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.

Versatile applications have driven a desire for dual-band detection that enables seeing objects in multiple wavebands through a single photodetector. In this paper, a concept of using graphene/p-GaN Schottky heterojunction on top of a regular AlGaN-based p-i-n mesa photodiode is reported for achieving solar-/visible-blind dual-band (275 nm and 365 nm) ultraviolet photodetector with high performance. The highly transparent graphene in the front side and the polished sapphire substrate at the back side allows both top illumination and back illumination for the dual band detection. A system limit dark current of 1×10−9 A/cm2 at a negative bias voltage up to -10 V has been achieved, while the maximum detectivity obtained from the detection wavebands of interests at 275 nm and 365 nm are ∼ 9.0 ×1012 cm·Hz1/2/W at -7.5 V and ∼8.0 × 1011 cm·Hz1/2/W at +10 V, respectively. Interestingly, this new type of photodetector is dual-functional, capable of working as either photodiode or photoconductor, when switched by simply adjusting the regimes of bias voltage applied on the devices. By selecting proper bias, the device operation mode would switch between a high-speed photodiode and a high-gain photoconductor. The device exhibits a minimum rise time of ∼210 µs when working as a photodiode and a maximum responsivity of 300 A/W at 6 μW/cm2 when working as a photoconductor. This dual band and multi-functional design would greatly extend the utility of detectors based on nitrides.

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 detection of rotated objects is a meaningful and challenging research work. Although the state-of-the-art deep learning models have feature invariance, especially convolutional neural networks (CNNs), their architectures did not specifically design for rotation invariance. They only slightly compensate for this feature through pooling layers. In this study, we propose a novel network, named LPNet, to solve the problem of object rotation. LPNet improves the detection accuracy by combining retina-like log-polar transformation. Furthermore, LPNet is a plug-and-play architecture for object detection and recognition. It consists of two parts, which we name as encoder and decoder. An encoder extracts images which feature in log-polar coordinates while a decoder eliminates image noise in cartesian coordinates. Moreover, according to the movement of center points, LPNet has stable and sliding modes. LPNet takes the single-shot multibox detector (SSD) network as the baseline network and the visual geometry group (VGG16) as the feature extraction backbone network. The experiment results show that, compared with conventional SSD networks, the mean average precision (mAP) of LPNet increased by 3.4% for regular objects and by 17.6% for rotated objects.