Microcontroller Unit Research Articles

High Performance Magnetic Mass‐Enhanced Triboelectric‐Electromagnetic Hybrid Vibration Energy Harvester Enabling Totally Self‐Powered Long‐Distance Wireless Sensing

AbstractWireless sensor networks play a significant role in various fields, and it is promising to construct a totally self‐powered wireless sensor network by harvesting unused mechanical vibration energy. Here, a magnetic mass‐enhanced triboelectric‐electromagnetic hybrid nanogenerator (MM‐HNG) is proposed for harvesting mechanical vibration energy. The additional magnets generate magnetic fields for electromagnetic power generation. As an additional mass effectively increases the membrane's amplitude, thereby enhancing the output performance of the MM‐HNG. The peak power density of TENG in the MM‐HNG reaches 380.4 W m−3, while the peak power density of EMG achieves 736 W m−3, which can charge a 0.1 F capacitor rapidly. In addition, a totally self‐powered wireless sensing system is constructed, with the integrated microcontroller unit (MCU), which detects and processes various sensing parameters and controls wireless transmission. The system features rapid transmission speeds and an extensive transmission range (up to 1 km), and its effectiveness has been validated in a practical application aboard an actual ship. The results illustrate the MM‐HNG's broad applicability across various Internet of Things (IoT) scenarios, including smart machinery, smart transportation, and smart factories.

Self-powered wireless sensor node enabled by ultra-high-output swinging hybrid generator toward real-time and in-situ marine meteorological observations

With the intensification of global ocean exploration, there is an increasing need for extensive and dispersed wireless sensor networks (WSNs) to meet the requirements of real-time, in-situ, and high-spatial-temporal-density marine meteorological observations. Nevertheless, providing a reliable power source and ensuring the ongoing maintenance of these WSNs pose significant challenges. Here, an innovative self-powered wireless sensor node is developed to address these challenges, featuring an ultra-high-output triboelectric-electromagnetic hybrid generator (T-EHG) with a swinging mechanism. The T-EHG, which combines triboelectric (TENG) and electromagnetic (EMG) generation, assisted by a universal power management system (UPMS), efficiently converts wave energy into a consistent voltage output between 1.5 V to 3.3 V. Additionally, the self-powered WSNs integrated with T-EHG, UPMS circuits, meteorological sensors, and microcontroller units (MCUs) with LoRa wireless transmission functionality are capable of executing data collection and transmission operations every 10 s under simulated wave conditions. These systems can achieve a maximum wireless transmission range of 1.5 km. Notably, a marine meteorological monitoring system utilizes these autonomous sensor nodes, along with base stations, cloud servers, and terminal devices to effectively implement real-time, on-site measurements of temperature, humidity, and air pressure within the monitored zone. This research underscores the substantial practical utility and extensive application prospects of these advanced self-powered WSNs for marine meteorological surveillance.

Optimizing Convolutional Neural Networks for Image Classification on Resource-Constrained Microcontroller Units

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%.

An acoustic pressure-gradient MEMS vector hydrophone system based on piezoelectric micromachined ultrasonic transducer array

This article presents an acoustic pressure-gradient MEMS vector hydrophone system, which consists of a 2 × 2 piezoelectric scalar hydrophone array and a microcontroller unit (MCU)-based signal-conditioning circuit. The reported scalar hydrophone is fabricated based on a scandium-doped aluminum nitride (Sc0.2Al0.8N) piezoelectric micromachined ultrasonic transducer (PMUT) array. Four identical scalar hydrophones are adopted and arranged in a 2 × 2 array for detecting the acoustic pressure gradient along different incident angles. The received electrical signals in each scalar hydrophone are collected and processed by the MCU directly. Experimental results show that the vector hydrophone system has a receive sensitivity of −168 dB (re: 1 V/μPa) in the omnidirectional receive mode, and a receive sensitivity of −198 dB (re: 1 V/μPa) at 10 kHz in the dipole receive mode, respectively. With the aid of an advanced data preprocessing approach, the reported vector hydrophone system achieves the direction-of-arrival (DOA) estimations of underwater acoustic signals with an error of less than 2°. The measurement results show that the reported MEMS vector hydrophone system has the potential for the detection of underwater sound sources.

