Detection Method Research Articles

FSPDD: A double-branch attention guided network for few-shot PCB defect detection

AbstractDuring the production of printed circuit board (PCB), there will be defects due to inappropriate operations, which will affect the use of electronic products. Majority defect detection methods cost a large number of annotated samples to train detection models. However, PCB defect samples are difficult to collect. Moreover, existing few-shot object detection methods tend to extracting low-level features from support and query images via the shared backbone such as ResNet-50. However, it is not sufficient to obtain fine-grained prior guidance. To address the above issues, we propose a few-shot PCB defect detection model with double-branch attention. Specifically, the joint attention enhancement (JAE) module is proposed to fully mine effective information of query PCB images in multiple dimensions to enhance the representation of latent defects. Then, the multi-scale guidance (MSG) module is proposed to integrate prior knowledge within support PCB images into vectors to reweight query PCB images. Experiments on the PCB defect dataset demonstrate that AP of FSPDD outperforms state-of-the-art methods under different shot settings (k=1,2,3,5,10,30) and our proposed FSPDD has a good generalization ability, in which AP reachs 0.273 when $$k=30$$ k = 30 and is 5.28% higher than SOTA methods.

An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis

Breast cancer is a leading cause of cancer-related deaths among women both worldwide and in Poland. Consequently, ensuring equitable access to diagnostic tests for all populations is crucial, alongside the urgent need to develop new, minimally invasive methods for early cancer detection with a particular focus on metastasis. Long noncoding RNAs (lncRNAs) have emerged as critical regulators of cancer metastasis. This review aims to investigate the potential of lncRNAs as novel biomarkers for breast cancer, focusing on their mechanisms, clinical relevance, and therapeutic implications. A comprehensive literature search was conducted using PubMed and Google Scholar databases, targeting publications from 2013 to 2024. Keywords included “lncRNA”, “biomarker”, “breast cancer”, “metastasis”, “prognosis”, and “diagnosis”. A total of 111 articles were selected based on their relevance and quality. Recent studies have identified numerous lncRNAs such as HOTAIR, MALAT1, BANCR, NEAT1, H19, and GAS5 as key regulators of various metastatic processes in breast cancer. They can be both upregulated and downregulated. Clinical studies have shown that abnormal lncRNA expression correlates with poor prognosis, higher metastatic potential, and therapy resistance in breast cancer patients. LncRNAs have significant potential as novel biomarkers for breast cancer metastasis because of their regulatory roles in metastasis-related processes and detectability in body fluids. Further research is essential to validate these findings in larger clinical studies and to develop lncRNA-based diagnostic and therapeutic tools, ultimately improving patient outcomes in breast cancer.

An Automated Diagnosis Method for Lung Cancer Target Detection and Subtype Classification-Based CT Scans

When dealing with small targets in lung cancer detection, the YOLO V8 algorithm may encounter false positives and misses. To address this issue, this study proposes an enhanced YOLO V8 detection model. The model integrates a large separable kernel attention mechanism into the C2f module to expand the information retrieval range, strengthens the extraction of lung cancer features in the Backbone section, and achieves effective interaction between multi-scale features in the Neck section, thereby enhancing feature representation and robustness. Additionally, depth-wise convolution and Coordinate Attention mechanisms are embedded in the Fast Spatial Pyramid Pooling module to reduce feature loss and improve detection accuracy. This study introduces a Minimum Point Distance-based IOU loss to enhance correlation between predicted and ground truth bounding boxes, improving adaptability and accuracy in small target detection. Experimental validation demonstrates that the improved network outperforms other mainstream detection networks in terms of average precision values and surpasses other classification networks in terms of accuracy. These findings validate the outstanding performance of the enhanced model in the localization and recognition aspects of lung cancer auxiliary diagnosis.

