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This study presents findings indicating that the ferroelectric tunnel junction (FTJ) or resistive random-access memory (RRAM) in one cell can be intentionally selected depending on the application. The HfAlO film annealed at 700 °C shows stable FTJ characteristics and can be converted into RRAM by forming a conductive filament inside the same cell, that is, the process of intentionally forming a conductive filament is the result of defect generation and redistribution, and applying compliance current prior to a hard breakdown event of the dielectric film enables subsequent RRAM operation. The converted RRAM demonstrated good memory performance. Through current–voltage fitting, it was confirmed that the two resistance states of the FTJ and RRAM had different transport mechanisms. In the RRAM, the 1/f noise power of the high-resistance state (HRS) was about ten times higher than that of the low-resistance state (LRS). This is because the noise components increase due to the additional current paths in the HRS. The 1/f noise power according to resistance states in the FTJ was exactly the opposite result from the case of the RRAM. This is because the noise component due to the Poole–Frenkel emission is added to the noise component due to the tunneling current in the LRS. In addition, we confirmed the potentiation and depression characteristics of the two devices and further evaluated the accuracy of pattern recognition through a simulation by considering a dataset from the Modified National Institute of Standards and Technology. [ABSTRACT FROM AUTHOR]

We investigate a synaptic device with short-term memory characteristics using IGZO/SnOx as the switching layer. The thickness and components of each layer are analyzed by using x-ray photoelectron spectroscopy and transmission electron microscopy. The memristor exhibits analog resistive switching and a volatile feature with current decay over time. Moreover, through ten cycles of potentiation and depression, we demonstrate stable conductance modulation, leading to high-accuracy Modified National Institute of Standards and Technology pattern recognition. We effectively emulate the learning system of a biological synapse, including paired-pulse facilitation, spiking-amplitude-dependent plasticity, and spiking-rate-dependent plasticity (SRDP) by pulse trains. Ultimately, 4-bit reservoir computing divided into 16 states is incarnated using a pulse stream considering short-term memory plasticity and decay properties. [ABSTRACT FROM AUTHOR]

This study presents a preliminary exploration of thermally oxidized TaOx-based memristors and their potential as artificial synapses. Unlike the 10-min annealed devices, which display instability due to current overshoots, the 5-min annealed device exhibits stable resistive switching, retention, and endurance characteristics. Moreover, our memristor showcases synaptic behaviors encompassing potentiation, depression, spike-timing-dependent plasticity, and excitatory postsynaptic currents. This synaptic emulation holds tremendous promise for applications in neuromorphic computing, offering the opportunity to replicate the adaptive learning principles observed in biological synapses. In addition, we evaluate the device's suitability for pattern recognition within a neural network using the modified National Institute of Standards and Technology dataset. Our assessment reveals that the Pt/TaOx/Ta memristor with an oxidized insulator achieves outstanding potential manifested by an accuracy of 93.25% for the identical pulse scheme and an impressive accuracy of 95.42% for the incremental pulse scheme. [ABSTRACT FROM AUTHOR]

Accurate medical recordkeeping is important for personal and public health. Conventional forms of on‐patient medical information, such as medical alert bracelets or finger‐markings, may compromise patient privacy because they are readily visible to other people. Here, the development of an invisible, temporary, and easily deployable on‐patient medical recordkeeping system is reported. Information is stored in unique patterns of spatially distributed near‐infrared (NIR) fluorescent quantum dots (QDs), which are delivered to the skin using dissolvable microneedle arrays. The patterns are invisible to the naked eye but detectable with an infrared camera, which can extract information with >98% accuracy using automated pattern recognition software. By encapsulating NIR QDs in an FDA‐approved biodegradable polymer, biodegradation rates can be tuned so that the encoded medical information can be conveyed in both a spatial and temporal manner, with some components fading within 100 days and others persisting for 6 months. This may be particularly useful for administering a series of vaccinations or treatments by indicating if enough time has passed for the patient to receive the next dose. Importantly, this system contains no personal information, does not require connection to a centralized database, and is not visible to the naked eye, ensuring patient privacy. [ABSTRACT FROM AUTHOR]

