Detection Algorithm Research Articles

Moving Target Detection for Array Antennas in Varying Multipath Environments

Detecting moving targets remains a significant challenge in radar applications, particularly in dense multipath environments where signal complexities present formidable obstacles. However, the application of time reversal (TR) technology offers promising prospects for enhancing target detection capabilities. Utilizing this technology, we propose a detection algorithm tailored specifically for a moving target, designed exclusively for array antennas (AA) operating in dense multipath environments with dynamic channel variations. We establish an AA-TR signal model within this framework and derive the AA-TR likelihood ratio test (AA-TR-LRT) detector. To assess the effectiveness of our proposed approach, we also develop the AA conventional LRT (AA-C-LRT) detector as a comparative benchmark. Comprehensive numerical tests consistently verify the outstanding effectiveness of our detection algorithm, underscoring its precision in detecting moving targets, particularly in highly complex multipath scenarios with varying channels.

Machine vision-based detection of surface defects in cylindrical battery cases

Automotive 21700 series lithium batteries are prone to surface defects during production and transportation, thus affecting their performance, so we propose a full-surface defect detection method for battery cases based on the synthesis of traditional image processing and deep learning to address this problem. First, the mechanism of surface defects on a battery case is analysed, and the types of surface defects are summarized. A suitable platform for image acquisition is designed for the severely reflective surface. Since there is no publicly available defect dataset for cylindrical battery cases, a defect dataset is established, and the dataset is augmented and expanded via the traditional method and the ACGAN model. For the defects on the top of the battery case, a traditional image processing algorithm is used to combine the roundness and the area of the area pixels to make a comprehensive judgement. For the defects on the bottom and side of the battery case, after comparing and analysing a variety of deep learning networks, the YOLOv7 model is selected to address the data characteristics of the large experimental inputs and the small targeted defects. To improve the accuracy of the model to meet the needs of real-time detection in industry, the CA attention mechanism, the DYHEAD dynamic detector head, and the slicing-assisted super inference (SAHI) method are used, with a final map accuracy reaching 98.1 %, which is 2.7 % higher than that of the initial model. Compared with mainstream target detection algorithms, the algorithm in this paper has good detection performance for cylindrical battery case defect detection and can be better applied to real-time detection in industry.

Optimizing multi-time series forecasting for enhanced cloud resource utilization based on machine learning

Due to its flexibility, cloud computing has become essential in modern operational schemes. However, the effective management of cloud resources to ensure cost-effectiveness and maintain high performance presents significant challenges. The pay-as-you-go pricing model, while convenient, can lead to escalated expenses and hinder long-term planning. Consequently, FinOps advocates proactive management strategies, with resource usage prediction emerging as a crucial optimization category. In this research, we introduce the multi-time series forecasting system (MSFS), a novel approach for data-driven resource optimization alongside the hybrid ensemble anomaly detection algorithm (HEADA). Our method prioritizes the concept-centric approach, focusing on factors such as prediction uncertainty, interpretability and domain-specific measures. Furthermore, we introduce the similarity-based time-series grouping (STG) method as a core component of MSFS for optimizing multi-time series forecasting, ensuring its scalability with the rapid growth of the cloud environment. The experiments performed demonstrate that our group-specific forecasting model (GSFM) approach enabled MSFS to achieve a significant cost reduction of up to 44%.

External Validation of a Previously Developed Deep Learning-based Prostate Lesion Detection Algorithm on Paired External and In-House Biparametric MRI Scans.

