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Potential transformers are vital for measuring and protecting the power grid. Their accuracy and reliability directly impact the stability and security of the power system. To address the issues with traditional high-precision potential transformer calibrators, such as cumbersome operation and low efficiency, a high-precision potential transformer calibrator has been developed. The calibrator is based on an embedded system architecture of FPGA and ARM. It uses a high-precision current comparator along with feedback control technology. By monitoring and adjusting the error feedback voltage, it can perform the automated calibration of potential transformers with an accuracy class of 0.0001. The measurement ranges from 0.00001% to 200.0%. This study can be adapted to meet the development needs of modern digital measurement systems. [ABSTRACT FROM AUTHOR]

The advantageous applications of magnetic bistable microwires have emerged during long-lasting research. They have a wide range of applications in the scientific sphere or technical practice. They can be used for various applications, including magnetic memories, biomedicine, and sensors. This manuscript is focused on the last-mentioned application of microwires—sensors—discussing various digital signal processing techniques used in practical applications. Thanks to the highly sensitive properties of microwires and their two stable states of magnetization, it is possible to perform precise measurements with less demanding digital processing. The manuscript presents four practical signal-processing methods of microwire response using three different experiments. These experiments are focused on detecting the signal in a simple environment without an external magnetic background, measuring with the external background of a ferromagnetic core, and measuring in harsh conditions with a strong magnetic background. The experiments aim to propose the best method under various conditions, emphasizing the quality and signal processing speed of the microwire signal. [ABSTRACT FROM AUTHOR]

This research addresses the issues of weak anti-disturbance ability, fast response, and incompatibility of overshoot in the control process of brushless DC motors (BLDCs). A six-step commutation control method based on second-order active disturbance rejection control (ADRC) is derived following the analysis of the BLDC model and the mathematical model of ADRC. A control model of the BLDC using both PI and ADRC is constructed. Detailed comparative and quantitative analyses of the simulation results using PI and ADRC are conducted, focusing on the anti-load disturbance capabilities using the integrated square error (ISE), integrated time square error (ITSE), integrated absolute error (IAE), and integrated time absolute error (ITAE). Experimental testing on the STM32F4 controller is also carried out, analyzing four error integral criteria in depth. The results indicate that both the ADRC and PI control modes can track the target signal without overshooting, demonstrating strong anti-load disturbance ability and robustness at varying working speeds. In the BLDC control system, using the ADRC control method can achieve fast and non-overshoot tracking of target signals compared to the PI control method, and ADRC has stronger resistance to load disturbances. [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]

Research on the electrical properties of rocks and ores plays a crucial role in the development of geophysical electromagnetism methods. However, currently available instruments suffer from high power consumption, a limited number of electrodes, inaccurate measurements, poor portability, and a limited ability to measure the electrical parameters of rocks and ores. To address these issues, this paper presents a three-dimensional electrical impedance tomography system for rock samples with high-density microelectrodes based on an Android system and STM32 microcontroller. The system features high observation accuracy, dense electrode arrays (with 384 current and potential electrodes), flexible electrode selection, user-friendly human–computer interaction, good stability, and real-time performance. Powered by a single power bank, the entire instrument can be controlled and monitored wirelessly via Bluetooth and Wi-Fi technology using an Android smartphone. Additionally, the system not only enables accurate measurement of electrical parameters, but also facilitates the generation of three-dimensional impedance imaging of specimens via inversion algorithms after data export, allowing for a comprehensive understanding of the electrical properties of rocks and ores. This system holds great potential for future research in this field. [ABSTRACT FROM AUTHOR]

This paper presents the development of a portable solar panel wireless charging device with an advanced charging algorithm. The device features a 6500 mAh Li-ion battery and is designed to efficiently charge smartphones and laptops. It incorporates a simulated solar panel, charging circuit, microcontroller, and wireless charging circuits. Rigorous testing has demonstrated its stable output voltage and current at 5 V/2 A, with high power transfer efficiency. The advanced charging algorithm optimizes power transfer efficiency and reduces charging time by adjusting the current and voltage to match the device's needs. This smart approach extends battery life and improves device performance. The device utilizes the Basic MPPT P&O Algorithm to dynamically track the solar panel's Maximum Power Point and optimize power extraction. The Lithium Battery Charging Characteristic Algorithm adjusts the charging levels to ensure safe and efficient charging. However, challenges were encountered during implementation and testing, highlighting the complexities of real-world scenarios and the need for further optimization. The device offers a sustainable and environmentally friendly charging alternative, particularly in areas with limited power access. The research findings contribute to the field of wireless charging, driving advancements in sustainable and efficient technologies. [ABSTRACT FROM AUTHOR]

