Current Issue : April-June Volume : 2026 Issue Number : 2 Articles : 5 Articles
This study presents the design and validation of an AI-enhanced embedded IoT system for real-time industrial sensor calibration. The proposed platform integrates a PT100 temperature sensor and a 4–20 mA pressure transmitter with an ESP32 microcontroller, enabling on-device data acquisition, processing, and wireless transmission. A lightweight multilayer perceptron (MLP) neural network, trained in Python with a hybrid dataset (synthetic and experimental) and deployed on the ESP32 via JSON weight files, performs local inference to estimate ideal sensor outputs and compute key performance metrics. Experimental tests under controlled laboratory conditions confirmed high accuracy, with efficiency above 98.6%, RMSE below 0.005V, and absolute uncertainty margins of ±0.5 ◦C and ±0.07 bar. Additionally, 95% confidence intervals for RMSE and standard deviation demonstrated statistical reliability across all operating points. The prototype also addresses practical constraints, including ESP32 ADC nonlinearity, energy consumption, and multisensor scalability, while remaining portable and low-cost. The integration of edge AI capabilities demonstrates the feasibility of executing accurate neural network models directly on embedded microcontrollers, eliminating reliance on cloud-based processing. The proposed solution provides a robust proof-of-concept that is scalable, cost effective, and suitable for industrial IoT applications, predictive maintenance, and Industry 4.0 environments, with future work focusing on long-term drift evaluation and validation under real industrial conditions....
With advances in computing power and deep learning, image-based pose estimation has become a viable tool for quantitative motion analysis. Compared to sensor-based systems, vision-based approaches are cost-effective, portable, and easy to deploy. However, when applied to golf swings, conventional similarity measures often fail to match expert perception, as they rely on static, frame-wise posture comparisons and require strict temporal alignment. We propose a Dynamic Motion Similarity Measurement (DMSM) framework that segments a swing into seven canonical phases—address, takeaway, half, top, impact, release, and finish—and evaluates the dynamic trajectories of joint keypoints within each phase. Unlike traditional DTW- or frame-based methods, our approach integrates continuous motion trajectories and normalizes joint coordinates to account for player body scale differences. Motion data are interpolated to improve temporal resolution, and numerical integration quantifies path differences, capturing motion flow rather than isolated postures. Quantitative experiments on side-view swing datasets show that DMSM yields stronger discrimination between same- and different-player pairs (phaseaveraged separation: 0.092 vs. 0.090 for the DTW + cosine baseline) and achieves a clear biomechanical distinction in spine-angle trajectories (Δ = 38.68). Statistical analysis (paired t-test) confirmed that the improvement was significant (p < 0.05), and coach evaluations supported perceptual alignment. Although DMSM introduces a minor computational overhead (≈169 ms), it delivers more reliable phase-wise feedback and biomechanically interpretable motion analysis. This framework offers a practical foundation for AI-based golf swing analysis and real-time feedback systems in sports training, demonstrating improved perceptual consistency, biomechanical interpretability, and computational feasibility....
With the increasing penetration of distributed energy resources (DERs) in distribution networks, voltage stability issues are becoming increasingly prominent, making the accurate characterization of the system’s security boundaries crucial. Traditional methods, such as the Continuation Power Flow (CPF) method, suffer from limitations, such as low efficiency and poor convergence when calculating high-dimensional feasible regions. This paper proposes a fast characterization method for the distribution network feasible region based on the Holomorphic Embedding Method (HEM). Firstly, the embedding approaches for the holomorphic embedding model at different types of nodes are presented, and the recursive relations for solving the power series coefficients are derived, noting that the model’s initial solution corresponds to the power flow solution of the system. Secondly, a distributed generator power injection space is introduced, and a holomorphic embedding model oriented towards limit violation point tracking is constructed. This model can efficiently characterize the operational feasible region of active distribution networks and quantify the hosting capacity and integration boundaries for DERs. Finally, case studies on the IEEE 33-node distribution system are conducted. Simulation results demonstrate that the proposed method effectively characterizes the security-constrained operational feasible region of active distribution networks, exhibits significant engineering practicality, and achieves markedly improved computational efficiency compared to the traditional CPF method. The method provides an important theoretical foundation and a practical tool for the planning and operation of active distribution networks....
Crowd counting is a task of estimating the number of the crowd through images, which is extremely valuable in the fields of intelligent security, urban planning, public safety management, and so on. However, the existing counting methods have some problems in practical application on embedded systems for these fields, such as excessive model parameters, abundant complex calculations, etc. The practical application of embedded systems requires the model to be real-time, which means that the model is fast enough. Considering the aforementioned problems, we design a super real-time model with a stem-encoder-decoder structure for crowd counting tasks, which achieves the fastest inference compared with state-of-the-arts. Firstly, large convolution kernels in the stem network are used to enlarge the receptive field, which effectively extracts detailed head information. Then, in the encoder part, we use conditional channel weighting and multi-branch local fusion block to merge multi-scale features with low computational consumption. This part is crucial to the super real-time performance of the model. Finally, the feature pyramid networks are added to the top of the encoder to alleviate its incomplete fusion problems. Experiments on three benchmarks show that our network is suitable for super real-time crowd counting on embedded systems, ensuring competitive accuracy. At the same time, the proposed network reasoning speed is the fastest. Specifically, the proposed network achieves 381.7 FPS on NVIDIA GTX 1080Ti and 71.9 FPS on NVIDIA Jetson TX1....
Backscatter technologies promise to enable large-scale, battery-free sensor networks by modulating and reflecting ambient radio frequency (RF) carriers rather than generating new signals. Translating this potential into practical deployments—such as distributed photovoltaic (PV) power systems—necessitates realistic modeling that accounts for deployment variabilities commonly neglected in idealized analyses, including uncertain hardware insertion loss, non-ideal antenna gain, spatially varying path loss exponents, and fluctuating noise floors. In this work, we develop a practical model for reliable backscatter communications that explicitly incorporates these impairing factors, and we complement the theoretical development with empirical characterization of each contributing term. To validate the model, we implement a frequency-shift keying (FSK)-based backscatter system employing a non-coherent demodulation scheme with adaptive bit-rate matching, and we conduct comprehensive experiments to evaluate communication range and sensitivity to system parameters. Experimental results demonstrate strong agreement with theoretical predictions: the prototype tag consumes 825 μW in measured operation, and an integrated circuit (IC) implementation reduces consumption to 97.8 μW, while measured communication performance corroborates the model’s accuracy under realistic deployment conditions....
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