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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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

This paper works on detecting a person in bed for sleep routine and sleep pattern monitoring based on the Micro-Electro-Mechanical Systems (MEMS) accelerometer and Internet of Things (IoT) embedded system board. This work provides sleep information, patient assessment, and elderly care for patients who live alone via tele-distance to doctors or family members. About 216,000 pieces of acceleration data were collected, including three classes: no person in bed, a static laying position, and a moving state for Artificial Intelligence (AI) application. Six well-known Machine-Learning (ML) algorithms were evaluated with precision, recall, F1-score, and accuracy in the workstation before implementing in the STM32-microcontroller for real-time state classification. The four best algorithms were selected to be programmed into the IoT board and applied for real-time testing. The results demonstrate the high accuracy of the ML performance, more than 99%, and the Classification and Regression Tree algorithm is among the best models with a light code size of 1583 bytes. The smart bed information is sent to the IoT dashboard of Node-RED via a Message Queuing Telemetry broker (MQTT). [ABSTRACT FROM AUTHOR]

This article deals with power supply linear and switching regulators commonly used in various applications for stabilizing an output voltage and ensuring a necessary power input to the load. It describes their basic parameters, performances, advantages, and disadvantages according to their topologies. We design a measurement chain for efficiency evaluation based on power monitors INA219 connected to an embedded system, Arduino UNO. Measurements were focused on evaluation linear regulators MA7805, LM317, switching regulators SZBK07, LM2575, SCW05B-05, and XH-M401. The resulting efficiency of linear and switching regulators was analyzed and errors in the measurement chain were evaluated. The second contribution is an innovative way of carrying out regulator noise estimation using a fast Allan variance method, focused on white noise and flicker noise (bias instability). The main contribution is employing a fast Allan variance method algorithm that dramatically decreases computation time by up to 11 s for 72 million measured (or generated) samples. It enables the analysis of large data sets of various physical quantities (for example, regulator output voltages). [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices often operate with limited resources while interacting with users and their environment, generating a wealth of data. Machine learning models interpret such sensor data, enabling accurate predictions and informed decisions. However, the sheer volume of data from billions of devices can overwhelm networks, making traditional cloud data processing inefficient for IoT applications. This paper presents a comprehensive survey of recent advances in models, architectures, hardware, and design requirements for deploying machine learning on low-resource devices at the edge and in cloud networks. Prominent IoT devices tailored to integrate edge intelligence include Raspberry Pi, NVIDIA's Jetson, Arduino Nano 33 BLE Sense, STM32 Microcontrollers, SparkFun Edge, Google Coral Dev Board, and Beaglebone AI. These devices are boosted with custom AI frameworks, such as TensorFlow Lite, OpenEI, Core ML, Caffe2, and MXNet, to empower ML and DL tasks (e.g., object detection and gesture recognition). Both traditional machine learning (e.g., random forest, logistic regression) and deep learning methods (e.g., ResNet-50, YOLOv4, LSTM) are deployed on devices, distributed edge, and distributed cloud computing. Moreover, we analyzed 1000 recent publications on "ML in IoT" from IEEE Xplore using support vector machine, random forest, and decision tree classifiers to identify emerging topics and application domains. Hot topics included big data, cloud, edge, multimedia, security, privacy, QoS, and activity recognition, while critical domains included industry, healthcare, agriculture, transportation, smart homes and cities, and assisted living. The major challenges hindering the implementation of edge machine learning include encrypting sensitive user data for security and privacy on edge devices, efficiently managing resources of edge nodes through distributed learning architectures, and balancing the energy limitations of edge devices and the energy demands of machine learning. [ABSTRACT FROM AUTHOR]