Hyposense: An Integrated Sensor Device for Hydroponics Farm Monitoring

In recent years, the monitoring of hydroponics farms has undergone a significant transformation due to the integration of sensors. However, the conventional method of manually observing measurements from multiple sensors has proven to be excessively time-consuming and costly. Additionally, the lack of automatic data recording options in most sensors has been a limiting factor. To address these challenges, we conducted an experimental study to introduce a novel integrated sensor device named “HypoSense”, designed to monitor essential parameters such as temperature, humidity, pH, light intensity, Total Dissolved Solids (TDS), and Electrical Conductivity (EC) in hydroponic systems. HypoSense consists of three microcontroller units (MCUs) incorporated with two controlling circuits based on the Arduino platform: the Arduino UNO, ESP32, and the ESP8266 (NodeMCU) microcontrollers. The device is designed using high-precision sensors, including the DHT11 for temperature and humidity monitoring, an Analog pH sensor kit for pH measurement, BH1750 for light intensity, and an Analog TDS sensor kit for TDS and electrical conductivity measurements. These sensors were chosen for their reliability, accuracy, and compatibility with the Arduino platform, ensuring that HypoSense delivers precise and consistent readings. HypoSense is a cost-effective, portable sensor device specially designed for monitoring growth parameters simultaneously in hydroponic farming, saving growers time and effort. The evaluation was carried out in two phases. Firstly, field sensors were employed to calibrate HypoSense device sensors. Next, a secondary evaluation was conducted to confirm the practicality and user-friendliness of the HypoSense device. To facilitate the evaluation of HypoSense, we set up an indoor hydroponic system for growing tomatoes and lettuce. The results of these evaluations, focusing on the performance and applicability of the HypoSense device, will provide valuable insights for hydroponic growers and future researchers in precision agriculture.

Stretchable, adhesive, conductive hydrogel initiated by liquid metal complex for multi-functional sensing

Liquid metal (LM) can serve as functional fillers, initiators, and cross-linkers for hydrogels. However, the high surface energy of LM makes it difficult to disperse uniformly and stably in the hydrogels. In this work, multi-walled carbon nanotubes (MWCNT-80) modified with the surfactant Tween 80, along with hyaluronic acid (HA), were employed to collectively stabilize LM to form LM complex for initiating the polymerization of vinyl monomers. The LM complex was used as a conductive filler, which not only enhanced the mechanical properties of the hydrogel, but also initiated free radical polymerization of the monomer. The prepared liquid metal conductive hydrogel (LMCH) has excellent stretchability (1192 %), adhesion (15.3 kPa) and conductivity (0.78 S/m). This LMCH hydrogel was further applied in the construction of multifunctional sensors. On one hand, strain sensors constructed with this hydrogel could monitor extensive joint range movements in real-time. On the other hand, a flexible triboelectric nanogenerator (L-TENG) constructed with the hydrogel as an electrode was employed for self-powered sensing. When integrated with a Microcontroller Unit (MCU), the joint training status of individuals can be transmitted in real-time to a mobile phone, allowing for the quantification and monitoring of human activities. This study presents a novel approach to the development of hydrogels for multifunctional sensors, and the prepared hydrogel holds significant potential in the fields of rehabilitation training and human–machine interaction.

Internet of thing based health monitoring system using wearable sensors networks

Maintaining mental and physical health is becoming increasingly important for maintaining independent living, particularly as the population of people suffering from chronic illnesses like diabetes, heart disease, obesity, and other conditions rises and the average age of many societies keeps rising. Using sensors, monitoring health remotely, and ultimately recognising daily activities have all been proposed as potential strategies. In this work, fatigue threshold and environmental bounds are assessed and provided via an external interface to a microcontroller unit (MCU) in addition to the required restrictions. Rerouting the required boundaries into the long range (LoRa) and Bluetooth module, the MCU is responsible for editing and analysing the raw data to remove the oxygen immersion, pulse, and temperature data. These important restrictions are sent to many terminals, such as PCs and mobile devices, using the remote Bluetooth and LoRa module. For data storage and retrieval, any IoT platform may be used. With caution, the patient is discharged home after the medical experts have carefully evaluated the diseases in light of the new features. To telemonitor patients with heart conditions, the test results show that the framework is efficient and dependable for collecting, sending, and presenting electrocardiogram (ECG) data constantly.

