Search

Abstract The Chinese cooking process, intricate and diverse, encompasses a wide range of dishes, generating substantial data that often poses significant challenges for automation. To address these challenges, this paper introduces a versatile, highly reliable, user-friendly, and intelligent data recorder specifically designed for Chinese cuisine-the Chinese Cuisine Intelligent Cooker Recorder-V1.0 (CCICR-V1.0). This device is intended to record data generated during the cooking process and provide essential support for intelligent cooking robot systems. The core of CCICR-V1.0 is a STM32 microcontroller, equipped with sensors that monitor temperature, pressure, and attitude. These sensors facilitate the intelligent collection of data related to dishes, ingredients, and the movements of cookware throughout the cooking process. To ensure efficient data acquisition and storage, CCICR-V1.0 employs a multi-task data buffering storage method that effectively allocates CPU resources. NAND Flash is used as the storage medium, guaranteeing secure storage, management, and preservation of high-frequency, multi-channel data. Experimental results demonstrate the effectiveness of CCICR-V1.0 in achieving multi-channel, high-frequency data acquisition and storage in complex environments. This leads to an increase in automation efficiency of 89.9%. The weighing module demonstrates a maximum relative error of only 0.288%, while the attitude sensor experiment shows an attitude information error of just 0.22°C. Furthermore, the non-contact infrared temperature measurement module exhibits impressive performance with a maximum absolute error of 0.258 $$^\circ C$$ ∘ C and a maximum relative error of 0.88%. Notably, this measurement module has achieved a cost reduction of approximately 91.7% and an accuracy improvement of around 40%. Additionally, the response time of CCICR-V1.0 is less than 3 ms. With its ability to effectively capture data related to Chinese cooking, CCICR-V1.0 holds commercial value for widespread adoption and application in the field of intelligent cooking.

[Objective] To meet the requirements of constant and adjustable rotation speed of rolling barrel and constant rotation speed of rolling barrel when tea is rolled. [Methods] The 6CR-55 single-column disc twisting machine was used as the research object in this paper. Fuzzy control algorithm and PWM closed-loop control method were investigated to study the stable control of rotating speed of rolling drum, and established the mathematical model of rotating speed control system of rolling drum based on the STM32 single-chip microcontroller. The simulation experiments of uncorrected, PID and fuzzy PID algorithm control system were carried out by MATLAB. [Results] Compared with uncorrected control and PID control, the output speed of the fuzzy PID speed control system was more stable and accurate, and the anti-interference ability was stronger. The speed error was controlled within 3 r/min. [Conclusion] The fuzzy PID control method can meet the requirement for the constant and adjustable rotating speed of the rolling drum during tea rolling, and improve the rate of tea forming and cell breakage.

Enhanced computations resulted in the evolution of cyber-physical systems (CPS). These systems are highly vulnerable to security threats. This work addresses the security concerns for a reliable CPS in the Internet of Things (IoT) through an improvised RSA with enhanced security features. The proposed method renders data throughput encryption at 11.67 Kbps with an overall average execution time of 0.02 s. The proposed algorithm exhibits the unique feature of being asymmetric, lightweight & enhanced security owing to randomization (average entropy of 0.98). The performance metrics of the proposed method are compared with other state-of-art techniques. This algorithm is implemented on commercially competent architecture STM32F401RET6 with cortex M4 processor running @ 84 MHz. The proposed encryption method exhibits great features thus making it a potential real-world solution. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Due to the height of coconut trees and the high risks associated with manual picking, there is a severe shortage of labor in the coconut picking industry in Hainan. To address this issue, a coconut picking robot was designed and constructed. In this study, the robot was mainly composed of tree climber and 3 degree of freedom (DoF) manipulator. The robot integrated two functions of tree climbing and coconut picking. The 3D model of the picking robot was designed through SOLIDWORKS. Furthermore, the statics and dynamics simulation of the robot were implemented through ANSYS and ADAMS. With STM32 as the core, an embedded remote control system based on CAN bus communication protocol was developed. And a multi-channel remote controller handle was used to control the robot to realize tree climbing and coconut picking. According to the test results, the robot could successfully climb trees up to 13.42 m tall. The average climbing time of coconut picking robot on 10 coconut trees was 152.3 s, and the average picking time was 222.1 s. Specifically, for coconut trees with height ranging from 7.03 m to 11.5 m, inclination ranging from 78° to 90° and diameter ranging from 21.08 cm to 30.20 cm, the average climbing time of the coconut picking robot was 147.7 s. The average climbing time for the remaining trees was 162.9 s. For the above types of trees, the robot could climb to the top of the trees more quickly, demonstrating better climbing performance. This study provides technical support for mechanized coconut picking and will inspire the development of coconut picking robots.