Purpose To evaluate the performance of an artificial intelligence (AI) model in detecting overall and clinically significant prostate cancer (csPCa)-positive lesions on paired external and in-house biparametric MRI (bpMRI) scans and assess performance differences between each dataset. Materials and Methods This single-center retrospective study included patients who underwent prostate MRI at an external institution and were rescanned at the authors' institution between May 2015 and May 2022. A genitourinary radiologist performed prospective readouts on in-house MRI scans following the Prostate Imaging Reporting and Data System (PI-RADS) version 2.0 or 2.1 and retrospective image quality assessments for all scans. A subgroup of patients underwent an MRI/US fusion-guided biopsy. A bpMRI-based lesion detection AI model previously developed using a completely separate dataset was tested on both MRI datasets. Detection rates were compared between external and in-house datasets with use of the paired comparison permutation tests. Factors associated with AI detection performance were assessed using multivariable generalized mixed-effects models, incorporating features selected through forward stepwise regression based on the Akaike information criterion. Results The study included 201 male patients (median age, 66 years [IQR, 62-70 years]; prostate-specific antigen density, 0.14 ng/mL2 [IQR, 0.10-0.22 ng/mL2]) with a median interval between external and in-house MRI scans of 182 days (IQR, 97-383 days). For intraprostatic lesions, AI detected 39.7% (149 of 375) on external and 56.0% (210 of 375) on in-house MRI scans (P < .001). For csPCa-positive lesions, AI detected 61% (54 of 89) on external and 79% (70 of 89) on in-house MRI scans (P < .001). On external MRI scans, better overall lesion detection was associated with a higher PI-RADS score (odds ratio [OR] = 1.57; P = .005), larger lesion diameter (OR = 3.96; P < .001), better diffusion-weighted MRI quality (OR = 1.53; P = .02), and fewer lesions at MRI (OR = 0.78; P = .045). Better csPCa detection was associated with a shorter MRI interval between external and in-house scans (OR = 0.58; P = .03) and larger lesion size (OR = 10.19; P < .001). Conclusion The AI model exhibited modest performance in identifying both overall and csPCa-positive lesions on external bpMRI scans. Keywords: MR Imaging, Urinary, Prostate Supplemental material is available for this article. © RSNA, 2024.

Autonomous Drone Navigation Using Computer Vision

Abstract: Autonomous drone navigation with computer vision is an innovative technology that allows drones to move through intricate surroundings without needing human control. This system uses live visual information to identify objects, steer clear of obstacles, and determine routes, improving operational safety and precision. The project's main objective is to create a visionbased navigation system that combines object detection and obstacle avoidance algorithms through deep learning methods like YOLO (You Only Look Once), alongside real-time sensor fusion. The drone uses computer vision algorithms to process aerial images and automatically changes its flight path to prevent crashes. A personalized dataset of aerial images is generated and utilized for improving object detection skills in various environments. In order to guarantee practicality in real- world situations, the system undergoes validation through simulations and field tests on different terrains, focusing on its resilience in changing environments. Improvements in navigation accuracy and obstacle detection are accomplished by implementing adaptive pathplanning and integrating multiple sensors, guaranteeing the drone's efficient operation in real-life situations. The goal of this method is to enhance the drone's ability to make decisions, minimize human mistakes, and expand its range of potential uses in areas like surveillance, agriculture, and disaster response

Accident Prevention System for Cars, Buses, Trucks

Driver drowsiness is a significant factor in road accidents, contributing to a large number of crashes annually. To enhance road safety, the development of a Drivers Drowsiness Detection System is essential. This system builds to detect early signs of lazyness in drivers and provide timely alerts to prevent accidents. The proposed system uses to track key indicators of drowsiness, such as eye closure, head position, yawning, and blink rate. By utilizing a camera-based real-time monitoring system, it continuously analyzes the driver’s facial expressions. The system employs cadcade algorithms to distinguish a featurebased object detection algorithm to detect objects from images between alert and drowsy states based on collected data. When drowsiness is detected, the system triggers alerts like audio warnings, seat vibrations, or even vehicle slowing mechanisms, depending on the integration. This technology has the potential to significantly reduce the number of road accidents related to driver fatigue and could be applied in both personal vehicles and commercial fleets. The implementation of such systems represents a critical step toward improving road safety and decreasing driver-related incidents. Nowadays it is very challenging to stay active all the time due to busy schedules. Falling asleep while driving can lead to serious consequences, accidents, and even death. This situation is much more common and therefore it is very important to solve this problem. So, to solve this problem, we developed a sleep alarm system for drivers. This system alerts the user when he falls asleep at the wheel, thus the main concern is preventing accidents and saving lives. This system is useful for long- distance travelers and late-night drivers.