Well test model identification is a challenging task due to the numerous types of well test interpretation models and the non-uniqueness of pressure responses generated by different reservoir models. An automated framework is crucial to aid in the identification of well test interpretation models. Since the identification of well test interpretation relies primarily on the various flow regimes appeared on different diagnostic plots. A novel approach is proposed for the well test model identification from the pressure transient test data using the syntactic pattern recognition in this study. In this study, the identification process of well test interpretation model is divided into six steps: preprocessing, feature primitive extraction, curve shape tracking, flow regime division, model preliminary inference, and model final validation incorporating TDS technology. The automatic identification framework developed with this method has been able to identify a variety of complex well test interpretation models correctly, and the non-uniqueness of model results can be well resolved by syntactic pattern recognition combined with TDS technology. In general, the findings of this study can help for better understanding of the process by which well test expert completes the task of model identification. [ABSTRACT FROM AUTHOR]

GLARE laminates have emerged as a favored option for the construction of fuselage and wing skins in large airliners, owing to their exceptional mechanical characteristics. Nevertheless, the incorporation of open‐hole designs poses a challenge as it disrupts the continuity of the laminates, resulting in stress concentration and subsequent damage. To investigate the impact of various layup orientations and hole sizes on the tensile properties of open‐hole GLARE laminates, this study conducted axial tensile tests. Additionally, the tensile damage process was monitored using DIC and AE techniques, enabling the identification of damage patterns and the analysis of their evolution. The results demonstrate a noteworthy decline in the ultimate strength and failure strain as the size of the opening increases. Moreover, the retention rate of failure strain displays a marked sensitivity to the layup orientation. In conjunction with observations made through DIC and SEM, the k‐means++ algorithm successfully clustered peak frequencies, thereby revealing distinct damage patterns and their corresponding frequency ranges as aluminum alloy damage [0–90 kHz], matrix cracking [104–174 kHz], fiber/matrix debonding [175–224 kHz], interlaminar delamination [234–300 kHz], and fiber fracture [304–469 kHz]. AE cumulative counts were utilized to evaluate the progression of individual damage modes. The results emphasize that matrix cracking demonstrates the most substantial cumulative counts, whereas damage to the fibers and aluminum alloy noticeably affects the load‐carrying capability of the laminate. Furthermore, the fibers/matrix debonding and interlaminar delamination, exhibit heightened susceptibility to layup orientation and hole size. Highlights: The tensile performance evaluation of GLARE laminates with open‐hole varying layup orientations and hole sizes was investigated.A combination of DIC and AE techniques was used to monitor the tensile damage process.Damage pattern was identified based on the observations of DIC and SEM.An approach based on Pearson's correlation coefficient and k‐means++ clustering algorithm was used for damage pattern recognition in GLARE laminates.AE cumulative counts were employed to assess the evolution of each damage mode. [ABSTRACT FROM AUTHOR]

Current computing systems rely on Boolean logic and von Neumann architecture, where computing cells are based on high-speed electron-conducting complementary metal-oxide-semiconductor (CMOS) transistors. In contrast, ions play an essential role in biological neural computing. Compared with CMOS units, the synapse/neuron computing speed is much lower, but the human brain performs much better in many tasks such as pattern recognition and decision-making. Recently, ionic dynamics in oxide electrolyte-gated transistors have attracted increasing attention in the field of neuromorphic computing, which is more similar to the computing modality in the biological brain. In this review article, we start with the introduction of some ionic processes in biological brain computing. Then, electrolyte-gated ionic transistors, especially oxide ionic transistors, are briefly introduced. Later, we review the state-of-the-art progress in oxide electrolyte-gated transistors for ionic neuromorphic computing including dynamic synaptic plasticity emulation, spatiotemporal information processing, and artificial sensory neuron function implementation. Finally, we will address the current challenges and offer recommendations along with potential research directions. [ABSTRACT FROM AUTHOR]