Evaluating Unmanned Aerial Vehicles for Vibrational Analysis: Exploiting Integrated Capabilities of Nano-Drones as Nodes in a WSN

Addressing the aging infrastructure challenge in both civil and industrial sectors has spurred the development of advanced sensor technologies and novel inspection methodologies, significantly contributing to the field of Structural Health Monitoring (SHM). While conventional sensors, such as structure-mounted accelerometers, have demonstrated high effectiveness, their deployment may be difficult or even impossible in high-risk environments like post-seismic areas, where the installation procedure hampers the operators' safety. Hence, this study aims to evaluate the characteristics of a novel sensing platform compatible with autonomous deployment for vibration inspection of infrastructures. In particular, we explore the use of Unmanned Aerial Vehicles (UAVs), with a specific focus on nano-drones, as a viable and promising solution due to their compact size and cost-effectiveness. In particular, Crazyflie 2.1 nano-drones are exploited as reference platforms, given their characteristics in terms of versatility and electrical parameters. The objective is to utilize the on-drone MEMS-based Inertial Measurement and Microcontroller Unit (IMU and MCU) to perform, at the same time, both flight control and navigation, and vibration inspection. This approach eliminates the need for additional hardware, going beyond other successful drone applications in SHM, such as visual inspections, sensors deployment, and distance measurements. The proposed solution is validated through a comparison with commercial sensors for SHM. Multiple drones can be combined to deploy wireless sensor networks in hard-to-reach locations, ready for vibration monitoring with minimal software adjustments, offering a novel approach to SHM. The results indicate that the Crazyflie drone is capable of detecting even small variations in the dynamic response of a beam, with a maximum deviation of 1% compared to commercially available counterparts designed for SHM purposes.

MCU-based Safer Coagulation Mode by Nonfixed Duty Cycle for an Electrosurgery Inverter.

The arcing-involved pulsating coagulation mode with both active and blank periods is essential for modern electrosurgery. This paper begins with a comprehensive introduction to such a pulsating mode, followed by its implementation challenges. Then, an industrial-scale low-speed microcontroller unit (MCU), TMS320F28379D, is utilized to exemplify the proposed output sampling and data-transferring strategy on a gallium nitride (GaN)-based high-frequency inverter that enables coagulation mode with interweaved active periods and blank periods. The inverter prototype fills the active period with 390 kHz sinusoids of amplitude ranging from hundreds to thousands of Volts, while maintaining null outputs during blank periods. The strategy of sampling the above-mentioned sinusoidal outputs, coupled with their data transfer facilitated by direct memory access (DMA), is also articulated for subsequential power computation. Besides that, a novel nonfixed duty cycle approach, featuring an alterable number of sinusoids as the active period, is proposed and integrated into the GaN-based inverter to enhance mode safety. Finally, the power tracking performance of the mode is evaluated initially on resistive load, secondarily on resistive plus capacitive load (R-C), and thirdly on fresh biotissue with the appearance of electrical arcing. The existing necessity of the null blank periods is examined at the end of the paper.

Simplified 1.5 μm Distributed Feedback Semiconductor Laser (DFB-LD) Frequency Stabilization System Based on Gas Absorption Chamber

The classical 1.5 μm band frequency-stabilized laser using acetylene gas saturated absorption can achieve high frequency stability and reproducibility, but its system design is complex and bulky. For some practical applications, a simple, compact system containing anti-interference abilities is preferred. In this study, a low-cost and simple-structured 1.5 μm frequency-stabilized laser is constructed using digital control methods, wavelength modulation technology, and acetylene gas absorption. The fiber input and output optical devices of the system significantly simplify the optical path and reduce the volume of the system. The error signal is obtained by the first-order differential method, and a combination of the high-speed comparator circuit and the microcontroller unit (MCU) is used to detect the error signal. Through the feedback control method of coarse temperature adjustment and fine current adjustment, the second-level frequency stability of the laser is stabilized within 100 kHz, that is, the frequency stability reaches 10​−10. The designed system achieved continuous and stable operation for more than 6 h, and the long-term frequency stability reached 10​−9.

