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Tectonic features on the Moon can reflect the state of stress during the formation of the structure, and sinuous rilles can provide further insight into the tectonic-thermal evolution of the Moon. Manual visual interpretation is the primary method for extracting these linear structures due to their complex morphology. However, extracting these features from the vast amount of lunar remote sensing data requires significant time and effort from researchers, especially for small-scale tectonic features, such as wrinkle ridges, lobate scarps, and high-relief ridges. In order to enhance the efficiency of linear structure detection, this paper conducts research on the automatic detection method of linear structures using sinuous rilles as an example case. In this paper, a multimodal semantic segmentation method, "Sinuous Rille Network (SR-Net)", for detecting sinuous rilles is proposed based on DeepLabv3+. This method combines advanced techniques such as ECA-ResNet and dynamic feature fusion. Compared to other networks, such as PSPNet, ResUNet, and DeepLabv3+, SR-Net demonstrates superior precision (95.20%) and recall (92.18%) on the multimodal sinuous rille test set. The trained SR-Net was applied in detecting lunar sinuous rilles within the range of 60°S to 60°N latitude. A new catalogue of sinuous rilles was generated based on the results of the detection process. The methodology proposed in this paper is not confined to the detection of sinuous rilles; with further improvements, it can be extended to the detection of other linear structures. [ABSTRACT FROM AUTHOR]

BACKGROUND: X-ray imaging plays an important role in security inspection. However, the objects are complex, which makes it difficult to automatically detect prohibited and restricted objects. OBJECTIVE: This study aims to develop and test a detection method based on a new image segmentation scheme to solve the problem of detecting prohibited and restricted objects from pseudo-color X-ray images with complex backgrounds. METHODS: The internal mechanism of the influence of different color spaces on image segmentation effect is explored, and the color space component Hi is studied. Furthermore, the mechanism of the new Hi component and the influence law of its adjustable coefficient are revealed. Additionally, a detection method based on Hi color space segmentation for pseudo-color X-ray images is proposed. The segmentation and detection methods are then tested on actual X-ray images. RESULTS: The results show that hue has the greatest influence on image segmentation effect of the pseudo-color X-ray images. For different pseudo-color X-ray images with complex backgrounds, applying the proposed new Hi color space segmentation method achieves overall accuracy of 0.974 and 1.0 in detecting the gun and knife, respectively. CONCLUSION: The new X-ray image detection method based on the Hi color space segmentation proposed in this paper enables to better solve the complex background problem including object overlap and adhesion and thus more effectively meet the requirements of actual security inspection. [ABSTRACT FROM AUTHOR]

In this paper, we present the implementation of a system that identifies the modulation of complex radio signals. This is realized using an artificial intelligence model developed, trained, and integrated with Microsoft Azure cloud. We consider that cloud-based platforms offer enough flexibility and processing power to use them instead of conventional computers for signal processing based on artificial intelligence. We tested the implementation using a software-defined radio platform developed in GNU Radio that generates and receives real modulated signals. This process ensures that the solution proposed is viable to be used in real signal processing systems. The results obtained show that for certain modulation types, the identification is performed with a high degree of success. The use of a cloud-based platform allows quick access to the system. The user is able to identify the signal modulation using only a laptop that has access to the internet. [ABSTRACT FROM AUTHOR]

Addressing the persistent delay problem in traditional network security antivirus protection systems, this paper introduces an innovative approach utilizing virtual reality (VR) technology. The primary objective is to significantly reduce detection delays and enhance the efficiency of network security measures. An enhanced decision-making algorithm is proposed to identify relevant features associated with network security. These features are then weighted and optimized to improve the overall detection process. An injection list is generated through web crawling techniques to strengthen security measures. A virtual protection block is also developed to serve as a barrier against potential threats. The proposed method claims a detection delay of only 75.33 milliseconds, significantly outperforming two traditional methods that recorded 290.11 milliseconds and 337.30 milliseconds, respectively. This substantial decrease in detection delay emphasizes the effectiveness of automatic detection within the context of VR technology. Practical implementation and empirical evidence further validate the success of this approach. The automatic detection of network security vulnerabilities within the VR technology framework is efficient and exhibits considerable progress. As such, this research offers a promising solution to the delay problem in network security antivirus protection. Embracing VR technology achieves shorter detection delays, ultimately improving the security posture of network systems. [ABSTRACT FROM AUTHOR]

Breast cancer was the most diagnosed cancer in 2020. Several thousand women continue to die from this disease. A better and earlier diagnosis may be of great importance to improving prognosis, and that is where Artificial Intelligence (AI) could play a major role. This paper surveys different applications of AI in Breast Imaging. First, traditional Machine Learning and Deep Learning methods that can detect the presence of a lesion and classify it into benign/malignant—which could be important to diminish reading time and improve accuracy—are analyzed. Following that, researches in the field of breast cancer risk prediction using mammograms—which may be able to allow screening programs customization both on periodicity and modality—are reviewed. The subsequent section analyzes different applications of augmentation techniques that allow to surpass the lack of labeled data. Finally, still concerning the absence of big datasets with labeled data, the last section studies Self-Supervised learning, where AI models are able to learn a representation of the input by themselves. This review gives a general view of what AI can give in the field of Breast Imaging, discussing not only its potential but also the challenges that still have to be overcome. [ABSTRACT FROM AUTHOR]