Smart home devices, also known as IoT devices, provide significant convenience; however, they also present opportunities for attackers to jeopardize homeowners' security and privacy. Securing these IoT devices is a formidable challenge because of their limited computational resources. Machine learning-based intrusion detection systems (IDSs) have been implemented on the edge and the cloud; however, IDSs have not been embedded in IoT devices. To address this, we propose a novel machine learning-based two-layered IDS for smart home IoT devices, enhancing accuracy and computational efficiency. The first layer of the proposed IDS is deployed on a microcontroller-based smart thermostat, which uploads the data to a website hosted on a cloud server. The second layer of the IDS is deployed on the cloud side for classification of attacks. The proposed IDS can detect the threats with an accuracy of 99.50% at cloud level (multiclassification). For real-time testing, we implemented the Raspberry Pi 4-based adversary to generate a dataset for man-in-the-middle (MITM) and denial of service (DoS) attacks on smart thermostats. The results show that the XGBoost-based IDS detects MITM and DoS attacks in 3.51 ms on a smart thermostat with an accuracy of 97.59%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed solution for the high-power IGBT driver can be used as a reference for the design of similar intelligent gate drives, improving the reliability of power electronic converters in traction, industrial, and renewable energy applications. High-power IGBTs are used in power electronic converters in a variety of applications: traction drives, renewable power converters, mining equipment, oil and water pumping, and so on. To control a transistor, a special gate driver board is required. This board converts the logical control signal into the appropriate voltage values necessary to turn the resistor on and off. Gate drivers can perform the protection functions of IGBTs using hardware and algorithmic approaches. Application-specific integrated circuits are often used in driver solutions to implement control and protection. The development of an application-specific integrated circuit is a time-consuming and expensive procedure, which increases the cost of the driver. This paper describes the control and protection algorithms implemented in an intelligent IGBT driver based on a low-cost microcontroller. The use of the microcontroller makes the gate driver design more flexible and allows for the accurate tuning of the protection thresholds. The gate driver protects the IGBT from short-circuiting, overcurrent, and overvoltage, monitors the voltage supply, and controls the switch on and switch off processes in the transistor. The performance of the protection algorithms was tested experimentally using a specialized test bench. [ABSTRACT FROM AUTHOR]

This research paper investigates an LED strip lighting system, whose LED lighting controller was developed based on the PIC24FV32KA302 microcontroller and PCA9685 PWM driver. During the study, various parameters of the system were evaluated, including the response time from the length of commands, the dependence of the PWM signal characteristics on the logic values generated by the microcontroller, and the dependence of the LED light flux and power on the percentage of the PWM signal and the power of the power supply channels. The results of the study revealed that the reaction time depended on the length of the sent command, where as the logic values of the microcontroller were changed from 1000 to 4000, the size of the PWM signal changed from 25 to 100%. The use of the I2C communication protocol, which is a master–slave architecture and uses data and synchronization lines, was also found to affect response times. When the percentage of the PWM signal was changed from 10 to 100%, the light flux of the LED strip changed from 100 to 1000 lm. These results reflect the advantages of applying microcontrollers and PWM drivers to LED control systems, emphasizing their flexibility, efficiency, and precise light control. [ABSTRACT FROM AUTHOR]

Concussions, a prevalent public health concern in the United States, often result from mild traumatic brain injuries (mTBI), notably in sports such as American football. There is limited exploration of smart-textile-based sensors for measuring the head impacts associated with concussions in sports and recreational activities. In this paper, we describe the development and construction of a smart textile impact sensor (STIS) and validate STIS functionality under high magnitude impacts. This STIS can be inserted into helmet cushioning to determine head impact force. The designed 2 × 2 STIS matrix is composed of a number of material layered structures, with a sensing surface made of semiconducting polymer composite (SPC). The SPC dimension was modified in the design iteration to increase sensor range, responsiveness, and linearity. This was to be applicable in high impact situations. A microcontroller board with a biasing circuit was used to interface the STIS and read the sensor's response. A pendulum test setup was constructed to evaluate various STISs with impact forces. A camera and Tracker software were used to monitor the pendulum swing. The impact forces were calculated by measuring the pendulum bob's velocity and acceleration. The performance of the various STISs was measured in terms of voltage due to impact force, with forces varying from 180 to 722 N. Through data analysis, the threshold impact forces in the linear range were determined. Through an analysis of linear regression, the sensors' sensitivity was assessed. Also, a simplified model was developed to measure the force distribution in the 2 × 2 STIS areas from the measured voltages. The results showed that improving the SPC thickness could obtain improved sensor behavior. However, for impacts that exceeded the threshold, the suggested sensor did not respond by reflecting the actual impact forces, but it gave helpful information about the impact distribution on the sensor regardless of the accurate expected linear response. Results showed that the proposed STIS performs satisfactorily within a range and has the potential to be used in the development of an e-helmet with a large STIS matrix that could cover the whole head within the e-helmet. This work also encourages future research, especially on the structure of the sensor that could withstand impacts which in turn could improve the overall range and performance and would accurately measure the impact in concussion-causing impact ranges. [ABSTRACT FROM AUTHOR]

