Implementation of microcontroller board on a sustainable and degradable PLA/flax composite substrate: a case study.

In this paper, we present a novel polylactic-acid/flax-composite substrate and the implementation of a demonstrator: a microcontroller board based on commercial design. The substrate is developed for printed circuit board (PCB) applications. The pre-preg is biodegradable, reinforced, and flame-retarded. The novel material was developed to counter the increasing amount of e-waste and to improve the sustainability of the microelectronics sector. The motivation was to present a working circuit in commercial complexity that can be implemented on a rigid substrate made of natural, bio-based materials with a structure very similar to the widely used Flame Retardant Class 4 (FR4) substrate at an early technological readiness level (2-3). The circuit design is based on the Arduino Nano open-source microcontroller board design so that the demonstration could be programmable and easy to fit into education, IoT applications, and embedded designs. During the work, the design was optimized at the level of layout. The copper-clad pre-preg was then prepared and processed with subtractive printed wiring technology and through hole plating. The traditional surface mounting methodology was applied for assembly. The resulting yield of PCB production was around 50%. Signal analysis was successful with analogue data acquisition (voltage) and low-frequency (4 kHz) tests, indistinguishable from sample FR4 boards. Eventually, the samples were subjected to highly accelerated stress test (HAST). HAST tests revealed limitations compared to traditional FR4 printed circuit materials. After six cycles, the weight loss was around 30% in the case of PLA/Flax, and as three-point bending tests showed, the possible ultimate strength (25 MPa at a flexural state) was reduced by 80%. Finally, the sustainability aspect was assessed, where we found that ∼95 vol% and ∼90 wt% of the traditional substrate can be substituted, significantly easing the load of waste on the environment.

The utilization of open-source microcontroller boards and single-board computers in liquid chromatography.

Electronic system control of analytical instrumentation remains a critical aspect of modern measurement science. Within the field of liquid chromatography (LC), this is especially relevant for automation, module operation, detection, data acquisition, and data analysis. Increasingly, home-built analytical tools used for liquid-phase separations rely upon open-source microcontrollers and single-board computers to aid in simplifying these operations. In this review, we detail literature reported within the past 5 years in which these types of devices were used to advance various aspects of the LC research field, including sample preparation, instrument control, and data collection.

Introducing PLEMS: the application of a low-cost, portable monitoring system in environmental walks.

The application of innovative systems using low-cost microcontrollers in human biometeorology studies is a promising alternative to conventional monitoring devices, which are usually cost-intensive and provide measurements at specific points, as in stationary meteorological stations. A Portable Low-cost Environmental Monitoring System (PLEMS) aimed at the pedestrian scale is introduced. The backpack-type equipment consists of a microcontroller with attached sensors that assess environmental conditions in a broad sense, integrating measurements of air quality, lighting and noise levels alongside variables typically measured at meteorological stations. The application of the system took place in altogether 12 environmental walks carried out with questionnaire-surveys with concurrent environmental monitoring with the PLEMS in Curitiba, Brazil, a subtropical location characterized by a Cfb climate type. Results allowed us to test the equipment and method of data gathering within a limited period (approximately 50 min) and for a short walking circuit of 800 m. The equipment was successfully able to capture even slightest differences in environmental conditions among points of interest, whereas subjective responses (n= 3843 responses to a total of 11 questions) showed consistency with measured data. From a multi-domain perspective, relevant insights could be obtained for the measured variables.

Implementation of a Universal Framework Using Design Patterns for Application Development on Microcontrollers.

This article focuses on the area of software development for microcontrollers and details the implementation of modern programming practices and principles in embedded systems and IoT applications. This article explains how we implemented previously unimplemented principles and applied design patterns for quality software design on microcontrollers, which are currently only used for developing applications on the higher layers of the IoT reference model. A custom modular framework for microcontrollers is presented, based on applying SOLID principles and adapting design patterns specific to the microcontrollers' application development needs. The implemented framework enables independent communication between modules and flexible integration of hardware components. It is designed with platform independence in mind, contributing to its wide adaptability and ease of use in diverse development environments. By applying these technological approaches, we can create applications that are not only testable and extensible in terms of application logic but also allow for easy adaptation to changes in these hardware resources. Utilizing these capabilities represents an innovative approach to development for microcontrollers that fundamentally improves the long-term sustainability and scalability of applications.

Cosmo ArduSiPM: An All-in-One Scintillation-Based Particle Detector for Earth and Space Application.

