Showing 102 results for Ica
Ayoub Hamidi, Ahmad Cheldavi, Asghar Habibnejad Korayem,
Volume 20, Issue 3 (9-2024)
Abstract
This paper proposes a structure for concrete composite materials that effectively attenuates transmitted power through the composite slab across a wide frequency range. The proposed structure is practical for electromagnetic interference shielding applications. To assess its effectiveness, the proposed structure has been compared with two other structures: a traditional wire mesh used in reinforced composites and an array of helices, a cutting-edge technique for manufacturing lightweight concretes with significant improvements in shielding properties. The comparison among full-wave simulation results indicates that the proposed method leverages the benefits of both techniques. It achieves a shielding effectiveness exceeding 30 dB from low frequencies up to 8.5 GHz and beyond 55 dB from low frequencies up to 4 GHz. Furthermore, an experimental measurement was conducted to validate the full-wave simulation results. An experimental sample was fabricated according to the simulated proposed structure, and the measured shielding effectiveness confirmed the composite's capability in wideband electromagnetic shielding. Theoretically, the proposed structure can enhance the concrete's mechanical characteristics while improving its shielding effectiveness, making it suitable for designing ultra-high-performance concretes.
Dalila Yessad,
Volume 20, Issue 4 (11-2024)
Abstract
This paper introduces the CTDRCepstrum, a novel feature extraction technique designed to differentiate various human activities using Doppler radar classification. Real data were collected from a Doppler radar system, capturing nine return echoes while monitoring three distinct human activities: walking, fast walking, and running. These activities were performed by three subjects, either individually or in pairs. We focus on analyzing the Doppler signatures using time-frequency reassignment, emphasizing its advantages such as improved component separability. The proposed CTDRCepstrum explores different window functions, transforming each echo signal into three forms of Short-Time Fourier Transform reassignments (RSTFT): time RSTFT (TSTFT), time derivative RSTFT (TDSTFT), and reassigned STFT (RSTFT). A convolutional neural network (CNN) model was then trained using the feature vector, which is generated by combining the cepstral analysis results of each RSTFT form. Experimental results demonstrate the effectiveness of the proposed method, achieving a remarkable classification accuracy of 99.83% by using the Bartlett-Hanning window to extract key features from real-time Doppler radar data of moving targets.
