To solve the problem where the low contrast and extremely small target size in the three-sky-screen target-integrated linear array CCD sensor measurement system under low-illumination conditions make it difficult to accurately identify projectile targets, this paper proposes a method of Frequency-Enhanced and Multi-scale Feature Fusion YOLOv11 (FEMFF-YOLOv11). It introduces a frequency-domain enhancement module in the backbone to improve feature discriminability, and deformable offset convolution is incorporated to handle geometric deformations. It also adds a multi-scale attention aggregation module in the neck to strengthen weak target features and suppress false targets such as near-lens flying objects. The detection head is optimized by replacing the low-resolution P5 layer with a high-resolution P2 layer for better projectile target localization. Experiments are conducted on a self-built linear array CCD projectile dataset. The results demonstrate that compared with YOLOv11 and other mainstream algorithms, our method achieves 87.35% precision and 85.13% recall under 300 lx low-illumination conditions. It also maintains 85.91% precision and 82.56% recall even at 50 lx, significantly outperforming all competitors.

To address the challenges of insufficient selectivity, environmentally induced signal instability, and limited quantitative reliability in the on-site rapid detection of tetracycline antibiotics (TCs) in food samples, this study developed a portable quantitative detection method for oxytetracycline (OTC) based on a molecularly imprinted fluorescent test strip. The proposed platform integrates a molecularly imprinted polymer–quantum dot (MIP–QD) recognition system with digital signal processing strategies. CdTe quantum dot-functionalized OTC-imprinted fluorescent test strips were fabricated to enable specific target recognition and fluorescence response. Digital filtering, characteristic peak identification, virtual baseline correction, and peak area integration were employed to improve the stability and accuracy of fluorescence signal extraction. A quantitative model for OTC determination was established based on the fluorescence intensity ratio between the test line and control line (T/C fluorescence ratio). The developed assay exhibited high selectivity toward OTC, with a limit of detection (LOD) of 0.01 μmol/L and a linear detection range of 0.02–0.24 μmol/L. In spiked milk and honey samples, the recoveries of OTC ranged from 96.1% to 100.9% and from 96.9% to 102.4%, respectively, with relative standard deviations (RSDs) below 5%. By integrating molecular imprinting recognition, quantum dot-based fluorescence signaling, and portable digital signal analysis, the proposed method demonstrated reliable quantitative performance in complex food matrices and provides a feasible approach for the on-site rapid screening of tetracycline antibiotics.

To address the recognition challenges caused by the high proportion, low resolution, and significant multi-scale variations of small objects in UAV small-object detection tasks, a UAV small-object detection and recognition algorithm based on CEF-YOLOv8n is proposed. The proposed algorithm uses YOLOv8n as the baseline network and introduces a Partial Convolution-based Cross Partial Feature (CPF) module into the backbone network to enhance the local feature extraction capability for low-resolution small objects. In the neck network, the concept of feature focusing and diffusion is adopted to construct a Focusing Generalized Feature Pyramid Network (FGFPN). A Feature Semantic Fusion Module (FSFM) based on a cross-attention mechanism is designed to complementarily fuse shallow detail features with deep semantic features, thereby enhancing information interaction among objects at different scales. In addition, a Lightweight Weight-Sharing Detection Head (LWSD) is proposed to improve the computational efficiency and real-time performance of the model while maintaining detection accuracy. Publicly available datasets are used for network training and detection evaluation, and comparative experiments are conducted with other algorithms. The results show that the proposed detection and recognition algorithm achieves 37.6% and 22.6% in terms of mAP50 and mAP50-95, respectively, representing improvements of 3.4 and 2.6 percentage points over the original YOLOv8n. Meanwhile, the number of parameters and FLOPs are reduced from 3.2 M and 8.7 G to 2.5 M and 6.9 G, respectively.