Ankle joint flexion and extension movements play an important role in the rehabilitation training of patients who have been injured or bedridden for a long time before and after surgery. Accurately guiding patients to perform ankle flexion and extension movements can significantly reduce deep vein thromboembolism. Currently, most ankle rehabilitation devices focus on assisting patients with ankle flexion and extension movements, and there is a lack of devices for effectively monitoring these movements. In this study, we designed an ankle joint flexion and extension movement-monitoring device based on a pressure sensor. It was composed of an STM32 microcontroller, a pressure sensor, an HX711A/D conversion chip, and an ESP8266 WiFi communication module. The value of the force and the effective number of ankle joint flexion and extension movements were obtained. An experimental device was designed to verify the accuracy of the system. The maximum average error was 0.068 N; the maximum average relative error was 1.7%; the maximum mean-squared error was 0.00464 N. The results indicated that the monitoring device had a high accuracy and could effectively monitor the force of ankle flexion and extension movements, ultimately ensuring that the patient could effectively monitor and grasp the active ankle pump movement. [ABSTRACT FROM AUTHOR]

There are various tools for real-time data acquisition and analysis of high-frequency signals. Usually, the tools for data acquisition and analysis are incompatible with each other, expensive, or require the utilization of various technologies. Designing a unique toolset for real-time data acquisition and analysis of high-frequency signals presents a large challenge, both in terms of hardware and software design. Some of the main challenges include fulfilling strict real-time performance requirements, using cost-effective hardware components, and designing a software architecture with minimal software latency for data sampling on an embedded platform and data analysis on a PC machine. This paper presents a software architecture design methodology for embedded platforms, including an RTOS and an Ethernet interface, along with the application architecture on the host side. To demonstrate the advantages of the proposed methodology, a prototype consisting of the STM32H747 microcontroller, FreeRTOS operating system, and a GUI application based on the Qt framework has been created. It has been shown that using the proposed architecture design methodology makes it possible to preserve the maximal hardware sampling rate utilizing a 100 Mbps Ethernet link. In the experiments, sampling rates up to 4.57 MSps for 10-bit resolution and real-time transfer of data over Ethernet link and visualization on host PC have been achieved with this specific MCU. [ABSTRACT FROM AUTHOR]

With the increasing level in the intensification of agricultural production in China, continuous cropping obstacles have become a problem that needs to be solved. The use of vertical rotary tillage technology and soil disinfection technology is an effective solution. In this paper, a vertical rotary soil-tilling variable disinfection combine was developed and an on-board control system with STM32 as the control core was designed to realize the real-time acquisition of powder monopoly torque information and the variable application of soil disinfection chemicals. Based on the obtained experimental soil parameters, a discrete element soil particle model was established, and orthogonal experiments were conducted to analyze the single-blade roller tillage process, and the optimal operating parameters were finally selected as 500 mm powder monopoly depth, 320 r/min knife roller speed, and 0.26 m/s forward speed, respectively. The field experiment found that the average tillage depth of the implement was 489 mm, the stability coefficient of tillage depth was 94.50%, the uniformity coefficient of soil disinfection was 85.57%, and the applied amount and the speed ratio coefficient of the given flow were linearly related, respectively. This research provides a technical reference for the deep tillage and soil disinfection of the powder monopoly. [ABSTRACT FROM AUTHOR]

In recent years, since edge computing has become more and more popular, its security issues have become apparent and have received unprecedented attention. Thus, the current research concentrates on security not only regarding devices such as PCs, smartphones, tablets, and IoTs, but also the automobile industry. However, since attack vectors have become more sophisticated than ever, we cannot just protect the zone above the system software layer in a certain operating system, such as Linux, for example. In addition, the challenges in IoT devices, such as power consumption, performance efficiency, and authentication management, still need to be solved. Since most IoT devices are controlled remotely, the security regarding system maintenance and upgrades has become a big issue. Therefore, a mechanism that can maintain IoT devices within a trusted environment based on localhost or over-the-air (OTA) will be a viable solution. We propose a mechanism called STBEAT, integrating an open-source project with ARM TrustZone to solve the challenges of upgrading the IoT system and updating system files more safely. This paper focuses on the ARMv7 architecture and utilizes the security stack from TrustZone to OP-TEE under the STM32 board package, and finally obtains the security key from the trusted application, which is used to conduct the cryptographic operations and then install the newer image on the MMC interface. To sum up, we propose a novel software update strategy and integrated ARM TrustZone security extension to beef up the embedded ecosystem. [ABSTRACT FROM AUTHOR]