The decipherment of ancient Chinese scripts, such as oracle bone and bronze inscriptions, holds immense significance for understanding ancient Chinese history, culture, and civilization. Despite substantial progress in recognizing oracle bone script, research on the overall recognition of ancient Chinese characters remains somewhat lacking. To tackle this issue, we pioneered the construction of a large-scale image dataset comprising 9233 distinct ancient Chinese characters sourced from images obtained through archaeological excavations. We propose the first model for recognizing the common ancient Chinese characters. This model consists of four stages with Linear Embedding and Swin-Transformer blocks, each supplemented by a CoT Block to enhance local feature extraction. We also advocate for an enhancement strategy, which involves two steps: firstly, conducting adaptive data enhancement on the original data, and secondly, randomly resampling the data. The experimental results, with a top-one accuracy of 87.25% and a top-five accuracy of 95.81%, demonstrate that our proposed method achieves remarkable performance. Furthermore, through the visualizing of model attention, it can be observed that the proposed model, trained on a large number of images, is able to capture the morphological characteristics of ancient Chinese characters to a certain extent. [ABSTRACT FROM AUTHOR]

In real‐world scenarios, pedestrian images often suffer from occlusion, where certain body features become invisible, making it challenging for existing methods to accurately identify pedestrians with the same ID. Traditional approaches typically focus on matching only the visible body parts, which can lead to misalignment when the occlusion patterns vary. To address this issue and alleviate misalignment in occluded pedestrian images, the authors propose a novel framework called body topology information generation and matching. The framework consists of two main modules: the body topology information generation module and the body topology information matching module. The body topology information generation module employs an adaptive detection mechanism and capsule generative adversarial network to restore a holistic pedestrian image while preserving the body topology information. The body topology information matching module leverages the restored holistic image from body topology information generation to overcome spatial misalignment and utilises cosine distance as the similarity measure for matching. By combining the body topology information generation and body topology information matching modules, the authors achieve consistency in the body topology information features of pedestrian images, ranging from restoration to retrieval. Extensive experiments are conducted on both holistic person re‐identification datasets (Market‐1501, DukeMTMC‐ReID) and occluded person re‐identification datasets (Occluded‐DukeMTMC, Occluded‐ReID). The results demonstrate the superior performance of the authors proposed model, and visualisations of the generation and matching modules are provided to illustrate their effectiveness. Furthermore, an ablation study is conducted to validate the contributions of the proposed framework. [ABSTRACT FROM AUTHOR]

Semantic segmentation from a three‐dimensional point cloud is vital in autonomous driving, computer vision, and augmented reality. However, current semantic segmentation does not effectively use the point cloud's local geometric features and contextual information, essential for improving segmentation accuracy. A semantic segmentation network that uses local feature fusion and a multilayer attention mechanism is proposed to address these challenges. Specifically, the authors designed a local feature fusion module to encode the geometric and feature information separately, which fully leverages the point cloud's feature perception and geometric structure representation. Furthermore, the authors designed a multilayer attention pooling module consisting of local attention pooling and cascade attention pooling to extract contextual information. Local attention pooling is used to learn local neighbourhood information, and cascade attention pooling captures contextual information from deeper local neighbourhoods. Finally, an enhanced feature representation of important information is obtained by aggregating the features from the two deep attention pooling methods. Extensive experiments on large‐scale point‐cloud datasets Stanford 3D large‐scale indoor spaces and SemanticKITTI indicate that authors network shows excellent advantages over existing representative methods regarding local geometric feature description and global contextual relationships. [ABSTRACT FROM AUTHOR]

