Search Results (50)

The wire tightener is a critical tool in the construction and maintenance of power lines. Failure to detect tension overload in a timely manner may lead to plastic deformation or even breakage of the tool, potentially causing serious safety accidents. To address this issue, a force monitoring sensor was developed to track the real-time load on wire tighteners. In terms of hardware design, a foil strain gauge was integrated with an ultra-low-power mixed-signal microcontroller based on the mechanical characteristics of the wire tightener, enabling accurate acquisition and processing of load data. Low-power LoRa technology was employed for wireless data transmission, and an adaptive sleep–wake strategy was implemented to optimize power efficiency during data collection. The sensor’s material, geometry, and structure were tailored to the tool’s composition and working environment. Experimental results showed that the average relative error between the sensor readings and the reference values was less than 0.5%. The sensor has been successfully deployed in practical engineering applications, consuming approximately 4500 mWh over an 8 h continuous monitoring period. Full article

This study develops a high-accuracy indoor positioning system using ultra-wideband (UWB) technology and the time-of-arrival (TOA) method. The system is built using Arduino Nano microcontrollers and DW1000 UWB chips to measure distances between anchor nodes and a mobile tag. Three positioning algorithms are tested: the triangle centroid algorithm (TCA), inner triangle centroid algorithm (ITCA), and the proposed intersection midpoint algorithm (IMA). Experiments conducted in a 732 × 488 × 220 cm indoor environment show that TCA performs well near the center but suffers from reduced accuracy at the edges. In contrast, IMA maintains stable and accurate positioning across all test points, achieving an average error of 12.87 cm. The system offers low power consumption, fast computation, and high positioning accuracy, making it suitable for real-time indoor applications such as hospital patient tracking and shopping malls where GPS is unavailable or unreliable. Full article

In this work, a low-cost low-power navigation solution for autonomous underwater vehicles is introduced utilizing a Micro-Electro-Mechanical System (MEMS) inertial sensor and an ultra short baseline (USBL) system. The complete signal processing is implemented on a cheap 16-bit fixed-point arithmetic microcontroller. For data fusion and calibration, an error state Kalman filter in square root form is used, which preserves stability in case of rounding errors. To further reduce the influence of rounding errors, a stochastic rounding scheme is applied. The USBL measurements are integrated using tightly coupled data fusion by deriving the observation functions separately for range, elevation, and azimuth angles. The effectiveness of the fixed point implementation with stochastic rounding is demonstrated on a simulation, and the the complete setup is tested in a field test. The results of the field test show an improved accuracy of the tightly coupled data fusion in comparison with loosely coupled data fusion. It is also shown that the applied rounding schemes can bring the fixed-point estimates to a near floating point accuracy. Full article

Surface-enhanced Raman scattering (SERS) spectroscopy is an ultra-sensitive analytical method with the powerful signal-molecule detection capability. Coupling with the polydimethylsiloxane (PDMS) material, SERS can be enabled on a polymeric substrate for fast-developing bio-compatible sensing applications. However, due to PDMS’s high viscosity, conventional PDMS-SERS substrates are typically thick and stiff, limiting their freedom for engineering flexible micro/nano functioning devices. To address this issue, we propose to adopt a low viscosity decamethylcyclopentasiloxane (D5) solvent as a diluent solution. Via controlling the mixture ratio of D5 and PDMS and the spin-coating speed for deposition, this method resulted in a film of a well-defined thickness from sub-millimeter down to a 100 nm scale. Furthermore, thanks to the unsaturated Si-H chemical bonds in the PDMS curing agent, the PDMS film could effectively reduce the $A{g}^{+}$ ions to Ag nanoparticles (NPs) directly bonding onto the substrate surface uniformly. Via adjusting the size and density of the AgNPs through reaction temperature and time, strong SERS was achieved and verified using R6G with the detection limit down to 0.1 ppm, attributed to the AgNPs’ plasmonic enhancement effect. Full article

Micro Light Emitting Diode (Micro-LED) technology, characterized by exceptional brightness, low power consumption, fast response, and long lifespan, holds significant potential for next-generation displays, yet its commercialization hinges on resolving challenges in high-density interconnect fabrication, particularly micrometer-scale bump formation. Traditional fabrication approaches such as evaporation enable precise bump control but face scalability and cost limitations, while electroplating offers lower costs and higher throughput but suffers from substrate conductivity requirements and uneven current density distributions that compromise bump-height uniformity. Emerging alternatives include electroless plating, which achieves uniform metal deposition on non-conductive substrates through autocatalytic reactions albeit with slower deposition rates; ball mounting and dip soldering, which streamline processes via automated solder jetting or alloy immersion but struggle with bump miniaturization and low yield; and photosensitive conductive polymers that simplify fabrication via photolithography-patterned composites but lack validated long-term stability. Persistent challenges in achieving micrometer-scale uniformity, thermomechanical stability, and environmental compatibility underscore the need for integrated hybrid processes, eco-friendly manufacturing protocols, and novel material innovations to enable ultra-high-resolution and flexible Micro-LED implementations. This review systematically compares conventional and emerging methodologies, identifies critical technological bottlenecks, and proposes strategic guidelines for industrial-scale production of high-density Micro-LED displays. Full article

