A Novel Symmetry Driven Siamese Network for THz Concealed Object Verification

Enhancing concealed object detection in active THz security images with adaptation-YOLO

The terahertz (THz) security scanner offers advantages such as non-contact inspection and the ability to detect various types of dangerous goods, playing an important role in preventing terrorist attacks. We aim to accurately and quickly detect concealed objects in THz security images. However, current object detection algorithms face many challenges when applied to THz images. The main reasons for the detection difficulty are that the concealed objects are small, the image resolution is low, and there is back-ground noise. Many methods often ignore the contextual dependency of the objects, hindering the effective capture of the object's features. To address this task, this paper first proposes an adaptive context-aware attention network (ACAN), which models global contextual association features in both spatial and channel dimensions. By dynamically combining local features and their global relationships, contextual association information can be obtained from the input features, and enhanced attention features can be achieved through feature fusion to enable precise detection of concealed objects. Secondly, we improved the adaptive convolution and developed the dynamic adaptive convolution block (DACB). DACB can adaptively adjust convolution filter parameters and allocate the filters to the corresponding spatial regions, then filter the feature maps to suppress interference information. Finally, we integrated these two components to YOLOv8, resulting in Adaptation-YOLO. Through wide-ranging experiments on the active THz image dataset, the results demonstrate that the suggested method effectively improves the accuracy and efficiency of object detectors.

Flexible terahertz phase shifter for optically controlled polydimethylsiloxane-vanadium dioxide composite film

In the terahertz (THz) band, modulation research has become a focal point, with precise control of the phase shift of THz waves playing a pivotal role. In this study, we investigate the optical control of THz phase shift modulation in a polydimethylsiloxane (PDMS)-vanadium dioxide (VO2) flexible material using THz time-domain spectroscopy. Under the influence of an 808-nm continuous wave (CW) laser with power densities ranging from 0 to 2.74 W/cm2, the PDMS-VO2 flexible material exhibits significant phase shift modulation in the frequency range of 0.2 to 1.0 THz. The maximum optical-pumping phase shift reaches 0.27π rad at 1.0 THz in a composite material with a VO2 mass fraction of 5% and a thickness of 360 µm, and the amplitude transmittance from 0.2 THz to 1.0 THz exceeds 70%. Furthermore, the composite material exhibits good stability under at least 640 switching cycle times, as confirmed through repeatability tests. The proposed composite devices offer a new approach for more flexible phase shift modulation owing to the flexibility of the composite material and the non-contact and precise modulation of light control. Additionally, the stress-adjustable characteristics of flexible materials make them highly suitable for use in wearable THz modulators, highlighting their significant application potential.

Artificial neural networks for material parameter extraction in terahertz time-domain spectroscopy

Terahertz time-domain spectroscopy (THz-TDS) is a proven technique whereby the complex refractive indices of materials can be obtained without requiring the use of the Kramers-Kronig relations, as phase and amplitude information can be extracted from the measurement. However, manual pre-processing of the data is still required and the material parameters require iterative fitting, resulting in complexity, loss of accuracy and inconsistencies between measurements. Alternatively approximations can be used to enable analytical extraction but with a considerable sacrifice of accuracy. We investigate the use of machine learning techniques for interpreting spectroscopic THz-TDS data by training with large data sets of simulated light-matter interactions, resulting in a computationally efficient artificial neural network for material parameter extraction. The trained model improves on the accuracy of analytical methods that need approximations while being easier to implement and faster to run than iterative root-finding methods. We envisage neural networks can alleviate many of the common hurdles involved in analyzing THz-TDS data such as phase unwrapping, time domain windowing, slow computation times, and extraction accuracy at the low frequency range.

Few-shot contrastive learning for image classification and its application to insulator identification

This paper presents a novel discriminative Few-shot learning architecture based on batch compact loss. Currently, Convolutional Neural Network (CNN) has achieved reasonably good performance in image recognition. Most existing CNN methods facilitate classifiers to learn discriminating patterns to identify existing categories trained with large samples. However, learning to recognize novel categories from a few examples is a challenging task. To address this, we propose the Residual Compact Network to train a deep neural network to learn hierarchical nonlinear transformations to project image pairs into the same latent feature space, under which the distance of each positive pair is reduced. To better use the commonality of class-level features for category recognition, we develop a batch compact loss to form robust feature representations relevant to a category. The proposed methods are evaluated on several datasets. Experimental evaluations show that our proposed method achieves acceptable results in Few-shot learning.