Real-time Detection Research Articles

CBS-YOLOv5: fault detection algorithm of electrolyzer plate with low-resolution infrared images based on improved YOLOv5

Abstract In the process of copper electrorefining, accurate detection of electrode plate faults is extremely challenging due to the low resolution of captured infrared images, significant noise interference, and dense electrode plate arrangements. To address these challenges, this paper proposes an improved YOLOv5-based electrode plate fault detection algorithm called CBS-YOLOv5. This algorithm introduces several innovations over the original YOLOv5, including: the incorporation of coordinate attention to enhance the ability of the feature extraction network to separate target information from noise; the construction of a small object detection module to improve the detection of dense small objects by increasing the resolution of the feature map; the replacement of the traditional path aggregation network with a Bi-directional Feature Pyramid Network (BiFPN) for more flexible multi-scale feature fusion; and the integration of the swin transformer to optimize the cross-stage partial bottleneck structure, significantly enhancing the model’s ability to detect densely packed small objects. Experimental results show that the proposed CBS-YOLOv5 model achieves an accuracy of 88.1%, which is an improvement of 5.7% over the base model. Furthermore, this algorithm demonstrates exceptional detection capabilities for dense small objects in low-resolution infrared images while maintaining real-time detection speed, making it suitable for various complex industrial scenarios, including fault detection in non-ferrous metal electrolysis processes. CBS-YOLOv5 not only improves detection accuracy and robustness but also has broad application prospects, offering a new solution for intelligent manufacturing and industrial inspection.

Assessment of Nine Real-Time PCR Kits for African Swine Fever Virus Approved in Republic of Korea.

The African swine fever virus (ASFV) causes severe disease in wild and domestic pigs, with high mortality rates, extensive spread, and significant economic losses globally. Despite ongoing efforts, an effective vaccine remains elusive. Therefore, effective diagnostic methods are needed to rapidly detect and prevent the further spread of ASF. This study assessed nine commercial kits based on real-time polymerase chain reaction (PCR) approved in the Republic of Korea using the synthesized ASFV plasmid, 20 food waste samples, and artificially spiked samples (ASSs). The kits were evaluated for their diagnostic sensitivity, specificity, cost per reaction, and reaction running time. In addition, the results were compared with those of the World Organization for Animal Health (WOAH) standard methods. Three commercial kits (VDx® ASFV qPCR Kit, Palm PCR™ ASFV Fast PCR Kit, and PowerChek™ ASFV Real-time PCR Detection Kit Ver.1.0) demonstrated the highest sensitivity (100 ag/μL), cost-effectiveness (less than KRW 10,000), and shortest running time (less than 70 min). These kits are suitable for the monitoring, early diagnosis, and prevention of the spread of ASF. This is the first report on the performance comparison of ASFV diagnostic kits approved in the Republic of Korea, providing valuable information for selecting kits for testing with food waste samples.

A New Tensor Summary Statistic for Real-Time Detection of Stealthy Anomaly in Avatar Interaction

A New Tensor Summary Statistic for Real-Time Detection of Stealthy Anomaly in Avatar Interaction

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.

An efficient multiscale enhancement network with attention mechanism for aluminium defect detection

ABSTRACT Real-time defect detection is required to efficiently control the quality of aluminium. However, aluminium defects have the characteristics of small-size, low contrast, and multiscale variations, which pose great challenges to defect detection. This article aims to improve the defect detection accuracy and propose an effective multiscale enhancement network with attention mechanism for aluminium defect detection (AMMENet). First, to capture key features and mitigate interference from the background, a pluggable parallel residual attention module (PRAM) is proposed for the feature extraction network. To compensate for the loss of deep features, a multilevel semantic enhancement module (C2f-MFF) is proposed to fuse multiscale feature maps. Finally, the model was applied to the Tianchi Aluminium Surface Defect Dataset (TC-ASDD) for ablation experiments and comparisons. The experimental results show that the mean average precision (mAP@0.5) of the proposed AMMENet is 73.6% with a real-time detection speed of 66.2 frames per second (FPS). Compared with YOLOv8 baseline network, AMMENet improves mAP@0.5 by 2.8% with only a slight loss in speed. Moreover, AMMENet is superior to the state-of-the-art detection methods in terms of detection accuracy.