To prevent environmental pollution caused by heavy metals, enabling people to intuitively and quantitatively detect the concentration of heavy metal ions. The paper aims to develop a portable heavy metal fluorescence polarization detection system. To make the sample emit polarized fluorescence, a fluorescence polarization detection optical system was designed. The system hardware circuit was designed to achieve fluorescence polarization signal acquisition and processing, stepper motor drive, and constant current drive for the light source. The fluorescence polarization detection program was developed on the Keil platform, and the fluorescence polarization detection host computer software was developed using C# language on the VS platform, achieving complete control of the embedded control system by the fluorescence polarization detection software. A copper ion concentration calibration experiment was conducted on the prototype, and the results showed that there was a significant linear relationship between copper ion concentration and detection range from 0.15 to 2.94 mg/L, with good system stability, making it a viable approach for detection in the field of heavy metals.

A low-cost portable microimaging system was designed to address the limitations of traditional microscopes in the immediate point-of-care testing (POCT) environment, including their inability to perform rapid image acquisition and their large size and high price. The system is based on a fully programmable ZYNQ system-on-chip to achieve rapid image acquisition and overall control, utilizing its parallel processing capability and efficient data stream processing to enable fast and simple sample detection. The mechanical structure was designed using NX12.0 and manufactured using three-dimensional printing technology. The device is compact (similar to 20 cm(3)), lightweight (similar to 2 kg), and low-cost (similar to 1400 yuan). Test results show that the system transmission rate reached 60 frame/s corresponding to Gigabit Ethernet transmission speed, and the resolution under the 10x objective lens reached the resolution plate limit of 2.19 mu m. Hence, the system enables quick acquisition and analysis of sample images, thereby meeting the need for simplified testing methods and portable equipment in the medical testing field.

In recent years, deep learning and related technologies have experienced rapid development due to their characteristics of high speed, accuracy, and recognition rates, as well as environmental sustainability. They are increasingly being applied in real-time inspection research involving underwater robots for the automatic identification and classification of underwater targets. This paper builds a self-constructed underwater image dataset, annotates the location information of four types of underwater targets: tortoises, fish, person, and corals, and performs preprocessing such as sharpening, histogram equalization, and normalization on the targets in the sample set to improve image quality. Based on this, a YOLO v5 network model is established. After repeated training, the loss function curve proves that the model is effective, and the average accuracy of the four types of targets is: 97.1% for tortoises, 93.5% for fish, 88.9% for person, and 74.7% for corals. Finally, to verify the reliability of the network model, this paper redivides the training set and test set in the ratio of 7:3 and 8:2 to achieve repeated training of the YOLO v5 network, and evaluates the model performance through three indicators: accuracy, recall rate, and mean average precision. Ultimately, the feasibility and stability of the YOLO v5 algorithm for underwater target detection are verified.

At present, it is highly subjective for pathologists to identify breast cancer cells in pathological cut images of breast cancer under microscope field of view by naked eyes. Therefore, we design a microscopic image based breast cancer cell recognition system, which includes microscopic image acquisition module and breast cancer cell recognition algorithm implementation module. Through USAF 1951 resolution test board, the microscopic image acquisition module of designed breast cancer recognition system is verified, and the final imaging resolution can reach 2. 19 mu m. In addition, the designed breast cancer cell recognition algorithm is verified by multiple sets of breast cancer pathological images, and the results show that the average accuracy of the designed breast cancer cell recognition system reaches 93. 4%.

Because phase has significantly higher contrast than amplitude, particularly for label-free specimens, and provides a new perspective for morphology and shape testing, quantitative phase microscopy has become an effective means in optical imaging and testing. We designed dual-view transport of intensity phase imaging, which comprehensively considers real-time imaging, simple configuration, and fast phase retrieval. This technique employs two imaging recorders to simultaneously capture under- and over-focus images and recovers the quantitative phase distributions from these defocused images by solving the Poisson equation. Based on such an idea, we designed PhaseRMiC as a phase real-time microscopy camera and PhaseStation as a compact phase imaging work station, and they have been successfully used in live cell observation, whole-slide imaging, and flow cytometry for various purposes, such as specimen detection, counting, recognition, and differentiation. In this work, we first briefly introduce the principle of the dual-view transport of intensity phase imaging, next provide the details of our designed PhaseRMiC and PhaseStation, and finally demonstrate their applications in real-time, field of view scanning, and microfluidic imaging, respectively. Besides, we also compare PhaseRMiC and PhaseStation with other quantitative phase microscopy equipment and lay out their prospects for future applications. We believe our designed dual-view transport of intensity phase imaging as well as its derivatives, PhaseRMiC and PhaseStation, can be a promising choice for quantitative phase microscopy. Because phase has significantly higher contrast than amplitude, particularly for label-free specimens, and provides a new perspective for morphology and shape testing, quantitative phase microscopy has become an effective means in optical imaging and testing.