Physical processes working in parallel with digital ones have transformed the way we view systems and have led to the creation of applications that boost the quality of people's lives, increase security as well as decrease production costs of goods. Critical to this evolution is the cost decrease in the components of such systems, among which are gas sensors. In this work, a custom-made Co3O4 gas sensing element is presented, which can potentially be used as part of a cyber-physical system (CPS) for O3 monitoring. To investigate its performance, a CPS is developed using low-cost, low-power micro-controller units (MCUs) and comparisons both with the laboratory equipment and commercial off-the-shelf (COTS) ozone sensors are provided. The experiments show that the Co3O4 sensor works at room temperature with low input voltage and low power consumption when used with the proposed MCUs. Moreover, an enhanced gas sensing performance against ozone is observed under low-pressure conditions due to the detection of low ozone concentrations (85.90 ppb) and good sensor response (113.1%) towards 1100 ppb O3. However, the drawbacks that need improvement relate to the kinetics of the charge carriers, which affect the response time and recovery behavior. The effect of humidity needs to be clarified in further works. [ABSTRACT FROM AUTHOR]

This paper presents a solution for remote classes where hardware is offered as a service. The infrastructure was based on Raspberry Pi mini computers to which a set of different developments boards were connected. The proposed software architecture allows students to connect to remote resources and interact with them. Moreover, the services monitoring status of remote resources were introduced to facilitate software development and the learning process. Furthermore, live video feedback is available to visually monitor operation of the resources. Finally, a debugging server was deployed allowing us to establish a remote debugging session between a user's PC and the dev board on the server premises. The solution offers a comprehensive remote service including user management. Safety risks of the Internet-exposed infrastructure and safety precautions were discussed. The presented RemoteLab system allows students of WUST to gain knowledge, practise and realize exercises in scope of academic courses such as robot controllers and advanced robot control. Thanks to advances in remote education and utilized tools, the RemoteLab was designed and deployed, allowing stationary classes to be substituted with remote ones, while maintaining a high level of class knowledge transfer. Up to the present, the system has been utilized by over 100 students who could realize exercises and prepare for classes thanks to 24 h system availability. [ABSTRACT FROM AUTHOR]

High-frequency trading is a method of intervention on the financial markets that uses sophisticated software tools, and sometimes also hardware, with which to implement high-frequency negotiations, guided by mathematical algorithms, that act on markets for shares, options, bonds, derivative instruments, commodities, and so on. HFT strategies have reached considerable volumes of commercial traffic, so much so that it is estimated that they are responsible for most of the transaction traffic of some stock exchanges, with percentages that, in some cases, exceed 70% of the total. One of the main issues of the HFT systems is the prediction of the medium-short term trend. For this reason, many algorithms have been proposed in literature. The author proposes in this work the use of an algorithm based both on supervised Deep Learning and on a Reinforcement Learning algorithm for forecasting the short-term trend in the currency FOREX (FOReign EXchange) market to maximize the return on investment in an HFT algorithm. With an average accuracy of about 85%, the proposed algorithm is able to predict the medium-short term trend of a currency cross based on the historical trend of this and by means of correlation data with other currency crosses using techniques known in the financial field with the term arbitrage. The final part of the proposed pipeline includes a grid trading engine which, based on the aforementioned trend predictions, will perform high frequency operations in order to maximize profit and minimize drawdown. The trading system has been validated over several financial years and on the EUR/USD cross confirming the high performance in terms of Return of Investment (98.23%) in addition to a reduced drawdown (15.97 %) which confirms its financial sustainability. [ABSTRACT FROM AUTHOR]