A dynamic object removing 3D reconstruction system based on multi-sensor fusion

Abstract Currently, one of the key technologies for autonomous navigation of unmanned mobile robots is SLAM, which faces many challenges in practical applications. These challenges include a lack of texture, deterioration in sensor performance, and interference from moving objects in dynamic outdoor environments, all of which have an impact on the mapping system. To address these issues, this paper proposes a framework for lidar, vision camera, and inertial navigation data, resulting in fusion and dynamic object removing. The system consists of three sub-modules: the Lidar-Inertial Module (LIM), the Visual-Inertial Module (VIM), and the Dynamic-Object-Removing Module (DORM). LIM and VIM assist each other, with lidar point clouds providing three-dimensional information for the global voxel map and the camera providing pixel-level color information. At the same time, the DORM performs synchronous dynamic object detection to remove dynamic objects from the global map. The system constructs a multi-sensor factor graph using the state and observation models, and the optimal solution is obtained using least squares. Furthermore, this paper employs triangle descriptors and bundle adjustment methods for loop closure detection in order to reduce accumulated errors and maintain consistency. Experimental results demonstrate that the system can perform clean state estimation, dynamic removing and scene reconstruction in a variety of complex scenarios.

Hydrogen-bond organic-framework-based electrochemical sensor for highly sensitive determination of trace cadmium ions in environmental and e-cigarette samples

BackgroundThe heavy metal ion Cd2+ is acutely toxic, and excessive concentrations can have adverse effects on human production and life, and even lead to significant public health risks and environmental impacts. There are several mature non-electrochemical methods for heavy metal detection, but these methods are characterized by high cost, which makes it difficult to be applied to the field for timely detection. Therefore, it is necessary to prepare a new electrochemical sensor that is environmentally friendly and capable of detecting Cd2+ in the environment quickly, easily and sensitively. ResultsIn this study, hydrogen-bonded organic frameworks (HOFs) were synthesized by a simple hydrothermal reaction. The prepared materials consisted of only C, N and O and had a thin lamellar structure. The HOFs were integrated into a novel electrochemical sensor to achieve accurate detection of Cd2+ ions in real aqueous environments by square wave anodic dissolution voltammetry. The sensor has a wide linear range and a detection limit as low as 0.13 μg/L. Several real water samples, such as tap water, lake water, and e-cigarette digest, were analyzed to simulate the working environment of the sensor, and the results showed that the recoveries of Cd2+ ranged from 95.75 % to 101.2 %. SignificanceWe pioneered the detection of heavy metal ions Cd2+ in e-cigarette digestate samples with the innovative use of HOFs as the sensor material, which demonstrated the potential application in electrochemical sensing with extremely low background current value and high sensitivity, providing new ideas for environmental monitoring and public health control.

QRNet: Query-based reparameterization net for real-time detection of power adapter surface defects

In industrial production, automated surface defect detection holds paramount importance. While current techniques can handle complex background power adapter surface defect detection with high precision, they have many parameters, low speed, slow convergence, and complex post-processing (such as non-maximum suppression). To this end, this study aims to develop a more efficient and effective method for surface defect detection. The primary objective of this research is to enhance the speed of detection without sacrificing accuracy. This paper designs the Separable-reparam Feature Module (SFM), which is based on depth-wise convolution, to fuse multi-branch features through reparameterization techniques. SFM has low parameters and excels in feature extraction without increasing the model’s Memory Access Cost (MAC). SFM combines cross-stage fusion to construct the backbone, enabling the extraction of multi-scale features. Following the standard query decoder architecture, a QRNet was built to learn the query set of objects, and predict the surface defect set for power adapter. Additionally, a novel two-stage mixed-supervised transfer-learning approach is proposed to expedite the convergence process. Experimental results on the power adapter defect dataset demonstrate QRNet’s remarkable performance. It achieved a mean average precision (mAP) of 98.94 % with just 0.841 M parameters and a latency of 5.72 ms. In conclusion, QRNet represents an advancement in surface defect detection, offering a balance between accuracy and efficiency.

One-Pot Detection of Proteins Using a Two-Way Extension-Based Assay with Cas12a.