In order to effectively monitor harmful gas leakage, this paper presents the design of an ultra-low-power IoT-based harmful gas monitoring system. The system is equipped with a custom-designed, low-power microcontroller motherboard, carefully selected low-power sensors, and high-efficiency, low-power communication modules. In addition, the system optimizes data acquisition and processing algorithms to segment gases of different concentrations. While ensuring real-time data acquisition and transmission, it achieves extremely low power consumption. By controlling the concentration of harmful gases and current for sensor performance testing, the experiment has shown that when the concentration of carbon monoxide reaches 500 ppm and methane reaches 2000 ppm, the system will trigger an alarm and upload relevant information; the sensor can detect and respond to the harmful gases within 60 s; and the system’s operating current fluctuation range remains within 0.5 mA, with an average power consumption much lower than that of other devices. Full article

Artificial neural networks (ANNs) have become essential components in various safety-critical applications, including autonomous vehicles, medical devices, and avionics, where system failures can lead to severe risks. Edge AI devices, which process data locally without relying on the cloud, are increasingly used to meet the performance and real-time demands of these applications. However, their reliability in radiation-prone environments is a significant concern. In this context, this paper evaluates the MAX78000, an ultra-low-power Edge AI microcontroller with a hardware-based convolutional neural network (CNN) accelerator, focusing on its behavior in radiation environments. To assess the reliability of the MAX78000, we performed a test campaign at the ChipIR neutron irradiation facility using two different ANNs. We implemented techniques to improve system observability during ANN inference and analyzed the radiation-induced errors observed. The results present a comparative analysis between the two ANN architectures, which shows that the complexity of the ANN directly impacts its reliability. Full article

Microfluidic devices promise to overcome the limitations of conventional hemodialysis and oxygenation technologies by incorporating novel membranes with ultra-high permeability into portable devices with low blood volume. However, the characteristically small dimensions of these devices contribute to both non-physiologic shear that could damage blood components and laminar flow that inhibits transport. While many studies have been performed to empirically and computationally study hemolysis in medical devices, such as valves and blood pumps, little is known about blood damage in microfluidic devices. In this study, four variants of a representative microfluidic membrane-based oxygenator and two controls (positive and negative) are introduced, and computational models are used to predict hemolysis. The simulations were performed in ANSYS Fluent for nine shear stress-based parameter sets for the power law hemolysis model. We found that three of the nine tested parameters overpredict (5 to 10×) hemolysis compared to empirical experiments. However, three parameter sets demonstrated higher predictive accuracy for hemolysis values in devices characterized by low shear conditions, while another three parameter sets exhibited better performance for devices operating under higher shear conditions. Empirical testing of the devices in a recirculating loop revealed levels of hemolysis significantly lower (<2 ppm) than the hemolysis ranges observed in conventional oxygenators (>10 ppm). Evaluating the model’s ability to predict hemolysis across diverse shearing conditions, both through empirical experiments and computational validation, will provide valuable insights for future micro ECMO device development by directly relating geometric and shear stress with hemolysis levels. We propose that, with an informed selection of hemolysis parameters based on the shear ranges of the test device, computational modeling can complement empirical testing in the development of novel high-flow blood-contacting microfluidic devices, allowing for a more efficient iterative design process. Furthermore, the low device-induced hemolysis measured in our study at physiologically relevant flow rates is promising for the future development of microfluidic oxygenators and dialyzers. Full article

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

Recently, there has been increased interest in adopting novel sensing technologies for continuously monitoring structural systems. In this respect, micro-electrical mechanical system (MEMS) sensors are widely used in several applications, including structural health monitoring (SHM), in which accelerometric samples are acquired to perform modal analysis. Thanks to their significantly lower cost, ease of installation in the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analyses show that MEMS can correctly detect modal frequencies, which are useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated, and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring. Full article

