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Amid escalating global challenges such as population growth, pollution, and climate change, access to safe and clean water has emerged as a critical issue. It is estimated that there are 4 billion cases of water-related diseases worldwide that cause 3.4 million deaths every year. This underscores the urgent need for efficient water quality monitoring and assessment. Traditional assessment techniques include laboratory-based methods that are manual, costly, time-consuming, and risky. Although some studies leveraged Internet of Things (IoT)-based systems to examine water quality, they only relied on a limited number of water quality parameters (and thus do not offer a comprehensive and accurate water quality assessment), mainly due to the technical difficulties to integrate multiple sensors to a single device. In fact, due to the issues of multimodality, heterogeneity, and complexity of data, the interoperability among sensors with various measurements, sampling rates, and technical requirements makes it very challenging to seamlessly integrate multiple sensors into one device. This study overcame these technical challenges by leveraging multisensor data fusion capabilities to develop an intelligent cloud-based IoT multimodal edge sensing device to provide an automated, real-time, and comprehensive assessment process of water quality. First, a total of nine water quality parameters were identified and considered. Second, the sensing device was designed and developed using an ESP32 embedded system, which is a low-cost, low-power system on a chip (SoC) microcontroller integrated with Wi-Fi and dual-mode Bluetooth connectivity by fusing data from six different sensors that measure the nine identified water parameters on the edge. Third, the overall water quality was evaluated using the National Sanitation Foundation Water Quality Index (NSFWQI). Fourth, a cloud-based server was created to publish the data instantly, and a graphical user interface (GUI) was developed to provide easy-to-understand real-time visualization and information of the water quality. The real-world applicability and practicality of the developed IoT-enabled sensing device was tested and verified in a pilot project (i.e., a case study) of a building located in Newark, New Jersey, for a duration of 6 months. This paper adds to the body of knowledge by being the first research developing a single IoT-enabled device that is capable of reporting NSFWQI in real-time based on 9 water quality indicators encompassing both physical [temperature, total dissolved solids (TDS), turbidity, and pH] and chemical [potassium, phosphorus, nitrogen, dissolved oxygen (DO), and 5-day biochemical oxygen demand (BOD5)] parameters. Thus, this study serves as a multifaceted improvement across different dimensions, fostering healthier, more efficient, and technologically advanced environments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Egypt faces extreme traffic congestion in its cities, which results in long travel times, large lines of parked cars, and increased safety hazards. Our study suggests a multi-modal approach that combines critical infrastructure improvements with cutting-edge technologies to address the ubiquitous problem of traffic congestion. Assuring vehicles owners of their timely arrival, cutting down on fuel usage, and improving communication using deep learning approach and optimization algorithm within the potential of IoT enabled 5G framework are the main goals. The traffic management system incorporates detection cameras, Raspberry Pi 3 microcontroller, an Android application, cloud connectivity, and traditional traffic lights that are powered using PV modules and batteries to secure the traffic controllers operation in case of grid outage and assure service continuity. The model examines the difficulties associated with Internet of Things (IoT) communication, highlighting possible interference from device-to-device (D2D) devices and cellular user equipment. This all-encompassing strategy aims to reduce fuel consumption, increase road safety and improve traffic efficiency. The model predicts a significant increase in Egypt's urban mobility by utilizing the possibilities of IoT and 5G technologies, which would improve Egypt's towns' livability and efficiency. The goal of this paper is to modernize Egypt's traffic management system and bring it into compliance with global guidelines for intelligent transportation networks. [ABSTRACT FROM AUTHOR]

There are appreciable physical losses to the header of a combine harvester operating in the fields during harvesting. To reduce these losses, it is desirable to effectively control the header height of the combine harvester. In the present study, a soil bund detection system has been developed for the header of the combine harvester using an ultrasonic sensor and tested under field conditions. In the laboratory, the best among five ultrasonic sensors (HC-SR04, US-100, JSN-SR04T, GY-US42 and MB-7092) was selected based on accuracy and response of the sensor operated for 27 treatments of soil bund height, sensor height and stubble density. Consequently, the soil bund detection system was developed using the US-100 sensor, Arduino UNO R3 microcontroller and two relays to control the actuator on prototype. The results showed that during sensor selection, the US-100 sensor had a maximum accuracy of 84.83% in BH3SH1SD1 treatment and a maximum response of 91.38 readings/sec in BH2SH2SD3 treatment. Furthermore, the developed system showed a maximum accuracy of 88.89% and 85.58% without the actuator in the field and with the actuator under laboratory conditions respectively. The highest response of 64.68 and 62.70 readings/sec was recorded for systems without the actuator in the field and with the actuator under laboratory conditions respectively. The study reveals that the increase in stubble density and sensor height causes a decrease in the accuracy, while the increase in bund height significantly increases the accuracy of the system. Moreover, an increase in vehicle speed causes a decrease in the response of the system, whereas with increasing bund height and stubble density, there is an increase in the response. [ABSTRACT FROM AUTHOR]

Microscopy is essential for detecting, identifying, analyzing, and measuring small objects. Access to modern microscopy equipment is crucial for scientific research, especially in the biomedical and analytical sciences. However, the high cost of equipment, limited availability of parts, and challenges associated with transporting equipment often limit the accessibility and operational capabilities of these tools, particularly in field sites and other remote or resource-limited settings. Thus, there is a need for affordable and accessible alternatives to traditional microscopy systems. We address this challenge by investigating the feasibility of using a simple microcontroller board not only as a portable and field-ready digital microscope, but furthermore as a versatile platform which can easily be adapted to a variety of imaging applications. By adding a few external components, we demonstrate that a low-cost ESP32 camera board can be used to build an autonomous in situ platform for digital time-lapse imaging of cells. Our prototype of this approach, which we call ESPressoscope, can be adapted to applications ranging from monitoring incubator cell cultures in the lab to observing ecological phenomena in the sea, and it can be adapted for other techniques such as microfluidics or spectrophotometry. Our prototype of the ESPressoscope concept achieves a low power consumption and small size, which makes it ideal for field research in environments and applications where microscopy was previously infeasible. Its Wi-Fi connectivity enables integration with external image processing and storage systems, including on cloud platforms when internet access is available. Finally, we present several web browser-based tools to help users operate and manage our prototype's software. Our findings demonstrate the potential for low-cost, portable microscopy solutions to enable new and more accessible experiments for biological and analytical applications. [ABSTRACT FROM AUTHOR]

