Excitons play an important role in semiconductor physics, whereas their electrical characteristics have rarely been explored. Here, AC differential capacitance response spectroscopy is applied to GaN-based multi-quantum-well (MQW) laser diodes (LDs) to investigate the transition-bound excitons. The capacitance response aligns with a modified rate equation, exhibiting negative activation energy, attributed to a sub-stable transition-bound state. Temperature dependent measurements yield binding energies of the transition-bound state consistent with weakly confined excitons in III–V quantum structures. These results confirm transition-bound excitons under continuous electrical excitation. As a comparison, the differential capacitance response of GaAs-based MQW LDs has shown that there is no negative activation energy, which further confirms the transition-bound excitons in GaN-based MQW LDs at room temperature. In addition, simulations of GaN-based MQW LDs’ capacitance response match experimental results well.

Traditional AlGaN electron blocking layer (EBL) has significant limitations in reducing electron leakage and enhancing electron injection, especially in high-Al-content environments. This study presents an EBL that combines superlattice (SL) and multi-gradient structures, aiming to enhance the performance of deep ultraviolet (DUV) light-emitting diodes (LEDs). Simulations were performed using the semiconductor device advanced physics model (APSYS) software, and comparisons were made with conventional high-Al content AlGaN EBL and SL EBL. The results indicate that the combination of SL and multi-gradient EBL significantly increases the electron barrier height, suppresses electron leakage, and enhances hole injection efficiency, leading to a 42.4% increase in internal quantum efficiency (IQE) and a notable 341.6% increase in output power compared to reference LED structure. Simultaneously, significant improvements were observed in the radiative recombination rate and spontaneous emission rate, offering new insights for the design of high-performance devices in ultraviolet applications.

The chemical oxygen demand (COD) online monitoring sensor, based on the spectral absorption, shows outstanding advantages in water quality monitoring, pollution source tracing, and environmental protection inspection. However, there are several factors that influence the stability and accuracy of the sensor detection in practical applications. Therefore, they must be carefully considered. This research analyzes the impact of changes in physical characteristics, chemical composition, and external environmental interference on the output result of the optical COD sensor systematically and comprehensively, from different fields including electronic technology, sensor technology, composition analysis, and environmental condition analysis, through both theoretical and experimental approaches. This study will provide guidance for the scientific research, product development, and upgrading of the optical water quality sensor, and provide reference for the long-term use, operation, and maintenance of the sensor.

In this paper, we present a wearable optical sensor based on microfiber for monitoring visual fatigue. The sensor consists of a double-layer polydimethylsiloxane (PDMS) enclosed microfiber. It was positioned on the subject’s orbicularis oculi muscle and secured with medical tape to ensure a tight fit against the skin. During a visual display terminal (VDT) task, we tracked various blinking feature signals associated with visual fatigue. As visual fatigue develops, the blinking characteristics observed in this study change significantly, offering a reliable method for the objective evaluation of VDT.

The aim of this study was to develop a simple, rapid, and reagent-free method for predicting the content of typical monocyclic aromatic hydrocarbons, benzene and toluene, in water using fluorescence spectroscopy data, chemometric methods, and random forest (RF) regression models. This was achieved through the reproducibility testing of fluorescence spectra configured at various concentrations of benzene and toluene, where the relative standard deviation (RSD) was approximately 5%. An extensive study of the spectral characteristics of a large dataset of fluorescence spectra revealed that the benzene spectrum produced three peaks, with the strongest peak at 277 nm, while the toluene spectrum produced a single peak at 284 nm. Subsequent spectral specificity analysis based on the substituents of benzene and toluene was conducted. An RF regression model was established using the relationship between concentrations of benzene and toluene and fluorescence intensity. The optimal RF regression models achieved a coefficient of determination (R²) of 0.997 and root mean square error (RMSE) of 3.06 for high concentrations of benzene, and an R² of 0.994 and RMSE of 0.04 for various concentrations of toluene. This study demonstrates that fluorescence spectroscopy, distinct from conventional mass spectrometric and chromatographic methods, provides a convenient and novel approach for the detection and online monitoring of typical monocyclic aromatic hydrocarbons, such as benzene and toluene, in water.

Optical inter-satellite links (ISLs) are critical to attaining high throughput transmission at long distance around the world. In this work, an ISL using both Hermite- and Laguerre-Gaussian beams are realized in 37 000 km range at aggregate data rate of 80 Gbit/s in a satellite communication system. The proposed optical ISL system can sustain maximum spot size and receiver pointing error of 9.3 µm and 1.4 µrad over 30 000 km distance, respectively, with better eye opening at 10⁻⁹ error rate with −42.57 dBm signal power. Also, the comparison analysis of the proposed work with existing ones depicts its superiority and offers high capacity, high throughput and reliable satellite connectivity.