Abstract During takeoff and landing, birds bounce and grab with their legs and feet. In this paper,the lower limb structure of the bionic bird is designed with reference to the function of jumping and grasping, and the PID algorithm based on the development module of stm32 development board is used to speed control the lower limb driving element, so that the motor and the bishaft steering gear move with the rate change of sine wave. According to the speed of grasping response time and the size of grasping force, the structure of the bionic bird paw is designed. Based on the photosensitive sensor fixed in the geometric center of the foot, the grasping action of the lower limb mechanism is intelligently controlled. Finally, the kinematic verification of the lower limb structure is carried out by ADAMS. Experiments show that the foot structure with four toes and three toes is more conducive to maintaining the stability of the body while realizing the fast grasping function. In addition, it can effectively improve the push-lift ratio of the bionic ornithopter by adjusting the sinusoidal waveform rate of the motor speed.

In response to the escalating inventory of laboratory equipment, the inadequacy of traditional manual management practices has become increasingly apparent. Current management challenges are underscored by historical data, which reveals a borrowing rate of approximately 30%, a concerning return rate of only about 5%, and an equipment loss rate of 10%. To address these inefficiencies, this paper introduces an advanced, intelligent laboratory equipment management system leveraging STM32 and GM65 technologies, designed to significantly enhance the return rate and minimize equipment loss. The system is meticulously engineered, featuring three integral modules: the scanning module, the core board module, and the communication module. The scanning module employs GM65 technology to rapidly scan and decode QR codes, facilitating the swift transmission of critical data to the core board for intelligent processing and interaction with the laboratory equipment. At the heart of the system, the core board module serves as the central processing unit, adeptly managing data from the scanning module. It intelligently discerns various information types and dynamically updates the TFT-LCD screen with the current status of equipment, ensuring that all users have access to real-time and accurate information regarding the borrowing and returning of equipment. The communication module is pivotal in bolstering the system's connectivity capabilities. Designed with a robust structure, the system is characterized by its simplicity of operation and robust practicality. It not only significantly improves the efficiency of laboratory equipment management but also plays a transformative role in modernizing laboratory management practices. By streamlining the management process and ensuring the optimal utilization of resources, this system provides a solid foundation for the sustainable advancement of laboratory operations. [ABSTRACT FROM AUTHOR]

Copyright of Food & Machinery is the property of Food & Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Human activity recognition (HAR) is one of the most interesting application for machine learning models running on low-cost and low-power devices, such as microcontrollers (MCUs). As a matter of fact, MCUs are often dedicated to performing inference on their own acquired data, and any form of model training and update is delegated to external resources. We consider this mainstream paradigm a severe limitation, especially when privacy concerns prevent data sharing, thus model personalization, which is universally recognized as beneficial in HAR. In this letter, we present our HAR solution where MCUs can directly fine-tune a deep learning model using locally acquired data. In particular, we enable training functionalities for 1-D convolutional neural networks (CNNs) on STM32 microcontrollers and provide a software tool to estimate the memory and computational resources required to accomplish model personalization. [ABSTRACT FROM AUTHOR]

Keyword spotting is a subfield of automatic speech recognition revolutionized in the last decade with the incorporation of artificial intelligence techniques based on deep learning. The implementation of keyword spotting models in microcontrollers implies as a first step the digital processing of audio signals to carry out the feature extraction in real time. The models implemented in microcontrollers found in literature use MFCC coefficients for feature extraction. It has been proved that when using deep learning techniques for classification, it is more effective the use of Mel spectrograms. In this paper, it is proposed an implementation of the algorithm for the obtention of Mel spectrograms in real time in STM32 microcontrollers compatible with the design of a real time keyword spotting system. Its use is evaluated in a test system based on the STM32G474RET6 microcontroller and the MEMS microphone SPH0645LM4H-B. The proposed implementation reduces the use of RAM memory in the microcontroller and fills the existing gap in the CMSIS-DSP driver of a routine for the computation of the Mel spectrum for a vector of signal samples. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The subject of study and research in this article is the list of modern microcontrollers and technologies of the programming for creation elements of home automation, embedded systems, and components of Internet of Things. The goal is to analyze the product range and nomenclature of modern microcontrollers for creation of components of IoT systems and home automation considering an available resources and hardware implementation of interfaces. The tasks: to analyze a significant parameters of microcontroller for practical use; to analyze the product range and a modern manufacturers list of the most widely used microcontrollers; to analyze the main features of AVR microcontrollers; to perform an analysis of the advantages of STM32 microcontrollers; to analyze the capabilities of ESP32 microcontrollers; to perform the comparison of hardware resources of these three groups of microcontrollers. According to the tasks, the following results were obtained. Fourteen leading manufacturers of microcontrollers are analyzed with taking into account the bit length and a set of series and families of devices. The main characteristics and requirements for the practical application of microcontrollers for both the creation of embedded systems and for training and prototyping are highlighted. The reasons of the great popularity of the use of AVR microcontrollers by the community for training and learning of programming are identified. The capabilities of hardware resources and the advantages of using for embedded systems of the set of models of STM32 microcontrollers series are analyzed. The reasonability of the use of ESP32 microcontrollers for building of components of IoT systems and devices with Internet access is concluded. The comparison of the hardware resources of microcontrollers of three manufacturers is performed with taking into account the available size of program memory, the number of interfaces and pins. Conclusions. The main contribution and scientific novelty of the obtained results is that the performed analysis allows to simplify the process of a decision making about the selection of the required product range of modern microcontrollers for the creation of the components of the systems of home automation and Internet of Things with taking into account the available hardware resources, the number of pins, and the presence of hardware interfaces. This reduces the required efforts and costs at the initial stages of prototyping such systems, as well as during the porting to new microcontrollers and integrated environments of an already existing project.