A junior-level four-unit electronics curriculum is described in which students learn by building their own automated electronic test equipment and performing a rigorous suite of electronics experiments outside the classroom. Each student builds an electronics "workbench" based on an Arduino Nano microcontroller under the control of a software scripting environment (in our case, MATLAB). In the course of their work, students (1) construct a DC power supply and function generator and iteratively add programmable serial interfaces; (2) create an oscilloscope and spectrum analyzer that provides real-time displays of two analog channels for measurement of AC voltage amplitudes, frequency, and phase difference; (3) automate measurements of device I–V curves and transistor gain; (4) design, build, and characterize audio amplifiers and filters through measurement of time constants and frequency responses; and (5) compare the measured and simulated Bode plots. Mixing unconventional topics such as automation, numerical simulation, and programming with basic electronics removes the drudgery of data collection and provides more exposure, repetition, and organization of key concepts. Without being restricted to making measurements within the imposed lab schedule, students can work where and when they are able, repeat automated experiments in seconds, and continue exploring electronics with their workbench. After completing this course, our students demonstrate more confidence and capability with integrating electronics, automation, and programming into other projects and in improving existing systems. Likewise, students glean hands-on experience with scripting repetitive activities, saving enormous effort in their data-gathering work and leaving more time for analysis and creativity. Editor's Note: In this paper, the author describes a junior-level flipped electronics instructional laboratory that combines electronics and computer interfacing instruction in one course. Students are provided with a low-cost electronics kit (including an Arduino microcontroller, a handheld multimeter, and various circuit components), along with a MATLAB license, from which they build a versatile electronics workbench at home. Many of the labs involve building permanent apparatus, including a programmable DC power supply and function generator, a spectrum analyzer, and an oscilloscope, that the students use to measure I–V characteristic curves, characterize transistors, work with Fourier transforms, Bode plots, and so on. Using their workbench, each student can work on their lab projects outside of the classroom whenever and wherever it is convenient for them. Various supplementary materials, including code, are provided, which are useful for those wishing to implement this curriculum at their own institution. [ABSTRACT FROM AUTHOR]

Recently, an increasing number of people have employed do-it-yourself (DIY) and do-it-with-others (DIWO) techniques and processes to develop unique technology products. This trend is commonly called the maker movement and fosters the creation of own electronic and mechanical devices and tools. Oscilloscopes are really useful tools to diagnose problems and analyze electronic devices and electrical circuits, and thus they should not stay outside this trend. To contribute to this field, an architecture to make low-cost portable digital oscilloscopes is proposed. The proposal is mainly based on general-purpose microcontrollers and operational amplifiers. Following this approach, a portable oscilloscope with two input channels, a graphic display, a synchronism detector, internal and external triggers, and a digital signal analyzer function is designed. Furthermore, different options for the implementation are proposed and discussed. [ABSTRACT FROM AUTHOR]

This research delves into simple Jerk-type dynamical networks, constructed using a specific bidirectional coupling scheme that influences the sub-oscillators and compound nonlinearity gradient functions that perturb each sub-unit of the network. The novelty of this work is to demonstrate a new pedagogical method able to stimulate the formation of higher-order multiscroll dynamics in the Jerk sub-oscillator. This is done by modeling a simple dynamic network built by applying a special bidirectional coupling between the Jerk sub-systems. Another novelty of this approach is that it allows us to study the collective dynamics of Jerk system networks, which has not yet been done in the literature. Multiscroll systems are exceedingly complex dynamically, making them valuable in chaos-based applications. The pedagogical approach used in this study is exceptional because it produces many additional equilibrium points (from 5 to 25 in dynamical network 1, and from 3 to 15 in dynamical network 2) in each Jerk sub-unit of the network. These equilibria elevate the complexity of Jerk systems by emulating higher-order multiscroll dynamics. The methodology used in this study is efficient and differs from those used in the literature, which mainly uses nonlinear multizero functions in dissipative systems. This research further explores the dynamic system characterization tools and conducts an experimental investigation on a microcontroller (ATMEGA2560) to confirm the predictions. [ABSTRACT FROM AUTHOR]

Optimizing the regeneration process of surgically created anastomoses (blood vessels, intestines, nerves) is an important topic in surgical research. One of the most interesting parameter groups is related to the biomechanical properties of the anastomoses. Depending on the regeneration process and its influencing factors, tensile strength and other biomechanical features may change during the healing process. Related to the optimal specimen size, the range and accuracy of measurements, and applicability, we have developed a custom-tailored microcontroller-based device. In this paper, we describe the hardware and software configuration of the latest version of the device, including experiences and comparative measurements of tensile strength and elasticity of artificial materials and biopreparate tissue samples. The machine we developed was made up of easily obtainable parts and can be easily reproduced on a low budget. The basic device can apply a force of up to 40 newtons, and can grasp a 0.05–1 cm wide, 0.05–1 cm thick tissue. The length of the test piece on the rail should be between 0.3 and 5 cm. Low production cost, ease of use, and detailed data recording make it a useful tool for experimental surgical research. [ABSTRACT FROM AUTHOR]

The vibrations that a shaft suffers when rotating affect both the friction and subsequent wear of the shaft. The main objective of this paper is to present an academic and experimental prototype that allows controlling the vibrations of a rotating shaft through magnetic levitation. The control was carried out with a microcontroller, electromagnets, and proximity sensors. Other sensors and regulators were also added to the model that allow parameterization and obtaining data for subsequent analysis. According to the experiments performed in the laboratory, the proposed digital controller is capable of mitigating vibrations of a rotating shaft. This theoretical-practical methodology enables students to acquire extensive knowledge of mechanics, electronics, computing, and control in an effective manner. Although this paper presents only an academic prototype, the same technology could be applied to a wide variety of mechanisms that require rotation shafts with friction problems. [ABSTRACT FROM AUTHOR]

The global population is rapidly increasing, so there is a critical requirement to satisfy the food production demand. Conventional methods of agriculture are inadequate to meet building demand which leads to declining farming sector and adaptable to other industries. Most of the farming activities are highly dependent on the labor which leads to increase in cost and time of operation. The rapid growth of mechanization for all farm activities cannot completely reduce the human involvement. As a result, agricultural automation is critically important. In terms of automation, this study emphasizes the crucial role of UAVs in precision and smart agriculture. The adoption of drones for various farm operations has the possibility to minimize labor requirements as well as operational time. This review provides overview of conceptual design, command flow operation, Micro-controller boards, remote-control systems and attachments like sensors, cameras, motors in UAVs for the purpose of automation in farm activities. The Internet of Things (IoT) employed in UAVs with image processing and machine learning algorithms provides accurate and precision results in farm activities. Furthermore, this study discusses future advancements, limitations and challenges for farmers in adapting to UAVs. Highlights: The recent literature from 2014 to 2024 on UAVs in agriculture were examined by PRISMA Methodology The overview of IOT system (sensors, cameras, micro-controllers and connectivity gateways) in UAVs were provided. The applications of UAVs in smart farming were compared and summarized by emphatical data and case studies. The limitations and future views of UAVs in farming are provided. [ABSTRACT FROM AUTHOR]

