Real-time Detection Method Research Articles

Vulnerability and Malware Detection

The increasingly linked digital world oftoday has made cybersecurity a top priority. The rise in cyber dangers, notably vulnerabilities and malware, poses major risks to individuals, organizations, and governments. This review article offers a thorough analysis of the approachesand instruments currently in use for malware and vulnerability detection. It examines and contrasts several detection methods, such as machine learning techniques, static and dynamic analysis, and signature-based detection. The study also examines new developments in the sector, such as the application of big data analytics and artificial intelligence (AI) to the detection of complex threats. It also covers the need for real-time detection methods, changing threat environments, and the difficulties posed by false positives.

X-ray fluoroscopy guided localization and steering of miniature robots using virtual reality enhancement

In developing medical interventions using untethered milli- and microrobots, ensuring safety and effectiveness relies on robust methods for real-time robot detection, tracking, and precise localization within the body. The inherent non-transparency of human tissues significantly challenges these efforts, as traditional imaging systems like fluoroscopy often lack crucial anatomical details, potentially compromising intervention safety and efficacy. To address this technological gap, in this study, we build a virtual reality environment housing an exact digital replica (digital twin) of the operational workspace and a robot avatar. We synchronize the virtual and real workspaces and continuously send the robot position data derived from the image stream into the digital twin with short average delay time around 20–25 ms. This allows the operator to steer the robot by tracking its avatar within the digital twin with near real-time temporal resolution. We demonstrate the feasibility of this approach with millirobots steered in confined phantoms. Our concept demonstration herein can pave the way for not only improved procedural safety by complementing fluoroscopic guidance with virtual reality enhancement, but also provides a platform for incorporating various additional real-time derivative data, e.g., instantaneous robot velocity, intraoperative physiological data obtained from the patient, e.g., blood flow rate, and pre-operative physical simulation models, e.g., periodic body motions, to further refine robot control capacity.

Real-time Actuator Fault Detection and Isolation for Quadrotor UAV via Directional Residuals

Abstract Fault diagnosis is critical for the safe and reliable operation of quadrotor unmanned aerial vehicles (UAVs), so that timely remedial measures can be taken to reduce its negative impact. This paper presents a real-time actuator fault detection and isolation (FDI) method for quadrotor UAV flight process. First, a linear parameter-varying (LPV) model is established to describe quadrotor UAV dynamics, which considers measurement noise and external disturbance. Then, an FDI strategy is developed based on directional residuals, in which only one observer-based residual generator is required and Η_/L∞ indices are incorporated to balance fault sensitivity and disturbance robustness. A threshold is derived from the L∞ performance condition, and actuator fault is detected through residual evaluation. Furthermore, by introducing fault feature vectors associated with the system model, the faulty actuator is isolated promptly by analyzing directional correlations between generated residuals and the defined fault feature vectors. Finally, based on a quadrotor UAV platform, the effectiveness and superiority of the proposed FDI method are verified through online trajectory tracking processes.

Real-time object detection method with single-domain generalization based on YOLOv8

Real-time object detection method with single-domain generalization based on YOLOv8

Circulation and Codetections of Influenza Virus, SARS-CoV-2, Respiratory Syncytial Virus, Rhinovirus, Adenovirus, Bocavirus, and Other Respiratory Viruses During 2022–2023 Season in Latvia

This retrospective study analysed the routine data obtained by multiplex real-time RT-qPCR methods for respiratory virus detection. A total of 4814 respiratory specimens collected during 1 September 2022–31 August 2023 were included in the study. A total of 38% of the specimens were positive for at least one target, with the incidence maximum (82%) for the small children (age group 0–4 years). The five dominant virus groups were rhinovirus (RV, 12%), influenza virus A (IAV, 7%), adenovirus (AdV, 6%), respiratory syncytial virus (RSV, 5%), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, 5%). The specimens with multi-detections represented 19% of the positives, unevenly distributed (n = 225, 56, 43, 24) among the age groups 0–4, 5–14, 15–64, and 65< years, respectively. The dominant virus groups in multi-positive specimens were RV (53%), AdV (43%), and bocavirus (BoV, 35%)—in mutual pairs as well as all three together—followed by RSV (21%), and IAV (15%). Our study focused on the specimens with codetections and provides an insight into the variety of the respiratory virus interactions in Latvia during the first year since pandemic-related social restriction measures were eased. The observations also emphasise the need to consider the differentiation between rhinoviruses and enteroviruses, especially for the youngest patients in the age group 0–4.