The article describes the implementation of IoT technology in the teaching of microprocessor technology. The method presented in the article combines the reality and virtualization of the microprocessor technology laboratory. A created IoT monitoring device monitors the students' microcontroller pins and sends the data to the server to which the teacher is connected via the control application. The teacher has the opportunity to monitor the development of tasks and student code of the program, where the functionality of these tasks can be verified. Thanks to the IoT remote laboratory implementation, students' tasks during the lesson were improved. As many as 53% (n = 8) of those students who could improve their results achieved an improvement of one or up to two tasks during class. Before the IoT remote laboratory application, up to 30% (n = 6) of students could not solve any task and only 25% (n = 5) solved two tasks (full number of tasks) during the class. Before implementation, 45% (n = 9) solved one problem. After applying the IoT remote laboratory, these numbers increased significantly and up to 50% (n = 10) of students solved the full number of tasks. In contrast, only 10% (n = 2) of students did not solve any task. [ABSTRACT FROM AUTHOR]

Atmospheric oxygen anions play an important role in medical health, clinical medicine, environmental health, and the ecological environment. Therefore, the concentration of atmospheric anions is an important index for measuring air quality. This paper proposes a monitoring system for atmospheric oxygen anions based on Beidou positioning and unmanned vehicles. This approach combines Beidou positioning technology, 4G pass-through, the unmanned capacitance suction method, electromagnetic field theory, and atmospheric detection technology. The proposed instrument can monitor the overall negative oxygen ion concentration, temperature, and humidity in a certain region over time and provide data visualization for the concentration of negative oxygen ions. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) is a common and practical technique to detect human activity and other physiological parameters and is commonly implemented in wearable devices. However, the PPG signal is often severely corrupted by motion artifacts. The aim of this paper is to address the human activity recognition (HAR) task directly on the device, implementing a recurrent neural network (RNN) in a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity. To reach this goal, (i) we first develop an RNN, which integrates PPG and tri-axial accelerometer data, where these data can be used to compensate motion artifacts in PPG in order to accurately detect human activity; (ii) then, we port the RNN to an embedded device, Cloud-JAM L4, based on an STM32 microcontroller, optimizing it to maintain an accuracy of over 95% while requiring modest computational power and memory resources. The experimental results show that such a system can be effectively implemented on a constrained-resource system, allowing the design of a fully autonomous wearable embedded system for human activity recognition and logging. [ABSTRACT FROM AUTHOR]

Star trackers are navigation sensors that are used for attitude determination of a satellite relative to certain stars. A star tracker is required to be accurate and also consume as little power as possible in order to be used in small satellites. While traditional approaches use lookup tables for identifying stars, the latest advances in star tracking use neural networks for automatic star identification. This manuscript evaluates two low-cost processors capable of running a star identification neural network, the Intel Movidius Myriad 2 Vision Processing Unit (VPU) and the STM32 Microcontroller. The intention of this manuscript is to compare the accuracy and power usage to evaluate the suitability of each device for use in a star tracker. The Myriad 2 VPU and the STM32 Microcontroller have been specifically chosen because of their performance on computer vision algorithms alongside being cost-effective and low power consuming devices. The experimental results showed that the Myriad 2 proved to be efficient and consumed around 1 Watt of power while maintaining 99.08% accuracy with an input including false stars. Comparatively the STM32 was able to deliver comparable accuracy (99.07%) and power measurement results. The proposed experimental setup is beneficial for small spacecraft missions that require low-cost and low power consuming star trackers. [ABSTRACT FROM AUTHOR]

A key objective of modern medicine is precision medicine, whose purpose is to personalize the treatment based on the specific characteristics of the patients and their illness. To guide treatment decisions, it is generally necessary to have a sample of the neoplastic tissue, which is obtained only with biopsies or similar invasive surgical procedures. As tumors are heterogeneous in their volume and change over time, a dynamic analysis of diagnostic medical images can provide a better understanding of the entire tumor, both in the screening and follow-up phase. In this work, the authors proposed the use of a radiomics pipeline which is able to characterize the possible response of the oncological patients to the anti- programmed death-ligand protein 1 (PD-L1) immunotherapeutic treatment. The immunotherapeutic treatment consists of a modern therapeutic approach in which the physicians try to reactivate the patient's immune system so that it recognizes and destroys cancer cells. The oncological biomarkers capable of characterizing patients who can benefit from immunotherapy from those who would not, are being studied. One of them is related to the expression of the PD-L1 inhibitor in the surface of neoplastic cells which are analyzed in this paper, considering that the analyzed immunotherapeutic treatment is of the anti-PD-L1 type. In this context, the authors propose a pipeline for an immunotherapy response prediction based on the analysis of only CT-scan images of patients with metastatic bladder cancer. Using a framework based on the use of deep Autoeconder network, CT-scan images were analyzed to extract the features capable of discriminating the patient's response to anti-PD-L1 immunotherapy treatment from those who are not. The preliminary results obtained (accuracy of approximately 86% with a sensitivity of approximately 80% against a specificity of approximately 89%) on the analyzed patient dataset, allows the confirmation of the feasibility of the proposed method. Although validated in a dataset containing patients with only one tumor histology (bladder cancer), the proposed method shows how modern radiomics techniques can contribute significantly in the implementation of non-invasive predictive systems that support the physician in the therapeutic choice. The idea of the authors is to create a form of oncological point of care on an embedded platform that allows physicians to always have a support tool in choosing the best therapy to suggest to the patient. [ABSTRACT FROM AUTHOR]