Generation of hidden attractor with complicated phase portrait in chaotic system with no equilibrium has presented a new research focus in the past decade. However, the existing approaches usually follow the rule that you reap what you sow, i.e., taking an no-equilibrium chaotic system as the seed. In this paper, a novel approach to the generation of no-equilibrium multi-fold hidden attractors is presented. The offset boosting and the operation of complex number are applied to several cases, including the seed chaotic systems with no equilibrium, with single unstable node, or with single stable node-focus. In specific, the offset boosting shifts the single non-zero equilibrium to the origin, then the operation of complex number rules out the equilibrium in the folded system and generates multi-fold trajectories. The highlight is that the proposed approach is not limited to the no-equilibrium seed system, but also applicable for different types of single-equilibrium seed systems, even the seed system with a single line of equilibrium. The detailed numerical simulations and experiments confirmed the feasibility of the proposed approach. Finally, an image encryption algorithm and its practical microcontroller-based implementation are presented to support the potential application. • Systematic approach for folding trajectory of chaotic system. • New multi-fold chaotic hidden attractors with no equilibrium. • New chaos-based encryption algorithm for image security. • Dynamic memory scheme for microcontroller implementation. [ABSTRACT FROM AUTHOR]

This proposed work investigates the performance of dc voltage of a three-phase Active Front-End (AFE) Sinusoidal Pulse Width Modulated (SPWM) rectifier during load switching. To obtain desired performances, a transfer function is derived in synchronous reference frame (dq), from which the gain of the PI controllers are estimated and observed the performance accordingly. In this research, the load current is feed-forwarded with d-axis current by a suitable choice of gain factor, which results a fractional feedback d-axis current control and improves the transient behaviour simultaneously. This will unalter the other performance parameters of the system such as, UPF operation, less Total Harmonic Distortion (THD) of input current, dc voltage boost etc. All the simulation results exhibits satisfactory performance and validated with a prototype developed using STM32F303RE microcontroller accordingly. [ABSTRACT FROM AUTHOR]

In today's technologically advanced landscape, sensors feed critical data for accurate decision-making and actions. Ensuring the integrity and reliability of sensor data is paramount to system performance and security. This paper introduces an innovative approach utilizing discrete wavelet transforms (DWT) embedded within microcontrollers to scrutinize sensor data meticulously. Our methodology aims to detect and isolate malfunctions, misuse, or any anomalies before they permeate the system, potentially causing widespread disruption. By leveraging the power of wavelet-based analysis, we embed computational intelligence directly into the microcontrollers, enabling them to monitor and validate their outputs in real-time. This proactive anomaly detection framework is designed to distinguish between normal and aberrant sensor behaviors, thereby safeguarding the system from erroneous data propagation. Our approach significantly enhances the reliability of embedded systems, providing a robust defense against false data injection attacks and contributing to overall cybersecurity. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The widespread use of smart wearable devices in various fields, including healthcare and sports, underscores the importance of their application in enhancing physical exercise among college students. Recent advancements in technology have facilitated the development of sophisticated methods to assess and predict physical activity outcomes, making their evaluation increasingly critical. OBJECTIVES: This study aims to develop a reliable assessment model for smart wearable devices used in college students' sports activities. The objective is to accurately predict and evaluate the effectiveness of these devices in improving students' physical health and promoting lifelong sports habits. Ultimately, the research seeks to integrate advanced computational methods to enhance the accuracy of physical exercise assessments. METHODS: The research introduces a novel assessment model that combines a zebra behavior-based heuristic optimization algorithm with a convolutional neural network (CNN). By analyzing user behavior data from wearable devices, the model constructs an evaluation index system tailored for college sports activities. The approach optimizes the parameters of the CNN using the zebra optimization algorithm, ensuring enhanced prediction accuracy. RESULTS: The evaluation model demonstrated high accuracy, with a significant improvement in predicting the outcomes of physical exercises among college students. Comparative analyses with traditional methods revealed that the new model reduced prediction errors and increased real-time performance metrics. Specifically, the model achieved a lower root mean square error (RMSE) in simulation tests, indicating more precise assessments. Figures and statistical data provided in the study illustrate the model's superior performance across various parameters. CONCLUSION: The developed assessment model significantly advances the application of smart wearable devices in monitoring and enhancing college students' physical activities. By integrating cutting-edge algorithms, the study not only improves the accuracy of exercise assessments but also contributes to the broader understanding of technology's role in health and fitness education. Future research could further refine this model by incorporating additional sensors and data points to expand its applicability and robustness. [ABSTRACT FROM AUTHOR]

This article is devoted to a comparison of two advanced control techniques applied to the same plant. The plant is a certain type of axial-piston pump. A linear-quadratic (LQR) controller and an H-infinity (H∞) controller were synthesized to regulate the displacement volume of the pump. The classical solution to such a problem is to use a hydro-mechanical controller (by pressure, flow rate, or power) but, in the available sources, there are solutions that implement proportional-integral-derivative (PID), LQR, model predictive control (MPC), etc. Unlike a classical solution, in our case, the hydro-mechanical controller is replaced by an electro-hydraulic proportional valve, which receives a reference signal from an industrial microcontroller. It is used as the actuator of the pump swash plate. The pump swash plate swivel angle determines the displacement volume, respectively, and the flow rate of the pump. The microcontroller is capable of embedding various control algorithms with different structures and complexities. The developed LQR and H∞ controllers are compared in the simulation and real experiment conditions. For this purpose, the authors have developed a laboratory experimental test bench, enabling a real-time function of the control system via USB/CAN communication. Both controllers are compared under different pump loading modes. Also, this paper contributes an uncertain model of an axial-piston pump with proportional valve control that is obtained from experimental data. Based on this model, the robust stability of the closed-loop system is investigated by comparing the structured singular value (μ). The investigations show that both control systems achieved robust stability. Moreover, they can tolerate up to four times larger uncertainties than modeled ones. The system with the H∞ controller attenuates approximately at least 30 times the disturbances with frequency up to 1 rad/s while the system with the LQR controller attenuates at least 10 times the same disturbances. [ABSTRACT FROM AUTHOR]

