Resistance strain force sensors have been applied to monitor the strains in various parts and structures for industrial use. Here, we review the working principles, structural forms, and fabrication processes for resistance strain gauges. In particular, we focus on recent developments in resistance stress transfer for resistance strain force sensors and the creep effect due to sustained loads and/or temperature variations. Various error compensation methods to reduce the creep effect are analyzed to develop a metrology standard for resistance strain force sensors. Additionally, the current status of carbon nanotubes (CNTs), silicon carbide (SiC), gallium nitride (GaN), and other wide band gap semiconductors for a wide range of strain sensors are reviewed. The technical requirements and key issues of resistance strain force sensors for future applications are presented.

Due to thermal carriers generated by a narrow mid-infrared energy gap, cooling is always necessary to achieve ideal photodetection. In quantum dot (QD), the electron thermal generation should be reduced with quantum confinement in all three dimensions. As a result, there would be a great potential to realize high-operating-temperature (HOT) QD mid-IR photodetectors, though not yet achieved. Taking the advantages of colloidal nanocrystals’ solution processability and precise doping control by surface dipoles, this work demonstrates a HOT mid-infrared photodetector with a QD gradient homojunction. The detector achieves background-limited performance with D^* = 2.7 × 10¹¹ Jones on 4.2 μm at 80 K, above 10¹¹ Jones until 200 K, above 10¹⁰ Jones until 280 K, and 7.6 × 10⁹ Jones on 3.5 μm at 300 K. The external quantum efficiency also achieves more than 77% with responsivity 2.7 A/W at zero bias. The applications such as spectrometers, chemical sensors, and thermal cameras, are also approved, which motivate interest in low-cost, solution-processed and high-performance mid-infrared photodetection beyond epitaxial growth bulk photodetectors.

With the development of infrared detection technology and the improvement of military remote sensing needs, infrared object detection networks with low false alarms and high detection accuracy have been a research focus. However, due to the lack of texture information, the false detection rate of infrared object detection is high, resulting in reduced object detection accuracy. To solve these problems, we propose an infrared object detection network named Dual-YOLO, which integrates visible image features. To ensure the speed of model detection, we choose the You Only Look Once v7 (YOLOv7) as the basic framework and design the infrared and visible images dual feature extraction channels. In addition, we develop attention fusion and fusion shuffle modules to reduce the detection error caused by redundant fusion feature information. Moreover, we introduce the Inception and SE modules to enhance the complementary characteristics of infrared and visible images. Furthermore, we design the fusion loss function to make the network converge fast during training. The experimental results show that the proposed Dual-YOLO network reaches 71.8% mean Average Precision (mAP) in the DroneVehicle remote sensing dataset and 73.2% mAP in the KAIST pedestrian dataset. The detection accuracy reaches 84.5% in the FLIR dataset. The proposed architecture is expected to be applied in the fields of military reconnaissance, unmanned driving, and public safety.

The potential of random pattern based computational ghost imaging (CGI) for real-time applications has been offset by its long image reconstruction time and inefficient reconstruction of complex diverse scenes. To overcome these problems, we propose a fast image reconstruction framework for CGI, called “DeepGhost”, using deep convolutional autoencoder network to achieve real-time imaging at very low sampling rates (10–20%). By transferring prior-knowledge from STL-10 dataset to physical-data driven network, the proposed framework can reconstruct complex unseen targets with high accuracy. The experimental results show that the proposed method outperforms existing deep learning and state-of-the-art compressed sensing methods used for ghost imaging under similar conditions. The proposed method employs deep architecture with fast computation, and tackles the shortcomings of existing schemes i.e., inappropriate architecture, training on limited data under controlled settings, and employing shallow network for fast computation.

Fourier single pixel imaging (FSPI) is well known for reconstructing high quality images but only at the cost of long imaging time. For real-time applications, FSPI relies on under-sampled reconstructions, failing to provide high quality images. In order to improve imaging quality of real-time FSPI, a fast image reconstruction framework based on deep learning (DL) is proposed. More specifically, a deep convolutional autoencoder network with symmetric skip connection architecture for real time 96 × 96 imaging at very low sampling rates (5–8%) is employed. The network is trained on a large image set and is able to reconstruct diverse images unseen during training. The promising experimental results show that the proposed FSPI coupled with DL (termed DL-FSPI) outperforms conventional FSPI in terms of image quality at very low sampling rates.