Fault Detection Technology for Ultrapure Water Plant using Virtual Sensors for Pressure Control Valve and Indicators in Return Line

Objectives:This study aims to propose a technology that utilizes virtual sensors to quickly and accurately diagnose faults in valves and measurement devices that may occur in the return line of an ultrapure water plant.Methods:The empirical equations were derived, and the error range was calculated using operational data collected from three pieces of equipment (pressure control valve, pressure transmitter, and flow transmitter) in the recovery system of the ultrapure water process. Based on this, a virtual sensor is established.Results and Discussion:It was confirmed that the utilization of 24-hour data collected from the operation of the ultrapure water plant was enough to establish virtual sensors and could also estimate monthly data with high accuracy. In Phase 1, the fault diagnosis technology was found to estimate values more accurately and had a meaningful margin of error compared to Phase 2.Conclusion:It was possible to develop a fault detection technology using the virtual sensor derived from the empirical equation. The technology showed high performance in detecting anomalies in the flow meter and pressure control valve opening but had limitations in estimating consistently pressure values. Additionally, considering that there was no issue in estimating monthly data using the equations derived using 24 hours operational data. The fault detection algorithm could be used continuously if the empirical equations are updated by collecting a day's data when operating conditions change.

A Review of Deep Learning Based Object Detection Algorithms

With the rapid development of artificial intelligence technology, the traditional manual feature-based object detection method has been gradually replaced by deep learning-based object detection technology. Object detection is the basis and prerequisite for many computer vision tasks aimed at recognizing targets in an image and determining their class and location. The purpose of object detection algorithm research based on deep learning is to improve the detection accuracy and detection speed of the detection algorithm, so that the object detection algorithm can be more safe and convenient applied to People's Daily production and life. This article reviews the evolution of object detection algorithms, focusing on two-stage, one-stage and object detection algorithms based on the transformer architecture. In addition, the performance, advantages and disadvantages of different algorithms are compared, and the development trend of object detection algorithms based on deep learning is summarized in the end.

Rail Detection Research Status of Multi -Sensing Technology

Traditional rail inspection methods, primarily relying on manual inspection and basic equipment, face significant challenges including low efficiency, poor accuracy, and susceptibility to human error. This paper presents a comprehensive review of the current research status and future trends in multi-sensor rail transit inspection vehicles. The focus is on the integrated application of various sensing technologies, particularly inertial systems and visual sensing, to achieve high-precision and efficient measurement of rail geometrical parameters. An in-depth analysis of key technologies and challenges in railway track inspection is provided, exploring how advanced sensing methods can overcome the limitations of traditional approaches. The paper also examines the market prospects for rail transit inspection, highlighting the growing demand for more sophisticated and reliable inspection systems. Furthermore, potential future research directions in this field are outlined, including the development of AI-powered data analysis, real-time defect detection algorithms, and the integration of emerging technologies such as LiDAR and thermal imaging. This review aims to provide valuable insights for researchers, engineers, and industry professionals working on advancing rail inspection technologies.

Digital twin-enhanced robotic system for remote diesel engine assembly defect inspection

Purpose This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork. Design/methodology/approach This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period. Findings Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers. Originality/value The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

Automated cardiac arrest DETECTion incorporated into a wristband: validation of a photoplethysmography algorithm in patients with spontaneous and induced cardiac arrest