This paper presents the development of a novel anti-violence device titled "no pAIn" (an acronym for Never Oppressed Protected by Artificial Intelligence Nonviolence system), which harnesses the power of artificial intelligence (AI). Primarily designed to combat violence against women, the device offers personal safety benefits for individuals across diverse demographics. Operating autonomously, it necessitates no user interaction post-activation. The AI engine conducts real-time speech recognition and effectively discerns genuine instances of aggression from non-violent disputes or conversations. Facilitated by its Internet connectivity, in the event of detected aggression, the device promptly issues assistance requests with real-time precise geolocation tracking to predetermined recipients for immediate assistance. Its compact size enables discreet concealment within commonplace items like candy wrappers, purpose-built casings, or wearable accessories. The device is battery-operated. The prototype was developed using a microcontroller board (Arduino Nano RP2040 Connect), incorporating an omnidirectional microphone and Wi-Fi module, all at a remarkably low cost. Subsequent functionality testing, performed in debug mode using the Arduino IDE serial monitor, yielded successful results. The AI engine exhibited exceptional accuracy in word recognition, complemented by a robust logic implementation, rendering the device highly reliable in discerning genuine instances of aggression from non-violent scenarios. [ABSTRACT FROM AUTHOR]

This work presents a configurable Internet of Things architecture for acoustical sensing and analysis for frequent remote respiratory assessments. The proposed system creates a foundation for enabling real-time therapy and patient feedback adjustment in a telemedicine setting. By allowing continuous remote respiratory monitoring, the system has the potential to give clinicians access to assessments from which they could make decisions about modifying therapy in real-time and communicate changes directly to patients. The system comprises a wearable wireless microphone array interfaced with a programmable microcontroller with embedded signal conditioning. Experiments on the phantom model were conducted to demonstrate the feasibility of reconstructing acoustic lung images for detecting obstructions in the airway and provided controlled validation of noise resilience and imaging capabilities. An optimized denoising technique and design innovations provided 7 dB more SNR and 7% more imaging accuracy for the proposed system, benchmarked against digital stethoscopes. While further clinical studies are warranted, initial results suggest potential benefits over single-point digital stethoscopes for internet-enabled remote lung monitoring needing noise immunity and regional specificity. The flexible architecture aims to bridge critical technical gaps in frequent and connected respiratory function at home or in busy clinical settings challenged by ambient noise interference. [ABSTRACT FROM AUTHOR]

Micro-ROS is widely used to bridge the performance gap between resource-constrained microcontrollers and powerful computing devices in ROS-based robotic applications. After modeling the callback scheduling module and the communication module in micro-ROS, we found that there are some design flaws that significantly impact the real-time performance of micro-ROS. To improve the timing predictability and run-time efficiency of micro-ROS, we design and implement a priority-driven chain-aware scheduling system (PoDS) based on the existing micro-ROS architecture. The experimental results demonstrate that our proposed PoDS exhibits significantly improved real-time performance compared to the default micro-ROS. [ABSTRACT FROM AUTHOR]