This paper mainly studies the task allocation problem in multi-UAV ground-to-ground coordinated operations, and establishes a corresponding mathematical model, which takes into account constraints such as time windows and tracks. Through the analysis of the mission scenario, this study conducts a basic theoretical analysis of the particle swarm algorithm, and balances the development and exploration capabilities of the algorithm by integrating the harmony search algorithm. At the same time, the golden sine strategy is introduced to increase the randomness of the search method. A particle swarm algorithm based on the golden sine harmony search is proposed, and the random search method is used to determine the value of the key control parameters. Finally, the effectiveness of the task allocation model and the superiority of the improved algorithm are verified by comparative simulation experiments. The simulation results show that the algorithm proposed in this paper shows higher convergence accuracy and robustness when dealing with the multi-UAV task allocation problem under the time window constraint.

Radar is a valuable tool for noncontact vital-sign detection. Interference from respiratory harmonics presents a major challenge in radar cardiogram (RCG) extraction—mainly when the frequency of respiratory harmonics is close to or equal to that of the cardiac sub-signals. To address this problem, a respiratory harmonic suppression method employing correlation analysis and an optimized feedback notch filter is proposed, which is based on 7.29-GHz center-frequency impulse-radio ultra-wideband radar. A genetic optimization algorithm is employed to optimize the parameters of the notch filter. Performance comparison analysis is conducted on the conventional notch filter and the feedback notch filter. Contact (ECG) and non-contact (RCG) data from 10 subjects were analyzed. The results verified that the performance of the optimized feedback notch filter is much better than that of the conventional notch filter in overshoot, bandwidth, and notch depth, and the proposed method can effectively locate, identify, and suppress respiratory harmonics from the RCG band while preserving heartbeat components. Consequently, this approach markedly enhances the precision of RCG extraction. The technique shows considerable promise for deployment in diverse practical settings, including non-contact auxiliary monitoring systems in both intelligent medical environments and home healthcare.

Though relatively good effect has been achieved by the image de-blurring method based on deep learning, the existing methods still suffer from the problem of unclear restoration of the edges. Therefore, brain-inspired image restoration model based on human attention and "fine vision" is proposed to improve the blind restoration quality of the image in this paper according to the response mechanism of the different cerebral cortices for high and low spatial resolutions. The designed brain-inspired model consists of dual-channel network available to realize the function of feature merger for low and high resolutions, which is used to extract the image edges with detailed information filtered out. Confirmatory experiment is implemented based on the blurred image in the data set of GOPRO, LIVE and set14. As per the result, the model proposed is available for relatively good restoration of blurred image and super-resolution, as well as looking results by visual inspection.

在印刷电路板(PCB)焊接后的质量检测中,针对单次拍摄的一整幅PCB图像不能达到高分辨率、高精度的图像要求,可以通过计算机视觉与图像加工处理方法,实现一种应用于PCB焊接质量检测的图像拼接算法.首先,使用尺度不变特征变换(SIFT)算子完成特征点的提取,进而完成粗匹配.接着,利用随机抽样一致性(RANSAC)算子,完成细匹配.最后,利用渐入渐出的加权平均融合算子完成图像融合,实现对PCB焊接图像的拼接.试验结果表明,该算法能够高效实现对PCB焊接图像的无缝拼接,可应用于高校实验室或研究院以及工厂对实际产品的检测中,具有良好的工程实用性.

