Thanks to the recent rapid technological advancement in IoT usage, there is a need for students to learn IoT-based concepts using a dedicated experimental platform. Furthermore, being forced into remote learning due to the ongoing COVID-19 pandemic, there is an urgent need for innovative learning methods. From our perspective, a learning platform should be reconfigurable to accommodate multiple applications and remotely accessible at any time, from anywhere, and on any connected device. Considering that many of the university courses are now held online, the reliability and scalability of the system become critical. This paper presents the design and development of a wireless configurable myRIO-based sensor node that connects to SystemLink Cloud. The sensors that were used are for ambient light, temperature, and proximity. A graphical programming environment (G-LabVIEW) and related APIs were used for rapid concept-to-development process. Distinct applications have been developed for the instructor and students, respectively. The students can select which sensor and application to run on the system and observe the measurements on the local student’s application or the cloud platform at a specific moment. They can also read the data on the cloud platform and use them in their LabVIEW application. In the context of remote education, we strongly believe that this platform is and will be suitable for the COVID and Post-COVID eras as well because it creates a much better remote laboratory experience for students. In conclusion, the system that was developed is innovative because it is software reconfigurable from the device, from the instructor’s application and cloud via a web browser, it is intuitive, and it has a user-friendly interface. It meets most of the necessary requirements in the current era, being also highly available and scalable in the cloud.

Music is important in everyday life, and music therapy can help treat a variety of health issues. Music listening is a technique used by music therapists in various clinical treatments. As a result, music therapists must have an intelligent system at their disposal to assist and support them in selecting the most appropriate music for each patient. Previous research has not thoroughly addressed the relationship between music features and their effects on patients. The current paper focuses on identifying and predicting whether music has therapeutic benefits. A machine learning model is developed, using a multi-class neural network to classify emotions into four categories and then predict the output. The neural network developed has three layers: (i) an input layer with multiple features; (ii) a deep connected hidden layer; (iii) an output layer. K-Fold Cross Validation was used to assess the estimator. The experiment aims to create a machine-learning model that can predict whether a specific song has therapeutic effects on a specific person. The model considers a person’s musical and emotional characteristics but is also trained to consider solfeggio frequencies. During the training phase, a subset of the Million Dataset is used. The user selects their favorite type of music and their current mood to allow the model to make a prediction. If the selected song is inappropriate, the application, using Machine Learning, recommends another type of music that may be useful for that specific user. An ongoing study is underway to validate the Machine Learning model. The developed system has been tested on many individuals. Because it achieved very good performance indicators, the proposed solution can be used by music therapists or even patients to select the appropriate song for their treatment.

This article describes the design and development of the NI myRIO device-based remote laboratory. The cloud-based, expandable, and reconfigurable remote laboratory is intended to give students access to an online web-based user interface to perform experiments. Multiple myRIO devices are programmed to host several experiments each. A finite state machine is used to select specific experiments, while a single state can contain several. The laboratory web virtual instruments interfaces are hosted on the SystemLink cloud and SystemLink server. A user-friendly interface has been designed to help students to understand important electronic concepts. Virtual and real experiments were fused to give students a wide range of experiments they can access online. The instructor can check outputs of an experiment being executed on the device. Achieving connection between myRIO and SystemLink through global variables and SystemLink ensured that the low-cost device was utilized, and this is suitable for third-world countries’ universities that cannot afford expensive equipment. Students can perform the experiments which have some resemblance to physical execution. The system is expandable in that the number of myRIO devices or number of experiments can be increased to suit changing requirements. The reconfigurability of the system is such that the finite state machine-based coding technique permits only one experiment to be selected, configure the system, and run while other experiments are idle.

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The continual research of electronic embedded system platforms for teaching and learning is of paramount importance. This is to increase the impact on a universities’ ability to lead in technological advancement with goals to enhance innovation and accelerate the concept-to-deployment process. Technological progress in the fields of electronics, wireless communication, cognitive computing, and robotics has caused almost everything which connects to electricity to have a small processor and sensor embedded with itself [ 1 ]. Cognitive or Intelligent embedded systems are the ‘core’ of trends such as: reduced energy consumption, deep learning applications, improved security for embedded devices, cloud connectivity and mesh networking, and visualization tools with real time data. This paper is aimed at stimulating design and innovation in electronics education through the rapid prototyping of configurable embedded systems. It also covers remote access functionality is also shown using cloud services. Divided into two parts, this paper gives design examples for both an elevator controller and a Data Acquisition (DAQ) system design. Focus was on the Programmable System on Chip (PSoC) 6 based kit, PSoC Creator software, and Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW) as rapid prototyping and learning platforms for digital and analog system designs. The Universal Digital Block (UDB) editor in PSoC Creator software was used to configure PSoC chip digital blocks and design a Finite State Machine (FSM) based elevator controller, which acquired digital signals and gave corresponding output. LabVIEW was used as a signal analysis tool and was also used to send results for online access and display. The PSoC and LabVIEW ecosystems are utilized here to bring an innovative paradigm into the embedded system design. These two platforms, especially combined with the virtual instrumentation concept, offer configurability and monetary value. This paper shows that the use of these ecosystems for the purpose of electronics education can accelerate learning and bridge the gaps within the online environments that students and universities find increasingly necessary.