The well-known subprime mortgage crisis, which began to manifest in early 2007, since when the effects of the speculative bubble begin to become evident from the increase in default rates in residential mortgages, has triggered a global crisis that has pushed various legislations over time to implement a series of financial reforms with the specific objective of avoiding that similar phenomena could be repeated over time. The ability to repay a loan is strongly influenced by the amortization algorithm that the bank has decided to adopt. This appears even more evident in variable interest rate loans since, as the economic conditions of the indexation parameter change, the definition of the loan balance and the related portion of interest will be decisive in relation to the borrower's ability to repay the loaned capital. A study of the main amortization algorithms and the related descriptions in the bank contracts will allow us to show which are the main issues due to an information asymmetry that, unfortunately, characterizes this type of contract and would seem to be one of the main reasons that lie at the root of the aforementioned crisis of subprime mortgages in the USA. Moreover, the authors will provide a clear analysis of the financial indicators usually reported in loan contracts and how often these indications are insufficient to characterize the actual cost of the loan. Furthermore, by highlighting the discretionary choice that banks often obtain following the contractual loan schemes commonly offered to retail and corporate clients, we will show how this often translates into greater cost to the borrower. Finally, we will propose two possible solutions to the problems highlighted, thus allowing us to reduce this information gap, which unfortunately translates into greater costs for customers with the associated increase in default rates, or the so-called nonperforming loan (NPLs) contracts. Therefore, the objective of this contribution is to show which are the most critical aspects of the bank contracts related to contractual transparency and to the presence or otherwise of hidden costs, i.e., not expressly shown in the contract. Specifically, we refer to the loan contracts issued in Italy both with reference to the local banking legislation and to the European one to which Italy must often refer. [ABSTRACT FROM AUTHOR]

Skin cancer is the most common type of cancer, as also among the riskiest in the medical oncology field. Skin cancer is more common in people who work or practice outdoor sports and those that expose themselves to the sun. It may also develop years after radiographic therapy or exposure to substances that cause cancer (e.g., arsenic ingestion). Numerous tumors can affect the skin, which is the largest organ in our body and is made up of three layers: the epidermis (superficial layer), the dermis (middle layer) and the subcutaneous tissue (deep layer). The epidermis is formed by different types of cells: melanocytes, which have the task of producing melanin (a pigment that protects against the damaging effects of sunlight), and the more numerous keratinocytes. The keratinocytes of the deepest layer are called basal cells and can give rise to basal cell carcinomas. We are interested in types of skin cancer that originate from melanocytes, i.e., the so-called melanomas, because it is the most aggressive. The dermatologist, during a complete visit, evaluates the personal and family history of the patient and carries out an accurate visual examination of the skin, thanks to the use of epi-luminescence (or dermoscopy), a special technique for enlarging and illuminating the skin. This paper mentions one of the most widely used diagnostic methods due to its simplicity and validity—the ABCDE method (Asymmetry, edge irregularity, Color Variegation, Diameter, Evolution). This methodology, based on "visual" investigation by the dermatologist and/or oncologist, has the advantage of not being invasive and quite easy to perform. This approach is affected by the opinion of who (physicians) applies it. For this reason, certain diagnosis of cancer is made, however, only with a biopsy, a procedure during which a portion of tissue is taken and then analyzed under a microscope. Obviously, this is particularly invasive for the patient. The authors of this article have analyzed the development of a method that obtains with good accuracy the early diagnosis of skin neoplasms using non-invasive, but at the same time, robust methodologies. To this end, the authors propose the adoption of a deep learning pipeline based on morphological analysis of the skin lesion. The results obtained and compared with previous approaches confirm the good performance of the proposed pipeline. [ABSTRACT FROM AUTHOR]