A magnetic SERS-imprinted sensor for the determination of cardiac troponin I based on proteolytic peptide technology

Acute myocardial infarction is a sudden and high-mortality disease that can be accurately diagnosed by measuring the level of cardiac troponin I in the blood. Currently, cTnI commonly used clinical detection methods usually have excellent sensitivity and are suitable for large-scale sample detection analysis. However, most of these methods are operated through multiple steps of fixation, incubation, reaction and separation, and most of them require professionals to operate complex instruments, which greatly limits their applicability in real-time rapid detection. Therefore, a method that requires low professional skills and can perform rapid detection is necessary. We presents an alternative strategy to quantify cTnI in clinical serum samples using a combination of SERS and magnetic molecularly imprinted polymers (MMIP). MMIPs was synthesized under Polyvinyl Pyrrolidone (PVP) conditions without vinyl modification using characteristic peptide as template, MAA and DMAm as functional monomers. The internal Raman probe 4-MBA was connected through Ag-SH bonds in MMIPs to solve the problem that the target object had no Raman characteristic peak. MMIPs with high magnetic and adsorptive properties showed characteristic absorption peaks at the Raman shift of 1582 cm−1 after specific capture of the target templates. The Raman signals of the 4-MBA were reduced due to shielding effects and the detection range of this method was 0.001–100 ng mL−1. The recoveries and relative standard deviations (RSDs) of the spiked experiments were 103.1%–106.3 % and 3.54 %–7.38 %, respectively. In summary, this work had appropriate sensitivity and specificity, and there was no significant difference in detection results after ELISA verification. It provided a flexible and practical analysis method for detecting cTnI based on SERS technology. In addition, the imprinting materials of other disease markers can be prepared by changing the template molecules, which provides a new idea for the detection of other biomarkers.

Resource-optimized cnns for real-time rice disease detection with ARM cortex-M microprocessors

This study explores the application of Artificial Intelligence (AI), specifically Convolutional Neural Networks (CNNs), for detecting rice plant diseases using ARM Cortex-M microprocessors. Given the significant role of rice as a staple food, particularly in Malaysia where the rice self-sufficiency ratio dropped from 65.2% in 2021 to 62.6% in 2022, there is a pressing need for advanced disease detection methods to enhance agricultural productivity and sustainability. The research utilizes two extensive datasets for model training and validation: the first dataset includes 5932 images across four rice disease classes, and the second comprises 10,407 images across ten classes. These datasets facilitate comprehensive disease detection analysis, leveraging MobileNetV2 and FD-MobileNet models optimized for the ARM Cortex-M4 microprocessor. The performance of these models is rigorously evaluated in terms of accuracy and computational efficiency. MobileNetV2, for instance, demonstrates a high accuracy rate of 97.5%, significantly outperforming FD-MobileNet, especially in detecting complex disease patterns such as tungro with a 93% accuracy rate. Despite FD-MobileNet’s lower resource consumption, its accuracy is limited to 90% across varied testing conditions. Resource optimization strategies highlight that even slight adjustments, such as a 0.5% reduction in RAM usage and a 1.14% decrease in flash memory, can result in a notable 9% increase in validation accuracy. This underscores the critical balance between computational resource management and model performance, particularly in resource-constrained settings like those provided by microcontrollers. In summary, the deployment of CNNs on microcontrollers presents a viable solution for real-time, on-site plant disease detection, demonstrating potential improvements in detection accuracy and operational efficiency. This study advances the field of smart agriculture by integrating cutting-edge AI with practical agricultural needs, aiming to address the challenges of food security in vulnerable regions.

Graded Nanotexturing Architectural Wearable Triboelectric Sensor for Programmable Haptic Exploration.

Emulating biological perception mechanisms to construct intelligent sensing devices and systems represents a paradigm for promoting human-computer interaction in the Internet of Everything era. Nonetheless, developing highly sensitive, real-time sensing and rapidly integrated intelligent interaction units remains a challenging and time-consuming endeavor. This study employs a low-temperature glow discharge technique to rapidly fabricate graded nanotexturing architectural triboelectric nanopaper, upon which wearable triboelectric sensors for real-time tactile detection are designed. The structure enhances the contact area under an external force. Additionally, the Z-stacking structure design enables the sensor to achieve a remarkable sensitivity of 10.3 kPa-1 and a rapid response time of 52 ms. Furthermore, a tactile sensor array was designed to demonstrate the triboelectric sensor's ability to recognize characteristic pressures. With programmable machine learning techniques, the object recognition rate reached 97%. This study supports material structural design across disciplines, laying a solid foundation for the rapid fabrication and integration of transient wearable electronics.