Based on the gas desorption rate method of coal, a CWH200 underground portable coal seam gas content rapid measurement instrument with a low-power microprocessor STM32F103RCT6 as the core control unit has been developed. The theoretical formula and overall design of the measuring instrument is introduced, and the hardware module principle and software flow of the measuring instrument is explained in detail. The two ranges automatically switch and can measure the coal seam gas content in the underground site within 10 minutes. The measurement results can be printed and uploaded in real time, which improves the measurement efficiency of coal seam gas content.

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]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a new contribution in the implementation of Volterra model predictive control for fast dynamics systems. The control approaches considered results on a switch paradigm that combine an online part based on suboptimal solution and an offline part referred to an offline neural network controller. The proposed approach has an advantage in comparison with nonlinear optimization-based control schemes. A real time application on STM32 to control a boost converter is studied and the results show very remarkable performances in time computing. [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]

Copyright of Light Industry Machinery is the property of Light Industry Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In modern agriculture IoT systems, edge computing is an indispensable component.In this context, it is feasible to deploy lightweight pest and disease image recognition tasks on edge devices.However, due to the constraints of device computation and storage capabilities, this task faces significant challenges.To address these challenges, an economically practical method was proposed for pest and disease image recognition on STM32 edge devices.Specifically, the MobileNetv2 structure was improved to better suit the characteristics of STM32, quantization-aware training technique was used to compresses the network, model portability was enhanced.Meanwhile, the X-CUBE-AI was used to arrange the model and evaluate the performance.Experimental results demonstrate that the proposed model not only ensures image classification accuracy but also reduces the Flash and RAM resource consumption on STM32 compared to other lightweight networks.

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.

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]

Embedded systems find extensive applications in various aspects of daily life and their presence in the highly connected world of today is growing. With a large number of remote devices and sensors, there is a growing need for remote data processing. Microcontrollers, often integral components of such devices, can enable remote data manipulation through the implementation of a web server called embedded web servers. The implementation of a server on such a system poses challenges, such as limited resources, so it is crucial for the web server implementation to be compatible with the available resources. This paper focuses on the requirements for the implementation of a web server. Also, an embedded web server has been designed for specific application of monitoring air parameters. The implementation utilizes the LwIP stack and allows for remote access to measurement results within a local network. The paper will delve into the operational principles of both the web server and the entire system. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of Nanjing University of Aeronautics & Astronautics is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Control of a certain object can be implemented using different principles, namely, a certain software-implemented algorithm, fuzzy logic, neural networks, etc. In recent years, the use of neural networks for applications in control systems has become increasingly popular. However, their implementation in embedded systems requires taking into account their limitations in performance, memory, etc. In this article, a neuro-controller for the embedded control system is proposed, which enables the processing of input technological data. A structure for the neuro-controller is proposed, which is based on the modular principle. It ensures rapid improvement of the system during its development. The neuro-controller functioning algorithm and data processing model based on artificial neural networks are developed. The neuro-controller hardware is developed based on the STM32 microcontroller, sensors and actuators, which ensures a low cost of implementation. The artificial neural network is implemented in the form of a software module, which allows us to change the neuro-controller function quickly. As a usage example, we considered STM32-based implementation of the control system for an intelligent mini-greenhouse. [ABSTRACT FROM AUTHOR]