Abstract Background Although survival from out-of-hospital cardiac arrest (OHCA) has improved, this does not apply to unwitnessed cases, as the arrest is often recognized too late. Automated cardiac arrest detection and alarming using a wristband is a promising technology to facilitate early assistance after cardiac arrest in a home setting. In the DETECT-1 study, a photoplethysmography(PPG) algorithm for circulatory arrest detection has been developed in patients with induced circulatory arrest. Purpose To assess the performance of the developed PPG algorithm for circulatory arrest detection in: 1) patients with spontaneous cardiac arrest; 2) subjects with induced circulatory arrest using blood pressure cuff inflation. Methods We selected patients with episodes of spontaneous cardiac arrest who participated in the DETECT-1 study and patients/healthy subjects with short-lasting induced circulatory arrest using blood pressure cuff inflations from the DETECT-1 and DETECT-2 studies. The DETECT-1 study included patients who underwent transcatheter aortic valve implantation (TAVI), subcutaneous ICD implantation, or ventricular tachycardia ablation. Included patients wore the PPG-wristband during the procedure. Continuous ECG and invasive blood pressure data were collected as reference standard. The DETECT-2 study included healthy volunteers who wore the PPG-wristband during physical exercise. Sensitivity for spontaneous and induced circulatory arrest and false positive alarms were assessed using the previously developed algorithm for circulatory arrest detection. All episodes were analyzed as individual events. Results A total of 39 individuals were included; nine patients with spontaneous cardiac arrests during TAVI and 20 subjects with 116 induced circulatory arrests. Of the patients with spontaneous cardiac arrest, two had ventricular fibrillation, five had pulseless electrical activity (figure), and two had asystole. Eight out of nine cardiac arrest events were detected by the circulatory arrest detection algorithm (sensitivity 89% [95% CI 51-99%]). Of the subjects with induced circulatory arrests, the PPG algorithm detected 114 induced circulatory arrests, yielding a sensitivity of 98% (95% CI 93-100%). In the entire group, there were no false positive alarms during 60 recording hours. Conclusions Automated cardiac arrest detection using a PPG-wristband is feasible with excellent sensitivity for both spontaneous and induced circulatory arrest. False positive alarms were absent in this controlled setting. Further study is warranted to assess algorithm performance in a larger sample of spontaneous cardiac arrests, and false positive alarms during daily life use. If available, automated cardiac arrest detection and alarming has the potential to markedly increase survival from OHCA in a home setting.Episode of spontaneous cardiac arrest

Automated cardiac arrest detection using a photoplethysmography wristband: algorithm performance in relation to sex, skin color type and arm hair density

Abstract Background While survival from witnessed out-of-hospital cardiac arrest (OHCA) has improved, this is not the case for unwitnessed cardiac arrest as the event goes unnoticed. Automated cardiac arrest detection and alerting through a wristband is considered a promising technique also facilitating early assistance for this large group. In the DETECT-1 study, a photoplethysmography(PPG) algorithm for circulatory arrest detection was developed in patients with induced circulatory arrest. However, patient characteristics, such as skin color and arm hair density, can influence the accuracy of the PPG signal. Thus, we performed further analyses to study the effect of patient characteristics on the algorithm’s performance. Purpose To assess the performance of the developed PPG algorithm for cardiac arrest detection in relation to sex, skin color type and arm hair density. Methods Patients who underwent short-lasting circulatory arrest induction during transcatheter aortic valve implantation, ventricular tachycardia ablation or subcutaneous ICD implantation wore a PPG wristband during the procedure. Continuous ECG and invasive blood pressure recordings served as reference standards. A PPG algorithm for circulatory arrest detection was developed. The algorithm’s performance was assessed in subgroups based on sex, skin color type (according to the Fitzpatrick scale), and arm hair density. The sensitivity for the detection of circulatory arrest and false positive alarms were compared between the different subgroups. Results In total, 151 patients were included in the current analysis. Analyzed data consisted of 159 hours of PPG data including 176 induced circulatory arrest events. The population consisted of 57 (38%) females and 94 (62%) males. The sensitivity for circulatory arrest detection was 100% in the female group vs. 98% in the male group (p=ns); false positive alarms occurred in 4 females and 9 males, resulting in a positive predictive value (PPV) of 94% and 92% (p=ns), respectively. Within the skin color subgroup analysis, 114 (75%) patients had skin color type I or II, and 37 (25%) patients had a skin color type III, IV, V or VI. There were no differences in sensitivity (p=ns) and PPV (p=ns) in the skin color subgroup (table 1). The population consisted of 106 (71%) patients with nil or sparse arm hair density, 43 (29%) patients with moderate or dense arm hair density, and 2 not reported. There were no differences in sensitivity (p=ns) and PPV (p=ns) within the arm hair density subgroup (table 1). Conclusions Automated cardiac arrest detection using a smartwatch is feasible and its accuracy is not affected by sex, skin color type or arm hair density in this study. Further study is needed to assess performance of the algorithm in a larger sample size and a more diverse study population. If proven to be effective, automated cardiac arrest detection has the potential to improve survival from OHCA.Sensitivity/PPV for arrest detection