Aircraft coating process has been an important part in manufacturing process of modern aviation products. For coating defect detection, the manual observation with naked eyes is usually utilized, which leads to low production efficiency. In this paper, the authors propose the improved YOLOv5‐OBB with the channel‐spatial attention block (CSAB), feature pyramid non‐local module (FPNM) and structured sparsity slimming criterion (SSSC). The CSAB can pay more attention to effective channel information features from the channel dimension and the target information area from the spatial dimension. The effective non‐local module called FPNM is proposed to further improve the detection accuracy. The authors utilize the oriented bounding boxes (OBB) to reduce redundant background information for coating defect detection. In addition, the SSSC is proposed to achieve network slimming and trade‐off between the efficiency and accuracy. The experimental results on several datasets demonstrate the effectiveness of the authors' scheme, which achieves superior performance. [ABSTRACT FROM AUTHOR]

Pattern recognition using the nanoparticle colorimetric sensor arrays is a powerful analytical tool for detection and discrimination of analytes. In this study, the unmodified silver nanoparticles (AgNPs) with different diameters were employed for discrimination of three opioid drugs methadone, morphine, and tramadol based on the difference in the reaction kinetics between the drug and AgNPs. Discrete wavelet transform with various mother wavelets was used to preprocess the obtained big data and reduce their dimensionality, and then neural network-based supervised pattern recognition methods like Counter Propagation Artificial Neural Networks (CPANN), Supervised Kohonen Networks (SKN), and XY-fused (X-YF) networks were used for discrimination after optimizing the network parameters with genetic algorithm. The best pattern recognition performance was achieved using the SKN and Daubechies (Db) mother wavelet with an accuracy of 98% in discrimination of the analytes in spiked real serum samples. [ABSTRACT FROM AUTHOR]

The article discusses the use of Structural Health Monitoring (SHM) techniques to ensure the safety and longevity of civil infrastructure. It focuses on damage identification methodologies and the use of numerical models to simulate the behavior of concrete structures under different fracture conditions. The article emphasizes the importance of accurate numerical strategies and comprehensive analysis of existing structures. It also presents the results of studies on the degradation of natural vibration frequencies and the detection of damage in plain concrete structures. Additionally, the article provides a compilation of research articles covering various topics related to vibration-based damage detection and structural health monitoring. Lastly, it includes a list of references cited in a research paper on the degradation of vibration characteristics in reinforced concrete beams and micro-fracture instabilities in granular solids. [Extracted from the article]

The domain name system (DNS) protocol has been used for over three decades. It plays a vital role in the functioning of the Internet by facilitating the conversion of domain names into IP addresses. However, DNS is an early and vulnerable network protocol that attackers frequently target due to its numerous security flaws. To address these security concerns, several improvements have been introduced over time. The most recent enhancement is DNS over HTTPS (DoH), which aims to enhance user privacy and security by safeguarding DNS requests and responses from eavesdropping and data manipulation. Nevertheless, DoH encounters several security and privacy issues, such as encrypted traffic hindering network administrators from inspecting DNS packets for Malicious activity. Consequently, this raises concerns regarding potential security breaches and increased risk. Identification and characterizing Malicious behavior of DoH network traffic helps mitigate these threats. To tackle these issues, this research proposes two statistical pattern recognition models based on logistic and linear regression. These proposed models aim to identify the profile of Malicious DoH network traffic behavior by recognizing data patterns. In this order, we proposed two models consisting of two primary stages: data preprocessing, which involves data preparation and the selection of optimal feature sets, and pattern recognition, in which the most suitable pattern is selected and used for data classification. We also presented the obtained Malicious DoH profile utilizing the correlation coefficients between the features. To assess the effectiveness of the proposed approaches, the CIRA-CIC-DoHBrw-2020 dataset is utilized, and a comparison is made against state-of-the-art machine learning and deep learning models. Experimental results indicate that the logistic regression-based model outperformed linear regression-based. Moreover, while the outcomes indicated that the effectiveness of the linear and logistic regression-derived models was lower than particular machine learning and deep learning models, our models employed a smaller set of features than earlier research endeavors. Furthermore, our proposed models offer several advantages over previous models, including low computational complexity, simple implementation, robustness to noise, and reduced data requirements This study is the first to use basic statistical models (linear and logistic regression) to profile Malicious behavior in DoH network traffic. [ABSTRACT FROM AUTHOR]