Protein biomarkers are an important class of biomarkers in disease diagnosis and are traditionally detected by enzyme-linked immunosorbent assay and mass spectrometry, which involve multiple steps and a complex workflow. In recent years, many CRISPR-Cas12a-based methods for protein detection have been developed; however, most of them have not overcome the workflow complications observed in traditional assays, limiting their applicability in point-of-care testing. In this work, we designed a single-step, one-pot, and proximity-based isothermal immunoassay integrating CRISPR Cas12a for homogeneous protein target detection with a simplified workflow and high sensitivity. Probes consisting of different binders (small molecule, aptamer, and antibody) conjugated with oligonucleotides undergo two-way extension upon binding to the protein targets, leading to downstream DNA amplification by a pair of nicking enzymes and polymerases to generate target sequences for Cas12a signal generation. We used the streptavidin-biotin model to demonstrate the design of our assay and proved that all three elements of protein detection (target protein binding, DNA amplification, and Cas12a signal generation) could coexist in one pot and proceed isothermally in a single buffer system at a low reaction volume of 10 μL. The plug-and-play applicability of our assay has been successfully demonstrated using four different protein targets, streptavidin, PDGF-BB, antidigoxigenin antibody, and IFNγ, with the limit of detection ranging from fM to pM.

A novel deep learning framework for rolling bearing fault diagnosis enhancement using VAE-augmented CNN model

In the context of burgeoning industrial advancement, there is an increasing trend towards the integration of intelligence and precision in mechanical equipment. Central to the functionality of such equipment is the rolling bearing, whose operational integrity significantly impacts the overall performance of the machinery. This underscores the imperative for reliable fault diagnosis mechanisms in the continuous monitoring of rolling bearing conditions within industrial production environments. Vibration signals are primarily used for fault diagnosis in mechanical equipment because they provide comprehensive information about the equipment's condition. However, fault data often contain high noise levels, high-frequency variations, and irregularities, along with a significant amount of redundant information, like duplication, overlap, and unnecessary information during signal transmission. These characteristics present considerable challenges for effective fault feature extraction and diagnosis, reducing the accuracy and reliability of traditional fault detection methods. This research introduces an innovative fault diagnosis methodology for rolling bearings using deep convolutional neural networks (CNNs) enhanced with variational autoencoders (VAEs). This deep learning approach aims to precisely identify and classify faults by extracting detailed vibration signal features. The VAE enhances noise robustness, while the CNN improves signal data expressiveness, addressing issues like gradient vanishing and explosion. The model employs the reparameterization trick for unsupervised learning of latent features and further trains with the CNN. The system incorporates adaptive threshold methods, the "3/5" strategy, and Dropout methods. The diagnosis accuracy of the VAE-CNN model for different fault types at different rotational speeds typically reaches more than 90 %, and it achieves a generally acceptable diagnosis result. Meanwhile, the VAE-CNN augmented fault diagnosis model, after experimental validation in various dimensions, can achieve more satisfactory diagnosis results for various fault types compared to several representative deep neural network models without VAE augmentation, significantly improving the accuracy and robustness of rolling bearing fault diagnosis.

Optimization of CRISPR/Cas12a detection assay and its application in the detection of Echinococcus granulosus

Cystic echinococcosis, resulting from infection with Echinococcus granulosus, poses a significant challenge as a neglected tropical disease owing to the lack of any known effective treatment. Primarily affecting under-resourced, remote, and conflict-ridden regions, the disease is compounded by the limitations of current detection techniques, such as microscopy, physical imaging, ELISA, and qPCR, which are unsuitable for application in these areas. The emergence of CRISPR/Cas12a as a promising tool for nucleic acid detection, characterized by its unparalleled specificity, heightened sensitivity, and rapid detection time, offers a potential solution. In this study, we present a one-pot CRISPR/Cas12a detection method for E. granulosus (genotype G1, sheep strain) integrating recombinase polymerase amplification (RPA) with suboptimal protospacer adjacent motif (PAM) and structured CRISPR RNA (crRNA) to enhance reaction efficiency. The evaluation of the assay's performance using hydatid cyst spiked dog feces and the examination of 62 dog fecal samples collected from various regions of Western China demonstrate its efficacy. The assay permits visual observation of test results about 15 minutes under blue light and displays superior portability and reaction speed relative to qPCR, achieving a sensitivity level of 10 copies of standard plasmids of the target gene. Analytic specificity was verified against four tapeworm species (E. multilocularis, H. taeniaeformis, M. benedeni, and D. caninum) and two other helminths (T. canis and F. hepatica), with negative results also noted for Mesocestoides sp. This study presents a rapid, sensitive, and time-efficient DNA detection method for E. granulosus of hydatid cyst spiked and clinical dog feces, potential serving as an alternative tool for field detection. This novel assay is primarily used to diagnose the definitive host of E. granulosus. Further validation using a larger set of clinical fecal samples is warranted, along with additional exploration of more effective approaches for nucleic acid release.