Eyes on nature: Embedded vision cameras for terrestrial biodiversity monitoring

Abstract We need comprehensive information to manage and protect biodiversity in the face of global environmental challenges, and artificial intelligence is required to generate that information from vast amounts of biodiversity data. Currently, vision‐based monitoring methods are heterogenous; they poorly cover spatial and temporal dimensions, overly depend on humans, and are not reactive enough for adaptive management. To mitigate these issues, we present a portable, modular, affordable and low‐power device with embedded vision for biodiversity monitoring of a wide range of terrestrial taxa. Our camera uses interchangeable lenses to resolve barely visible and remote targets, as well as customisable algorithms for blob detection, region‐of‐interest classification and object detection to automatically identify them. We showcase our system in six use cases from ethology, landscape ecology, agronomy, pollination ecology, conservation biology and phenology disciplines. Using the same devices with different setups, we discovered bats feeding on durian tree flowers, monitored flying bats and their insect prey, identified nocturnal insect pests in paddy fields, detected bees visiting rapeseed crop flowers, triggered real‐time alerts for waterfowl and tracked flower phenology over months. We measured classification accuracies (i.e. F1‐scores) between 55% and 95% in our field surveys and used them to standardise observations over highly resolved time scales. Our cameras are amenable to situations where automated vision‐based monitoring is required off the grid, in natural and agricultural ecosystems, and in particular for quantifying species interactions. Embedded vision devices such as this will help addressing global biodiversity challenges and facilitate a technology‐aided agricultural systems transformation.

Data enhanced YOLOv8s algorithm for X-ray weld defect detection

Data enhanced YOLOv8s algorithm for X-ray weld defect detection

Artificial intelligence-enabled electrocardiogram for left ventricular diastolic dysfunction and long-term risk of new-onset atrial fibrillation

Abstract Background Heart failure (HF) and atrial fibrillation (AF) are intertwined, sharing common risk factors and influencing each other in a reciprocal manner. While left ventricular diastolic dysfunction (LVDD) stands as a hallmark of HF's hemodynamic profile, its direct correlation with heightened AF risk remains uncertain. Our recent validation of an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm for LVDD detection has opened new avenues. Yet, whether AI-ECG-detected LVDD correlates with long-term AF risk remains unexplored. Methods We performed a retrospective study among all patients with a comprehensive LVDD assessment from the test population of the AI-ECG LVDD study between September 2001 and June 2022. Patients with prior documented AF according to health records or previous ECG were excluded. Patients were classified by AI-ECG as normal diastolic function (DF), grade 1 (G1), grade 2 (G2), or grade 3 (G3) LVDD. We assessed the risk of AF across AI-ECG LVDD grade, with normal DF as reference, with competing risk models. Results Of 97,023 patients free of AF at baseline, 63,533 (65.4%), 10,776 (11.1%), 18,940 (19.5%), and 3,774 (3.9%) were classified as normal DF, G1-LVDD, G2-LVDD, and G3-LVDD, respectively. Worse AI-ECG LVDD was associated with older age, lower left ventricular ejection fraction (LVEF), and higher rates of HF, diabetes, hypertension, chronic pulmonary disease, renal failure, and cardiovascular disease (p-of-trend&amp;lt;0.001 for all). Over a median follow-up of 4.8 years (interquartile range 1.8-9.2 years), new-onset AF occurred in 6,795 (7.0%) patients. In multivariable survival analysis, adjusted to multiple risk factors including LVEF and clinical HF, G2 and G3 LVDD, representing patients with high filling pressures, were independently associated with a higher AF-risk [G2-LVDD: adjusted hazard ratio (aHR) 1.64 (95% confidence interval (95%CI) 1.52-1.73), G3-LVDD: aHR 2.24 (95%CI 2.02-2.48)]. Conclusion The AI-ECG LVDD algorithm independently predicts long-term AF risk, regardless of LVEF and clinical HF status, highlighting its potential for identifying high-risk populations and guiding proactive monitoring and management.