This paper presents the development and validation of a cost-efficient and uncomplicated real-time localization system (RTLS) for use in mobile robotics, specifically within indoor and storage environments. By harnessing ultrasonic waves to measure distances from three beacons, the system provides stable and reliable localization. This method utilizes the time-of-flight (TOF) principle, allowing for accurate distance calculations with simple microcontrollers. The system is designed to update the robot's position at a frequency of at least 10 times per second, ensuring smooth navigation. Our trilateration-based approach allows for the precise determination of the robot's position with a notable standard deviation accuracy of up to 15 mm. The aim was to design a simple yet sufficiently accurate system and verify its precision through experimental measurements. The experimental results demonstrate the system's efficacy and lay a solid foundation for advancing this technology. Furthermore, the cost for the components required to build this indoor localization system (ILS) with three beacons and one tag is remarkably low, under EUR 80. [ABSTRACT FROM AUTHOR]

Internet-of-things networks consist of multiple sensor devices spread over a wide area. In order to protect the data from unauthorized access and tampering, it is essential to ensure secure communication between the sensor devices and the central server. This security measure aims to guarantee authenticity, confidentiality, and data integrity. Unlike traditional computing systems, sensor node devices are often limited regarding memory and computing power. Lightweight communication protocols, such as LoRaWAN, were introduced to overcome these limitations. However, despite the lightweight feature, the protocol is vulnerable to different types of attacks. This proposal presents a highly secure key establishment protocol that combines two cryptography schemes: Elliptic Curve Qu–Vanstone and signcryption key encapsulation. The protocol provides a method to establish a secure channel that inherits the security properties of the two schemes. Also, it allows for fast rekeying with only one exchange message, significantly reducing the handshake complexity in low-bandwidth communication. In addition, the selected schemes complement each other and share the same mathematical operations in elliptic curve cryptography. Moreover, with the rise of a community-friendly platform like RISC-V, we implemented the protocol on a RISC-V system to evaluate its overheads regarding the cycle count and execution time. [ABSTRACT FROM AUTHOR]

Hypertension, often termed "the silent killer", is associated with cardiovascular risk and requires regular blood pressure (BP) monitoring. However, existing methods are cumbersome and require medical expertise, which is worsened by the need for physical contact, particularly during situations such as the coronavirus pandemic that started in 2019 (COVID-19). This study aimed to develop a cuffless, continuous, and accurate BP measurement system using a photoplethysmography (PPG) sensor and a microcontroller via PPG signals. The system utilizes a MAX30102 sensor and ESP-WROOM-32 microcontroller to capture PPG signals that undergo noise reduction during preprocessing. Peak detection and feature extraction algorithms were introduced, and their output data were used to train a machine learning model for BP prediction. Tuning the model resulted in identifying the best-performing model when using a dataset from six subjects with a total of 114 records, thereby achieving a coefficient of determination of 0.37/0.46 and a mean absolute error value of 4.38/4.49 using the random forest algorithm. Integrating this model into a web-based graphical user interface enables its implementation. One probable limitation arises from the small sample size (six participants) of healthy young individuals under seated conditions, thereby potentially hindering the proposed model's ability to learn and generalize patterns effectively. Increasing the number of participants with diverse ages and medical histories can enhance the accuracy of the proposed model. Nevertheless, this innovative device successfully addresses the need for convenient, remote BP monitoring, particularly during situations like the COVID-19 pandemic, thus making it a promising tool for cardiovascular health management. [ABSTRACT FROM AUTHOR]