Thanks to advancements in silicon photomultiplier sensors (SiPMs) and system-on-chip (SoC) technology, our INFN Roma1 group developed ArduSiPM in 2012, the first all-in-one scintillator particle detector in the literature. It used a custom Arduino Due shield to process fast signals, utilizing the Microchip Sam3X8E SoC's internal peripherals to control and acquire SiPM signals. The availability of radiation-tolerant SoCs, combined with the goal of reducing system space and weight, led to the development of an innovative second-generation board, a better-performing device called Cosmo ArduSiPM, suitable for space missions. The architecture of the new detector is based on the Microchip SAMV71 300 MHz, 32-bit ARM® Cortex®-M7 (Microchip Technology Inc., Chandler, AZ, USA). While the analog front-end is essentially identical to the ArduSiPM, it utilizes components with the smallest possible package. The board fits in a CubeSat module. Thanks to the compact design, the board has two independent channels, with a total weight of only 40 grams within a CubeSat form factor. The ArduSiPM architecture is based on a single microcontroller and fast discrete analog electronics. It benefits from the continued development of SoCs related to the IoT (Internet of Things) market. Compared with a system with a custom ASIC, this architecture based on software and SoC capabilities offers considerable advantages in terms of cost and development time. The ability to incorporate new commercial SoCs, continuously emerging from advancements in the aerospace and automotive industries, provides the system with a robust foundation for sustained growth over the years. A detailed characterization of the hardware and the system's response to different photon fluxes is presented in this article. Additionally, coupling the device with a scintillator was tested at the end of this article as a preliminary trial for future measurements, showing potential for further enhancement of the detector's capabilities.

Developing and Testing High-Performance SHM Sensors Mounting Low-Noise MEMS Accelerometers.

Recently, there has been increased interest in adopting novel sensing technologies for continuously monitoring structural systems. In this respect, micro-electrical mechanical system (MEMS) sensors are widely used in several applications, including structural health monitoring (SHM), in which accelerometric samples are acquired to perform modal analysis. Thanks to their significantly lower cost, ease of installation in the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analyses show that MEMS can correctly detect modal frequencies, which are useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated, and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring.

Microcontroller-Optimized Measurement Electronics for Coherent Control Applications of NV Centers.

Long coherence times at room temperature make the NV center a promising candidate for quantum sensors and quantum computers. The necessary coherent control of the electron spin triplet in the ground state requires microwave π pulses in the nanosecond range, obtained from the Rabi oscillation of the mS spin states of the magnetic resonances of the NV centers. Laboratory equipment has a high temporal resolution for these measurements but is expensive and, therefore, uninteresting for fields such as education. In this work, we present measurement electronics for NV centers that are optimized for microcontrollers. It is shown that the Rabi frequency is linear to the output of the digital-to-analog converter (DAC) and is used to adapt the time length π of the electron spin flip, to the limited pulse width resolution of the microcontroller. This was achieved by breaking down the most relevant functions of conventional laboratory devices and replacing them with commercially available integrated components. The result is a cost-effective handheld setup for coherent control applications of NV centers.

Different Scenarios of Autonomous Operation of an Environmental Sensor Node Using a Piezoelectric-Vibration-Based Energy Harvester.

This paper delves into the application of vibration-based energy harvesting to power environmental sensor nodes, a critical component of modern data collection systems. These sensor nodes play a crucial role in structural health monitoring, providing essential data on external conditions that can affect the health and performance of structures. We investigate the feasibility and efficiency of utilizing piezoelectric vibration energy harvesters to sustainably power environmental wireless sensor nodes on the one hand. On the other hand, we exploit different approaches to minimize the sensor node's power consumption and maximize its efficiency. The investigations consider various sensor node platforms and assess their performance under different voltage levels and broadcast frequencies. The findings reveal that optimized harvester designs enable real-time data broadcasting with short intervals, ranging from 1 to 3 s, expanding the horizons of environmental monitoring, and show that in case the system includes a battery as a backup plan, the battery's lifetime can be extended up to 9 times. This work underscores the potential of vibration energy harvesting as a viable solution for powering sensor nodes, enhancing their autonomy, and reducing maintenance costs in remote and challenging environments. It opens doors to broader applications of sustainable energy sources in environmental monitoring and data collection systems.

Response of the TEROS 12 Soil Moisture Sensor under Different Soils and Variable Electrical Conductivity.