In today's globalized economy, various industries are promoting product transformation and upgrading. The textile industry is also facing a harsh international situation and fierce market competition. While constantly promoting the upgrade of automation equipment, the original quality control mode relying on manual testing has been unable to meet the modern production requirements and the market demand for product quality. This paper investigates the product inspection and quality control in the textile industry at home and abroad, and puts forward the application of machine vision technology in textile automated inspection and quality control, so As to strengthen the product quality control system and improve the product's physical quality. By studying the composition of machine vision detection technology, this paper studies the two core technologies of image acquisition and image processing in machine vision, summarizes and analyzes the common defects of textile Products, and proves that the common defects can be detected and repaired in time by machine vision detection method. [ABSTRACT FROM AUTHOR]

Bladder urothelial carcinoma is the most common malignant tumor disease in urinary system, and its incidence rate ranks ninth in the world. In recent years, the continuous development of hyperspectral imaging technology has provided a new tool for the auxiliary diagnosis of bladder cancer. In this study, based on microscopic hyperspectral data, an automatic detection algorithm of bladder tumor cells combining color features and shape features is proposed. Support vector machine (SVM) is used to build classification models and compare the classification performance of spectral feature, spectral and shape fusion feature, and the fusion feature proposed in this paper on the same classifier. The results show that the sensitivity, specificity, and accuracy of our classification algorithm based on shape and color fusion features are 0.952, 0.897, and 0.920, respectively, which are better than the classification algorithm only using spectral features. Therefore, this study can effectively extract the cell features of bladder urothelial carcinoma smear, thus achieving automatic, real-time, and noninvasive detection of bladder tumor cells, and then helping doctors improve the efficiency of pathological diagnosis of bladder urothelial cancer, and providing a reliable basis for doctors to choose treatment plans and judge the prognosis of the disease. [ABSTRACT FROM AUTHOR]

The control process of welding requires manual operations, and this consumes time. Robot classifier can help by automatic detection of welding defect and by taking rapid actions to correct in situ the defect. This paper presents a convolutional neural network (CNN) model developed to classify the welding defect like splash, twisty, overlap, edge and copper adhesion based on machine vision. Using a resistance spot welding (RSW) dataset the CNN model was trained and evaluated to achieve the best performance. The batch size was varied to quantify its effect on the precision of the model. The model can predict the type of welding surface by confidence of 99.86%. [ABSTRACT FROM AUTHOR]

Obstructive Sleep Apnea (OSA) is a common sleep-breathing disorder that highly reduces the quality of human life. The most powerful method for the detection and classification of sleep apnea is the Polysomnogram. However, this method is time-consuming and cost-inefficient. Therefore, several methods focus on using electrocardiogram (ECG) signals to detect sleep apnea. This paper proposed a novel automated approach to detect and classify apneic events from single-lead ECG signals. Wavelet Scattering Transformation (WST) was applied to the ECG signals to decompose the signal into smaller segments. Then, a set of features, including higher-order statistics and entropy-based features, was extracted from the WST coefficients to formulate a search space. The obtained features were fed to a random forest classifier to classify the ECG segments. The experiment was validated using the 10-fold and hold-out cross-validation methods, which resulted in an accuracy of 91.65 % and 90.35 % , respectively. The findings were compared with different classifiers to show the significance of the proposed approach. The proposed approach achieved better performance measures than most of the existing methodologies. [ABSTRACT FROM AUTHOR]

The application of Airborne Laser Scanning methods (ALS) in archaeology has opened a new chapter for prospective research in forest areas. Previously, these were under-explored due to the lack of effective methods for recording archaeological remains in forests. With LiDAR data, researchers can now use available analytical tools. Also, they have developed new tools by creating so-called image derivatives, which have expanded the possibilities of microrelief interpretation for the identification of archaeological objects. This paper presents the results of DTM analyses and field investigations that were carried out at the Osie archaeological site (Poland). The site was discovered thanks to DTM analyses, which made it possible to identify an extensive settlement complex from the Late Roman Period. The object of our research was to identify remains connected with modern forest management in the form of traces of ploughing. The aim was to see how spatial data analyses, by means of which we identify ploughing traces, correlate with the results of archaeological surveys. [ABSTRACT FROM AUTHOR]

In recent years, with the widespread popularity of the Internet, social media has become an indispensable part of people’s lives. People regard online social media as an essential tool for interaction and communication. Due to the convenience of data acquisition from social media, mental health research on social media has received a lot of attention. The early detection of psychological disorder based on social media can help prevent further deterioration in at-risk people. In this paper, depression detection is performed based on non-verbal (acoustics and visual) behaviors of vlog. We propose a time-aware attention-based multimodal fusion depression detection network (TAMFN) to mine and fuse the multimodal features fully. The TAMFN model is constructed by a temporal convolutional network with the global information (GTCN), an intermodal feature extraction (IFE) module, and a time-aware attention multimodal fusion (TAMF) module. The GTCN model captures more temporal behavior information by combining local and global temporal information. The IFE module extracts the early interaction information between modalities to enrich the feature representation. The TAMF module guides the multimodal feature fusion by mining the temporal importance between different modalities. Our experiments are carried out on D-Vlog dataset, and the comparative experimental results report that our proposed TAMFN outperforms all benchmark models, indicating the effectiveness of the proposed TAMFN model. [ABSTRACT FROM AUTHOR]

This paper studies the vibration detection technique based on the piezoelectric cantilever structure, and analyzes the relationship between the resonance frequency and the structure parameters. Although the output of the electrical signal is too small, it can be used to detect vibration during resonance with the maximum output voltage. A vibration testing device is designed based on piezoelectric cantilever structure, because in the vibration environment of the same frequency the vibration detection technology can obtain larger electrical signals. It can realize the automatic detection of real-time vibration state, and the vibration energy can charge the power supply through the energy storage circuit. Through the experimental method to verify relationship between the output voltage of piezoelectric cantilever beam, mass block, piezoelectric cantilever structure parameters and frequency, the experimental value of resonance frequency is very close with theoretical calculation value. The experimental resonant frequency of the designed piezoelectric cantilever beam is 16.5 Hz, which is slightly lower than the theoretical value of resonant frequency of 17.6 Hz, and the peak value of the piezoelectric output open-circuit voltage is 7.1 V. [ABSTRACT FROM AUTHOR]