To achieve quantitative detection of unlabeled samples, a miniaturized phase microscope is independently designed using NX12. 0 and related devices. Compared to the expensive phase microscopes currently on the market, the newly designed microscope has an approximately 60% smaller volume while achieving the same system resolution, significantly improving portability; it does not require coherent devices, and the cost is only approximately 5000 yuan. The system also incorporates an autofocus algorithm and a field of view correction algorithm, based on transform domain techniques, to accelerate the detection speed and accuracy of phase recovery. After testing, the resolution of the system reached the resolution board limit of 2. 19 mu m using a 10x objective lens. Further, the detection of random phase plates indicates that the accuracy of phase recovery also meets the basic requirements. Additionally, defects in the structure of living cells and flat glass are also tested. The results indicate that the proposed system can quantitatively measure living cells and play an important role in detecting transparent/ semitransparent plane defects. They also prove the feasibility of this low-cost and portable unlabeled-sample quantification system design scheme.

The porcine epidemic diarrhea virus (PEDV) is a major pathogen of swine enteric diseases. Various etiology and serological methods have been employed for PEDV detection, but most of their applications are limited to laboratories. To extend PEDV detection to on-site applications, we design a homogeneous fluorescence resonance energy transfer (FRET)-based enzyme-linked immunosorbent assay (ELISA). Both donor and acceptor fluorescence microspheres modified PEDV antibodies can be linked only to the occurrence of PEDV antigen, thus generating FRET signals, which can be collected by our designed portable FRET immunoassay station (FRETIS). Verified by standard samples, FRET immunoassay reached high sensitivity with a detection limit as TCID50 (median tissue culture infective dose) of 10/mL, which is 10 times more sensitive than colloidal gold test strips; and verified by clinical samples, it was also proved with high accuracy, good selectivity, and repeatability. More importantly, FRET immunoassay could detect PEDV in a 96-well plate in 35 min with only one step of incubation without any further washing steps using field-portable devices and field-operable procedures, well supporting on-site applications. Considering these advantages, this reported FRET immunoassay provides a promising way for multi-sample on-site PEDV detection and can be potentially used in the swine industry.

目的 针对飞行员经常需要打破昼夜节律出紧急任务的现状,研究一种经颅神经电刺激睡眠干预方法来缩短入睡时间、提高睡眠质量,以达到短时间内提高作战效能、提升飞行安全性的目的.方法 提出了一种基于低频正弦交流电经颅刺激的睡眠干预方法,按照设计要求招募20名被试,每名被试参与3组睡眠干预实验,分别为对照组、伪刺激组和刺激组,其中刺激组使用经颅神经电刺激仪根据刺激方案对受试者进行电刺激干预,伪刺激组仅模拟刺激仪开关时的电流,其余时间不刺激,实验全程进行脑电、眼电、肌电信号的同步监测,记录相关数据.对采集到的脑电信号进行delta/theta功率谱密度比值分析,结合眼电、肌电信号确定受试者的入睡时间.结果 对照组、伪刺激组和刺激组入睡时间分别为(17.29±7.72)、(11.41±4.71)、(5.21±3.36)min,刺激组入睡时间与对照组结果比较,差异具有统计学意义(P＜0.05),伪刺激组入睡时间与对照组结果比较,差异无统计学意义(P＞0.05).结论 在入睡时间上刺激组＜伪刺激组＜对照组,证明该经颅神经交流电刺激方法可有效缩短被试入睡时间.

