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Study on the Conductivity Effect on the Characteristics of a Wideband Printed Dipole Antenna Implemented with Silver Nanoparticle Ink

This study examined a flexible composite wideband dipole antenna implemented with conductive silver nanoparticle ink having different conductivities. Two identical split ring resonators (SRRs) were designed to encompass each arm of a dipole element, and each dipole arm and its coupled SRR were printed on the top and bottom sides of a dielectric substrate. The overall dimensions of the compact antenna were 10 mm × 74.8 mm × 0.254 mm (0.053λ<sub>o</sub> × 0.399λ<sub>o</sub> × 0.0014λ<sub>o</sub> at 1.6 GHz). The characteristics of the antenna with different conductivities were numerically investigated and experimentally confirmed. Our investigation aimed to ascertain the proposed antenna's response to different conductivity values and to determine the range of conductivity values that generates major changes in the antenna's performance characteristics in terms of impedance bandwidth, gain, and radiation efficiency. At conductivity values less than approximately 6.0 × 10<sup>4</sup> S/m, the number of generated resonances changed from three to two, and the antenna experienced a nearly 3-dB gain reduction when the conductivity approached 5.8 × 10<sup>4</sup> S/m.

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SRCNN: Stacked-Residual Convolutional Neural Network for Improving Human Activity Classification Based on Micro-Doppler Signatures of FMCW Radar

Current methods for daily human activity classification primarily rely on optical images from cameras or wearable sensors. Despite their high detection reliability, camera-based approaches suffer from several drawbacks, such as low-light conditions, limited range, and privacy concerns. To address these limitations, this article proposes the use of a frequency-modulated continuous wave radar sensor for activity recognition. A stacked-residual convolutional neural network (SRCNN) is introduced to classify daily human activities based on the micro-Doppler features of returned radar signals. The model employs a two-layer stacked-residual structure to reuse former features, thereby improving the classification accuracy. The model is fine-tuned with different hyperparameters to find a trade-off between classification accuracy and inference time. Evaluations are conducted through training and testing on both simulated and measured datasets. As a result, the SRCNN model with six stacked-residual blocks and 64 filters achieves the best performance, with accuracies exceeding 95% and 99% at 0 dB and 10 dB, respectively. Remarkably, the proposed model outperforms several state-of-the-art CNN models in terms of classification accuracy and execution time on the same datasets.

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