A novel fatigue driving detection method based on whale optimization and Attention-enhanced GRU

Fatigue driving has emerged as the predominant causative factor for road traffic safety accidents. The fatigue driving detection method, derived from laboratory simulation data, faces challenges related to imbalanced data distribution and limited recognition accuracy in practical scenarios. In this study, we introduce a novel approach utilizing a gated recurrent neural network method, employing whale optimization algorithm for fatigue driving identification. Additionally, we incorporate an attention mechanism to enhance identification accuracy. Initially, this study focuses on the driver's operational behavior under authentic vehicular conditions. Subsequently, it employs wavelet energy entropy, scale entropy, and singular entropy analysis to extract the fatigue-related features from the driver's operational behavior. Subsequently, this study adopts the cross-validation recursive feature elimination method to derive the optimal fatigue feature index about operational behavior. To effectively capture long-range dependence relationships, this study employs the gated recurrent unit neural network method. Lastly, an attention mechanism is incorporated in this study to concentrate on pivotal features within the data sequence of driving behavior. It assigns greater weight to crucial information, mitigating information loss caused by the extended temporal sequence. Experimental results obtained from real vehicle data demonstrate that the proposed method achieves an accuracy of 89.84% in third-level fatigue driving detection, with an omission rate of 10.99%. These findings affirm the feasibility of the approach presented in this study.

Enhancing Community E-Commerce Repurchase Prediction Through Information Entropy Analysis

In this study, we delve into the prediction and recommendation algorithm for user repurchase behavior within the community e-commerce landscape, leveraging the framework of information entropy. By analyzing consumer behavior data from a product-centered perspective, we observe that the majority of consumers engage in approximately 0-8 information search clicks before making a purchase, primarily focusing on product details, with an average of about 4 browsing interactions. Subsequently, a minimal portion of consumers exhibit 0-5 instances of preference behavior after browsing activities. Similarly, consumers who add items to their shopping carts typically engage in this behavior from 0 to 4 times, showcasing limited participation. Leveraging attribute probability, information entropy serves as a driving force for predicting community e-commerce users' repurchase behavior, contrasting with traditional outlier detection methods and underscoring the advantages of information entropy.

Fluorescence detection of valence speciation of Cr(III) based on the catechol oxidase mimic enzyme activity of CuSeNP nanozymes.

Copper selenide nanoparticles (CuSeNP) weresynthesized using histidine, ethylenediamine, and sodium selenate as precursors by one-step microwave digestion methods. The as-prepared CuSeNPs exhibit excellent catechol oxidase mimic enzyme and catalase (CAT)-like activities. Dopamine (DA) can be oxidized to aminochrome with H2O2 by CuSeNPs, and the intermediate product aminochrome can further react with α-naphthol to yield a highly fluorescent derivative. It was confirmed that Cr(III) could adsorb on the surface of CuSeNPs and inhibit the production of semiquinone radicals in the reaction system, and the catalytic activity of CuSeNPs was inhibited. The detection mechanisms, kinetics, and catalytic properties of CuSeNPs were systematically investigated. As a result, a novel fluorescence method for the assay of Cr(III) was established. The feasibility of CuSeNP nanozyme in detecting speciation Cr(III) in food samples was explored with satisfactory results. It showed the obvious potential for developing effective and dependable fluorescent detection method for protecting food safety.

