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2024-04-20
PIER M
Vol. 127, 11-22, 2024
download: 13
Supervised Manifold Learning-Based Polarimetric-Spatial Feature Extraction for PolSAR Image Classification
Hui Fan , Wei Wang , Sinong Quan , Xi He and Jie Deng
In order to improve the classification performance of Polarimetric Synthetic Aperture Radar (PolSAR) image by synthesizing various polarimetric features, a supervised manifold learning method is proposed in this paper for PolSAR feature extraction and classification. Under the umbrella of tensor algebra, the proposed method characterizes each pixel with a feature tensor by combining the high-dimensional feature information of all the pixels within its local neighborhood. The tensor representation mode integrates the polarimetric information and spatial information, which is beneficial for alleviating the influence of speckle noise. Then, the tensor discriminative locality alignment (TDLA) method is introduced to seek the multilinear transformation from the original polarimetric-spatial feature tensor to the low-dimensional feature. The label information of training samples is utilized during feature transformation and feature mapping; therefore, the discriminability of different classes can be well preserved. Based on the extracted features in the low-dimensional space, the SVM classifier is applied to achieve the final classification result. The experiments implemented on two real PolSAR data sets verify that the proposed method can extract the features with better stability and separability, and obtain superior classification results compared to several state-of-the-art methods.
Supervised Manifold Learning-based Polarimetric-spatial Feature Extraction for PolSAR Image Classification
2024-04-20
PIER M
Vol. 127, 1-10, 2024
download: 16
Flexible Wearable Antenna Based on AMC with Different Materials for Bio-Telemetry Applications
Yara Ashraf Kamel , Hesham Abd Elhady Mohamed , Hala Elsadek and Hadia El-Hennawy
In this work, a low-profile and flexible antenna operating in the ISM (2.4-2.4835) GHz band for bio-telemetry applications is presented. This antenna is designed on two flexible substrate materials: Roger RO3003 with a thickness of 0.254 mm and jeans fabric material with a thickness of 0.7 mm, of an overall foot print of 20 × 30 mm2. The deformation bending of the designed antenna in two different cases is studied. The designed antenna is backed by a 3 × 3 artificial magnetic conductor (AMC) array structure, which resulted in the final design configuration. The antenna is backed by an AMC array structure to achieve a lower specific absorption rate (SAR) as well as high gain when it is mounted on biological tissue. For validation, the antenna is fabricated on two flexible substrate materials and then measured in free space as well as on four different parts of the realistic human (chest, back, arm, and leg) body with and without AMC structure. Furthermore, the SAR is measured on cSAR3D flat. Finally, for reliable communication, the link margin is calculated.
Flexible Wearable Antenna Based on AMC with Different Materials for Bio-telemetry Applications