Vision-Based Real-Time Bolt Loosening Detection by Identifying Anti-Loosening Lines

Bolt loosening detection is crucial for ensuring the safe operation of equipment. This paper presents a vision-based real-time detection method that identifies bolt loosening by recognizing anti-loosening line markers at bolt connections. The method employs the YOLOv10-S deep learning model for high-precision, real-time bolt detection, followed by a two-step Fast-SCNN image segmentation technique. This approach effectively isolates the bolt and nut regions, enabling accurate extraction of the anti-loosening line markers. Key intersection points are calculated using ellipse and line fitting techniques, and the loosening angle is determined through spatial projection transformation. The experimental results demonstrate that, for high-resolution images of 2048 × 1024 pixels, the proposed method achieves an average angle detection error of 1.145° with a detection speed of 32 FPS. Compared to traditional methods and other vision-based approaches, this method offers non-contact measurement, real-time detection capabilities, reduced detection error, and general adaptability to various bolt types and configurations, indicating significant application potential.

ETLSH-YOLO: An Edge–Real-Time Transmission Line Safety Hazard Detection Method

Using deep learning methods to detect potential safety hazards in transmission lines is the mainstream method for power grid security monitoring. However, the existing model is too complex to adapt to edge device deployment and real-time detection. Therefore, an edge–real-time transmission line safety hazard detection method (ETLSH-YOLO) was proposed to reduce the model’s complexity and improve the model’s robustness. Firstly, a re-parameterized Ghost efficient layer aggregation network (RepGhostCSPELAN) was designed to effectively fuse the feature information of different layers while enhancing the model’s expression ability and reducing the number of model parameters and floating-point operations. Then, a spatial channel decoupled downsampling block (CSDovn) was designed to reduce computational redundancy and improve the computational efficiency of the model. Then, coordinate attention (CA) was added in the process of multi-scale feature fusion to suppress the interference of complex background and improve the global perception ability of the model object. Finally, the Mish activation function was used to improve the network’s training speed, convergence, and generalization ability. The experimental results show that the mAP50 of this model improved by 1.73% compared with the baseline model, and the number of parameters and floating-point operations were reduced by 33.96% and 22.22%, respectively. This model lays the foundation for solving the dilemma of edge device deployment.

MAFIKD: A Real-Time Pest Detection Method Based on Knowledge Distillation

MAFIKD: A Real-Time Pest Detection Method Based on Knowledge Distillation

Leaf Moisture Content Detection Method Based on UHF RFID and Hyperdimensional Computing

Leaf moisture content (LMC) directly affects the life activities of plants and becomes a key factor to evaluate the growth status of plants. To explore a low-cost, real-time, rapid, and accurate method for LMC detection, this paper employs Ultra-High-Frequency Radio-Frequency Identification (UHF RFID) sensor technology. By reading the tag information attached to the back of leaves, the parameters of the RSSI, phase, and reading distance of the tags are collected. In this paper, we propose an enhanced Multi-Feature Fusion algorithm based on Hyperdimensional Computing (HDC) called MFFHDC. In our proposed method, the real-valued features are encoded into hypervectors and then combined with Multi-Linear Discriminant Analysis (MLDA) for the feature fusion of different features. Finally, a retraining method based on Cosine Annealing with Warm Restarts (CAWR) is proposed to improve the model and further enhance its accuracy. Tests conducted in the experimental forest show that the proposed mechanism can effectively predict the LMC. The model’s Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2) reached 0.0195, 0.0255, and 0.9131, respectively. Additionally, comparisons with other methods demonstrate that the presented system performs excellently in most aspects. As a lightweight model, this study shows great practical application value, particularly for the limited data volume and low hardware costs.

Molecular imaging of excitability difference between alkaloids/salts (nicotine, nicotinic benzoate, caffeine and arecoline hydrobromide)