The approach of dynamic tracking and counting for obscured citrus based on machine vision is a key element to realizing orchard yield measurement and smart orchard production management. In this study, focusing on citrus images and dynamic videos in a modern planting mode, we proposed the citrus detection and dynamic counting method based on the lightweight target detection network YOLOv7-tiny, Kalman filter tracking, and the Hungarian algorithm. The YOLOv7-tiny model was used to detect the citrus in the video, and the Kalman filter algorithm was used for the predictive tracking of the detected fruits. In order to realize optimal matching, the Hungarian algorithm was improved in terms of Euclidean distance and overlap matching and the two stages life filter was added; finally, the drawing lines counting strategy was proposed. ln this study, the detection performance, tracking performance, and counting effect of the algorithms are tested respectively; the results showed that the average detection accuracy of the YOLOv7-tiny model reached 97.23%, the detection accuracy in orchard dynamic detection reached 95.12%, the multi-target tracking accuracy and the precision of the improved dynamic counting algorithm reached 67.14% and 74.65% respectively, which were higher than those of the pre-improvement algorithm, and the average counting accuracy of the improved algorithm reached 81.02%. The method was proposed to effectively help fruit farmers grasp the number of citruses and provide a technical reference for the study of yield measurement in modernized citrus orchards and a scientific decision-making basis for the intelligent management of orchards. [ABSTRACT FROM AUTHOR]

This paper proposes the implementation of and experimentation with GPR for real-time automatic detection of buried IEDs. GPR, consisting of hardware and software, was implemented. A UWB antenna was designed and implemented, particularly for the operation of the GPR. The experiments were conducted in order to demonstrate the real-time automatic detection of buried IEDs using GPR with an R-CNN algorithm. In the experiments, the GPR was mounted on a pickup truck and a maintenance train in order to find the IEDs buried under a road and a railway, respectively. B-scan images were collected using the implemented GPR. R-CNN-based detection for the hyperbolic pattern, which indicates the buried IED, was performed along with pre-processing, for example, using zero offset removal, and background removal and filtering. Experimental results in terms of detecting the hyperbolic pattern in B-scan images were shown and verified that the proposed GPR system is superior to the conventional one using region analysis processing-based detection. Results also showed that pre-processing is required in order to improve and/or clean the hyperbolic pattern before detection. The GPR can automatically detect IEDs buried under roads and railways in real time by detecting the hyperbolic pattern appearing in the collected B-scan image. [ABSTRACT FROM AUTHOR]

The digital optical fiber repeater (DOFR) is an important infrastructure in the LTE networks, which solve the problem of poor regional signal quality. Various types of conventional measurement data from the LTE network cannot indicate whether a working DOFR is present in the cell. Currently, the detection of DOFRs relies solely on maintenance engineers for field detection. Manual detection methods are not timely or efficient, because of the large number and wide geographical distribution of DOFRs. Implementing automatic detection of DOFR can reduce the maintenance cost for mobile network operators. We treat the DOFR detection problem as a classification problem and employ a deep convolutional neural network (DCNN) to tackle it. The measurement report (MR) we used in this paper are tabular data, which is not an ideal input for DCNN. We propose a novel MR representation method that takes the overall MR data of a cell as a sample rather than a single record in the table, and represents the MR data as a pseudo-image matrix (PIM). The PIM will be used as the input for training DCNN, and the trained DCNN will be used to perform DOFR detection tasks. We conducted a series of experiments on real MR data, and the classification accuracy can achieve 93%. The proposed AI-based method can effectively detect the DOFR in a cell. [ABSTRACT FROM AUTHOR]

The Loess Plateau is an ecologically fragile area in China; furthermore, loess landslides are typical forms of geological disasters, which severely limit the sustainable development of the local societies and the economy. Studying the automatic detection of landslides can facilitate disaster prevention and mitigation in the Loess Plateau, and help realize the climate action goal (SDG 13) of the United Nations Sustainable Development Goals (SDGs). This paper takes typical loess areas in China as the research object, and establishes a historical loess landslide sample database based on Google Earth (GEE) image data, with a total of 1451 loess landslides. The automatic detection of loess landslides is implemented by improving the You Only Look Once X (YOLOX) algorithm. The results show that the average accuracy of landslide detection in this method is 95.43%, and the accuracy rate is 96.32%, which effectively combines the earth's big data to realize the automatic detection of loess landslides. The research results provide technical support for the promotion of disaster prevention and mitigation in China's loess regions, the realization of sustainable development goals, and the improvement of natural disaster prevention–resistance–reduction systems. [ABSTRACT FROM AUTHOR]

The vehicles have become an absolute necessity in our daily life and with the rapid growth in population, the number of vehicles on the road has also increased. This in turn increases the number of road accidents causing a high loss of lives. One of the main reasons for more than half of the deaths due to accidents is the delay in receiving medical assistance on time. If immediate medical assistance is provided to the accident victims, then their chances of survival may increase. Nowadays, manufacturing companies are designing many accident prevention systems aiming to the reduction of the accidents or lessening its impact. But they still lack the facility of communicating with the emergency services if the vehicle is met with an accident. This paper proposes FlashRescue, a system that addresses these issues by equipping the vehicle with the technology of automatic accident detection and immediate notification to the emergency care services. It is developed using a camera, a GPS receiver, MPU 6050 accelerometer-gyroscope module all integrated with the Raspberry Pi. The system detects the accident using the sensors like accelerometer and gyroscope, finds total number of passengers, gets the location of the accident spot and informs the hospital and police station available nearest to the site of the accident using Google APIs and Twilio APIs. A testbed implementation and experimentation of FlasRescue shows that the system works without human intervention, is user friendly, compact and cost-efficient. [ABSTRACT FROM AUTHOR]