Performance of apple watch for detecting atrial fibrillation among a known atrial fibrillation population

Abstract Background Photoplethysmography (PPG) based consumer accessible devices with algorithms for irregular rhythm detection are gaining popularity as an opportunistic screening tool for atrial arrhythmias, and are FDA cleared for use in patients without a history of atrial fibrillation (AF). However, these devices are increasingly used for the management of AF, a purpose for which some algorithms are not intended. Purpose To compare the performance of the Apple Watch Irregular Rhythm Notification (IRN) with an implantable cardiac monitor (ICM) for the detection of atrial fibrillation (AF) in patients with known AF from the DEFINE AFib study. Methods Patients with known AF, a LINQ or LINQ II ICM, and an Apple Watch with PPG-based IRN activated were included in the analysis. ICM detected AF episodes served as the standard to which Apple Watch IRNs were compared. All AF episodes detected by the ICM device were manually adjudicated by two trained, independent reviewers with a third independent reviewer in situations of disagreement. Only AF episodes ≥60 minutes during a 75-minute window in which the Apple Watch was monitoring were included in accordance with the known Apple Watch algorithm detection parameters. To eliminate oversampling of ICM detected episodes compared to Apple Watch IRNs, 1,000 simulations of one randomly selected episode per subject was done to avoid single-subject bias. Results In total, 97 subjects were analyzed of which 42 had an AF episode ≥60 minutes. Applying the 75-minute window of Apple Watch monitoring to episodes ≥60 minutes, 20 subjects had qualifying, adjudicated ICM detected AF. These 20 subjects accounted for 481 qualifying AF episodes. Of the ICM detected episodes, 191 (40%) occurred while an Apple Watch was not being worn. In total, 11/20 (55%) subjects had an IRN generated by the Apple Watch. Adjudication of the ICM episodes resulted in 290 true positive AF episodes ≥60 minutes during a 75-minute window while the watch was actively monitoring, with 74 (26%) of those episodes triggering an IRN resulting in a per episode sensitivity of 24 ± 7% and PPV of 91 ± 4% (Figure 1A). The efficacy of Apple Watch to detect AF episodes of differing lengths was assessed for episodes between 1-6 hours, 6-12 hours, 12-24 hours, and &amp;gt; 24 hours with sensitivities of 19 ± 19%, 38 ± 8%, 32 ± 10%, and 31 ± 8%, respectively (Figure 1B). The Apple Watch generated an annualized false positive rate of 2.25 IRNs for every patient year of monitoring. At the subject level, Apple Watch had a sensitivity of 48%, specificity of 100%, PPV of 100%, and NPV of 71% (Figure 1A). Conclusions In patients with a history of AF, the Apple Watch IRN was most sensitive at the individual subject level while watch performance was lower by episode. The current IRN algorithm favors longer episode durations. The limited sensitivity, dependence on user wear-time, and reliance on long-durations of AF limit the use of Apple Watch for AF management.

Improved algorithm for fracture-dissolution pore detection in resistivity imaging logging based on dung beetle optimization

Abstract Resistivity imaging logging has become a direct and precise method for visualizing the structural complexities of reservoir fractures and dissolution pores. The current use of Otsu's thresholding for segmentation results in poor segmentation quality and significant noise. Accurate segmentation of sub-images containing fracture and dissolution pore targets is essential for automated structure identification and subsequent parameter calculation. This study leverages the rapid convergence and robust global optimization capabilities of the Dung Beetle Optimizer to develop enhanced image segmentation approaches. Specifically, it introduces a refined K-Means algorithm for multi-category image segmentation and an Otsu algorithm for multi-threshold image segmentation, both optimized by the Dung Beetle Optimizer. Compared to conventional binary segmentation algorithms, this new algorithm effectively isolates noise and extracts multi-category information. Using the segmented sub-images, this paper integrates mathematical morphology techniques to compute parameters such as area, perimeter, tortuosity length, and pore shape factor for identified targets. Additionally, Principal Component Analysis is used to derive recognition factors for fractures and dissolution pores. Applications show that this factor can identify matrix, fracture, and dissolution pore targets in complex background images. By combining parameter information of the target area, the method effectively removes false information in resistivity imaging and segments sub-images of fractures and dissolution pores, calculating fracture area ratio, dissolution pore area ratio, and total area ratio.