In this work, the performance of the TEROS 12 electromagnetic sensor, which measures volumetric soil water content (θ), bulk soil electrical conductivity (σb), and temperature, is examined for a number of different soils, different θ and different levels of the electrical conductivity of the soil solution (ECW) under laboratory conditions. For the above reason, a prototype device was developed including a low-cost microcontroller and suitable adaptation circuits for the aforementioned sensor. Six characteristic porous media were examined in a θ range from air drying to saturation, while four different solutions of increasing Electrical Conductivity (ECw) from 0.28 dS/m to approximately 10 dS/m were used in four of these porous media. It was found that TEROS 12 apparent dielectric permittivity (εa) readings were lower than that of Topp's permittivity-water content relationship, especially at higher soil water content values in the coarse porous bodies. The differences are observed in sand (S), sandy loam (SL) and loam (L), at this order. The results suggested that the relationship between experimentally measured soil water content (θm) and εa0.5 was strongly linear (0.869 < R2 < 0.989), but the linearity of the relation θm-εa0.5 decreases with the increase in bulk EC (σb) of the soil. The most accurate results were provided by the multipoint calibration method (CAL), as evaluated with the root mean square error (RMSE). Also, it was found that εa degrades substantially at values of σb less than 2.5 dS/m while εa returns to near 80 at higher values. Regarding the relation εa-σb, it seems that it is strongly linear and that its slope depends on the pore water electrical conductivity (σp) and the soil type.

Application of microcontroller-based systems in human biometeorology studies: a bibliometric analysis.

Urban development creates several inadvertent impacts related to urban climate and human biometeorology. Monitoring systems based on microcontrollers are slowly emerging as an alternative to conventional devices for monitoring outdoor thermal comfort (OTC), thus overcoming limitations imposed by the high costs of commercially available equipment. This review was conducted using the Scopus database, searching for articles and conference papers according to a pre-defined search string, which included the terms "microcontrollers" and "human thermal comfort" up to 2022. From a total sample of 113 articles, 52 papers met the desired criteria (written in English, published in peer-reviewed journals, and within the given time frame). Results show a growing, yet timid trend of published material on low-cost, open-source technologies for diverse applications in human biometeorology.

Design and Implementation of an Embedded Data Acquisition System for Vehicle Vertical Dynamics Analysis.

With the expansion of electronics in recent decades, it is notorious to observe that embedded systems are increasingly necessary to improve people's quality of life and to facilitate the diagnosis of systems in general, ranging from pacemakers to control systems. The increased use of electronic components for technological support, such as telemetry systems, electronic injection, and automotive diagnostic scanners, enhances the perspective of data analysis through an embedded system aimed at vehicular systems. Thus, this work aims to design and implement an embedded data acquisition system for the analysis of vehicle vertical dynamics. The methodology for this study was structured into several stages: mathematical modeling of a motorcycle's mass-spring-damper system, coding for the Arduino microcontroller, computational data analysis supported by MATLAB software version 9.6, electronic prototyping of the embedded system, implementation on the vehicle, and the analysis of motorcycle vertical dynamics parameters. In addition, a mathematical modeling of the mass-spring-damper system was performed using the state-space method. The system was implemented on the Arduino microcontroller platform, enabling real-time data transfer from a motorcycle. The experimental results have successfully validated the proposed data acquisition system.

Design and Validation of a Low-Level Controller for Hierarchically Controlled Exoskeletons.

In this work, a generalized low-level controller is presented for sensor collection, motor input, and networking with a high-level controller. In hierarchically controlled exoskeletal systems, which utilize series elastic actuators (SEAs), the hardware for sensor collection and motor command is separated from the computationally expensive high-level controller algorithm. The low-level controller is a hardware device that must collect sensor feedback, condition and filter the measurements, send actuator inputs, and network with the high-level controller at a real-time rate. This research outlines the hardware of two printed circuit board (PCB) designs for collecting and conditioning sensor feedback from two SEA subsystems and an inertial measurement unit (IMU). The SEAs have a joint and motor encoder, motor current, and force sensor feedback that can be measured using the proposed generalized low-level controller presented in this work. In addition, the high and low-level networking approach is discussed in detail, with a full breakdown of the data storage within a communication frame during the run-time operation. The challenges of device synchronization and updates rates of high and low-level controllers are also discussed. Further, the low-level controller was validated using a pendulum test bed, complete with full sensor feedback, including IMU results for two open-loop scenarios. Moreover, this work can be extended to other hierarchically controlled robotic systems that utilize SEA subsystems, such as humanoid robots, assistive rehabilitation robots, training simulators, and robotic-assisted surgical devices. The hardware and software designs presented in this work are available open source to enable researchers with a direct solution for data acquisition and the control of low-level devices in a robotic system.