Power grid dispatching is among the forefront issues in the power industry for it can highly influence the efficiency of electricity-related industries. At present, power grid dispatching is usually managed manually, which is quite time-consuming with the continuous growth of scale and complexity of electric power systems. To mitigate the time cost of power grid dispatching, an automatic power grid dispatching system is proposed. The system contains devices configuration detection, flexible and automatic device detection and vertical encryption to ensure the efficiency, adaptability and security of power gird dispatching. In this paper, modeling and algorithm details of the automatic power grid dispatching system are presented, and the effectiveness and efficiency of the proposed system was evaluated with power grid data in Kunming, China. [ABSTRACT FROM AUTHOR]

To address the complexity and low detection accuracy issues in the intrinsic frequency detection process of fiber optic gyroscopes, a highly precise automatic measurement method based on optimal projection approximation is proposed. Building upon the digital closed-loop fiber optic gyroscope hardware circuit, this method generates a frequency-adjustable square wave with a 25% duty cycle through field-programmable gate array (FPGA) fractional division. Subsequently, it calculates the difference between the high and low levels of the sign bits of the modulated optical intensity signal sampled by the analog-to-digital converter (ADC). This yields an error signal related to the modulation frequency and intrinsic frequency. The modulation frequency is then approximated using the nearest projection of this error signal, thereby achieving automatic detection of the fiber optic gyroscope's intrinsic frequency. The experimental results indicate that this method, requiring no external auxiliary equipment, enables rapid and highly accurate measurement of the intrinsic frequency of the fiber optic gyroscope. The measurement of intrinsic frequency takes approximately 300 milliseconds, achieving a measurement accuracy of up to 0.001 Hz. [ABSTRACT FROM AUTHOR]

The running band on the rail surface serves as a crucial indicator of the wheel-rail contact relationship, making quantitative detection of the running band vital for railroad inspection and maintenance. However, current rail running band detection methods remain manual, and leveraging state-of-the-art deep learning technology can significantly enhance detection efficiency and minimize the reliance on human labor. This paper presents an improved version of ShuttleNet, called ShuttleNetM2T, for rail surface running band detection, emphasizing high stability. Efficient self-attention in ShuttleNetM2T captures global information, maintaining efficiency and rectifying issues like misrecognition and incomplete edges. Experimental results show ShuttleNetM2T achieves 0.9823 average F-measure and 0.9653 average Intersection-Over-Union on test images. For automatic running band width detection, we propose a pixel-to-physical distance conversion-based calculation, yielding just 0.625% relative error. Overall, the findings in this paper facilitate accurate detection and automatic calculation of the running band width, with promising applications in intelligent rail surface detection. • A stable ShuttleNetM2T model for detecting running bands on track surfaces has been proposed. • The ShuttleNetM2T can fully recognize the running bands' edges on the surface of rails. • A precise automatic detection algorithm for running band width has been developed. [ABSTRACT FROM AUTHOR]

Sleep spindles are thought to be related to some sleep diseases and play an important role in memory consolidation. They were traditionally identified by physiology experts based on rules and recently detected by automatic algorithms. However, many automatic approaches were validated on the different electroencephalogram (EEG) using various assessment methods, making it difficult to appraised a method objectively and fairly. In this paper, we proposed a sliding window-based probability estimation (SWPE) method for sleep spindle detection. We performed a continuous wavelet transform with Mexican hat wavelet function, following by a sliding window to find out the candidate spindle points corresponding to the large wavelet coefficients at the frequencies of spindles and estimated their probabilities. To enhance the results, we used the envelope of the rectified signal to reject some false sleep spindle candidates. This was an enhanced method and we called it SWPE-E in this paper. Finally, we compared our approaches with four approaches on the same public available EEG database, and the result showed the significative improvement of our proposed approaches. [ABSTRACT FROM AUTHOR]

Tectonic features on the Moon can reflect the state of stress during the formation of the structure, and sinuous rilles can provide further insight into the tectonic-thermal evolution of the Moon. Manual visual interpretation is the primary method for extracting these linear structures due to their complex morphology. However, extracting these features from the vast amount of lunar remote sensing data requires significant time and effort from researchers, especially for small-scale tectonic features, such as wrinkle ridges, lobate scarps, and high-relief ridges. In order to enhance the efficiency of linear structure detection, this paper conducts research on the automatic detection method of linear structures using sinuous rilles as an example case. In this paper, a multimodal semantic segmentation method, “Sinuous Rille Network (SR-Net)”, for detecting sinuous rilles is proposed based on DeepLabv3+. This method combines advanced techniques such as ECA-ResNet and dynamic feature fusion. Compared to other networks, such as PSPNet, ResUNet, and DeepLabv3+, SR-Net demonstrates superior precision (95.20%) and recall (92.18%) on the multimodal sinuous rille test set. The trained SR-Net was applied in detecting lunar sinuous rilles within the range of 60°S to 60°N latitude. A new catalogue of sinuous rilles was generated based on the results of the detection process. The methodology proposed in this paper is not confined to the detection of sinuous rilles; with further improvements, it can be extended to the detection of other linear structures.