The dynamical probing and linear offset boosting of constants in Josephson junction (JJ) instigated by the Wien bridge oscillator (JJIWBO) are investigated in this paper. Via numerical simulations, the JJIWBO unveiled bistable periodic oscillations, bistable periodic doubling with an evolving path to bistable chaotic characteristics, monostable chaotic behaviours and monostable periodic characteristics. The numerical dynamics are realised by the microcontroller validation (MCV) scheme. Two constant parameters are introduced in the rate equations of JJIWBO to implement the linear offset boosting of constants based on the two voltage variables. It is demonstrated that the polarity of the chaotic voltage and phase difference signals can be flexibly changed by varying one of the two constant parameters which is different from zero and the other constant parameter is zero. When two constant parameters have the same value, the chaotic voltages and phase difference signals can switch between bipolar and unipolar signals flexibly by changing the unique constant parameter. [ABSTRACT FROM AUTHOR]

This article presents an analysis and control approach for a non-smooth air-gap permanent magnet synchronous motor (NSAG-PMSM) in the absence of external disturbances. The analytical study of NSAG-PMSM shows the existence of equilibrium points. Based on the Routh–Hurwitz criterion, the stability of the equilibrium points reveals the existence of transcritical bifurcation. NSAG-PMSM exhibits various dynamical behaviours, such as bistable chaos, periodic spiking oscillations, chaotic spiking characteristics, coexistence between periodic and chaotic behaviours and periodic evolution towards monostable chaos as system parameters change. The research uses microcontroller implementation to validate the dynamical characteristics observed during the numerical simulations of the NSAG-PMSM. The study of NSAG-PMSM proposes a strategy to mitigate chaos and stabilise the system using two simple controllers, with a comparative study presented using peak overshoot and settling time diagrams. By combining these different aspects, this article significantly contributes to the understanding of the operation in NSAG-PMSM, highlighting specific aspects related to the application of microcontroller techniques in the field of electrical engineering and solutions to chaos control. [ABSTRACT FROM AUTHOR]

In recent years, various solutions have been developed to control power electronic converters using devices available on the market that are powerful and easy to use. These solutions, in most cases, offer high performance. However, these have high implementation costs because the required devices are expensive. For this reason, this document presents the implementation of two discrete-time controllers widely used in the literature for a boost converter implemented on a low-cost platform. The objective is to obtain a constant voltage at the converter's output for photovoltaic system applications. The proportional-integral control is implemented as the first case, and the second case is a sliding mode control. In addition, a prior analysis is presented through simulation. Both control algorithms are implemented on the TMS320F28379D microcontroller from Texas Instruments through the same manufacturer's integrated development software based on an optimized C/C++ language compiler. The results of the non-linear algorithm reveal better performance in reducing the time response, the overshoot of the transient state, and the steady-state error. Finally, the significant economic savings associated with the implementation costs of the controllers tested on a low-cost platform differentiate this work from other similar ones. [ABSTRACT FROM AUTHOR]

With the rapid development of the Industrial Internet of Things in rotating machinery, the amount of data sampled by mechanical vibration wireless sensor networks (MvWSNs) has increased significantly, straining bandwidth capacity. Concurrently, the safety requirements for rotating machinery have escalated, necessitating enhanced real-time data processing capabilities. Conventional methods, reliant on experiential approaches, have proven inefficient in meeting these evolving challenges. To this end, a fault detection method for rotating machinery based on mobileNet in MvWSNs is proposed to address these intractable issues. The small and light deep learning model is helpful to realize nearly real-time sensing and fault detection, lightening the communication pressure of MvWSNs. The well-trained deep learning is implanted on the MvWSNs sensor node, an edge computing platform developed via embedded STM32 microcontrollers (STMicroelectronics International NV, Geneva, Switzerland). Data acquisition, data processing, and data classification are all executed on the computing- and energy-constrained sensor node. The experimental results demonstrate that the proposed fault detection method can achieve about 0.99 for the DDS dataset and an accuracy of 0.98 in the MvWSNs sensor node. Furthermore, the final transmission data size is only 0.1% compared to the original data size. It is also a time-saving method that can be accomplished within 135 ms while the raw data will take about 1000 ms to transmit to the monitoring center when there are four sensor nodes in the network. Thus, the proposed edge computing method shows good application prospects in fault detection and control of rotating machinery with high time sensitivity. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive guide for the design and development of a power electronic converter, specifically focusing on the Buck converter. The guide aims to bridge the gap between theoretical knowledge and practical application, providing engineering students with a hands-on experience in digital control using the STM32 microcontroller. The workshop experience described covers all necessary steps, from the initial design and simulation using tools like MATLAB-Simulink and Plecs to the final implementation and testing of the converter. The educational value of this approach is highlighted, emphasizing the importance of practical engagement in understanding complex concepts related to power electronics. [ABSTRACT FROM AUTHOR]

This paper presents a high-frequency (HF) magnetic pulse generator designed for low-intensity transcranial magnetic stimulation (LI-TMS) applications. HF pulse stimulation can induce a strong electric field with minimal current and enhance the penetration depth of the electric field in human tissue. The HF magnetic pulse generator was designed and fabricated using a microcontroller unit, gate driver, full-bridge coil driver, and stimulation coil. Measurements with a full-bridge circuit supply voltage of 10 V demonstrated an electric field intensity of 6.8 Vpp/m at a frequency of 1 MHz with a power dissipation of 2.45 W. Achieving a similar electric field intensity at a frequency of 100 kHz required approximately ten times the coil current. Additionally, a quasi-resonant LC load was introduced by connecting a capacitor in series with the stimulation coil, which set the resonant frequency to approximately 10% higher than the frequency of 1 MHz. This approach reduced the coil impedance, achieving higher current with the same bias supply voltage. Experimental results showed an enhanced electric field intensity of 19.1 Vpp/m with a supply voltage of only 1.8 V and reduced power dissipation of 1.11 W. The proposed HF pulse train with quasi-resonant coil system is expected to enable a low-power LI-TMS system. [ABSTRACT FROM AUTHOR]