In the category of sports supplements, whey protein powder is one of the popular supplements for muscle building applications. Therefore, verification of the sport supplements as authentic products has become a universal concern. This work aimed to propose vibrational spectroscopy including near infrared (NIR) and infrared (IR) as rapid and non-destructive testing tools for the detection and quantification of maltodextrin, milk powder and milk whey powder in whey protein supplements. Initially, principal component analysis was applied to data for pattern recognition and the results displayed a fine pattern of discrimination. Partial least square discrimination analysis (PLS-DA) and K-nearest neighbours (KNN) were exploited as supervised method modelling classification. This process was done in order to respond to two vital questions whether the sample is adulterated or not and what is the kind of adulteration. PLS-DA showed better classification results rather than KNN according to the figure of merits of the model. Partial least square regression (PLSR) was employed on pre-treated spectra to quantify the amount of adulteration in sport whey supplements. Eventually, it seems vibrational spectroscopy could be implemented as a simple, and low-cost analysis method for the detection and quantification of mentioned adulterants in whey protein supplements. [ABSTRACT FROM AUTHOR]

In recent years, various studies have been conducted on SVMs and their applications in different area. They have been developed significantly in many areas. SVM is one of the most robust classification and regression algorithms that plays a significant role in pattern recognition. However, SVM has not been developed significantly in some areas like large-scale datasets, unbalanced datasets, and multiclass classification. Efficient SVM training in large-scale datasets is of great importance in the big data era. However, as the number of samples increases, the time and memory required to train SVM increase, making SVM impractical even for a medium-sized problem. With the emergence of big data, this problem becomes more significant. This paper presents a novel distributed method for SVM training in which a very small subset of training samples is used for classification, which reduces the problem size and thus the required memory and computational resources. The solution of this problem almost converges to standard SVM. This method includes three steps: first, detecting a subset of distributed training samples, second, creating local models of SVM and obtaining partial vectors, and finally combining the partial vectors and obtaining the global vector and the final model. In addition, the datasets which suffer from unbalanced number of samples and tend to the majority class, the proposed method balances the samples of the two classes and it can be used in unbalanced datasets. The empirical results show that using this method is efficient for large-scale problems. [ABSTRACT FROM AUTHOR]

This study introduces parallel coordinates plot as an innovative visualization method, offering an alternative to traditional score plots in Principal Component Analysis (PCA) for exploratory data analysis (EDA). EDA is pivotal for unraveling intricate data structures, and PCA serves as a powerful tool for simplifying complex datasets. The paper explores the application of parallel coordinates plot in visualizing and interpreting PCA results, emphasizing their effectiveness in revealing multivariate patterns and detecting outliers. Through the examination of simulated datasets, where two distinct classes are generated in three-dimensional and five-dimensional spaces and real Raman spectroscopic data, the research demonstrates the utility of parallel coordinates plot in enhancing data exploration and providing valuable insights in pattern recognition methodologies. The results highlight the complementary nature of traditional score plots and parallel coordinates plots, offering a comprehensive approach to understanding high-dimensional datasets. This innovative visualization technique proves valuable in discerning subtle patterns and relationships within complex data structures, contributing to a more profound exploration of datasets in various domains. [ABSTRACT FROM AUTHOR]