Three-Dimensional Outdoor Object Detection in Quadrupedal Robots for Surveillance Navigations

Quadrupedal robots are confronted with the intricate challenge of navigating dynamic environments fraught with diverse and unpredictable scenarios. Effectively identifying and responding to obstacles is paramount for ensuring safe and reliable navigation. This paper introduces a pioneering method for 3D object detection, termed viewpoint feature histograms, which leverages the established paradigm of 2D detection in projection. By translating 2D bounding boxes into 3D object proposals, this approach not only enables the reuse of existing 2D detectors but also significantly increases the performance with less computation required, allowing for real-time detection. Our method is versatile, targeting both bird’s eye view objects (e.g., cars) and frontal view objects (e.g., pedestrians), accommodating various types of 2D object detectors. We showcase the efficacy of our approach through the integration of YOLO3D, utilizing LiDAR point clouds on the KITTI dataset, to achieve real-time efficiency aligned with the demands of autonomous vehicle navigation. Our model selection process, tailored to the specific needs of quadrupedal robots, emphasizes considerations such as model complexity, inference speed, and customization flexibility, achieving an accuracy of up to 99.93%. This research represents a significant advancement in enabling quadrupedal robots to navigate complex and dynamic environments with heightened precision and safety.

A Radial Distribution Function Based Recognition Algorithm of Point Defects in Large-Scale β-Ga2O3 Systems.

The atomic configurations and concentrations of intrinsic defects profoundly influence the electrical and optical properties of the semiconductor materials. This influence is particularly significant in the case of β-Ga2O3, which is a highly promising ultrawide bandgap semiconductor characterized by highly complex intrinsic defect configurations. Despite its importance, there is a notable absence of an accurate method to recognize these defects in large-scale atomistic computational modeling. We design an effective algorithm for the explicit identification of various intrinsic point defects in the β-Ga2O3 lattice, which constitutes the integration of the particle swarm optimization (PSO) and K-means clustering (K-MC) methods. Our algorithm attains the recognition accuracy exceeding 95%. Finally, the algorithm is applied to dynamic simulations, where the feasibility of dynamic real-time detection is explored.

Antibiotic-Mediated Plasmonic Resonance on a Novel Nanopillar Metasurface Array.

This study demonstrates a silicon nanopillar metasurface coupled with localized surface plasmon resonance (LSPR) mediated by the presence of cephalexin antibiotics in solution for biosensing applications. A facile fabrication process was developed to create the metasurface on silicon wafers with a unique resonance signature. The resulting metasurface consists of periodic nanopillars approximately 180 nm in diameter, 210 nm deep, and with a controlled edge-to-edge separation of 200 nm. These dimensions were chosen based on a finite element method simulation that was used to investigate the ideal parameters to produce the desired resonance effect in the metasurface reflection spectra. Optimization of the nanopillar surface properties and the sidewall angle allowed for replication of the simulations. This metasurface was coupled with BSA-coated gold nanospheres (BSANS) to mediate the redshift of peak resonance wavelength values, occurring only in the presence of the antibiotic linker. The device fabricated herein exhibits a significant 22 nm wavelength shift resulting from changes to the local refractive index in the presence of the BSANS-antibiotic coupling. Further enhancement of the binding events is promoted by the LSPR hot spots formed between the nanoparticles and the metasurface allowing for sensitive and real-time detection.

Electrode Droplet Microarray (eDMA): An Impedance Platform for Label-Free Parallel Monitoring of Cellular Drug Response in Nanoliter Droplets.