Design and Implementation of an Intelligent Ultrasonic System based on 51 Microcontroller

With the rapid development of microelectronics and intelligent technology, microcontroller units (MCUs) are increasingly applied in various fields. This paper proposes the design and implementation of an intelligent ultrasonic system based on a 51 microcontroller, integrating ultrasonic sensors, an LCD display, and a 51 MCU to achieve real-time distance measurement, data display, and intelligent control functions. The system utilizes a CD4069 ultrasonic sensor to transmit and receive ultrasonic waves, measuring the distance between the sensor and an object by calculating the time difference between transmission and reception. The measured distance data is processed and displayed on an LCD screen in real-time. Furthermore, the system features intelligent control capabilities, automatically adjusting and controlling devices based on the measured data, thereby enhancing the system's level of intelligence. The intelligent ultrasonic system based on the 51 microcontroller offers advantages such as low cost, high precision, and strong versatility. It can be widely used in various fields, including intelligent robots, obstacle avoidance systems, and automatic parking systems. The design and implementation of this system not only demonstrate innovative applications of ultrasonic ranging technology but also provide valuable references for the extensive application of microcontroller units in intelligent technology fields.

A Trusted Execution Environment RISC-V System-on-Chip Compatible with Transport Layer Security 1.3

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.

Tiny-Machine-Learning-Based Supply Canal Surface Condition Monitoring.

The South-to-North Water Diversion Project in China is an extensive inter-basin water transfer project, for which ensuring the safe operation and maintenance of infrastructure poses a fundamental challenge. In this context, structural health monitoring is crucial for the safe and efficient operation of hydraulic infrastructure. Currently, most health monitoring systems for hydraulic infrastructure rely on commercial software or algorithms that only run on desktop computers. This study developed for the first time a lightweight convolutional neural network (CNN) model specifically for early detection of structural damage in water supply canals and deployed it as a tiny machine learning (TinyML) application on a low-power microcontroller unit (MCU). The model uses damage images of the supply canals that we collected as input and the damage types as output. With data augmentation techniques to enhance the training dataset, the deployed model is only 7.57 KB in size and demonstrates an accuracy of 94.17 ± 1.67% and a precision of 94.47 ± 1.46%, outperforming other commonly used CNN models in terms of performance and energy efficiency. Moreover, each inference consumes only 5610.18 μJ of energy, allowing a standard 225 mAh button cell to run continuously for nearly 11 years and perform approximately 4,945,055 inferences. This research not only confirms the feasibility of deploying real-time supply canal surface condition monitoring on low-power, resource-constrained devices but also provides practical technical solutions for improving infrastructure security.

TinyML for smart agriculture: Comparative analysis of TinyML platforms and practical deployment for maize leaf disease identification