A novel “turn-on” NIR fluorescent probe based on benzothianone for the detection of palladium species and its application in cell and zebrafish

The development of an efficient palladium probe holds significant application value, considering the detrimental impact of palladium contaminants on human health. Thus, it is critical to create a sensitive detection method. To this end, a fluorescent probe TM-TPA-Pd based on benzothianone structure was designed, using allyl carbonate as the Pd0 recognition unit. TM-TPA-Pd exhibited high sensitivity (1.4 eq), selectivity, near-infrared (NIR) fluorescence (798 nm), and low detection limit (0.46 μM) for Pd0 with a rapid “turn-on” fluorescence signal (5 min). Furthermore, TM-TPA-Pd has extremely low cytotoxicity and has been successfully applied to detecting cells and zebrafish, which has great potential for palladium detection in biological systems.

An improved YOLOv5-based apple leaf disease detection method

The effective identification of fruit tree leaf disease is of great practical significance to reduce pesticide spraying, improve fruit yield and realize ecological agriculture. Computer vision technology can be effectively identifying and prevent plant diseases and insect pests. However, the lack of consideration of disease diversity and accuracy of existing detection models hinders their application and development in the field of plant pest detection. This paper proposes an efficient detection model of apple leaf disease spot through the improvement of the traditional Yolov5 detection network called A-Net. In order to significantly increase the A-Net's detection speed and accuracy, the A-Net model applies the loss function Wise-IoU, which includes the attention mechanism and the dynamic focusing mechanism, to the Yolov5 network model. The RepVGG module is then used to replace the original model's convolution module. The experimental results show that the improved model effectively suppresses the growth of some error weights. Compared with several object detection models, the improved A-Net model has a Mean Average Precision across IoU threshold 0.5 and an accuracy of 92.7%, which fully proves that the improved A-Net model has more advantages in detecting apple leaf diseases.

Two deep learning methods in comparison to characterize droplet sizes in emulsification flow processes

AbstractFor reliable supervision in multiphase processes, the droplet size represents a critical quality attribute and needs to be monitored. A promising approach is the use of smart image flow sensors since optical measurement is the most commonly used technique for droplet size distribution determination. For this, two different AI-based object detection methods, Mask RCNN and YOLOv4, are compared regarding their accuracy and their applicability to an emulsification flow process. Iterative optimization steps, including data diversification and adaption of training parameters, enable the models to achieve robust detection performance across varying image qualities and compositions. YOLOv4 shows better detection performances and more accurate results which leads to a wider application window than Mask RCNN in determining droplet sizes in emulsification processes. The final droplet detection model YOLOv4 with Hough Circle (HC) for feature extraction determines reliable droplet sizes across diverse datasets of liquid-liquid flow systems (disperse phase content 1–15 vol.-%, droplet size range 5–150 μm). Evaluating the adjustment of Confidence Scores (CS) ensures statistical representation of even smaller droplets. The droplet detection performance of the final YOLOv4 model is compared with a manual image processing method to validate the model in general as well as its accuracy and reliability. Since YOLOv4 in combination with Hough Circle (HC) shows an accurate and robust detection and size determination, it is applicable for online monitoring and characterization of various liquid-liquid flow processes. Graphical abstract

Heterogeneous Views and Spatial Structure Enhancement for triple error detection

Knowledge graph error detection is to identify erroneous triples in knowledge graphs that are inconsistent with objective facts in the real world. In practice, the quality of knowledge graphs is an indispensable foundation for the widespread and accurate knowledge application services such as intelligent retrieval and human machine dialogue. Technically, the existing knowledge graph error detection methods face the following two problems: few available negative samples of triples, and an uneven data distribution. That distribution is caused by the large disparity in the number of head and tail entities belonging to the same relationship and the disparity in the number of triples with different relationships. To alleviate these problems, this paper proposes an approach based on the Heterogeneous Views and Spatial Structure Enhancement (HVSSE) in a contrastive learning framework for triple error detection task. Specifically, the heterogeneous views are constructed to include four kinds of triple views, i.e., positive and negative triple views based on head or tail entity co-occurrence. Moreover, Graph-Spatial-Transformer with an explicit spatial structure encoding is designed to fully capture the contextual information of triple nodes. Thereby, driven by the framework of contrastive learning, our HVSSE model can not only learn more discriminative embedding of triples, but also capture the local structure of triples and global contextual information of knowledge graphs. Experimental results on five public datasets indicate that our proposed approach is superior to the state-of-the-art methods, showing its the effectiveness on triple error detection.