Label-free real-time monitoring of cellular behavior using impedance spectroscopy is important for drug development and toxicological assessments. Parallelization and miniaturization of such experiments are essential for increasing throughput and enabling experiments with low abundant stem or primary cells. Traditional methods are not miniaturized and require large volumes of reagents and number of cells, limiting their suitability for cost effective high-throughput screening of cells of limited availability. Here, the fabrication, optimization, and application of a bioelectrical signaling monitoring system - electrode droplet microarray (eDMA) are demonstrated. The eDMA platform is based on preparation of a hydrophilic-superhydrophobic patterns covering an array of individually addressable microelectrodes, which confines cells to individual microelectrodes, allowing for parallel, real-time, and label-free detection of cellular responses to drug treatments in nanoliter droplets. The real-time monitoring of cytotoxic effect of an anticancer drug is demonstrated over 48 h with real-time calculation of the half-inhibitory concentration (IC50) values through impedance spectroscopy. This demonstrates eDMA's ability to dynamically assess responses to various drugs in parallel at any given time point, which is crucial for functional personalized oncology. Specifically, the platform can be employed for monitoring anticancer drug toxicity using limited patient samples, where the miniaturization provided by eDMA is essential.

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

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

Adaptive sensor attack detection and defense framework for autonomous vehicles based on density

The security of autonomous vehicles heavily depends on localization systems that integrate multiple sensors, which are vulnerable to sensor attacks and increase the risk of accidents. Given the diversity of sensor attacks and the dynamic changing of driving scenarios of autonomous vehicles, an adaptive and effective attack detection and defense framework faces a considerable challenge. This paper proposes a novel real-time adaptive attack detection and defense framework based on density, which can detect and identify attacked sensors and effectively recover data. We first develop a reinforcement learning multi-armed Bandit-based Density-Based Spatial Clustering of Applications with Noise (BDBSCAN) algorithm that selects hyperparameters adaptively. The Adaptive Extended Kalman Filter (AEKF) combines with the vehicle dynamic model on the localization system and extracts data features used for the BDBSCAN algorithm to monitor potential sensor attacks. If attack detection indicates possible system compromise, AEKF is further employed on localization sensors with anomalies identified through the BDBSCAN algorithm of the attacked sensors. To ensure precision and reliability, the data recovery incorporates a redundancy mechanism to apply a decision tree to select the optimal state estimation between AEKF and Extended Kalman Filter (EKF) to replace corrupted sensor data. To evaluate the effectiveness and adaptability of the proposed framework, we conducted 15,000 experiments using the real-world KITTI and V2V4Real datasets across various driving and sensor attack scenarios. The results demonstrate that our proposed framework achieves 100% accuracy and 0% false alarm rate in various driving scenarios for attack detection within 0.15 s, with a recovery time of 0.08 s.

Modeling and experimental characterization of a compact Mach–Zehnder electro-optic modulator on the SOI

The Mach–Zehnder electro-optic modulator (MZM) plays a crucial role in photonics integration technology during signal transmission. We propose the design and fabrication of a silicon-based electro-optic modulator based on the M-Z structure and design a modulator using silicon as the waveguide core layer on a silicon dioxide substrate. The detailed design involves a 1×2 splitter, branch waveguides, modulating arm waveguides, and a 2×1 combiner. The MZM fabrication and characterization results reveal that the half-wave voltage of the silicon-based MZM is 2 V, with an optical loss of −2.464dB and a device core size of 450µm×2800µm. The experimental results indicate that the MZM with a low half-wave voltage, a low loss, and high integration has significant value. The proposed MZM exhibits considerable improvements, featuring a low half-wave voltage and a low loss, and this device may serve as a fundamental component in wearable large-scale photonic integrated circuits for weak ECG real-time detection.

Application of multimodal intraoperative neurophysiological monitoring technology in neurofibromatosis type 1 related peripheral nerve tumor surgery

