Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
Large language models (LLMs) have shown strong potential for automated code generation in software development, yet their effectiveness in embedded systems programming— requiring understanding of software logic and hardware constraints—has not been well studied. Existing evaluation frameworks do not comprehensively cover practical microcontroller development scenarios in real-world Internet of Things (IoT) projects. This study systematically evaluates 27 state-of-the-art LLMs across eight embedded systems scenarios of increasing complexity, from basic sensor reading to complete cloud database integration with visualization dashboards. Using ESP32 microcontrollers with environmental and motion sensors, we employed the Analytic Hierarchy Process with four weighted criteria: functional, instructions, output and creativity, evaluated independently by two expert reviewers. Top-performing models were Claude Sonnet 4.5, Claude Opus 4.1, and Gemini 2.5 Pro, with scores from 0.984 to 0.910. Performance degraded with complexity: 19–23 models generated compilable code for simple applications, but only 3–5 produced functional solutions for complex scenarios involving Grafana and cloud databases. The most frequent failure was hallucinated non-existent libraries or incorrect API usage, with functional capability as the primary barrier and instruction-following quality the key differentiator among competent models. These findings provide empirical guidance for embedded developers on LLM selection and identify limitations of zero-shot prompting for hardware-dependent IoT development....
In this study, we present a new EEG-based drowsiness-detection system using a single EEG channel and IoT technology. The aim of this work is to develop a person-dependent system capable of overcoming interpersonal variability due to aging while sending alert signals to the cloud. We used a set of five features computed from the power spectral density, based on variations in power spectral energy during the transition from wakefulness to drowsiness (stage one of sleep) for each individual. The results demonstrate that the proposed system can accurately detect driver drowsiness, achieving an accuracy of 95% using a reduced set of features and a single differential EEG channel. The main advantage of the proposed system lies in its ability to overcome interpersonal variability while maintaining high detection accuracy. The system was validated using the MIT-BIH Polysomnography dataset, comprising ten subjects....
Industrial dehumidification plays a pivotal role in spice processing industries, where precise moisture control directly influences product quality, shelf life, and processing efficiency. However, in many industrial facilities, these systems are operated manually or using basic on-off control methods. Such practices often result in unstable operating conditions, frequent compressor switching, increased energy consumption, and reduced equipment lifespan. This study addresses the lack of affordable, hardware-level automation for multi-evaporator systems by presenting the design and implementation of a dedicated embedded control architecture. The proposed objective was to develop a dual-microcontroller system where a primary controller manages real-time decision-making based on temperature and relative humidity, while a secondary controller is strictly dedicated to safety and time-delay protection. The system was implemented and tested in an industrial spice processing facility. Key findings demonstrate that the autonomous mode reduced outlet air temperature variation to ±1 – 2 oC and relative humidity fluctuation to ±4 – 5%, compared to significantly higher variations in manual operation. Furthermore, the system reduced operator interventions from 1-2 per shift to 0-1 and minimized compressor cycling frequency. Beyond operational efficiency, the stabilization of the drying environment directly contributes to the preservation of critical quality parameters, such as volatile oil retention and color uniformity, which are frequently compromised under manual control regimes. These results imply that low-cost embedded automation can significantly enhance operational stability and safety in agro-industrial processing without requiring expensive infrastructure upgrades....
We present an optical deformation sensor additively manufactured via an embedded printing process that enables the direct integration of colloidal quantum dots into multimode silicone (PDMS) waveguides. The sensor consists of two parallel waveguide strands, one of which is locally functionalized with CdSe/CdS quantum dots serving as fluorescent emitters. When narrow-band UV light at 405 nm is coupled into the non-functionalized strand, structural deformation alters the conditions of total internal reflection, thereby changing the optical interaction between both strands. This leads to a deformation-dependent variation in the fluorescence shift-affected intensity ratio, which serves as a self-referenced signal for angle determination. Using ratiometric evaluation, angular deflections of up to 9.5° are detected with a resolution below 1° (2σ confidence), representing the performance of an initial functional prototype. The embedded printing process allows the voxel-wise adjustment of the material composition within a viscoplastic support medium and thus the spatially resolved integration of quantum dot-functionalized silicone. Attenuation losses of 0.81±0.02 dB/cm at 625 nm confirm the optical suitability of the printed waveguides. This approach combines optical sensing and structural flexibility within a single manufacturing step and establishes a pathway toward fully integratable deformation-sensing elements for soft robotic and wearable systems....
In this work, we study the linear and nonlinear optical properties of a novel quinolinone-chalcone derivative, namely, 4(1H)-quinolinone-(E)-4-chlorobenzylidene-4-chlorophenyl-phenylsulfonyl with formula C28H19Cl2NO3S. Theoretical calculations of the electrical properties of the quinolinone-chalcone derivative crystal were performed at density functional theory DFT/CAM-B3LYP/6-311++G(d, p) level, both in the static and dynamic regimes. To simulate the crystalline environment, an electrostatic iterative charge embedding approach was employed, which revealed a redistribution of electronic density arising from crystalline polarization effects. This approach revealed a significant enhancement in the molecular dipole moment (μ 5.95D) due to crystal packing effects. The calculated third-order nonlinear susceptibility at 532 nm was found to be χ ð3Þ Kerr 162.52 × 10−22ðm=VÞ2, with a highest occupied molecular orbital-lowest unoccupied molecular orbital gap of 4.14 eV, indicating a good potential for optical switching applications. Future experimental validations via Z-scan and third-harmonic generation measurements are proposed to corroborate these theoretical predictions....
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