Search Results (377)

This work presents a comprehensive first-principles investigation of the structural, electronic, magnetic, optical, and thermodynamic properties of Ti-based full-Heusler compounds TiMCu₂ (M = Al, Ga, In). Using density functional theory within the GGA+U framework, the compounds were optimized and analyzed to evaluate their stability and potential for functional applications. The results confirm robust structural and dynamic stability, as verified by elastic constants and phonon dispersion curves. All studied systems exhibit metallic character with pronounced spin polarization, while TiGaCu₂ shows the strongest total magnetization, highlighting its suitability for spintronic devices. Optical analyses reveal strong absorption across the visible and near-UV regions, low reflectivity, and favorable dielectric behavior, indicating promise for photovoltaic and optoelectronic applications. Thermodynamic modeling further confirms stability under high temperature and pressure, reinforcing their practical viability. Overall, the TiMCu₂ family demonstrates multifunctional characteristics, positioning them as eco-friendly and cost-effective candidates for next-generation renewable energy, spintronic, and optoelectronic technologies. Full article

Anaxyelidae, a relict lineage of sawflies, are represented by a single extant species today but displayed remarkable Mesozoic diversity. Here, we describe the Orthosyntexis mascula sp. nov. from mid-Cretaceous Burmese amber. The new species can be readily distinguished by its forewing, with a normally sized, uniformly sclerotized pterostigma; 1-Rs shorter than 1-M; cell 1M more than twice as long as wide; absence of 1r-rs; 1-Cu, distinctly shorter than 2-Cu; 3-Cu shorter than 4-Cu; 2m-cu shorter than 1m-cu; and 3rs-m twice as short as 4-M. In the hind wing, abscissa 2-M+Cu present, 1-M shorter than 2-M, crossvein m-cu absent, and cell R1 closed. Mesotibia with two apical spurs. Examination of high-resolution photographs of Kempendaja jacutensis enables a revised interpretation of its venation, confirming its placement in Anaxyelinae. Comparative analysis of syntexine taxa further reveals that variation in the forewing crossvein 1r-rs consistently corresponds with hind wing venation, suggesting that multiple evolutionary trajectories may have existed within Syntexinae. These findings not only expand the known diversity of Cretaceous Anaxyelidae but also provide new evidence for reconstructing the evolutionary history and internal diversification of Anaxyelidae. Full article

Microcontroller units (MCUs) serve as the core components of embedded systems. In the era of smart IoT, embedded devices are increasingly deployed on mobile platforms, leading to a growing demand for low-power consumption. As a result, low-power technology for MCUs has become increasingly critical. This paper systematically reviews the development history and current technical challenges of MCU low-power technology. It then focuses on analyzing system-level low-power optimization pathways for integrating MCUs with artificial intelligence (AI) technology, including lightweight AI algorithm design, model pruning, AI acceleration hardware (NPU, GPU), and heterogeneous computing architectures. It further elaborates on how AI technology empowers MCUs to achieve comprehensive low power consumption from four dimensions: task scheduling, power management, inference engine optimization, and communication and data processing. Through practical application cases in multiple fields such as smart home, healthcare, industrial automation, and smart agriculture, it verifies the significant advantages of MCUs combined with AI in performance improvement and power consumption optimization. Finally, this paper focuses on the key challenges that still need to be addressed in the intelligent upgrade of future MCU low power consumption and proposes in-depth research directions in areas such as the balance between lightweight model accuracy and robustness, the consistency and stability of edge-side collaborative computing, and the reliability and power consumption control of the sensor-storage-computing integrated architecture, providing clear guidance and prospects for future research. Full article

The novel design and modular implementation of a distributed control system for a fuel cell generator, aimed at monitoring and actuation, are presented. Two ESP32 NodeMCU microcontrollers and MCP2515 modules are used for the controller area network (CAN) bus communication protocol. To compare this setup with a traditional battery management system (BMS), small rated-power fuel cell generators were connected individually via the CAN bus to form a larger stacked output. An RFID interface was introduced into the CAN bus system to enhance its applicability in stacked fuel cells, without interfering with original message frames, arbitration mechanisms, or CRC efficiency across various sectors. Additionally, to provide a clearer understanding of the system’s features and functions, a PC-based logic analyzer was employed as an analytical tool to monitor and analyze data transmitted over the CAN bus. Comprehensive insights into the system’s performance are supported by logic analysis of its complex applications in series-connected fuel cells. The advantages of the RFID-based CAN bus are further enhanced by modern communication protocols, offering greater scalability and flexibility, with potential applications in industrial automation, autonomous vehicles, and smart green power grids. Full article