Legitimate identification of devices is crucial to ensure the security of present and future IoT ecosystems. In this regard, AI-based systems that exploit intrinsic hardware variations have gained notable relevance. Within this context, on-chip sensors included for monitoring purposes in a wide range of SoCs remain almost unexplored, despite their potential as a valuable source of both information and variability. In this work, we introduce and release a dataset comprising data collected from the on-chip temperature and voltage sensors of 20 microcontroller-based boards from the STM32L family. These boards were stimulated with five different algorithms, as workloads to elicit diverse responses. The dataset consists of five acquisitions (1.3 billion readouts) that are spaced over time and were obtained under different configurations using an automated platform. The raw dataset is publicly available, along with metadata and scripts developed to generate pre-processed T–V sequence sets. Finally, a proof of concept consisting of training a simple model is presented to demonstrate the feasibility of the identification system based on these data. Dataset: https://doi.org/10.5281/zenodo.10042177. Dataset License: The dataset is available under CC-BY 4.0 licence [ABSTRACT FROM AUTHOR]

Microcontroller Units (MCUs) play a vital role in embedded devices due to their energy efficiency and scalability. The firmware in MCUs contains vulnerabilities that can lead to digital and physical harm. However, testing MCU firmware faces challenges due to various tool limitations and unavailable firmware details. To address this problem, research is turning to fuzzing and rehosting. Due to the inherent imbalance in computational resources of the fuzzing algorithm and the lack of consideration for the computational resource requirements of rehosting methods, some hardware behavior-related paths are difficult to discover. In this work, we propose a novel Dynamically Co-directional Guidance Fuzzing (DCGFuzz) method to improve security analysis efficiency. Our method dynamically correlates computational resource allocation in both fuzzing and rehosting, computing a unified power schedule score. Using the power schedule score, we adjust test frequencies for various paths, boosting testing efficiency and aiding in the detection of hardware-related paths. We evaluated our approach on nine real-world pieces of firmware. Compared to the previous approach, we achieved a maximum increase of 47.9% in path coverage and an enhancement of 27.6% in effective model coverage during the fuzzing process within 24 h. [ABSTRACT FROM AUTHOR]

In a cooling system for radio astronomy receivers, maintaining cold heads and compressors is essential for consistent performance. This project focuses on monitoring the power currents of the cold head's motor to address potential mechanical deterioration, which could jeopardize the overall functionality of the system. Using Hall effect sensors, a microcontroller-based electronic board, and artificial intelligence, the system detects and predicts anomalies. The model operates using an unsupervised approach based on incremental clustering. Since potential fault scenarios can be multiple and often challenging to simulate or identify during training, the system is initially trained using known operational categories. Over time, the system adapts and evolves by incorporating new data, which can be assigned to existing categories or, in the case of new anomalies, form new categories. This incremental approach enables the system to enhance its performance over the years, adapting to new anomaly scenarios and ensuring precise and reliable monitoring of the cold head's health. [ABSTRACT FROM AUTHOR]

As it concerns remote laboratories (RLs) for teaching microcontroller programming, the related literature reveals several common characteristics and a common architecture. Our search of the literature was constrained to papers published in the period of 2020–2023 specifically on remote laboratories related to the subject of teaching microcontroller programming of the Arduino family. The objective of this search is to present, on the one hand, the extent to which the RL platform from the Hellenic Mediterranean University (HMU-RLP) for Arduino microcontroller programming conforms to this common architecture and, on the other hand, how it extends this architecture with new features for monitoring and assessing users' activities over remote labs in the context of pervasive and supervised learning. The HMU-RLP hosts a great number of experiments that can be practiced by RL users in the form of different scenarios provided by teachers as activities that users can perform in their self-learning process or assigned as exercises complementary to the theoretical part of a course. More importantly, it provides three types of assessments of the code users program during their experimentation with RLs. The first type monitors each action users perform over the web page offered by the RL. The second type monitors the activities of users at the hardware level. To this end, a shadow microcontroller is used that monitors the pins of the microcontroller programmed by the users. The third type automatically assesses the code uploaded by the users, checking its similarity with the prototype code uploaded by the instructors. A trained AI model is used to this end. For the assessments provided by the HMU-RLP, the experience API (xAPI) standard is exploited to store users' learning analytics (LAs). The LAs can be processed by the instructors for the students' evaluation and personalized learning. The xAPI reporting and visualization tools used in our prototype RLP implementation are also presented in the paper. We also discuss the planned development of such functionalities in the future for the use of the HMU-RLP as an adaptive tool for supervised distant learning. [ABSTRACT FROM AUTHOR]