This paper presents a single-phase Full-Bridge (FB) inverter with a hybrid commutation technique designed to reduce the harmonic distortion caused by the loss of the controller capability around the zero-crossing point in the unipolar commutation region. The hybrid modulation changes from unipolar to bipolar commutation under the loss of the reference control, improving the robustness and efficiency of the method. The commutation technique improves the switching performance and reduces the switching losses. Simulation models are developed in MATLAB/Simulink R2023b to evaluate their performance under different operating conditions. The results show that the proposed commutation technique can achieve high efficiency, low total harmonic distortion (THD), and fast dynamic response. The experimental implementation of sliding mode control (SMC) implemented in an STM32 microcontroller confirms that the hybrid commutation technique can reduce the THD by 0.96 percentage points for local (off-grid) loads and up to 2.45 in an industrial grid-tie network, compared with unipolar commutation. These findings highlight the potential of the proposed modulation technique for applications like solar panels and offer crucial insights for ongoing research and development in this field. [ABSTRACT FROM AUTHOR]

Computer numerical control (CNC) machine tools mainly use transmission elements such as screws and gears as drive systems. To obtain better accuracy, equipment manufacturers apply pre-pressure to the screw to reduce the occurrence of backlash errors. Backlash errors are one of the important factors affecting positioning accuracy. When the pre-pressure fails, a backlash error occurs. Backlash error can cause abnormal vibration or drive delays in CNC drive systems, leading to reduced transmission efficiency and poor positioning accuracy. Therefore, in most research, vibration sensors are installed on the fixed end of the screw or the nut to detect abnormal phenomena and to estimate the backlash error using the algorithmic model. This traditional method has been established on the basis of the relationship between the amount of vibration and backlash error using an algorithmic model. The backlash can be estimated using this traditional method. However, this traditional method cannot directly sense backlash errors, and the accuracy of the measurement is limited by the algorithm model. Therefore, in this study, we attempted to use a photoelectric switch sensor combined with a microcontroller unit to detect the backlash errors of the CNC drive system. This system can detect a time change of 10 µs. After error corrections, the gain values of each backlash amount and delay time can be used to predict the backlash amount. Through the final verification with commercially available instruments, the system accuracy was found to reach 0.001 mm on the linear axis and 0.001° on the rotating axis. Owing to the streamlined sensing principle of the proposed system, in the future, the system can be integrated into the relevant rotary table components or within the CNC drive system. [ABSTRACT FROM AUTHOR]

In this contribution, a chaos‐based microcontroller electrocardiogram (ECG) signal acquisition‐security‐transmission system is proposed. It is designed based on a Novel 4Wings‐4D Chaotic Oscillator (N4W4DCO) with a hyperbolic sine nonlinearity unbalanced. The classical nonlinear dynamics tools, such as 2D bifurcation and the highest Lyapunov exponent curves, basins of attraction, and power spectral density, help us see that the proposed chaotic oscillator generates periodic oscillations, intermittency + crisis routes to chaos, transient chaos, and the coexistence of 4/2 wings attractors just to name a few dynamics. The data generated using highly chaotic regime are tested using the well‐known NIST TEST ‐800‐22 Rev A and the results passed the test successfully. The N4W4DCO oscillator is embedded in an Arduino microcontroller where the discovered interesting dynamics are confirmed. A low‐cost ECG acquisition circuit with an AD8232 ECG sensor is also designed and experimented. ECG signals are acquired and directly loaded into MATLAB‐Simulink and are successfully encrypted with random data from the N4W4DCO in its chaos regime. The scrambled ECG signals from experiment are sent through an added white gaussian noise (AWGN) channel and thereafter received and decrypted. These results are promising and open the possibility of improving secure telemedicine transmission systems. [ABSTRACT FROM AUTHOR]

Simulation-based Fault Injection (FI) is crucial for validating system behaviour in safety-critical applications, such as the automotive industry. The ISO 26262 standard's Part 11 extension provides failure modes for digital components, driving the development of new fault models to assess software-implemented mechanisms against random hardware failures (RHF). This paper proposes a Fault Injection framework, QEFIRA, and shows its ability to achieve the failure modes proposed by Part 11 of the ISO 26262 standard and estimate relevant metrics for safety mechanisms. QEFIRA uses QEMU to inject permanent and transient faults during runtime, whilst logging the system state and providing automatic post-execution analysis. Complemented with a confusion matrix, it allows us to gather standard compliant metrics to characterise and evaluate different designs in the early stages of development. Comparatively to the native QEMU implementation, the tool only shows a slowdown of 1.4 × for real-time microcontroller-based applications. [ABSTRACT FROM AUTHOR]

In this work, we present a study on seat suspension technology that integrates pneumatic muscles, marking a significant advancement in active vibration control. This innovative approach addresses the limitations of traditional suspension systems, providing enhanced comfort and adaptability. A key achievement is the development of a mathematical model for controlling horizontal seat vibration, which serves as a valuable design tool for evaluating seat suspension under various conditions and control strategies. The creation of a custom microcontroller, benchmarked against a standard from National Instruments, highlights the practical applications of this research. Positive results suggest a promising future for this technology in industrial settings, where vibration reduction is critical. The system's scalability and user-adjustable signal levels further enhance its potential for widespread industrial adoption. [ABSTRACT FROM AUTHOR]

Electric mobility is pervasive and strongly affects everyone in everyday life. Motorbikes, bikes, cars, humanoid robots, etc., feature specific battery architectures composed of several lithium nickel oxide cells. Some of them are connected in series and others in parallel within custom architectures. They need to be controlled against over current, temperature, inner pressure and voltage, and their charge/discharge needs to be continuously monitored and balanced among the cells. Such a battery management system exhibits embarrassingly parallel computing, as hundreds of cells offer the opportunity for scalable and decentralized monitoring and control. In recent years, tiny machine learning has emerged as a data-driven black-box approach to address application problems at the edge by using very limited energy, computational and storage resources to achieve under mW power consumption. Examples of tiny devices at the edge include microcontrollers capable of 10–100 s MHz with 100 s KiB to few MB embedded memory. This study addressed battery management systems with a particular focus on state-of-charge prediction. Several machine learning workloads were studied by using IEEE open-source datasets to profile their accuracy. Moreover, their deployability on a range of microcontrollers was studied, and their memory footprints were reported in a very detailed manner. Finally, computational requirements were proposed with respect to the parallel nature of the battery system architecture, suggesting a per cell and per module tiny, decentralized artificial intelligence system architecture. [ABSTRACT FROM AUTHOR]