Transfer Learning for Logistic Regression with Differential Privacy

Transfer learning, as a machine learning approach enhancing model generalization across different domains, has extensive applications in various fields. However, the risk of privacy leakage remains a crucial consideration during the transfer learning process. Differential privacy, with its rigorous mathematical foundation, has been proven to offer consistent and robust privacy protection. This study delves into the logistic regression transfer learning problem supported by differential privacy. In cases where transferable sources are known, we propose a two-step transfer learning algorithm. For scenarios with unknown transferable sources, a non-algorithmic, cross-validation-based transferable source detection method is introduced, to mitigate adverse effects from non-informative sources. The effectiveness of the proposed algorithm is validated through simulations and experiments with real-world data.

Comparing methylation levels assayed in GC-rich regions with current and emerging methods

DNA methylation is an epigenetic mechanism that regulates gene expression, and for mammals typically occurs on cytosines within CpG dinucleotides. A significant challenge for methylation detection methods is accurately measuring methylation levels within GC-rich regions such as gene promoters, as inaccuracies compromise downstream biological interpretation of the data. To address this challenge, we compared methylation levels assayed using four different Methods Enzymatic Methyl-seq (EM-seq), whole genome bisulphite sequencing (WGBS), Infinium arrays (Illumina MethylationEPIC, “EPIC”), and Oxford Nanopore Technologies nanopore sequencing (ONT) applied to human DNA. Overall, all methods produced comparable and consistent methylation readouts across the human genome. The flexibility offered by current gold standard WGBS in interrogating genome-wide cytosines is surpassed technically by both EM-seq and ONT, as their coverages and methylation readouts are less prone to GC bias. These advantages are tempered by increased laboratory time (EM-seq) and higher complexity (ONT). We further assess the strengths and weaknesses of each method, and provide recommendations in choosing the most appropriate methylation method for specific scientific questions or translational needs.

Research Progress of Biosensor Based on Organic Photoelectrochemical Transistor.

In order to solve the food safety problem better, it is very important to develop a rapid and sensitive technology for detecting food contamination residues. Organic photoelectrochemical transistor (OPECT) biosensor rely on the photovoltage generated by a semiconductor upon excitation by light to regulate the conductivity of the polymer channels and realize biosensor analysis under zero gate bias. This technology integrates the excellent characteristics of photoelectrochemical (PEC) bioanalysis and the high sensitivity and inherent amplification ability of organic electrochemical transistor (OECT). Based on this, OPECT biosensor detection has been proven to be superior to traditional biosensor detection methods. In this review, we summarize the research status of OPECT biosensor in disease markers and food residue analysis, the basic principle, classification, and biosensing mechanism of OPECT biosensor analysis are briefly introduced, and the recent applications of biosensor analysis are discussed according to the signal strategy. We mainly introduced the OPECT biosensor analysis methods applied in different fields, including the detection of disease markers and food hazard residues such as prostate-specific antigen, heart-type fatty acid binding protein, T-2 toxin detection in milk samples, fat mass and objectivity related protein, ciprofloxacin in milk. The OPECT biosensor provides considerable development potential for the construction of safety analysis and detection platforms in many fields, such as agriculture and food, and hopes to provide some reference for the future development of biosensing analysis methods with higher selectivity, faster analysis speed and higher sensitivity.

Flame and smoke detection using Kafka on edge devices

This paper presents object detection methods to accurately identify the sources of flame and smoke in vast circumstances. Aerial drones collected the data, analyzed the recognition outputs in real time on an edge device, and then transferred them to the back-end for data processing and warnings using Kafka. To detect flame and smoke occurrences, the models were compared using various convolutional neural networks (CNN). Several factors considered include streaming speed, accuracy, portability, efficiency, and power consumption on edge devices. This work conducted training comparisons of YOLOV4, YOLOV5, YOLOV7, YOLOV8, and Faster RCNN. The inference performance was then evaluated on an edge computing device. The findings showed an accuracy of 0.91 and 0.87, while maintaining a processing speed of roughly 1 frame per second on the Nvidia Jetson NX without acceleration.