Image processing is an important area to bring out the best in an image for human interpretation. This process has been widely used in various fields such as machine vision, remote sensing image analysis, medical diagnosis, image restoration, image pattern recognition, and video processing. During the acquisition and transmission of digital images, several random signals, known as noise, can affect image quality. To reduce or to remove efficiently the noise in the acquired or transmitted images, various image denoising techniques can be applied. The performance of denoising methods increases progressively when the noise parameters are taken into account as input parameters. Traditional denoising approaches adopt some assumptions to model noise, such noise is known as purely additive or multiplicative, pixel-independent, and channel-invariant. Usually, these assumptions limit the denoising effect due to inaccurate estimation of noise parameters in these algorithm models. However, the real noise model is signal-dependent and even device-dependent. In this paper, a new denoising method called signal-dependant noise-reducing anisotropic diffusion is developed, which is a version of the speckle reducing anisotropic diffusion (SRAD) filter. It differs from the standard SRAD filter approach by the insertion of a suitable noise parameters estimation framework. The new filter is designed to handle a variety of images corrupted by several common types of signal-dependent noises that are produced by charge-coupled device sensors. As well as it offers great potential for denoising with preserving textures and fine details. Extensive experiments demonstrate a significant increase in the image denoising performance in terms of SNR and RMSE. Qualitative (visual) results underline the efficacy of the proposed algorithm for filtering mixed signal-dependent noise. [ABSTRACT FROM AUTHOR]

This article explores the potential use of dual-process theories of thought as architectures for developing neuro-symbolic AI models. It discusses the limitations of large language models (LLMs) in handling structured tasks that rely on symbolic reasoning and proposes integrating LLMs with symbolic systems to address these limitations. The article explores different models for the interaction between rapid, associative thinking (System 1) and deliberate, rule-based thinking (System 2), and suggests that these architectures could be useful in developing and testing symbolic/subsymbolic systems integration in AI. It emphasizes the need for further exploration of the interaction between the two systems in neuro-symbolic architectures, while also prioritizing minimizing errors and achieving optimal performance. The document contains a list of references and citations from various sources related to bias in artificial intelligence and decision-making processes, providing additional sources for further research. [Extracted from the article]

The climate science community aims to improve our understanding of climate change due to anthropogenic influences on atmospheric composition and the Earth's surface. Yet not all climate interactions are fully understood, and uncertainty in climate model results persists, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. We synthesize current challenges and emphasize opportunities for advancing our understanding of the interactions between atmospheric composition, air quality, and climate change, as well as for quantifying model diversity. Our perspective is based on expert views from three multi-model intercomparison projects (MIPs) – the Precipitation Driver Response MIP (PDRMIP), the Aerosol Chemistry MIP (AerChemMIP), and the Radiative Forcing MIP (RFMIP). While there are many shared interests and specializations across the MIPs, they have their own scientific foci and specific approaches. The partial overlap between the MIPs proved useful for advancing the understanding of the perturbation–response paradigm through multi-model ensembles of Earth system models of varying complexity. We discuss the challenges of gaining insights from Earth system models that face computational and process representation limits and provide guidance from our lessons learned. Promising ideas to overcome some long-standing challenges in the near future are kilometer-scale experiments to better simulate circulation-dependent processes where it is possible and machine learning approaches where they are needed, e.g., for faster and better subgrid-scale parameterizations and pattern recognition in big data. New model constraints can arise from augmented observational products that leverage multiple datasets with machine learning approaches. Future MIPs can develop smart experiment protocols that strive towards an optimal trade-off between the resolution, complexity, and number of simulations and their length and, thereby, help to advance the understanding of climate change and its impacts. [ABSTRACT FROM AUTHOR]