Comparison of the excitability of four different alkaloids/salts, including nicotine, nicotinic benzoate, caffeine and arecoline hydrobromide. Based on positron emission tomography (PET) imaging and 18F-Fallypride, a novel technique for measuring alkaloid/salt excitability in SD rats was developed. Different doses and types of alkaloids/salts were administered to the SD rats in a single nebulised inhalation.The results showed that: (1) PET imaging technology can detect the excitability intensity of SD rats after single inhalation of alkaloids/salts in non-invasive real time and the optimal PET scanning time of four different alkaloids/salts (nicotine, nicotinic benzoate, caffeine and arecoline hydrobromide) were slightly different. (2) The excitatory saturation effect of four alkaloids/salts was observed in SD rats after single inhalation and the saturation effect doses of nicotine, nicotine benzoate, caffeine and arecine hydrobromide were 0.063 mg/kg, 0.075 mg/kg, 0.33 mg/kg and 0.075 mg/kg, respectively. (3) In the case of single inhalation of the same dose of four alkaloids/salts, male SD rats inhaled arecoline hydrobromide with the strongest excitability, while female SD rats inhaled nicotinic benzoate. A PET method for noninvasive real-time detection of alkaloid/salt excitability in SD rats was established.The finding of an excitatory saturation effect for four alkaloids/salts (nicotine, nicotinic benzoate, caffeine and arecoline hydrobromide) and the presence of excitatory intensity and gender differences at the same dose of inhalation of four alkaloids/salts, which provide a new theoretical basis for determiningthe content of alkaloids/salts.

Single-atom nanozymes possessing robust multienzyme-mimetic catalytic properties for sensing of volatile alkaline gas

Volatile alkaline gases (VAGs) emitted from various sources, such as the food or chemical industry, pose potential harm to the natural environment and human health. Therefore, the development of a real-time, rapid, and cost-effective detection method is crucial. In this study, the enzyme-like catalytic types and activities of four different metal-based single-atom enzymes (Ga, Cu, Mn, and Zn SAzymes) were selected and evaluated. Among them, Ga SAzyme exhibited the most promising catalytic properties. Furthermore, the multienzyme catalytic properties of Ga SAzyme were utilized for the detection of ammonia, and it was found that its peroxidase-like (POD-like) activity showed a strong affinity for ammonia (Km = 0.05 mM). This detection strategy demonstrated high sensitivity, with a limit of detection (LOD) of 3.0 mM in the linear range of 0.01–0.05 M and 7.0 mM in the linear range of 0.075–0.25 M. Additionally, the method was characterized by fast response time (15 s) and low cost ($0.035 per sample). The proposed method holds great potential for the detection of VAGs from various sources in the future.

Weakly Supervised Underwater Object Real-time Detection Based on High-resolution Attention Class Activation Mapping and Category Hierarchy

Recently, deep learning-based underwater object detection technology has achieved remarkable success. However, the accuracy and completeness of dataset instance annotation are crucial for its success. The quality of underwater images is low, severe objects clustering, and occlusion, acquiring object's annotations demands substantial time and labor costs, while mis annotation and missed annotation can also degrade model performance and limit their application in practical scenarios. To address this issue, this paper presents a novel weakly supervised underwater object real-time detection method, which is divided into two subtasks: weakly supervised object localization and real-time object detection. In the weakly supervised object localization task, we design a novel category hierarchy structure network that integrates the high-resolution attention-class activation mapping algorithm to obtain high-quality object class activation maps, weaken background interference, and obtain more complete object regions. The parameterized spatial loss module is devised to enable the model to escape from local optimal solutions, thus accurately and efficiently obtaining object pseudo-detection annotation boxes. For the real-time object detection task, the single-stage detector YOLOv7 is selected as the basic detection model, and an object perception loss function is designed based on the class activation map to jointly supervise the training process. A method for filtering noisy pseudo-supervision information is proposed to enhance the pseudo-supervision information involved in training. Ablation experiments and multi-method comparison experiments were conducted on the URPC and RUOD datasets, and the results verify the effectiveness of the proposed strategy, and our model exhibits significant advantages in detection performance and detection efficiency compared to current mainstream and advanced models.

REAL-TIME DETECTION AND CLASSIFICATION OF FISH IN UNDERWATER ENVIRONMENT USING YOLOV5: A COMPARATIVE STUDY OF DEEP LEARNING ARCHITECTURES

This article explores techniques for the detection and classification of fish as an integral part of underwater environmental monitoring systems. Employing an innovative approach, the study focuses on developing real-time methods for high-precision fish detection and classification. The implementation of cutting-edge technologies, such as YOLO (You Only Look Once) V5, forms the basis for an efficient and responsive system. The study also evaluates various approaches in the context of deep learning to compare the performance and accuracy of fish detection and classification. The results of this research are expected to contribute to the development of more advanced and effective aquatic monitoring systems for understanding underwater ecosystems and conservation efforts.

NIR-Sensitive Squaraine Dye-Peptide Conjugate for Trypsin Fluorogenic Detection.