Hybrid energy storage systems significantly impact the renewable energy sector due to their role in enhancing grid stability and managing its variability. However, implementing these systems requires advanced control strategies to ensure correct operation. This paper presents an algorithm for designing the power and control stages of a hybrid energy storage system formed by a battery, a supercapacitor, and a bidirectional Zeta converter. The control stage involves an adaptive sliding-mode controller co-designed with the power circuit parameters. The design algorithm ensures battery protection against high-frequency transients that reduce lifespan, and provides compatibility with low-cost microcontrollers. Moreover, the continuous output current of the Zeta converter does not introduce current harmonics to the battery, the microgrid, or the load. The proposed solution is validated through an application example using PSIM electrical simulation software (version 2024.0), demonstrating superior performance in comparison with a classical cascade PI structure. [ABSTRACT FROM AUTHOR]

The paper presents the essential theoretical elements related to the construction and operation of numerical control machines, starting from a study of the theoretical aspects related to the design of a CNC typewriter. The block diagrams of the machine, the interconnections between the component blocks and the operation of the whole system are highlighted. The paper also describes the implementation of the typewriter. It starts from a detailed diagram of the wiring and connections between the component elements, up to the presentation and commentary of how to create the program for the microcontroller used. Finally, the testing and operation of this CNC is detailed, from the design of the drawing to its actual realization, with the help of the designed and implemented assembly. CNC systems allow the repetitive production of the same parts, exactly the same and without deviations. Unlike a human operator who cannot execute two identical parts, the repeatability achieved by machines with numerical controls is clearly superior, so the reproducibility component is ensured. [ABSTRACT FROM AUTHOR]

Environmental sound classification (ESC) is one of the important research topics within the nonspeech audio classification field. While deep neural networks (DNNs) have achieved significant advances in ESC recently, their high computational and memory demands render them highly unsuitable for direct deployment on resource-constrained Internet of Things (IoT) devices based on microcontroller units (MCUs). To address this challenge, we propose a novel DNN compression framework specifically designed for such devices. On the one hand, we leverage pruning techniques to significantly compress the large number of model parameters in DNNs. To reduce the accuracy loss that follows pruning, we propose a knowledge distillation scheme based on feature information from multiple intermediate layers. On the other hand, we design a two-stage quantization-aware knowledge distillation scheme to mitigate the accuracy degradation of mandatory quantization required by MCU hardware. We evaluate our framework on benchmark ESC datasets (UrbanSound8K, ESC-50) using the STM32F746ZG device. The experimental results demonstrate that our framework can achieve compression rates up to 97% while maintaining competitive inference performance compared to the uncompressed baseline. [ABSTRACT FROM AUTHOR]

The Trusted Execution Environment (TEE) is designed to establish a safe environment that prevents the execution of unauthenticated programs. The nature of TEE is a continuous verification process with hashing, signing, and verifying. Such a process is called the Chain-of-Trust, derived from the Root-of-Trust (RoT). Typically, the RoT is pre-programmed, hard-coded, or embedded in hardware, which is locally produced and checked before booting. The TEE employs various cryptographic processes throughout the boot process to verify the authenticity of the bootloader. It also validates other sensitive data and applications, such as software connected to the operating system. TEE is a self-contained environment and should not serve as the RoT or handle secure boot operations. Therefore, the issue of implementing hardware for RoT has become a challenge that requires further investigation and advancement. The main objective of this proposal is to introduce a secured RISC-V-based System-on-Chip (SoC) architecture capable of securely booting a TEE using a versatile boot program while maintaining complete isolation from the TEE processors. The suggested design has many cryptographic accelerators essential for the secure boot procedure. Furthermore, a separate 32-bit MicroController Unit (MCU) is concealed from the TEE side. This MCU manages sensitive information, such as the root key, and critical operations like the Zero Stage BootLoader (ZSBL) and key generation program. Once the RoT is integrated into the isolated sub-system, it becomes completely unavailable from the TEE side, even after booting, using any method. Besides providing a secured boot flow, the system is integrated with essential crypto-cores supporting Transport Layer Security (TLS) 1.3. The chip is finally fabricated using the Complementary Metal–Oxide–Semiconductor (CMOS) 180 nm process. [ABSTRACT FROM AUTHOR]

Currently, industrial engineers are witnessing a rapid development of innovation in modern industry and the integration of critical elements of Industry 4.0 into production processes in order to remain competitive. Such changes are characterized by a large amount of effort and financial resources. To successfully deploy these changes requires not only the purchase of hardware and software but also the training of employees and the modification of the company's organizational structure. The main objective of the article is to propose a framework for the modernization of SMEs to a level close to the Smart Factory by using the necessary attributes of Industry 4.0. The framework design is based on the initial state of a standard SME and consists of the design of fitting new IoT devices for efficient data collection, the design of a data warehouse for storing process data using Hadoop, and the integration of process- and operational-level data into the prepared data warehouse. The resulting design is developed in the form of a methodology and is generalized for use in manufacturing enterprises. The universal design is independent of the initial state of the enterprise. [ABSTRACT FROM AUTHOR]

Hand gesture recognition using a camera provides an intuitive and promising means of human-computer interaction and allows operators to execute commands and control machines with simple gestures. Research in hand gesture recognition-based control systems has garnered significant attention, yet the deploying of microcontrollers into this domain remains relatively insignificant. In this study, we propose a novel approach utilizing micro-hand gesture recognition built on micro-bottleneck Residual and micro-bottleneck Conv blocks. Our proposed model, comprises only 42K parameters, is optimized for size to facilitate seamless operation on resource-constrained hardware. Benchmarking conducted on STM32 microcontrollers showcases remarkable efficiency, with the model achieving an average prediction time of just 269ms, marking a 7× faster over the state-of-art model. Notably, despite its compact size and enhanced speed, our model maintains competitive performance result, achieving an accuracy of 99.6% on the ASL dataset and 92% on OUHANDS dataset. These findings underscore the potential for deploying advanced control methods on compact, costeffective devices, presenting promising avenues for future research and industrial applications. [ABSTRACT FROM AUTHOR]

Running machine learning algorithms for image classification locally on small, cheap, and low-power microcontroller units (MCUs) has advantages in terms of bandwidth, inference time, energy, reliability, and privacy for different applications. Therefore, TinyML focuses on deploying neural networks on MCUs with random access memory sizes between 2 KB and 512 KB and read-only memory storage capacities between 32 KB and 2 MB. Models designed for high-end devices are usually ported to MCUs using model scaling factors provided by the model architecture's designers. However, our analysis shows that this naive approach of substantially scaling down convolutional neural networks (CNNs) for image classification using such default scaling factors results in suboptimal performance. Consequently, in this paper we present a systematic strategy for efficiently scaling down CNN model architectures to run on MCUs. Moreover, we present our CNN Analyzer, a dashboard-based tool for determining optimal CNN model architecture scaling factors for the downscaling strategy by gaining layer-wise insights into the model architecture scaling factors that drive model size, peak memory, and inference time. Using our strategy, we were able to introduce additional new model architecture scaling factors for MobileNet v1, MobileNet v2, MobileNet v3, and ShuffleNet v2 and to optimize these model architectures. Our best model variation outperforms the MobileNet v1 version provided in the MLPerf Tiny Benchmark on the Visual Wake Words image classification task, reducing the model size by 20.5% while increasing the accuracy by 4.0%. [ABSTRACT FROM AUTHOR]