During the past decades, radar sounder (RS) instruments have been effectively used to detect subglacial lakes (SLs). SLs appear as flat, smooth, and bright reflectors in RS radargrams. The visual interpretation has been the main approach to SL detection in radargrams. However, this approach is subjective and inappropriate for processing large amounts of radargrams. While the analysis of RS data for understanding the subglacial hydrology has recently received increased attention, the literature on the development of automatic methods specifically designed for SL detection is still limited. In order to fill this gap, in this paper, we propose a novel automatic technique for SL detection. The technique is made up of two steps: 1) feature extraction and 2) automatic detection. In the first step, we define and extract three families of features for discriminating between the lake and nonlake radar reflections. The features model locally the basal topography, the shape of the basal reflected waveforms, and the statistical properties of the basal signal. In the second step, we provide the features as input to a support vector machine classifier to perform the automatic SL detection. The proposed technique has been applied to radargrams acquired over two large regions in East Antarctica and Siple Coast. The obtained results, which are validated both quantitatively and qualitatively, confirm the robustness of the features and their capabilities to effectively characterize SLs. Moreover, they prove the potentiality of the method to process large amounts of radargrams and update the current SL inventory. [ABSTRACT FROM AUTHOR]

The identification of animal fibers has always been an important topic in the textile field. Therefore, it is very important to identify fiber types accurately and quickly for textile fiber quality inspection. Although some traditional image-based fiber recognition techniques can identify one to four fibers in an image, the methods usually need to segment the fibers in the image into a single fiber image, which is a time-consuming process. Therefore, the FiberNet model is proposed to automate this task. Different from other methods, this study used the case segmentation model for the first time to detect multiple fibers and their species. FiberNet was developed based on BlendMask, introducing a denoising module and an attention module to improve the final detection accuracy and speed. Therefore, the model performance and identification accuracy of FiberNet are higher than those of the fiber detection algorithm, with identification accuracy of up to 99.51%. [ABSTRACT FROM AUTHOR]

Shelf-out-of-stock is one of the leading motivations of technology innovation in the shelf of the future. The Shelf Detector project described in this paper aims to solve the problem of data knowledge in the shelf-out-of-stock problem. This paper is mainly focused on the information layer of the system and main novelties illustrated in this work are in the information field demonstrating the huge number of insights that can be derived from the use of such a tool able to gather data in real time from the store. The tool presented is the first being installed for a long time in a high number of stores and products, demonstrating the ability to gather data and extract interesting insights. This paper aims to demonstrate the feasibility and the scalability of our system in providing a high number of data and interesting insights for store and marketing teams. The cloud based architecture developed and tested in this project is a key feature of our system together with the ability to collect data from a distributed sensor network. [ABSTRACT FROM AUTHOR]

Digital image processing is turning out to be increasingly more significant in the health care field used to diagnose skin cancer. The death rate is increasing by 1% every year due to skin cancer. One of the major causes of casualties due to this cancer is the non-predictability at the early stages. This paper will help in future research work when it comes to early detection of a tumor. In this work, the proposed model comprises of two important steps which are preprocessing and segmentation. In a pre-processing case, unwanted artifacts like hair, illumination, or many other artifacts are reduced by an enhanced technique using threshold and morphological operations and In the second step, segmentation of skin lesion using k-mean segmentation algorithm with optimized firefly algorithm (FFA) technique is used to achieve high accuracy. Input sample images are taken from the International skin imaging collaboration (ISIC) archive dataset and dermatology service of Hospital Pedro Hispano (PH2) dataset which are available online. The results of the proposed method are measured in terms of different parameters. It provides an accuracy of 99.1% and 98.9% using ISIC and PH2 datasets and shows better performance than existing techniques such as K-Mean and K-Mean with Particle Swarm Optimization (PSO). The performance of this research work is, in fact, quite promising. [ABSTRACT FROM AUTHOR]

The paper describes a software package developed for automated monitoring of seismic activity of the Khibiny mountain massif. The general structure of the software package is described, and the applied methods and algorithms are considered in detail. In particular, the algorithm for locating the source of a seismic event via grid searches, which are specified as sets of circles and are categorized during execution of the algorithm, is described in detail. The proposed location methods are combined into several packages—scenarios to optimize the time spent by a geophysicist in routine interactive processing of a large dataset. Because the described software package was created to process data from seismic networks located directly in the mining area, much attention is paid to increasing the reliability of detecting target signals in a high-noise environment and the accuracy in determining the arrival times of body waves. To solve these problems, the system implements a new approach to locating close events, based on the idea of a holistic perception of a signal record, which is very close to the concept of human perception of objects. The algorithms and methods for detecting and locating seismic event generation sites implemented in the software package can significantly increase the reliability of the solutions obtained and increase the accuracy and efficiency of the final interactive processing of seismic signal records. [ABSTRACT FROM AUTHOR]

In the shipbuilding industry, the non-destructive testing for welding quality inspection is mainly used for the permanent storage of the testing results and the radio-graphic testing which can visually inspect the interior of the welded part. Experts are required to properly detect the test results and it takes a lot of time and cost to manually Interpret the radio-graphic testing image of the structure over 500 blocks. The algorithms that automatically interpret the existing radio-graphic testing images to extract features through image pre-processing and classify the defects using neural networks, and only partial automation is performed. In order to implement the feature extraction and classification in one algorithm and to implement the overall automation, this paper proposes a method of automatically detecting welding defect using Faster R-CNN which is a deep learning basis. We analyzed the data to learn algorithms and compared the performance improvements using data augmentation method to artificially increase the limited data. In order to appropriately extract the features of the radio-graphic testing image, two internal feature extractors of Faster R-CNN were selected, compared, and performance evaluation was performed. [ABSTRACT FROM AUTHOR]