To summarize application effect and clinical experience of multimodal intraoperative neurophysiological monitoring (IONM) technology in the surgery of neurofibromatosis type 1 (NF1) related peripheral nerve tumors. A retrospective study was conducted on NF1 patients, who admitted between January 2019 and December 2023 and treated with peripheral nerve tumor resection surgery assisted by multimodal IONM technology. There were 49 males and 45 females. The age ranged from 5 to 78 years, with an average of 33.7 years. Tumor morphological classification included 71 cases of nodular type, 13 cases of diffuse type, and 10 cases of mixed type. Target tumors were distributed in craniofacial region (47 cases), neck (11 cases), trunk (12 cases), and limbs (24 cases). Preoperatively, 44 cases had no obvious neurological symptoms, while the remaining patients had neurological symptoms, including 15 cases of visual impairment, 5 cases of hearing impairment, 16 cases of somatic movement disorders, and 31 cases of somatic sensory disorders, of which 7 cases had more than one symptom. IONM plans were selected based on the relevant nerves and adjacent important structures of the target tumor, including visual evoked potential (17 cases), somatosensory evoked potential (44 cases), motor evoked potential (88 cases), and electromyogram (94 cases). All surgeries were successfully completed. Ninety-three patients underwent total/near total resection and 1 patient underwent palliative resection. Pathological examination showed 80 cases of neurofibroma and 14 cases of malignant peripheral nerve sheath tumors. Complications included 2 cases of hematoma and 3 cases of incision infection. All patients were followed up 3-61 months (median, 15 months). During follow-up, no significant changes in neurological symptoms or tumor recurrence were found. Among the patients with preoperative visual impairment, there were 14 cases with no improvement in symptoms and 1 with improvement after surgery. Among the patients with somatic movement disorders, there were 11 cases with no improvement in symptoms, 3 cases with improvement, 2 cases with aggravation, 4 newly onset cases, and 1 case with significant impact on daily life after surgery. Among the patients with somatic sensory disorders, there were 17 cases with no improvement in symptoms, 14 cases with improvement, and 13 newly onset cases. The patients with hearing impairment showed improvement after surgery. The clinical manifestations of NF1 related peripheral nerve tumors are complex. Multimodal IONM technology can provide real-time detection of nerve provocation and damage. Surgical treatment with multimodal IONM technology is safe and can reduce complications.

Efficient Credit Card Fraud Detection System Using Big Data and Machine Learning

This review offers a detailed strategy to address the growing threat of credit card fraud in today's digital landscape. By utilizing Big Data analytics alongside machine learning methods, the system aims to transform fraud detection processes. It tackles the challenges arising from the increasing volume and complexity of credit card transactions, enabling the real-time detection and prevention of fraudulent actions. The system employs sophisticated machine learning algorithms to identify patterns and anomalies linked to fraudulent activities, allowing for proactive responses to emerging fraud tactics. Additionally, the system is optimized to handle and analyze large datasets efficiently, ensuring timely and precise detection of fraud. It also incorporates strong security protocols to protect sensitive customer data while adhering to privacy regulations. This review ultimately seeks to enhance the safety and reliability of electronic payments, protecting financial institutions and consumers from the harmful effects of credit card fraud.

Preparation of a V–COF@SWCNTs-COOH/SPCE supported molecularly imprinted electrochemical sensor for real-time detection of trace sulfadimidine

To overcome the limitations of insufficient sensitivity and poor specificity of portable screen-printed carbon electrode-electrochemical sensors (SPCE-EC) in practical applications, we prepared carrier composites of carboxylic single-walled carbon nanotubes vertically grafted by covalent organic frameworks (v-COF@SWCNTs-COOH) and coated with a molecularly imprinted polymer (MIP) of sulfadimidine (SM2). 55 °C hot steam elution is more eco-friendly than traditional organic solvent elution. The results showed that when the mass ratio of DBA to DBA-SWCNTs was 1:1, the v-COF@SWCNTs-COOH obtained by the two-step synthesis method could increase the electrical signal up to 2.33-fold of the bare electrode. The bifunctional monomer MIP prepared on the above structure enhanced the signal response by 2.91-fold, with a high imprint factor of 20. The assembled MIP/v-COF@SWCNTs-COOH/SPCE were analyzed by differential pulse voltammetry (DPV) with a high sensitivity of 0.21 nM for LOD and 0.70 nM for LOQ. In milk and fish samples, the recovery rate was 95.0 %–104.8 %. The validation of authentic pork samples with the statutory LC-MS/MS method showed no significant difference (P > 0.05). The sensor's performance indicators remained robust after five repeated uses. Therefore, the MIP/v-COF@SWCNTs-COOH/SPCE combines the cheapness and portability of SPCE, while the sensitivity and specificity of small molecule detection were significantly improved.

A Low-Power IoT Architecture for Real-Time Detection and Notification of Unauthorized Tree Poaching

A Low-Power IoT Architecture for Real-Time Detection and Notification of Unauthorized Tree Poaching

Machine learning applications of network security enhancement: review

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).