This article presents a new firmware update paradigm for optimising the procedure in microcontrollers. The aim is to allow updating during program execution, without interruptions or restarts, replacing only specific code segments. The proposed method uses static and absolute addresses to locate and isolate the code segment to be updated. The work focuses on Microchip's PIC18F27K42 microcontroller and includes an example of updating functionality without affecting ongoing applications. This approach is ideal for band limited channels, reducing the amount of data transmitted during the update process. It also allows incremental changes to the program code, preserving network capacity, and reduces the costs associated with data transfer, especially in firmware update scenarios using cellular networks. This ability to update the normal operation of the device, avoiding service interruption and minimising downtime, is of remarkable value. [ABSTRACT FROM AUTHOR]

Datasets are key to developing new machine learning-based applications but are very costly to prepare, which hinders research and development in the field. We propose an edge-to-cloud end-to-end system architecture optimized for sport activity recognition dataset collection and application deployment. Tests in authentic contexts of use in four different sports have revealed the system's ability to effectively collect machine learning-usable data, with an energy consumption compatible with the timeframe of most of the sport types. The proposed architecture relies on a key feature of the Measurify internet of things framework for the management of measurement data (i.e.,.csv dataset management) and supports a workflow designed for efficient data labeling of signal timeseries. The architecture is independent of any specific sport, and a new dataset generation application can be set up in a few days, even by novice developers. With a view to concretely supporting the R&D community, our work is released open-source. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach for error prediction (EP) tailored to near-threshold operations, addressing the energy-efficient requirements of digital circuits in applications such as IoT devices and wearables. The novel EP technique combines the benefits of error prediction and detection, effectively addressing critical issues associated with each method by enabling adaptive prediction capability and voltage scaling. More specifically, the presented EP method requires no modifications to the processor pipeline and mitigates the generation of false-positive errors, ensuring stable operation of the system at high-efficiency points. The effectiveness of this strategy is demonstrated through its implementation in a near-threshold 32-bit microprocessor system with a modest 5.82% area overhead. Silicon measurements validate the adaptive EP system from 0.59 to 0.66 V (4–32 MHz) and confirm its removal of all voltage margins. Here, the EP technique reduces the energy consumption by 18.6–25.1% with respect to the signoff margins and it allows the system to operate without energy overhead compared to its ideal non-margined critical operation point, with less than a 5% throughput loss. [ABSTRACT FROM AUTHOR]

In the era of widespread edge computing, energy conservation modes like complete power shutdown are crucial for battery-powered devices, but they risk data loss in volatile memory. Energy autonomous systems, relying on ambient energy, face operational challenges due to power losses. Recent advancements in emerging nonvolatile memories (NVMs) like FRAM, RRAM, MRAM, and PCM offer mature solutions to sustain work progress with minimal energy overhead during outages. This paper thoroughly reviews utilizing emerging NVMs in microcontroller units (MCUs), comparing their key attributes to describe unique benefits and potential applications. Furthermore, we discuss the intricate details of NVM circuit design and NVM-driven compute-in-memory (CIM) architectures. In summary, integrating emerging NVMs into MCUs showcases promising prospects for next-generation applications such as Internet of Things and neural networks. [ABSTRACT FROM AUTHOR]