In order to solve the problem of automatic detection of artificial objects in high resolution remote sensing images, a method for detection of artificial areas in high resolution remote sensing images based on multi-scale and multi feature fusion is proposed. Firstly, the geometric features such as corner, straight line and right angle are extracted from the original resolution, and the pseudo corner points, pseudo linear features and pseudo orthogonal angles are filtered out by the self-constraint and mutual restraint between them. Then the radiation intensity map of the image with high geometric characteristics is obtained by the linear inverse distance weighted method. Secondly, the original image is reduced to multiple scales and the visual saliency image of each scale is obtained by adaptive weighting of the orthogonal saliency, the local brightness and contrast which are calculated at the corresponding scale. Then the final visual saliency image is obtained by fusing all scales' visual saliency images. Thirdly, the visual saliency images of artificial areas based on multi scales and multi features are obtained by fusing the geometric feature energy intensity map and visual saliency image obtained in previous decision level. Finally, the artificial areas can be segmented based on the method called OTSU. Experiments show that the method in this paper not only can detect large artificial areas such as urban city, residential district, but also detect the single family house in the countryside correctly. The detection rate of artificial areas reached 92%. [ABSTRACT FROM AUTHOR]

One impeding factor for determining the correct number of solar filaments in H- $$\upalpha $$ solar images is its broken appearance, which, in turn, affects the accuracy of tracking it and extracting its attributes accurately. By integrating image processing (IP) and artificial intelligence techniques, these fragmented threads have been more effectively merged. In this paper, different IP methods are utilized to extract the filament attributes, where these attributes act as inputs to a neural network to obtain a 92% true-positive rate. The method introduced in this work is fully automated and not threshold-based-unlike in previous works. [ABSTRACT FROM AUTHOR]

Glaucoma is a leading cause of irreversible vision impairment globally, and cases are continuously rising worldwide. Early detection is crucial, allowing timely intervention that can prevent further visual field loss. To detect glaucoma an examination of the optic nerve head via fundus imaging can be performed, at the center of which is the assessment of the optic cup and disc boundaries. Fundus imaging is noninvasive and low-cost; however, image examination relies on subjective, time-consuming, and costly expert assessments. A timely question to ask is: "Can artificial intelligence mimic glaucoma assessments made by experts?" Specifically, can artificial intelligence automatically find the boundaries of the optic cup and disc (providing a so-called segmented fundus image) and then use the segmented image to identify glaucoma with high accuracy? We conducted a comprehensive review on artificial intelligence-enabled glaucoma detection frameworks that produce and use segmented fundus images and summarized the advantages and disadvantages of such frameworks. We identified 36 relevant papers from 2011 to 2021 and 2 main approaches: 1) logical rule-based frameworks, based on a set of rules; and 2) machine learning/statistical modeling-based frameworks. We critically evaluated the state-of-art of the 2 approaches, identified gaps in the literature and pointed at areas for future research. [ABSTRACT FROM AUTHOR]

Accurate and timely detection of river change attaches importance to regional ecological security. Since river change is a complex and dynamic process, there are difficulties to describe it properly. Inspired by geographic events research, river pattern events are innovatively proposed to quantify river change process, combining multi-dimensional information thoroughly. First, inter- and intra- events are defined by comparing typologies changes of river patterns. Afterwards, a river pattern events model (RPEM), integrating "Event types-Spatial locations-River patterns-Morphological features-Hydrological attributes", was proposed to maximally characterize river change process. River pattern events were detected using the object increment difference (OID), and were interpreted as the semantic information in realistic rivers. Experimental results in the Lower Yellow River (LYR) demonstrated that the proposed method achieves good performance in assessing the morphological and hydrological change process. River pattern events are expected to support rapid detection and dynamic analysis of regional and global river change. [ABSTRACT FROM AUTHOR]

In order to prevent abnormal communication packets from spreading in the Internet, an automatic abnormal communication packet detection system is designed to detect abnormal communication packets accurately. The Internet uses the Web server as the carrier to transmit communication packets, and the data packet capture module adopts WinPcap technology to obtain effective data packets. In the protocol parsing module, the decoding work of TCP/IP protocol, CMP protocol and UDP protocol is completed, and the decoded data is input into the abnormal communication packets detection model of electronic equipment based on SVM. The SVM model seeks the optimal hyperplane to divide the abnormal communication packets of electronic equipment into abnormal and normal packets, and completes the automatic detection of abnormal communication packets of electronic equipment according to the results. Experimental results show that the false positive rate of abnormal communication packets is less than 3%. Compared with the same type of system, the system has the advantages of high receiving capacity and high efficiency. [ABSTRACT FROM AUTHOR]

Automatic detection of kitchen waste is of great significance, which provides support for its subsequent full quantitative consumption and harmless treatment. In addition, manual sorting of kitchen waste is inefficient and toxic waste is harmful to human health, making automatic detection of kitchen waste technology crucial. Automatic detection of kitchen waste in complex scenes faces the challenge of diversity of category outlines and uneven distribution. In this paper, we propose a detector based on statistical adaptive modeling(namely SA-Det) for the automatic detection of kitchen waste in complex scenes. Firstly, to solve the issue of diversity of category outlines, we propose a category statistics adaptive (CSA) module. The CSA module constructs dynamic thresholds for each instance to accurately assign positive and negative samples by fusing category statistics and instance shape information, thereby improving detection performance. Moreover, to solve the issue of uneven distribution, we propose a distribution adaptive (DA) module, which dynamically adjusts the loss weights by adaptively sensing the number of labels during training process. Extensive experiments on our constructed kitchen waste dataset (KWD) demonstrate that SA-Det consistently and significantly improves the performance of existing state-of-the-art methods (e.g., Rotated_RetinaNet (Lin et al., 2017) and RoI_Transformer (Ding et al., 2019)) by around 2% to 3.5%. • We propose a detector based on statistical adaptive modeling (namely SA-Det) for the automatic detection of kitchen waste in complex scenes. Extensive experiments on our constructed kitchen waste dataset (KWD) demonstrate that SA-Det consistently and significantly improves the performance of existing state-of-the-art methods by around 2% to 3.5%. • To solve the issue of diversity of category outlines, we design a category statistical adaptive (CSA) module by fusing the statistical characteristics of each category and instance shape information, which provides a dynamic threshold for each target instance to accurately allocate positive and negative samples and improve detection precision. • According to the distribution difference faced by kitchen waste in detection, we propose a distribution adaptive (DA) module, which dynamically allocates loss weights by adaptively sensing the number of targets during the training process, thereby effectively alleviating the issue of uneven distribution of kitchen waste. [ABSTRACT FROM AUTHOR]