An Eggplant Fruit and Shoot Borer (EFSB) is a disease that affects the entirety of the eggplant fruit if not detected. Hence, a detector was proposed in the form of a handheld gun. It was designed and developed to non-invasively classify eggplant fruits that are noninfested and infested with EFSB. Using an Arduino Nano as its microcontroller and a near-infrared spectroscopy (NIRS) module, insect infestation is determined and displayed through its OLED display. Measured reflectance data through the NIRS module of the detector is then stored inside a MicroSD module for further use. Since the prototype was developed for online monitoring, portability was given of utmost importance, pattering the design in the form of a handheld gun, inside of which was powered by a 9V rechargeable battery. The 3D-printed chassis of the detector houses the aforementioned components and modules, alongside with switches for power and near-infrared detection. Through Support Vector Machine (SVM), the classifier model was trained and developed using Jupyter and was extracted as a C++ code for the Arduino Nano module. Compared with a farmer's traditional performance in terms of accuracy, precision, and speed, the prototype performed better with an accuracy of 84%, precision of 72.83%, and an average speed of 9.736 seconds [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A significant attention has concentrated on systems with "hidden attractors" in recent years. The aim of this work is to study an oscillator displaying "hidden attractors". The oscillator has all nonlinear terms but no equilibrium. The oscillator exhibits attractive dynamics such as chaos, bubble bifurcation, symmetrical attractors, and boosting attractors. Oscillator's feasibility is verified by a microcontroller. Utilizing the oscillator for biomedical image encryption illustrates its application. In addition, the adaptive control is designed for the oscillator. [ABSTRACT FROM AUTHOR]

The memristor, a revolutionary electronic component, mimics both neural synapses and electromagnetic induction phenomena. Recent study challenges are the development of effective neural models and discovering their dynamics. In this study, we propose a novel Hopfield neural network model leveraging multistable memristors, showcasing its efficacy in encoding biomedical images. We investigate the equilibrium states and dynamic behaviors of our designed model through comprehensive numerical simulations, revealing a rich array of phenomena including periodic orbits, chaotic dynamics, and homogeneous coexisting attractors. The practical realization of our model is achieved using a microcontroller, with experimental results demonstrating strong agreement with theoretical analyses. Furthermore, harnessing the chaos inherent in the neural network, we develop a robust biomedical image encryption technique, validated through rigorous computational performance tests. [ABSTRACT FROM AUTHOR]

This paper deals with the position detection of automated guided vehicles (AGVs) in dynamic resonant-inductive wireless power transfer (WPT) systems. A position detection is necessary to activate the correct transmitting coil. One of the simplest and most effective approaches for a position detection method is to use optical or magnetic position sensors for each coil. However, due to needing a high number of sensors, this technique is relatively expensive. Therefore, an AGV position detection technique based on a reduced number of optical or magnetic sensors (by a factor of two) is proposed. The proposed detection technique was verified experimentally by using a scaled-down prototype of the dynamic WPT system. The proposed approach can be easily implemented by uploading a specific program code to a microcontroller. The microcontroller with the code developed by us was used for processing data from AGV position detection sensors, activating a suitable transmitting coil and controlling an inverter of the dynamic WPT system. As shown by the experiments, due to the proposed approach for the position detection of AGVs and activation of transmitting coils, the number of the position detection sensors is reduced by a factor of two, leading to reductions in the overall cost and level of complexity of the dynamic WPT system without degrading its performance. [ABSTRACT FROM AUTHOR]

Neonatal electrocardiogram (ECG) monitoring is an important diagnostic tool for identifying cardiac issues in infants at birth. Long-term remote neonatal dryelectrode ECG monitoring solutions can be an additional step for preventive healthcare measures. In these solutions, power and computationally efficient embedded signal processing techniques for denoising newborn ECGs can assist in increasing neonatal medical wearable time. Wavelet denoising is an appropriate denoising mechanism with low computational complexity that can be implemented on embedded microcontrollers for long-term remote ECG monitoring. Discrete wavelet transform (DWT) denoising for neonatal dry-electrode ECG using different wavelet families is investigated. The wavelet families and mother wavelets used include Daubechies (db1, db2, db3, db4, and db6), symlets (sym5), and coiflets (coif5). Different levels of added white Gaussian noise (AWGN) were added to 19 newborn ECG signals, and denoising was performed to select the appropriate wavelets for neonatal dry-electrode ECG. The selected wavelets then undergo real noise additions of baseline wander and electrode motion to determine their robustness and accuracy. Signal-to-noise ratio (SNR), mean squared error (MSE), and power spectral density (PSD) are used to examine denoising performance. db1, db2, and db3 wavelets are eliminated from analysis where the 30 dB AWGN led to negative SNR improvement for at least one newborn ECG, removing important ECG information. db4 and sym5 are eliminated from selection due to their different waveform morphology compared to the dry-electrode newborn ECG's QRS complex. db6 and coif5 are selected due to their highest SNR improvement and lowest MSE of 6.26 × 10-6 and 1.65 × 10-7 compared to other wavelets, respectively. Their wavelet shapes are more like a newborn ECG's QRS morphology, validating their selection. db6 and coif5 showed similar denoising performance, decreasing electrode motion and baseline wander noisy ECG signals by 10 dB and 14 dB, respectively. Further denoising of inherent dry-electrode noise is observed. DWT with coif5 or db6 wavelets is appropriate for denoising newborn dry-electrode ECGs for long-term neonatal dry-electrode ECG monitoring solutions under different noise types. Their similarity to newborn dry-electrode ECGs yields accurate and robust reconstructed denoised newborn dry-electrode ECG signals. [ABSTRACT FROM AUTHOR]