The paper presents an adaptation of the microinverter platform from Texas Instruments to incorporate a battery energy storage system (BESS) alongside the development of the BESS system itself. Initially designed for unidirectional power flow between PV panels and an electric grid, the platform required modifications to accommodate bidirectional energy transfer for BESS integration. These modifications encompass software adjustments and hardware enhancements, which are all detailed within the paper. The electrical configuration includes selecting and deploying components such as DCDC power converters, microcontrollers, measured signals, and actuating signals to facilitate battery connection to the platform's DC bus. Furthermore, a supervisory control and data acquisition (SCADA) system is devised for supervisory control and monitoring, with its implementation outlined. Control software tailored for the chosen microcontroller of the DCDC converters is described in terms of structure and functionality. A hardware-in-the-loop (HIL) methodology is employed to validate the proposed modifications and microgrid configuration. Utilizing the real-time simulator OPAL-RT, the paper presents experimental results and their analysis within the considered microgrid environment. [ABSTRACT FROM AUTHOR]

Eye stimulation research plays a critical role in advancing our understanding of visual processing and developing new therapies for visual impairments. Despite its importance, researchers and clinicians still face challenges with the availability of cost-effective, precise, and versatile tools for conducting these studies. Therefore, this study introduces a high-resolution, compact, and budget-friendly multi-wavelength LED light source tailored for precise and versatile eye stimulation, addressing the aforementioned needs in medical research and visual science. Accommodating standard 3 mm or 5 mm package LEDs, the system boasts broad compatibility, while its integration with any microcontroller capable of PWM generation and supporting SPI and UART communication ensures adaptability across diverse applications. Operating at high resolution (18 bits or more) with great linearity, the LED light source offers nuanced control for sophisticated eye stimulation protocols. The simple 3D printable optical design allows the coupling of up to seven different wavelengths while ensuring the cost-effectiveness of the device. The system's output has been designed to be fiber-coupled with standard SMA connectors to be compatible with most solutions. The proposed implementation significantly undercuts the cost of commercially available solutions, providing a viable, budget-friendly option for advancing eye stimulation research. [ABSTRACT FROM AUTHOR]

This thesis introduces a nondestructive inspection and weight grading device for chicken wings to replace the traditional manual grading operation. A two-sided quality nondestructive inspection model of chicken wings based on the YOLO v7-tiny target detection algorithm is designed and deployed in a Jetson Xavier NX embedded platform. An STM32 microcontroller is used as the main control platform, and a wing turning device adapting to the conveyor belt speed, dynamic weighing, and a high-efficiency intelligent grading unit are developed, and the prototype is optimized and verified in experiments. Experiments show that the device can grade four chicken wings per second, with a comprehensive accuracy rate of 98.4%, which is better than the traditional grading methods in terms of efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Photosynthetically Active Radiation (PAR) is an important parameter in the plant photosynthesis process, which can relate to plant growth, crop water use, and leaf gas exchange. Previously, many researchers utilized commercially available sensors to monitor PAR. The high cost of the commercially available PAR sensors has limited researchers, agricultural professionals, and farmers to use and expand PAR monitoring in agricultural lands. Thus, this paper focuses on designing an affordable Internet of Things (IoT)-based PAR sensor monitoring system including 3D-printed enclosures (waterproof) for the sensors, performance evaluation of multiple light sensors, solar powering configuration, cloud setup, and cost analysis. Three sensors, including VTB8440BH photodiode, SI 1145, and LI-190R sensors, were evaluated. The 3D-printed waterproof enclosures were designed for the photodiode and SI 1145. Particle Boron was used for recording and sending the sensor data to the IoT webserver. Both the photodiode and SI 1145 were compared to LI-190R, which is the industry standard. In the calibration process, the R2 values of the photodiode and SI 1145 with LI-190R were 0.609 and 0.961, respectively. Field validation data shows that SI 1145 had a strong correlation with LI-190R. In addition, the performance evaluation data shows the photodiode had a weaker correlation with LI-190R than SI 1145. In conclusion, the study successfully developed and designed affordable and reliable IoT-based PAR sensor monitoring systems, including a 3D-printed housing, hardware, programming, and IoT website. SI 1145 with a glass filter is an alternative sensor to monitor PAR at a low cost and has the advantage of being connected to IoT microcontrollers. [ABSTRACT FROM AUTHOR]