The rapid expansion of the Internet of Things (IoT) demands scalable, efficient, and user-friendly gateway solutions that seamlessly connect resource-constrained edge devices to cloud services. Low-cost, widely available microcontrollers, such as the ESP32 and its ecosystem peers, offer integrated Wi-Fi/Bluetooth connectivity, low power consumption, and a mature developer toolchain at a bill of materials cost of only a few dollars. For smart-home deployments where budgets, energy consumption, and maintainability are critical, these characteristics make MCU-class gateways a pragmatic alternative to single-board computers, enabling always-on local control with minimal overhead. This paper presents the design and implementation of an embedded IoT gateway powered by the ESP32 microcontroller. By using lightweight communication protocols such as Message Queuing Telemetry Transport (MQTT) and REST APIs, the proposed architecture supports local control, distributed intelligence, and secure on-site data storage, all while minimizing dependence on cloud infrastructure. A real-world deployment in an educational building demonstrates the gateway’s capability to monitor energy consumption, execute control commands, and provide an intuitive web-based dashboard with minimal resource overhead. Experimental results confirm that the solution offers strong performance, with RAM usage ranging between 3.6% and 6.8% of available memory (approximately 8.92 KB to 16.9 KB). The initial loading of the single-page application (SPA) results in a temporary RAM spike to 52.4%, which later stabilizes at 50.8%. These findings highlight the ESP32’s ability to serve as a functional IoT gateway with minimal resource demands. Areas for future optimization include improved device discovery mechanisms and enhanced resource management to prolong device longevity. Overall, the gateway represents a cost-effective and vendor-agnostic platform for building resilient and scalable IoT ecosystems. Full article

In this paper, three alternative solutions to the problem of detecting and cleaning anomalies in soil signal time series, involving the use of artificial neural networks deployed on in situ data measurement end devices, are proposed and investigated. These models are designed to perform calculations on MCUs, characterized by significantly limited computing capabilities and a limited supply of electrical power. Training of neural network models is carried out based on data from multiple sensors in the supporting computing cloud instance, while detection and removal of anomalies with a trained model takes place on the constrained end devices. With such a distribution of work, it is necessary to achieve a sound compromise between prediction accuracy and the computational complexity of the detection process. In this study, neural-primed heuristic (NPH), autoencoder-based (AEB), and U-Net-based (UNB) approaches were tested, which were found to vary regarding both prediction accuracy and computational complexity. Labeled data were used to train the models, transforming the detection task into an anomaly segmentation task. The obtained results reveal that the UNB approach presents certain advantages; however, it requires a significant volume of training data and has a relatively high time complexity which, in turn, translates into increased power consumption by the end device. For this reason, the other two approaches—NPH and AEB—may be worth considering as reasonable alternatives when developing in situ data cleaning solutions for IoT measurement systems. Full article

This paper presents ModuLab, a modular, low-cost sensor platform designed to simplify real-time environmental monitoring for laboratory research and educational settings. Centered on the APP-All MCU 2023 development board with an AVR128DA48 microcontroller (Microchip Technology Inc., Taiwan) ModuLab supports plug-and-play integration of multiple sensor types—including temperature, pH, light, and humidity—using a robust I²C communication protocol. The system features configurable sampling rates, built-in signal conditioning, and a Python-based interface for real-time data visualization. As a proof-of-concept, ModuLab was operated continuously for 48 h to evaluate system stability and filtering capabilities. However, due to institutional data ownership and confidentiality policies, the underlying datasets cannot be disclosed in this submission. The architecture and implementation details described herein are intended to guide future users and research groups seeking accessible alternatives to conventional data acquisition solutions. Comprehensive performance validation and open-access data sharing are planned as the next steps in this ongoing project. Full article