Experiments for the Assessment of the Probabilistic Multistage Algorithm for Damage Detection in Flat and Curved CFRP Panels

The Probabilistic Multistage (PM) algorithm was developed by authors for identifying and localizing damages in isotropic plates. More specifically, PM algorithm consists of a fully automated probabilistic damage imaging methodology based on ultrasonic guided wave propagation and a 5-sensor array working in a pitch-catch approach. The algorithm processes the gathered data in three steps to identify key features associated with damage. The aim of this work is to assess the effectiveness of the PM algorithm on more complex structures. Specifically, the study investigates three cases of study, made of Carbon Fiber Reinforced Plastic (CFRP) composite, characterized by different geometries and layups. The algorithm is initially tested on panels with artificial damage in the form of a Teflon disk located in a specific location between the middle laminae of the panels, which is used to replicate the effect of delamination. In order to expand the experimental dataset without incurring additional costs or waste, new damage conditions are simulated by adding masses on the upper surface of the panels. Each plate is investigated considering three different damage sizes and 16 different damage locations. The proposed algorithm successfully detects damages both within and outside the sensor network. The PM algorithm produces a clear damage positioning map and a positioning (probabilistic field) range for the identified damage. This information can be used to assist operators in conducting inspections more efficiently by focusing on the highlighted areas, which may potentially lead to reduced maintenance and repair expenses.

Tightly coupled stereo vision-inertial odometry based on point and line feature

Abstract To improve the accuracy and robustness of visual simultaneous localization and mapping (SLAM) in low-texture environments, this paper proposes a robust and fast stereo vision inertial SLAM pose estimation method that combines point and line features with an inertial measurement unit (IMU). The method tightly couples visual point and line features with IMU constraints, forming a least-squares problem through the error of each constraint term for nonlinear optimization. To address the issues of over-segmentation and time consumption in traditional line segment detection (LSD) algorithms, an improved LSD algorithm is adopted to accelerate line feature extraction. This approach merges nearby line segments based on spatial geometric relationships and filters out invalid segments, improving the time efficiency of the algorithm. Finally, experiments conducted in low-texture environments demonstrate that our algorithm achieves high localization accuracy and robustness.

Regional microbial content of fermented traditional and industrial East Mediterranean sausages from the islands of Cyprus and Mytilini

Objective: To characterize the microbial biodiversity of fermented sausages from the East Mediterranean islands of Cyprus and Mytilini, and to identify differences between the microbial diversity of traditionally and industrially produced Cypriot sausages. Method: The microbial diversity of thirty sausages from Cyprus and Mytilini (traditionally and industrially produced) was analyzed using high throughput sequencing (HTS) (amplicon sequencing) of 16S rRNA gene and ITS loci fragments. By applying microbial signature detection and machine learning algorithms, we identified key microbes that distinguish traditionally produced sausages from those produced industrially. Focusing on selected microbial taxa and using interaction network analysis, we identified associations among the sausages’ microbiota that may affect the shaping of the sausages’ microbial consortia. Results: Cypriot and Mytilini sausages indicated increased relative representation of Lactobacillus and Leuconostoc . Cypriot sausages were distinguished by the presence of the fungi Debaryomyces hansenii and Candida spp. , whereas Mytilini sausages by the bacteria Lactococcus and Streptococcus . Traditionally produced sausages from the Pitsilia region of Cyprus were differentiated by the presence of the species Lactobacillus helveticus , whereas industrially produced sausages were differentiated by the species Leuconostoc mesenteroides . Focusing mainly on Lactobacillus and Leuconostoc , negative associations with pathogenic bacteria, such as Salmonella , and spoilage fungi, such as Fusarium poae , were revealed. Conclusion: The present metataxonomic analysis provided insights into the microbial communities that characterize Cypriot and Mytilini sausages. The findings provide an indication that the microbial diversity might be applied as an additional marker of Cypriot sausages’ authenticity.