Maize, a vital global food crop, is susceptible to various diseases impacting agricultural yield and quality, necessitating effective monitoring strategies. This study presents a comprehensive approach utilizing advanced Convolutional Neural Network (CNN) architecture for efficient maize leaf disease detection and classification. Leveraging datasets from Kaggle, Harvard Dataverse, and Mendeley, the proposed CNN maize disease detection (MDD) system integrates optimization techniques, data augmentation, batch size adjustments, and fine-tuning strategies. Evaluation through data splitting reveals significant correlations among maize leaf classifications and the influence of data augmentation on image quality. Following training, the CNN-MDD model is deployed on a Microcontroller Unit (MCU) simulation, achieving promising optimization parameters: latency of 7.60 ms, RAM size of 726.60 KB, classifier flash size of 344.70 KB, and an accuracy of 94.60 %. These optimized parameters align well with MCU specifications, highlighting the potential for practical deployment on low-cost edge devices like Arduino Ble 33 Sense. Additionally, this research contributes to the creation of a Ghanaian maize crop pest and disease dataset and the development of an optimal and cost-effective CNN-MDD MCU edge device, aiming to provide local farmers in Ghana and Sub-Saharan Africa with a swift, offline solution for maize disease detection, thus fostering sustainable agricultural practices. We conduct a comparative analysis of Edge Impulse and TensorFlow, two prominent machine-learning platforms for TinyML model development and deployment in various applications, including smart crop disease detection. Utilizing a dataset comprising 12,344 raw images and 7,454 augmented images of healthy and diseased maize leaves, we evaluate the workflow, features, performance, and usability of each platform. Our findings indicate that Edge Impulse holds a slight advantage over TensorFlow in terms of ease of use, reliability, and memory footprint, whereas TensorFlow offers greater flexibility, customization options, and higher accuracy. We conclude that while both platforms are suitable for crop disease detection, a combined approach allows for leveraging TensorFlow's robust training capabilities for complex neural networks alongside the simplicity of Edge Impulse for deploying models on low-powered edge devices. This synergistic approach enhances the effectiveness and efficiency of crop disease detection systems, ultimately benefiting agricultural practices and food security.

Reducing False Alarms in Wearable Seizure Detection With EEGformer: A Compact Transformer Model for MCUs.

The long-term, continuous analysis of electroencephalography (EEG) signals on wearable devices to automatically detect seizures in epileptic patients is a high-potential application field for deep neural networks, and specifically for transformers, which are highly suited for end-to-end time series processing without handcrafted feature extraction. In this work, we propose a small-scale transformer detector, the EEGformer, compatible with unobtrusive acquisition setups that use only the temporal channels. EEGformer is the result of a hardware-oriented design exploration, aiming for efficient execution on tiny low-power micro-controller units (MCUs) and low latency and false alarm rate to increase patient and caregiver acceptance.Tests conducted on the CHB-MIT dataset show a 20% reduction of the onset detection latency with respect to the state-of-the-art model for temporal acquisition, with a competitive 73% seizure detection probability and 0.15 false-positive-per-hour (FP/h). Further investigations on a novel and challenging scalp EEG dataset result in the successful detection of 88% of the annotated seizure events, with 0.45 FP/h.We evaluate the deployment of the EEGformer on three commercial low-power computing platforms: the single-core Apollo4 MCU and the GAP8 and GAP9 parallel MCUs. The most efficient implementation (on GAP9) results in as low as 13.7 ms and 0.31 mJ per inference, demonstrating the feasibility of deploying the EEGformer on wearable seizure detection systems with reduced channel count and multi-day battery duration.

Development of an energy-autonomous eddy current sensor system for in-situ component monitoring

Energy-autonomous non-destructive testing (NDT) systems provide new opportunities for structural health monitoring. An NDT system that is energy-autonomous and transmits the recorded data wirelessly can be used in places that are not easily accessible for inspection. In addition, it can provide for continuous monitoring, which can improve the operational safety of various components and structures. This study presents a concept that enables energy-autonomous and wireless structural health monitoring using a splined shaft as a demonstrator. To monitor mechanical overloads, a material sensor is applied to critical areas of these components by a laser heat treatment. The structural change that occurs as a result of an overload can be monitored by the developed smart sensor system. This system consists of a material sensor, a low-cost eddy current sensor, an energy-efficient evaluation unit with an ultra-low-power microcontroller unit (MCU) and a Long-Range Wide Area Network (LoRaWAN) data transmission unit. Wireless data transmission via LoRaWAN provides access to the Industrial Internet of Things network, enabling the collection of NDT data from multiple smart sensors in a cloud and the processing of these data to assess the condition of the component from any location. It is shown that the MCU-based eddy current testing system is able to generate a similar signal quality for material characterisation compared to an industrial eddy current testing system, despite a much more compact design and a significantly higher energy efficiency. The energy autonomy of the smart sensor system is achieved by an energy harvesting system integrated into the splined shaft. In addition, three different testing scenarios are presented, which differ mainly in the scope of data processing and evaluation on the MCU, and thus also in the energy required.