The ionospheric quasi-periodic wave is a type of typical and common electromagnetic wave phenomenon occurring in extremely low-frequency (ELF) and very low-frequency ranges (VLF). These emissions propagate in a distinct whistler-wave mode, with varying periodic modulations of the wave intensity over time scales from several seconds to a few minutes. We developed an automatic detection model for the QP waves in the ELF band recorded by the China Seismo-Electromagnetic Satellite. Based on the 827 QP wave events, which were collected through visual screening from the electromagnetic field observations, an automatic detection model based on the Transformer architecture was built. This model, comprising 34.27 million parameters, was trained and evaluated. It achieved mean average precision of 92.3% on the validation dataset, operating at a frame rate of 39.3 frames per second. Notably, after incorporating the proton cyclotron frequency constraint, the model displayed promising performance. Its lightweight design facilitates easy deployment on satellite equipment, significantly enhancing the feasibility of on-board detection. [ABSTRACT FROM AUTHOR]

Background/Objectives: The aim of this study was to assess the diagnostic accuracy of the AI-driven platform Diagnocat for evaluating endodontic treatment outcomes using cone beam computed tomography (CBCT) images. Methods: A total of 55 consecutive patients (15 males and 40 females, aged 12–70 years) referred for CBCT imaging were included. CBCT images were analyzed using Diagnocat's AI platform, which assessed parameters such as the probability of filling, adequate obturation, adequate density, overfilling, voids in filling, short filling, and root canal number. The images were also evaluated by two experienced human readers. Diagnostic accuracy metrics (accuracy, precision, recall, and F1 score) were assessed and compared to the readers' consensus, which served as the reference standard. Results: The AI platform demonstrated high diagnostic accuracy for most parameters, with perfect scores for the probability of filling (accuracy, precision, recall, F1 = 100%). Adequate obturation showed moderate performance (accuracy = 84.1%, precision = 66.7%, recall = 92.3%, and F1 = 77.4%). Adequate density (accuracy = 95.5%, precision, recall, and F1 = 97.2%), overfilling (accuracy = 95.5%, precision = 86.7%, recall = 100%, and F1 = 92.9%), and short fillings (accuracy = 95.5%, precision = 100%, recall = 86.7%, and F1 = 92.9%) also exhibited strong performance. The performance of AI for voids in filling detection (accuracy = 88.6%, precision = 88.9%, recall = 66.7%, and F1 = 76.2%) highlighted areas for improvement. Conclusions: The AI platform Diagnocat showed high diagnostic accuracy in evaluating endodontic treatment outcomes using CBCT images, indicating its potential as a valuable tool in dental radiology. [ABSTRACT FROM AUTHOR]

Epilepsy, as a serious neurological disorder, can be detected by analyzing the brain signals produced by neurons. Electroencephalogram (EEG) signals are the most important data source for monitoring these brain signals. However, these complex, noisy, nonlinear and nonstationary signals make detecting seizures become a challenging task. Feature-based seizure detection algorithms have become a dominant approach for automatic seizure detection. This study presents an algorithm for automatic seizure detection based on novel features with clinical and statistical significance. Our algorithms achieved the best results on two benchmark datasets, outperforming traditional feature-based methods and state-of-the-art deep learning algorithms. Accuracy exceeded 99.99% on both benchmark public datasets, with the 100% correct detection of all seizures on the second one. Due to the interpretability and robustness of our algorithm, combined with its minimal computational resource requirements and time consumption, it exhibited substantial potential value in the realm of clinical application. The coefficients of variation of datasets proposed by us makes the algorithm data-specific and can give theoretical guidance on the selection of appropriate random spectral features for different datasets. This will broaden the applicability scenario of our feature-based approach. [ABSTRACT FROM AUTHOR]

Background/Objectives: The purpose of this preliminary study was to evaluate the diagnostic performance of an AI-driven platform, Diagnocat (Diagnocat Ltd., San Francisco, CA, USA), for assessing endodontic treatment outcomes using panoramic radiographs (PANs). Materials and Methods: The study included 55 PAN images of 55 patients (15 males and 40 females, aged 12–70) who underwent imaging at a private dental center. All images were acquired using a Hyperion X9 PRO digital cephalometer and were evaluated using Diagnocat, a cloud-based AI platform. The AI system assessed the following endodontic treatment features: filling probability, obturation adequacy, density, overfilling, voids in filling, and short filling. Two human observers independently evaluated the images, and their consensus served as the reference standard. The diagnostic accuracy metrics were calculated. Results: The AI system demonstrated high accuracy (90.72%) and a strong F1 score (95.12%) in detecting the probability of endodontic filling. However, the system showed variable performance in other categories, with lower accuracy metrics and unacceptable F1 scores for short filling and voids in filling assessments (8.33% and 14.29%, respectively). The accuracy for detecting adequate obturation and density was 55.81% and 62.79%, respectively. Conclusions: The AI-based system showed very high accuracy in identifying endodontically treated teeth but exhibited variable diagnostic accuracy for other qualitative features of endodontic treatment. [ABSTRACT FROM AUTHOR]