To ensure the safety and quality of grain during storage requires distributed monitoring of temperature and relative humidity within the bulk material, where hundreds of sensors may be needed. Conventional multi-channel systems are often constrained by the limited number of sensors connectable to a single acquisition unit, high hardware cost, and poor scalability. To address these challenges, this study proposes a novel design method for a multi-channel temperature and relative humidity acquisition system (MTRHAS). The system integrates sequential sampling control and a time-division multiplexing mechanism, enabling efficient data acquisition from multiple sensors while reducing hardware requirements and cost. This system employs sequential sampling control using a single complex programmable logic device (CPLD), and uses multiple CPLDs for multi-channel sensor expansion with a shared address and data bus for communication with a microcontroller unit (MCU). A prototype was developed using two CPLDs and one MCU, achieving data collection from 80 sensors. To validate the approach, a simulated grain silo experiment was conducted, with nine sensors deployed to monitor temperature and relative humidity during aeration. Calibration ensured sensor accuracy, and real-time monitoring results revealed that the system effectively captured spatial and temporal variation patterns of intergranular air conditions. Compared with conventional designs, the proposed system shortens the sampling cycle, decreases the number of acquisition units required, and enhances scalability through the shared bus architecture. These findings demonstrate that the MTRHAS provides an efficient and practical solution for large-scale monitoring of grain storage environments. Full article

This work proposes a methodology for the automated sizing of transistors in analog integrated circuits, based on a modular and hierarchical representation of the circuit. The methodology combines structured design techniques and systematic design flow to generate a hierarchy of simplified macromodels that define their specifications locally and are interconnected with other macromodels or transistor-level primitive blocks. These primitive blocks can be described using symbolic models or pre-characterized data from look-up tables (LUTs). The symbolic representation of the system is obtained using Modified Nodal Analysis (MNA), and the exploration of each block is performed using local design spaces constrained by top-level specifications. The methodology is validated through the design of low dropout voltage regulators (LDOs) for DC-DC integrated power systems using open-source tools and three process design kits: Sky130A, GF180MCU, and IHP-SG13G2. Results show that the methodology allows the exploration of several topologies and technologies, demonstrating its versatility and modularity, which are key aspects in analog design. Full article

Improving the catalytic performance of the oxygen evolution reaction (OER) for water splitting in acidic media is crucial for the production of clean and renewable hydrogen energy. Herein, we study the OER electrocatalytic properties of various active sites on four exposed (110) and ($\overline{1}$10) surfaces of Sn-doped RuO₂ (Sn/RuO₂) with antiferromagnetic arrangements in acidic environments. The Sn/RuO₂ bulk structure with the Cm space group exhibits favorable thermodynamic stability. The coordinatively unsaturated metal (M_cus) sites distributed on the right branch of the volcano plot are generally more active than the bridge-bonded lattice oxygen (O_br) sites located on the left. Different from the conventional knowledge that the most active site is located in the nearest neighbor of the doped atom, it has a lower OER overpotential when the active site is 3.6 Å away from the doped Sn atom. Among the sites studied, the 46-Ru_cus site exhibits the optimal OER catalytic performance. The inherent factors affecting the OER activity of each site on the Sn/RuO₂ surface are further analyzed, including the center of the d/p band at the active sites, the average electrostatic potential of the ions, and the number of transferred electrons. This work provides a reminder for the selection of active sites used to evaluate catalytic performance, which will benefit the development of efficient OER electrocatalysts. Full article

This study presents a portable gas sensor system that achieves high performance while minimizing power consumption and production costs for biomedical applications. The proposed system integrates a low-power readout integrated circuit (ROIC) capable of processing large-amplitude sensor signals using a 1.2 V ADC, significantly reducing the power consumption compared with conventional high-voltage solutions. To address the inherent limitations of single-core/single-thread microcontrollers, an optimized Wi-Fi communication algorithm is implemented, enabling real-time data transmission and accurate signal reconstruction without data loss. Experimental validation using a hydrogen gas detection setup demonstrated that the system achieves less than 0.15% error in reconstructed signals, while substantially reducing overall power consumption and component cost. Comparative analysis confirms that the proposed approach achieves a performance comparable to conventional systems while offering significant reductions in energy usage and hardware expense. These results demonstrate the feasibility of a scalable, low-cost solution for portable gas sensing, particularly in biomedical applications requiring precise and reliable monitoring. Full article