Trypsin enzyme has gained recognition as a potential biomarker in several tumors, such as colorectal, gastric, and pancreatic cancer, highlighting its importance in disease diagnosis. In response to the demand for rapid, cost-effective, and real-time detection methods, we present an innovative strategy utilizing the design and synthesis of NIR-sensitive dye-peptide conjugate (SQ-3 PC) for the sensitive and selective monitoring of trypsin activity by fluorescence ON/OFF sensing. The current research deals with the design and synthesis of three unsymmetrical squaraine dyes SQ-1, SQ-2, and SQ-3 along with a dye-peptide conjugate SQ-3-PC as a trypsin-specific probe followed by their photophysical characterizations. The absorption spectral investigation conducted on both the dye alone and its corresponding dye-peptide conjugates in water, utilizing SQ-3 and SQ-3 PC respectively, reveals enhanced dye aggregation and pronounced fluorescence quenching compared to observations in DMSO solution. The absorption spectral investigation conducted on dye only and corresponding dye-peptide conjugates in water utilizing SQ-3 and SQ-3 PC, respectively, reveals not only the enhanced dye aggregation but also pronounced fluorescence quenching compared to that observed in the DMSO solution. The trypsin-specific probe SQ-3 PC demonstrated a fluorescence quenching efficiency of 61.8% in water attributed to the combined effect of aggregation-induced quenching (AIQ) and fluorescence resonance energy transfer (FRET). FRET was found to be dominant over AIQ. The trypsin-mediated hydrolysis of SQ-3 PC led to a rapid and efficient recovery of quenched fluorescence (5-fold increase in 30 min). Concentration-dependent changes in the fluorescence at the emission maximum of the dyes reveal that SQ-3 PC works as a trypsin enzyme-specific fluorescence biosensor with linearity up to 30 nM along with the limit of detection and limit of quantification of 1.07 nM and 3.25 nM, respectively.

A droplet digital PCR method for the detection of scale drop disease virus in yellowfin seabream (Acanthopagrus latus).

In this study, a ddPCR method for the detection of scale drop disease virus (SDDV) in yellowfin seabream (Acanthopagrus latus) was established based on Real-time fluorescence quantitative PCR detection methods and principles. The reaction conditions were optimized, and the sensitivity, specificity, accuracy, and reproducibility were assessed. The results showed that threshold line position was determined to be 1900 by the ddPCR method; the optimum annealing temperature for SDDV detection by the ddPCR method was 60°C; the limit of detection was 1.4-1.7 copies/μL; the results of specific detection of other common viruses, except for SDDV specific amplification, were all negative; and the relative standard deviation (RSD) for the reproducibility validation was 0.77%. The samples of yellowfin seabream (Acanthopagrus latus) liver, spleen, kidney, heart, intestine, brain, blood, muscle, skin and ascites with three replicates, respectively, were tested using the ddPCR method, and the results were consistent with clinical findings. The ddPCR method established in this study has the advantages of high sensitivity, high specificity, good reproducibility and simple steps for the quantitative detection of SDDV, which could be used for the nucleic acid detection of clinical SDDV samples, and provided a new quantitative method for the diagnosis of yellowfin seabream SDDV in the early stage of pathogenesis.

A lightweight real-time detection method of small objects for home service robots

A lightweight real-time detection method of small objects for home service robots

ECM-YOLO: a real-time detection method of steel surface defects based on multiscale convolution

Steel surface defects, characterized by multiple types, varied scales, and overlapping occurrences, directly impact the quality, performance, and reliability of industrial products. Proposing a high-precision and high-speed steel surface defect detection algorithm is crucial for ensuring product quality. In this regard, this paper introduces ECM-YOLO, a detection network based on YOLOv8n. First, addressing the insufficient information capture of the C2f module, the C2f enhanced multiscale convolution processing (C2f_EMSCP) module is proposed, enhancing global and local feature capture capabilities through multiscale convolutions. Second, to further enhance the network’s robustness and focus on critical information, the channel prior convolutional attention (CPCA) mechanism is integrated between the backbone and neck networks to facilitate more efficient information transmission. Last, a novel, to the best of our knowledge, detection head, i.e., multiscale simple and efficient anchor matching head (MultiSEAMHead), is proposed to mitigate accuracy issues arising from overlaps between different types of defects. Experimental results demonstrate that ECM-YOLO achieves mAPs of 78.9% and 68.2% on the NEU-DET and GC 10-DET data sets, respectively, outperforming YOLOv8n by 2.5% and 4.4%. Moreover, ECM-YOLO excels in model parameters, computational efficiency, and inference speed compared with other models. These findings highlight the applicability of ECM-YOLO for real-time defect detection in industrial settings.