Because of the vast ocean area and the large amount of high-resolution image data, ship detection and data processing have become more difficult. These difficulties can be solved using the artificial intelligence interpretation method. The efficient and accurate detection ability of ship target detection has been widely recognized with the increasing application of deep learning technology. It is widely used in the practice of ship target detection. Firstly, we set up a data set concerning ship targets by collecting and training a large number of images. Then, we improved the YOLO v5 algorithm. The feature specify module (FSM) is used in the improved algorithm. The improved YOLO v5 algorithm was applied to ship detection practice under the framework of Anaconda. Finally, the training results were optimized, and the false alarm rate was reduced. The detection rate was improved. According to the statistics pertaining to experimental results with other algorithm models, the improved YOLO v5 algorithm can effectively suppress conflicting information, and the detection ability of ship details is improved. This work has accumulated valuable experience for related follow-up research. [ABSTRACT FROM AUTHOR]

Cognitive engagement involves mental and physical involvement, with observable behaviors as indicators. Automatically measuring cognitive engagement can offer valuable insights for instructors. However, object occlusion, inter-class similarity, and intra-class variance make designing an effective detection method challenging. To deal with these problems, we propose the Object-Enhanced–You Only Look Once version 8 nano (OE-YOLOv8n) model. This model employs the YOLOv8n framework with an improved Inner Minimum Point Distance Intersection over Union (IMPDIoU) Loss to detect cognitive engagement. To evaluate the proposed methodology, we construct a real-world Students' Cognitive Engagement (SCE) dataset. Extensive experiments on the self-built dataset show the superior performance of the proposed model, which improves the detection performance of the five distinct classes with a precision of 92.5%. [ABSTRACT FROM AUTHOR]

The movement-related cortical potential (MRCP) is a brain signal related to planning and execution of motor tasks. From an MRCP, three notable features can be identified: the early Bereitschaftspotential (BP1), the late Bereitschaftspotential (BP2), and the negative peak (PN). These features have been used in past studies to quantify neurophysiological changes in response to motor training. Currently, either manual labeling or a priori specification of time points is used to extract these features. The limitation of these methods is the inability to fully model the features. This paper proposes the segmented regression along with a local peak method for automated labeling of the features. The proposed method derives the onsets, amplitudes at onsets, and slopes of BP1 and BP2 along with time and amplitude of the PN in a typical average MRCP. To choose the most suitable regression technique a bounded segmented regression method, a change point method and multivariate adaptive regression splines were evaluated using the root-mean-square error on a dataset of 6000 simulated MRCPs. The best-performing regression technique combined with the local peak method was then applied to a smaller set of 123 simulated MRCPs. Error in onsets of BP1 and BP2 and time of PN were compared with the errors in manual labeling by an expert. The performance of the proposed method was also evaluated on an experimental dataset of MRCPs derived from electroencephalography (EEG) recorded across two sessions from 22 healthy participants during a lower limb task. The Bland–Altman plots were used to evaluate the absolute reliability of the proposed method. On experimental data, the proposed method was also compared with manual labeling by an expert. Bounded segmented regression produced the smallest error on the simulation data. For the experimental data, our proposed method did not exhibit statistically significant bias in any of the modeled features. Furthermore, its performance was comparable to manual labeling by experts. We conclude that the proposed method can be used to automatically obtain robust estimates for the MRCP features with known measurement error. [ABSTRACT FROM AUTHOR]

This paper introduces a new detection algorithm to calculate the maize kernel row number automatically. The algorithm is based on convex characteristics of the ear cross section. The convex hull of the contour of an ear cross section is the same as that of the cross section itself. We use an improved 2-d OTSU algorithm as the threshold segmentation algorithm to obtain the binary image of the ear cross section and its exact contour. Afterward we adopt Quickhull algorithm to calculate the convex hull of the contour and fill it with the pixel value the same as that of the ear cross section. The binary image of the ear cross section is subtracted from the convex hull image to obtain the convex deficiency image, the connected component of which is labeled to achieve the number of the kernel row. Particularly, a new hybrid optimization algorithm is introduced to the image segmentation. By experiments, we detected 500 samples. The one-sample detection time was 0.427s and the precision rate was 100%. The algorithm proposed can reach the results more quickly and more accurately. [ABSTRACT FROM AUTHOR]

This paper mainly presents an orientation detection methodology and corresponding system for developing platform of interface and ARM single chip unit to measure the magnetic field of the irregular shaped permanent magnet of multi- DOF motor. The data acquisition and data processing are implemented by two means -- traditional Gauss meter measuring method and automated test system. Then the results by the two methods are compared to verify the availability of presented automated test system consequently. Experiment results show that this test system can detect the rotor's orientation effectively and provide useful references for further research. [ABSTRACT FROM AUTHOR]

For improving the detection of micro-calcifications (MCs), this paper proposes an automatic detection of MC system making use of multi-fractal spectrum in digitized mammograms. The approach of automatic detection system is based on the principle that normal tissues possess certain fractal properties which change along with the presence of MCs. In this system, multi-fractal spectrum is applied to reveal such fractal properties. By quantifying the deviations of multi-fractal spectrums between normal tissues and MCs, the system can identify MCs altering the fractal properties and finally locate the position of MCs. The performance of the proposed system is compared with the leading automatic detection systems in a mammographic image database. Experimental results demonstrate that the proposed system is statistically superior to most of the compared systems and delivers a superior performance. [ABSTRACT FROM AUTHOR]