In this letter, a coplanar waveguide (CPW) dual-band bandpass filter (BPF) for wireless local area network (WLAN) and worldwide interoperability for microwave access (WiMAX) applications is proposed. The dual-band response is achieved by etching two pairs of spiral-shaped defected ground structures (DGS) on the CPW ground planes, a rectangular gap and T-shaped slots on the center strip, and extending T-shaped slots forming U-shaped structures on the grounds. Measured results show passbands centered at 3.67 GHz and 5.82 GHz, with return loss better than 10 dB over 3.33–4.01 GHz and 5.55–6.08 GHz. Three transmission zeros are generated, one in each stopband adjacent to the passbands, providing sharp frequency selectivity.

We propose a distributed fiber sensor based on optical frequency domain reflectometry (OFDR) for strain measurement on a wind turbine blade. Due to the varying cross sections along the heterotypic blade, the strain distribution is nonlinearly demonstrated in experiment as estimated and predicted in numerical analysis. Negative strains (compression) and positive strains (tension) are detected in the 177–198 cm and 224–245 cm regions of the fiber, respectively. Moreover, responsivities of −0.775 71 µε/g and 0.599 53 µε/g at 195 cm and 228 cm are proved to be inversely proportional to the local cross sections, with a linearity quantified by R² values of 0.999 87 and 0.999 9. This technique provides an effective method to characterize strain in heterotypic structure, showing potential in defect positioning by stress concentration as well.

A pedestrian and vehicle detection algorithm CFAI-YOLO based on you only look once v11 small (YOLOv11s) is proposed to process the issues of insufficient accuracy, high false detection and missed detection rates in existing pedestrian and vehicle detection algorithms. Firstly, the introduction of context and spatial feature calibration network (CSFCN) effectively solves the problems of pixel context mismatch and spatial feature misalignment. Secondly, an adaptive fine-grained channel attention (FCA) mechanism is added to the backbone network to achieve more effective feature weight allocation. Finally, using the ADown downsampling module preserves the target feature information and reduces the number of model parameters. The experimental results of the improved model on the KITTI dataset achieved 88.1% mean average precision (mAP), which is 2.4% higher than the original model.

In low-light environments, the signal-to-noise ratio (SNR) of complementary metal-oxide-semiconductor (CMOS) image sensors decreases significantly. In order to improve the imaging quality, this paper proposes a transformer-based denoising method, illumination prompting dual-attention transformer (IPDT). In the noise model, the effect of dark current noise on the low light images is emphasized. The denoising network contains the dual-attention transformer block (DTB) module including spatial-channel attention and the prompt-block (PB). The DTB captures global and detail information of low-light images through cross-window and cross-channel interactions, respectively. The PB acquires illumination information through learnable prompt parameters, dynamically guiding the denoising network. Experiments on see-in-the-dark (SID) dataset and smartphone image denoising dataset (SIDD) indicate that the proposed method effectively suppresses the noise of low-light images and demonstrates superior performance in terms of blind referenceless image spatial quality evaluator (BRISQUE) and multi-scale structural similarity index (MS-SSIM) metrics.

To address challenges of limited computational resources on drones and interference from low resolution and background noise in infrared electrical equipment detection, this study proposes a hybrid ratio-tunable you only look once (HRT-YOLO) adaptive framework. Inspired by RepViT, the lightweight RepViTBlock (LRepViTBlock) was proposed, enabling the decoupling of the token-channel mixer, improving computational efficiency, and enhancing the model’s learning ability. A hybrid ratio-tunable convolution (HRTConv) was proposed, enabling computation-accuracy trade-offs through channel allocation factor α. A C3K2 module incorporating HRTConv was proposed, effectively reducing the computational complexity while maintaining negligible accuracy loss. When α is 0.75, evaluations on thermal imagery show HRT-YOLO achieves 95.5% mAP@0.5 and 78.6% mAP@0.5:0.9 with 4.7 giga floating-point operations per second (GFLOPS), reducing computations by 25.4% versus baseline while outperforming YOLOv5 by 6.8% in mAP@0.5:0.9. Furthermore, HRT-YOLO achieves rapid adjustment of model capacity through scaling factor α modulation, achieving computational-accuracy trade-offs for diverse situation requirements.