Experimental investigation of a hybrid photovoltaic-thermal energy system for hot air production

Solar energy as a non-fossil alternative energy source has become the best choice to overcome the problem of energy demand in most countries in the world. There are two different techniques to convert solar energy: photovoltaic (PV) panels to produce electricity and thermal collectors to generate heat. The two technologies can be combined to provide electrical and thermal energy either simultaneously or separately. In order to optimize the performance of a hybrid photovoltaic-thermal (PVT) solar air heater, it is necessary to collect experimental data on solar irradiation and temperature. This paper emphasized the development of a PVT energy system for hot air production in a temperature range of 50-55 °C. Additionally, experiments were constructed to monitor the information acquired from the proposed PVT solar air heater and the environment, such as hot air temperature, ambient temperature, and solar irradiation. The real-time monitoring system was set for five sample days. A microcontroller unit was used to control the hot air temperature and save the measurement data into memory. The experimental results showed that the proposed PVT solar air heater is capable of maintaining a certain level of hot air temperature throughout the day and night.

A chaotic map with two-dimensional offset boosting.

A chaotic map with two-dimensional offset boosting is exhaustively studied, which is derived from the Lozi map and shows the controllability of amplitude control. The mechanism of two-dimensional offset boosting is revealed based on the cancelation of offset-involved feedback terms. Furthermore, the coexistence of homogeneous multistability and heterogeneous multistability is disclosed when the offset boosting turns to the initial condition. It is also found that the independent constant term rescales the amplitude of all the sequences without changing the Lyapunov exponents. More strikingly, the parameters for amplitude control and offset boosting are bound together introducing hybrid control. The circuit implementation based on the microcontroller unit is used to validate the theoretical analysis and numerical simulations. This chaotic map is applied for particle swarm optimization showing its stronger performance and robustness in solving optimization problems.

Tiny Machine Learning for Concept Drift.

Tiny machine learning (TML) is a new research area whose goal is to design machine and deep learning (DL) techniques able to operate in embedded systems and the Internet-of-Things (IoT) units, hence satisfying the severe technological constraints on memory, computation, and energy characterizing these pervasive devices. Interestingly, the related literature mainly focused on reducing the computational and memory demand of the inference phase of machine and deep learning models. At the same time, the training is typically assumed to be carried out in cloud or edge computing systems (due to the larger memory and computational requirements). This assumption results in TML solutions that might become obsolete when the process generating the data is affected by concept drift (e.g., due to periodicity or seasonality effect, faults or malfunctioning affecting sensors or actuators, or changes in the users' behavior), a common situation in real-world application scenarios. For the first time in the literature, this article introduces a TML for concept drift (TML-CD) solution based on deep learning feature extractors and a k -nearest neighbors ( k -NNs) classifier integrating a hybrid adaptation module able to deal with concept drift affecting the data-generating process. This adaptation module continuously updates (in a passive way) the knowledge base of TML-CD and, at the same time, employs a change detection test (CDT) to inspect for changes (in an active way) to quickly adapt to concept drift by removing obsolete knowledge. Experimental results on both image and audio benchmarks show the effectiveness of the proposed solution, whilst the porting of TML-CD on three off-the-shelf micro-controller units (MCUs) shows the feasibility of what is proposed in real-world pervasive systems.

Lifting platform PWM control system design combining distance detection

Abstract The design of this system aims to achieve running speed control of a lifting platform based on pulse width modulation (PWM) technology, combined with weight and distance detection. The system consists of a microcontroller unit (MCU), pressure sensor, ultrasonic ranging, L298N motor drive, LCD1602 display, keys, and alarm modules. The system generates corresponding PWM signals based on the weight of load and distance between the platform and the starting point to control the speed of lifting the platform DC motor, ensuring the safety and stability of the lifting process. The system’s overall functionality has been verified through simulation by PROTEUS 8.15 and tested through physical circuit construction. This control system can achieve real-time and accurate lifting speed control of the platform, and has good performance and stability. The design is simple and can be applied to various occasions such as industrial production lines, warehouse material handling, and high-altitude operations, improving work efficiency and reducing the risk and burden of manual operations.