Building collapse leads to mechanical injury, which is the main cause of injury and death, with crush syndrome as its most common complication. During the post-disaster search and rescue phase, if rescue personnel hastily remove heavy objects covering the bodies of injured individuals and fail to provide targeted medical care, ischemia-reperfusion injury may be triggered, leading to rhabdomyolysis. This may result in disseminated intravascular coagulation or acute respiratory distress syndrome, further leading to multiple organ failure, which ultimately leads to shock and death. Using bio-radar to detect vital signs and identify compression states can effectively reduce casualties during the search for missing persons behind obstacles. A time-domain ultra-wideband (UWB) bio-radar was applied for the non-contact detection of human vital sign signals behind obstacles. An echo denoising algorithm based on PSO-VMD and permutation entropy was proposed to suppress environmental noise, along with a wounded compression state recognition network based on radar-life signals. Based on training and testing using over 3000 data sets from 10 subjects in different compression states, the proposed multiscale convolutional network achieved a 92.63% identification accuracy. This outperformed SVM and 1D-CNN models by 5.30% and 6.12%, respectively, improving the casualty rescue success and post-disaster precision.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a causal agent of Coronavirus Disease 2019 (COVID-19) has led to the global pandemic. Though the real-time reverse transcription polymerase chain re-action (RT-PCR) acting as a gold-standard method has been widely used for COVID-19 diagnostics, it can hardly support rapid on-site applications or monitor the stage of disease development as well as to identify the infection and immune status of rehabilitation patients. To suit rapid on-site COVID-19 diagnostics under various appli-cation scenarios with an all-in-one device and simple detection reagents, we propose a high-throughput multi -modal immunoassay platform with fluorescent, colorimetric, and chemiluminescent immunoassays on the same portable device and a multimodal reporter probe using quantum dot (QD) microspheres modified with horseradish peroxidase (HRP) coupled with goat anti-human IgG. The recombinant nucleocapsid protein fixed on a 96-well plate works as the capture probe. In the condition with the target under detection, both reporter and capture probes can be bound by such target. When illuminated by excitation light, fluorescence signals from QD microspheres can be collected for target quantification often at a fast speed. Additionally, when pursuing simple detection without using any sensing devices, HRP-catalyzed TMB colorimetric immunoassay is employed; and when pursuing highly sensitive detection, HRP-catalyzed luminol chemiluminescent immunoassay is established. Verified by the anti-SARS-CoV-2 N humanized antibody, the sensitivities of colorimetric, fluorescent, and chemiluminescent immunoassays are respectively 20, 80, and 640 times more sensitive than that of the lateral flow colloidal gold immunoassay strip. Additionally, such a platform can simultaneously detect multiple samples at the same time thus supporting high-throughput sensing; and all these detecting operations can be imple-mented on-site within 50 min relying on field-operable processing and field-portable devices. Such a high -throughput multimodal immunoassay platform can provide a new all-in-one solution for rapid on-site di-agnostics of COVID-19 for different detecting purposes.

乳腺癌是女性致死率最高的疾病之一,严重威胁女性的身体健康.目前,病理医生判断病症等级,仍通过观察病理切片中的增殖细胞个数,具有很大的主观性.本文提出了 一种将RGB彩色空间和HSV彩色空间模型结合的方法,采用各分量的色度特征,通过量化分析建立色度特征方程,实现从病理图像中检测识别乳腺癌增殖细胞.经20组乳腺癌病理切片图像验证,准确性达到了 94.2％,优于单色彩空间模型方法约4％,本文提出方法能够更精确识别乳腺癌细胞,为乳腺癌细胞识别提供了一种新方法.

We designed a portable Raspberry Pi-based spectrometer, which mainly consists of a white LED acting as the wide-spectrum source, a reflection grating for light dispersion, and a CMOS imaging chip aiming at spectral recording. All the optical elements and Raspberry Pi were integrated using 3-D printing structures with a size of 118 mm × 92 mm × 84 mm, and home-built software was also designed for spectral recording, calibration, analysis, and display implemented with a touch LCD. Additionally, the portable Raspberry Pi-based spectrometer was equipped with an internal battery, thus supporting on-site applications. Tested by a series of verifications and applications, the portable Raspberry Pi-based spectrometer could reach a spectral resolution of 0.065 nm per pixel within the visible band and provide spectral detection with high accuracy. Therefore, it can be used for on-site spectral testing in various fields.

Abstract The key challenge in RBGT tracking is how to fuse dual‐modality information to build a robust RGB‐T tracker. Motivated by CNN structure for local features, and visual transformer structure for global representations, the authors propose a two‐stream hybrid structure, termed CMC2R, to take advantage of convolutional operations and self‐attention mechanisms to lean the enhanced representation. CMC2R fuses local features and global representations under different resolutions through the transformer layer of the encoder block, and the two modalities are collaborated to get contextual information by the spatial and channel self‐attention. The temporal association is performed with the track query, each track query models the entire track of an object, and updated frame‐by‐frame to build the long‐range temporal relation. Experimental results show the effectiveness of the proposed method, and achieve the SOTAs performance.