This paper describes the design and optimization of a smart algorithm based on artificial intelligence to increase the accuracy of an ocean water current meter. The main purpose of water current meters is to obtain the fundamental frequency of the ocean waves and currents. The limiting factor in those underwater applications is power consumption and that is the reason to use only ultra-low power microcontrollers. On the other hand, nowadays extraction algorithms assume that the processed signal is defined in a fixed bandwidth. In our approach, belonging to the edge computing research area, we use a deep neural network to determine the narrow bandwidth for filtering the fundamental frequency of the ocean waves and currents on board instruments. The proposed solution is implemented on an 8 MHz ARM Cortex-M0+ microcontroller without a floating point unit requiring only 9.54 ms in the worst case based on a deep neural network solution. Compared to a greedy algorithm in terms of computational effort, our worst-case approach is 1.81 times faster than a fast Fourier transform with a length of 32 samples. The proposed solution is 2.33 times better when an artificial neural network approach is adopted. Full article

Modern, commonly used cryptosystems based on encryption keys require that the length of the stream of encrypted data is approximately the length of the key or longer. In practice, this approach unnecessarily complicates strong encryption of very short messages commonly used for example in ultra-low-power and resource-constrained wireless network sensor nodes based on microcontrollers (MCUs). In such cases, the data payload can be as short as a few bits of data while the typical length of the key is several hundred bits or more. The article proposes an idea of employing a complex of two algorithms, initially applied for data compression, acting as a standard-length encryption key algorithm to increase the transmission security of very short data sequences, even as short as one or a few bytes. In this article, we present and evaluate an approach that uses LZW and Huffman coding to achieve data transmission obfuscation and a basic level of security. Full article

Underwater sensor networks play a crucial role in collecting valuable data to monitor offshore aquaculture infrastructures. The number of deployed devices not only impacts the bandwidth for a highly constrained communication environment, but also the cost of the sensor network. On the other hand, industrial and literature current meters work as raw data loggers, and most of the calculations to determine the fundamental frequencies are performed offline on a desktop computer or in the cloud. Belonging to the edge computing research area, this paper presents an algorithm to extract the fundamental frequencies of water currents in an underwater sensor network deployed in offshore aquaculture infrastructures. The target sensor node is based on a commercial ultra-low-power microcontroller. The proposed fundamental frequency identification algorithm only requires the use of an integer arithmetic unit. Our approach exploits the mathematical properties of the finite impulse response (FIR) filtering in the integer domain. The design and implementation of the presented algorithm are discussed in detail in terms of FIR tuning/coefficient selection, memory usage and variable domain for its mathematical formulation aimed at reducing the computational effort required. The approach is validated using a shallow water current model and real-world raw data from an offshore aquaculture infrastructure. The extracted frequencies have a maximum error below a 4%. Full article

This paper analyses the conceptual application of a wireless power transfer (WPT) system with multiple resonators supplying outdoor sensors using a mobile charger. The solution is based on the idea of using sensors, located in open space, to monitor environmental parameters. Instead of the typical two-coil WPT with a single charger, energy transfer is realized simultaneously, using a group of identical planar coils as transmitters and receivers connected to the independent power supply circuits of each sensor and microcontroller. By isolating these charged circuits, a higher reliability and powering flexibility of the weather station can be achieved. The concept of the proposed system was discussed, and it was proposed to include the main devices in it. A theoretical analysis was performed considering all mutual couplings and the skin effect; hence, the system is characterized by a matrix equation and sufficient formulae are given. The calculations were verified experimentally for different frequencies, two possible distances between the transmitters and receivers, and equivalent loads. Both the efficiency and load power are compared and discussed, showing that this solution can provide power to ultra-low-power devices, yet the efficiency must still be improved. At the small distance between the transmitting and receiving coils (5 mm), the maximum efficiency value was about 40%, with a load resistance of 10 Ω. By doubling the distance between the coils, the efficiency of the WPT system decreased by three times. Full article

This study proposes a heterogeneous hardware-based framework for network intrusion detection using lightweight artificial neural network models. With the increase in the volume of exchanged data, IoT networks’ security has become a crucial issue. Anomaly-based intrusion detection systems (IDS) using machine learning have recently gained increased popularity due to their generation’s ability to detect unseen attacks. However, the deployment of anomaly-based AI-assisted IDS for IoT devices is computationally expensive. A high-performance and ultra-low power consumption anomaly-based IDS framework is proposed and evaluated in this paper. The framework has achieved the highest accuracy of 98.57% and 99.66% on the UNSW-NB15 and IoT-23 datasets, respectively. The inference engine on the MAX78000EVKIT AI-microcontroller is 11.3 times faster than the Intel Core i7-9750H 2.6 GHz and 21.3 times faster than NVIDIA GeForce GTX 1650 graphics cards, when the power drawn was 18mW. In addition, the pipelined design on the PYNQ-Z2 SoC FPGA board with the Xilinx Zynq xc7z020-1clg400c device is optimised to run at the on-chip frequency (100 MHz), which shows a speedup of 53.5 times compared to the MAX78000EVKIT. Full article