Development and application of a quantitative real-time PCR method for detection of Decapod iridescent virus 1.

As a newly discovered virus, Decapoda iridovirus 1 (DIV1) can cause a mortality rate of up to 100% in crustaceans, leading to huge economic losses. At present, there is no effective prevention and control measures for this disease. In the present study, the specific primers targeting highly conserved regions of MCP gene were designed, and then a quantitative real-time PCR method was established. The results indicate that DIV1 quantitative real-time PCR established has good specificity and does not cross react with other pathogens including white spot syndrome virus (WSSV), infectious subcutaneous and hematopoietic necrosis virus (IHHNV) and Vibrio parahaemolyticus induced acute hepatopancreatic necrosis disease (VpAHPND). The real-time PCR was capable of detecting DIV1 DNA at a minimum concentration of 10 copies/μL within 34 cycles. The method has good repeatability, with intra group and inter group coefficients of variation both less than 2%. Thirty-two clinical samples were assessed using both the real-time PCR and conventional PCR. The results shown real-time PCR we established are more sensitive than conventional PCR. In conclusion, this method has strong specificity, stable repeatability, and high sensitivity, providing technical support for clinical diagnosis, epidemiology investigation and monitoring of DIV1.

Standardization and application of ARMS TaqMan real-time PCR for screening of folate metabolism genes in Han Chinese.

Folate has antioxidant properties, and low concentration in seminal plasma may be associated with increased DNA damage in sperm. Mutations of the methylenetetrahydrofolate reductase (MTHFR) and methionine synthase reductase (MTRR) genes, including MTHFR C677T (rs1801133), MTHFR A1298C (rs1801131), and MTRR A66G (rs1801394), can lead to decreased activity of the encoded folate metabolic enzymes, thereby affecting male reproduction. The current SNP detection methods commonly used in clinical practice have some shortcomings, such as long time-consuming, complex detection steps, or high cost. The purpose of this study was to establish a simple, time-saving, sensitive, accurate, and easy to clinical popularization method for folate metabolism gene detection. We combined ARMS-PCR with TaqMan fluorescent probe to establish an ARMS TaqMan real-time PCR detection method. According to the variation of rs1801131, rs1801133, and rs1801394, two specific primers (one wild type and one mutant) were designed. Mismatched nucleotides were introduced at the penultimate or third position to improve the specificity of the primer. Specific TaqMan probe was introduced to detect PCR products to improve the sensitivity of the method. The results showed that the sensitivity of ARMS TaqMan real-time PCR in SNP genotyping was 1ng, and the accuracy was 100%. A total of 249 clinical samples were detected by the established method, and the correlation between three SNPs and semen quality was analyzed. We found that individuals carrying the AG+GG genotype of rs1801394 had a lower risk of abnormal semen quality. In conclusion, we developed a highly sensitive, accurate, rapid, and easy to be popularized method for detecting SNPs of rs1801394, rs1801131, and rs1801133. ARMS TaqMan real-time PCR is a reliable SNP genotyping method in folate metabolism genes.

Optimizing self-sensing performance of conductive mortar via gradation of graphene coated aggregate

With the increasing focus on structural health monitoring of concrete during its service life, self-sensing functional concrete has emerged as an effective method for real-time detection and assessment of structural conditions. We recently proposed a new methodology of preparing conductive cementitious mortar with graphene-coated aggregates (Gr@Ag). Instead of the traditional approach, where conductive fillers are directly intermixed and uniformly distributed in the binding matrix, the development of Gr@Ag has created a more efficient path for electron transport through the connections of aggregates. The main objective of this study is to understand the effect of aggregate gradation on the electrical performances of conductive mortars, and the piezoresistivity is optimized by adjusting the aggregate packing. Electrochemical impedance spectroscopy was employed to investigate the underlying mechanisms of how aggregate gradation affects the mortar's electrical conductivity. It was found that both the internal resistance and resistivity of the conductive mortar considerably decreased with the reduction in Gr@Ag particle size. The contribution of conductive sand to the conductivity of mortar can reach 80.89–92.36 %, effectively transforming the conductive path from a disconnected state to a connected state. Reducing the particle size of the conductive aggregates contributes to an increase in particle contact rate, with the particle contact rate reaching up to 59 %. The presence of completely isolated conductive aggregates is noticeably reduced. As the particle size decreases, there is a significant reduction in mechanical performance. The conductive aggregates become relatively isolated from the matrix, and both the size of the conductive aggregates and the interfacial transition zone play vital roles to affect the mechanical properties.