The regulation of calcium (Ca²⁺) homeostasis is a critical process in both plant and animal systems, involving complex interplay between various organelles and a diverse network of channels, pumps, and transporters. This review provides a concise overview of inter-organellar Ca²⁺ homeostasis, highlighting key regulators and mechanisms in plant and animal cells. We discuss the roles of key Ca²⁺ channels and transporters, including IP₃Rs, RyRs, TPCs, MCUs, TRPMLs, and P2XRs in animals, as well as their plant counterparts. Here, we explore recent innovations in structural biology and advanced microscopic techniques that have enhanced our understanding of these proteins’ structure, functions, and regulations. We examine the importance of membrane contact sites in facilitating Ca²⁺ transfer between organelles and the specific expression patterns of Ca²⁺ channels and transporters. Furthermore, we address the physiological implications of inter-organellar Ca²⁺ homeostasis and its relevance in various pathological conditions. For extended comparability, a brief excursus into bacterial intracellular Ca²⁺ homeostasis is also made. This meta-analysis aims to bridge the gap between plant and animal Ca²⁺ signaling research, identifying common themes and unique adaptations in these diverse biological systems. Full article

The growing adoption of IoT applications has led to increased use of low-power microcontroller units (MCUs) for energy-efficient, local data processing. However, deploying deep neural networks (DNNs) on these constrained devices is challenging due to limitations in memory, computational power, and energy. Traditional methods like cloud-based inference and model compression often incur bandwidth, privacy, and accuracy trade-offs. This paper introduces a novel Decentralized Distributed Sequential Neural Network (DDSNN) designed for low-power MCUs in Tiny Machine Learning (TinyML) applications. Unlike the existing methods that rely on centralized cluster-based approaches, DDSNN partitions a pre-trained LeNet across multiple MCUs, enabling fully decentralized inference in wireless sensor networks (WSNs). We validate DDSNN in a real-world predictive maintenance scenario, where vibration data from an industrial pump is analyzed in real-time. The experimental results demonstrate that DDSNN achieves 99.01% accuracy, explicitly maintaining the accuracy of the non-distributed baseline model and reducing inference latency by approximately 50%, highlighting its significant enhancement over traditional, non-distributed approaches, demonstrating its practical feasibility under realistic operating conditions. Full article

In the wide range of human diseases, Parkinson’s disease (PD) has a high incidence, according to a recent survey by the World Health Organization (WHO). According to WHO records, this chronic disease has affected approximately 10 million people worldwide. Patients who do not receive an early diagnosis may develop an incurable neurological disorder. PD is a degenerative disorder of the brain, characterized by the impairment of the nigrostriatal system. A wide range of symptoms of motor and non-motor impairment accompanies this disorder. By using new technology, the PD is detected through speech signals of the PD victims by using the reduced instruction set computing 5th version (RISC-V) processor. The RISC-V microcontroller unit (MCU) was designed for the voice-controlled human-machine interface (HMI). With the help of signal processing and feature extraction methods, the digital signal is impaired by the impairment of the nigrostriatal system. These speech signals can be classified through classifier modules. A wide range of classifier modules are used to classify the speech signals as normal or abnormal to identify PD. We use Matrix Laboratory (MATLAB R2021a_v9.10.0.1602886) to analyze the data, develop algorithms, create modules, and develop the RISC-V processor for embedded implementation. Machine learning (ML) techniques are also used to extract features such as pitch, tremor, and Mel-frequency cepstral coefficients (MFCCs). Full article

Format-compatible encryption can be used to ensure the security and privacy of JPEG images. Recently, a JPEG image encryption method proved to be secure against known plaintext attacks by employing an adaptive encryption key, which depends on the histogram of the number of non-zero alternating current coefficients (ACC) in Discrete Cosine Transform (DCT) blocks. However, this scheme has been demonstrated to be vulnerable to chosen-plaintext attacks (CPA) based on the run consistency of MCUs (RCM) between the original image and the encrypted image. In this paper, an improved CPA scheme is proposed. The method of incrementing run-length values instead of permutation is utilized to satisfy the uniqueness of run sequences of different minimum coded units (MCUs). The experimental results show that the proposed method can successfully recover the outlines of plaintext images from the encrypted images, even with lower-quality factors. Full article