The Development of a Cost-Effective Imaging Device Based on Thermographic Technology.

Thermal vision-based devices are nowadays used in a number of industries, ranging from the automotive industry, surveillance, navigation, fire detection, and rescue missions to precision agriculture. This work describes the development of a low-cost imaging device based on thermographic technology. The proposed device uses a miniature microbolometer module, a 32-bit ARM microcontroller, and a high-accuracy ambient temperature sensor. The developed device is capable of enhancing RAW high dynamic thermal readings obtained from the sensor using a computationally efficient image enhancement algorithm and presenting its visual result on the integrated OLED display. The choice of microcontroller, rather than the alternative System on Chip (SoC), offers almost instantaneous power uptime and extremely low power consumption while providing real-time imaging of an environment. The implemented image enhancement algorithm employs the modified histogram equalization, where the ambient temperature sensor helps the algorithm enhance both background objects near ambient temperature and foreground objects (humans, animals, and other heat sources) that actively emit heat. The proposed imaging device was evaluated on a number of environmental scenarios using standard no-reference image quality measures and comparisons against the existing state-of-the-art enhancement algorithms. Qualitative results obtained from the survey of 11 subjects are also provided. The quantitative evaluations show that, on average, images acquired by the developed camera provide better perception quality in 75% of tested cases. According to qualitative evaluations, images acquired by the developed camera provide better perception quality in 69% of tested cases. The obtained results verify the usability of the developed low-cost device for a range of applications where thermal imaging is needed.

Real-Time Sensor-Embedded Neural Network for Human Activity Recognition.

This article introduces a novel approach to human activity recognition (HAR) by presenting a sensor that utilizes a real-time embedded neural network. The sensor incorporates a low-cost microcontroller and an inertial measurement unit (IMU), which is affixed to the subject's chest to capture their movements. Through the implementation of a convolutional neural network (CNN) on the microcontroller, the sensor is capable of detecting and predicting the wearer's activities in real-time, eliminating the need for external processing devices. The article provides a comprehensive description of the sensor and the methodology employed to achieve real-time prediction of subject behaviors. Experimental results demonstrate the accuracy and high inference performance of the proposed solution for real-time embedded activity recognition.

Preventive Conservation of a Short Theatre Skit (Valencian "Sainete") with Cloud Data Storage and Internet of Things.

Preventing the progressive deterioration of works of art over time is a topic of great interest to collectors and museums. With this aim, time capsules where environmental conditions remain unchanged are well known for preserving art. In this paper, a prototype of an IoT time capsule is presented with a focus on low cost in order to make it accessible to private collectors or small museums with tight budgets. Valencian 'sainetes' (small plays), which are considered materials of artistic interest, have been placed in a "time capsule", which is a manually made container with insulating materials for keeping small pieces for a long time. Environmental control has been performed with a low-cost microcontroller, sensors and actuators connected to a free online IoT platform. This platform recorded data and made decisions based on these data, sending cooling or heating orders to an environmental control system. The results obtained are very satisfactory and open interesting perspectives for future research. However, they also highlight some relevant technical and economic limitations that will have to be considered in the future.

Implementation of Parameter Observer for Capacitors.

This paper describes the implementation of a parameter observer (PO) intended to estimate the capacitance and equivalent serial resistance of a capacitor (ESR). The implemented observer consists of a dynamic second-order discrete-time system. The input signal of the observer is the voltage at the terminals of the capacitor measured during its discharge across a variable resistance in two steps. The implemented observer can be used in quasi-online or offline mode. The theoretical and experimental supporting materials provide a comprehensive picture of the implementation and conditions of use of the PO. The experimental verification was carried out with a microcontroller with Cortex®-M7 core architecture. The sampling time of the PO was 20 µs, and the estimation of the parameters was obtained before the end of the discharge of the capacitor. In the cases described in the paper, this means approximately 25 ms. Due to the PO's capabilities (estimation speed, reduced computational complexity and precision)-proved by the experiments carried out on three electrolytic capacitors of 100 µF, 220 µF and 440 µF-the implementation is of interest for several applications, primarily in the field of power electronic applications.

Edge-Machine-Learning-Assisted Robust Magnetometer Based on Randomly Oriented NV-Ensembles in Diamond.

Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in nano- or micro-diamonds is a promising technology for precise magnetic-field sensors. Here, we propose a new, low-cost and stand-alone sensor setup that employs machine learning on an embedded device, so-called edge machine learning. We train an artificial neural network with data acquired from a continuous-wave ODMR setup and subsequently use this pre-trained network on the sensor device to deduce the magnitude of the magnetic field from recorded ODMR spectra. In our proposed sensor setup, a low-cost and low-power ESP32 microcontroller development board is employed to control data recording and perform inference of the network. In a proof-of-concept study, we show that the setup is capable of measuring magnetic fields with high precision and has the potential to enable robust and accessible sensor applications with a wide measuring range.

Creation and Analysis of a Respiratory Sensor Using the Screen-Printing Method and the Arduino Platform.

The aim of this paper is to present novel highly sensitive and stretchable strain sensors using data analysis to report on human live parameters using the Arduino embedded system as a proof of concept in developing new and innovative solutions for health care. The article introduces the solution of textile sensor origination with electrical resistance measurement using the mobile Arduino microcontroller in the designed/elaborated textile printed sensor. The textile sensor was developed by the screen printing technique based on the water dispersion of carbon nanotubes during printing composition. By stretching and squeezing the T-shirt during breathing, the electrical resistances of the printed sensor were changed. The measured resistance corresponded to the number of breaths of the person wearing the T-shirt. The microcontroller calculated the number of breaths as a number of electrical resistance peaks, which then led to monitoring human live parameters.

A Privacy-Preserving Desk Sensor for Monitoring Healthy Movement Breaks in Smart Office Environments with the Internet of Things.

Smart workplace Internet of Things (IoT) solutions rely on several sensors deployed efficiently in the workplace environment to collect accurate data to meet system goals. A vital issue for these sensor-based IoT solutions is privacy. Ideally, the occupants must be monitored discreetly, and the strategies for maintaining privacy are dependent on the nature of the data required. This paper proposes a new sensor design approach for IoT solutions in the workplace that protects occupants' privacy. We focus on a novel sensor that autonomously detects and captures human movements in the office to monitor a person's sedentary behavior. The sensor guides an eHealth solution that uses continuous feedback about desk behaviors to prompt healthy movement breaks for seated workers. The proposed sensor and its privacy-preserving characteristics can enhance the eHealth solution system's performance. Compared to self-reporting, intrusive, and other data collection techniques, this sensor can collect the information reliably and timely. We also present the data analysis specific to this new sensor that measures two physical distance parameters in real-time and uses their difference to determine human actions. This architecture aims to collect precise data at the sensor design level rather than to protect privacy during the data analysis phase.

Gait Stride Length Estimation Using Embedded Machine Learning.

INTRODUCTION: Spatiotemporal gait parameters, e.g., gait stride length, are measurements that are classically derived from instrumented gait analysis. Today, different solutions are available for gait assessment outside the laboratory, specifically for spatiotemporal gait parameters. Such solutions are wearable devices that comprise an inertial measurement unit (IMU) sensor and a microcontroller (MCU). However, these existing wearable devices are resource-constrained. They contain a processing unit with limited processing and memory capabilities which limit the use of machine learning to estimate spatiotemporal gait parameters directly on the device. The solution for this limitation is embedded machine learning or tiny machine learning (tinyML). This study aims to create a machine-learning model for gait stride length estimation deployable on a microcontroller. MATERIALS AND METHOD: Starting from a dataset consisting of 4467 gait strides from 15 healthy people, measured by IMU sensor, and using state-of-the-art machine learning frameworks and machine learning operations (MLOps) tools, a multilayer 1D convolutional float32 and int8 model for gait stride length estimation was developed. RESULTS: The developed float32 model demonstrated a mean accuracy and precision of 0.23 ± 4.3 cm, and the int8 model demonstrated a mean accuracy and precision of 0.07 ± 4.3 cm. The memory usage for the float32 model was 284.5 kB flash and 31.9 kB RAM. The int8 model memory usage was 91.6 kB flash and 13.6 kB RAM. Both models were able to be deployed on a Cortex-M4F 64 MHz microcontroller with 1 MB flash memory and 256 kB RAM. CONCLUSIONS: This study shows that estimating gait stride length directly on a microcontroller is feasible and demonstrates the potential of embedded machine learning, or tinyML, in designing wearable sensor devices for gait analysis.