Over the past few years, ARM has been the dominant player in embedded systems and System-on-Chips (SoCs). With the emergence of hardware platforms based on the RISC-V architecture, a practical comparison focusing on their energy efficiency and performance is needed. In this study, our goal is to comprehensively evaluate the energy efficiency and performance of ARM and RISC-V SoCs in three different systems. We will conduct benchmark tests to measure power consumption and overall system performance. The results of our study are valuable to developers and researchers looking for the most appropriate hardware platform for energy-efficient computing applications. Our observations suggest that RISC-V Instruction Set Architecture (ISA) implementations may demonstrate lower average power consumption than ARM, but this does not automatically imply a superior performance per watt ratio for RISC-V. The primary focus of the study is to evaluate and compare these ISA implementations, aiming to identify potential areas for enhancing their energy efficiency. Furthermore, to ensure the practical applicability of our findings, we will use the Computational Fluid Dynamics software OpenFOAM. This step serves to validate the relevance of our results in real-world scenarios. It allows us to fine-tune execution parameters based on the insights gained from our initial study. By doing so, we aim not only to provide meaningful conclusions but also to investigate the transferability of our results to practical applications. Our analysis will also scrutinize the capabilities of these SoCs when handling nonsynthetic software workloads, thereby broadening the scope of our evaluation. [ABSTRACT FROM AUTHOR]

Over the last two decades, a considerable amount of equipment has been acquired (spreaders, seeders, sprayers, among others) to respond to the challenges of the precision agriculture (PA) concept. Most of this equipment has been purchased at a high cost. However, many of them, despite still being functional and equipped with sensors, actuators, and electronic processing units capable of adjusting to variations in speed, have become obsolete in terms of communication and incompatible with new monitoring and control systems based on the "Isobus" protocol. This work aims to present a solution for updating the control system ("Ferticontrol") of a "Vicon RS-EDW" spreader with variable rate application (VRA), making it compatible with the "InCommand" system from "Ag Leader". The solution includes serial protocol mediation using low-cost tools such as "Arduino" and "Raspberry Pi" microcontrollers and open-source software. The development shows that it is possible to implement a solution that is accessible to farmers in general. It also provides a niche business opportunity for young researchers to set up small technology-based enterprises associated with universities and research centers. These partnerships guarantee permanent innovation and represent a decisive step towards modern, technological, competitive, and sustainable agriculture. [ABSTRACT FROM AUTHOR]

The growing demand for agricultural output and limited resources encourage precision applications to generate higher-order output by utilizing minimal inputs of seed, fertilizer, land, and water. An electronically operated planter was developed, considering problems like ground-wheel skidding, field vibration, and the lack of ease in field adjustments of ground-wheel-driven seed-metering plates. The seed-metering plate of each unit of the developed planter is individually driven by a brushless direct current (BLDC) motor, and a BLDC motor-based aspirator is attached for pneumatic suction of seeds. The revolutions per minute (RPM) of the seed-metering plate are controlled by a microcontroller as per the received data relating to RPM from the ground wheel and the current RPM of the seed-metering plate. A feedback loop with proportional integral derivative (PID) control is responsible for reducing the error. Additionally, each row unit is attached to a parallelogram-based depth control system that can provide depth between 0 and 100 mm. The suction pressure in each unit is regulated as per seed type using the RPM control knob of an individual BLDC motor-based aspirator. The row-to-row spacing can be changed from 350 mm to any desired spacing. The cotton variety selected for the study was RCH 659, and the crucial parameters like orifice size, vacuum pressure, and forward speed were optimized in the laboratory with the adoption of a central composite rotatable design. An orifice diameter of 2.947 mm with vacuum pressure of 3.961 kPa and forward speed of 4.261 km/h was found optimal. A quality feed index of 93% with a precision index of 8.01% was observed from laboratory tests under optimized conditions. Quality feed index and precision index values of 88.8 and 12.75%, respectively, were obtained from field tests under optimized conditions. [ABSTRACT FROM AUTHOR]

Recent technological advances in microcontroller systems enable novel biogas monitoring capabilities. This study investigates microcontroller-based quantification of methane and hydrogen sulfide concentrations in biogas derived from anaerobic digestion. Anaerobic digesters were fed either 100% cow dung substrates or a 50:50 mixture of cow dung with municipal solid waste (MSW). Methane levels were monitored using an MQ-4 sensor, hydrogen sulfide via an MQ-136 sensor, and temperature with a K-type thermocouple, all integrated with an ATmega 2560 microcontroller system. The 100% cow dung digester produced biogas with maximum methane concentrations of 3488 ppm at 21 days, indicating improved methane production compared to the 50:50 mixture of cow dung with MSW. Hydrogen sulfide reached 195 ppm and 192 ppm for the 100% cow dung and mixed digesters. Mesophilic temperature conditions were maintained throughout the digestion process. Real-time quantification of biogas composition demonstrates the capabilities of microcontroller-based anaerobic digester monitoring to provide precise methane and hydrogen sulfide measurements. [ABSTRACT FROM AUTHOR]

This work provides the solutions to the technical challenges of output power fluctuations and intermittencies associated with wind turbines due to a continuously varying input of wind speed and an everchanging wind direction. The design comprises of the wind sensing, electronic control and electric motor sections. Microcontroller technology using Atmega328P, integration of control algorithms, local wind data from wind sensors, precise motor control mechanisms and simulation techniques were applied. When incorporated into a conventional wind turbine, it equips the turbine with a wind-speed reinforcement capability. Simulation results show that the designed circuit has demonstrated tremendous capability to making-up for low power output emanating from low wind speed and to harvest available wind irrespective of originating wind direction. This, it does by generating appropriate speed compensation data in order to reinforce the rotors speed. It was recommended that the wind turbine control device so designed be modelled and evaluated in order to validate the optimized performance. [ABSTRACT FROM AUTHOR]

Wireless short-range communication has become widespread in the modern era, partly due to the advancement of the Internet of Things (IoT) and smart technology. This technology is now utilized in various sectors, including lighting, medical, and industrial applications. This article aims to examine the historical, present, and forthcoming advancements in wireless short-range communication. Additionally, the review will analyze the modifications made to communication protocols, such as Bluetooth, RFID and NFC, in order to better accommodate modern applications. Batteryless technology, particularly batteryless NFC, is an emerging development in short-range wireless communication that combines power and data transmission into a single carrier. This modification will significantly influence the trajectory of short-range communication and its applications. The foundation of most low-power, short-range communication applications relies on an ultra-low-power microcontroller. Therefore, this study will encompass an analysis of ultra-low-power microcontrollers and an investigation into the potential limitations they might encounter in the future. In addition to offering a thorough examination of current Wireless short-range communication, this article will also attempt to forecast future patterns and identify possible obstacles that future research may address. [ABSTRACT FROM AUTHOR]