatory setting. Finally, in the at-home scenario, we look at how robots can be employed for both rural/remote healthcare delivery and home-based care. In addition to assessing the current state of robotics at the interface of healthcare delivery, we describe critical problems for the future where such technology will be ubiquitous. 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He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"2",type:"subseries",title:"Prosthodontics and Implant Dentistry",keywords:"Osseointegration, Hard tissue, Peri-implant soft tissue, Restorative materials, Prosthesis design, Prosthesis, Patient satisfaction, Rehabilitation",scope:"