Many features of leaves, including color, texture, and shape, are used in automated leaf recognition models. The curvature of a leaf is one of the least studied characteristics. This is primarily due to the fact that curvature is not an invariant feature and can fluctuate significantly in a single leaf in its many peripheral positions. The second reason is that if one considers curvature in a pixel-by-pixel manner, it seems entirely inappropriate as a single representative feature. In this study, we focused mostly on curvature and talked about how curvature might be used as a distinguishing feature for identifying leaves. We have provided a step-by-step method for dealing with curvature, starting with the fundamentals like the average curvature of a leaf and working our way up to a fine tuned representation that may be used as a significant feature. To determine whether the new feature is appropriate, we added it to the existing feature sets and fed the data to various standard classification models. The findings show a noticeable improvement in accuracy, demonstrating the usefulness of curvature as a feature. [ABSTRACT FROM AUTHOR]

We demonstrate an InGaZnO (IGZO)-based synaptic transistor with a TiO2 buffer layer. The structure of the synaptic transistor with TiO2 inserted between the Ti metal electrode and an IGZO semiconductor channel O2 trapping layer produces a large hysteresis window, which is crucial for achieving synaptic functionality. The Ti/TiO2/IGZO synaptic transistor exhibits reliable synaptic plasticity features such as excitatory post-synaptic current, paired-pulse facilitation, and potentiation and depression, originating from the reversible charge trapping and detrapping in the TiO2 layer. Finally, the pattern recognition accuracy of Modified National Institute of Standards and Technology handwritten digit images was modeled using CrossSim simulation software. The simulation results present a high image recognition accuracy of ∼89%. Therefore, this simple approach using an oxide buffer layer can aid the implementation of high-performance synaptic devices for neuromorphic computing systems. [ABSTRACT FROM AUTHOR]

Computational thinking is increasingly acknowledged as important in education to support the development of problem-solving skills in mathematics. Pattern recognition plays a significant role in solving mathematical problems using computational thinking. This qualitative research explores how students recognize patterns when solving mathematical problems using computational thinking. The study adopts a case study approach, with 46 subjects then chooses two students from the Mathematics Education Study Program selected through purposive sampling. Data collection methods involve tests and interviews in August. The data is qualitatively analyzed, involving reduction, data presentation, and concluding the research findings. The results indicate that the subjects recognized a pattern related to determining the height of a triangle, which was one of the assigned questions. The pattern emerged as the subjects identified the problem, decomposed it, recognized known and unknown information, and identified the relationship between sub-problems and task information. The identified pattern is crucial in addressing the problem in the given task. [ABSTRACT FROM AUTHOR]

Fermatean fuzzy sets (FFSs) serve as a nascent yet potent approach for coping with fuzziness, with their efficacy recently being demonstrated across a spectrum of practical contexts. Nevertheless, the scholarly literature remains limited in exploring the similarity and distance measures tailored for FFSs. The limited existing measures on FFSs sometimes yield counter-intuitive outcomes, which can obfuscate the accurate quantification of similarity and difference among FFSs. This paper introduces a suite of similarity and distance measures tailored for FFSs, drawing inspiration from the Tanimoto measure. We delve into the characteristics of these novel measures and offer some comparative studies with existing FFSs measures, highlighting their superior efficacy in the processing of fuzzy data from FFSs. Our proposed measures effectively rectify the counter-intuitive situations encountered with many existing measures and demonstrate a significant enhancement in differentiating between diverse FFSs. Moreover, we showcase the real-world applicability of our proposed measures through case studies in pattern recognition, medical diagnostics, and multi-attribute decision-making. [ABSTRACT FROM AUTHOR]

Speech emotion recognition is of great significance in the industry such as social robots, health care, and intelligent education. Due to the obscurity of emotional expression in speech, most works on speech emotion recognition (SER) ignore the consistency of speech emotion recognition, leading to fuzzy expression and low accuracy in emotional recognition. In this paper, we propose a semantic aware speech emotion recognition model to alleviate this issue. Specifically, a speech feature extraction module based on CNN and Transformer is designed to extract local and global information from the speech. Moreover, a semantic embedding support module is proposed to use text semantic information as auxiliary information to assist the model in extracting emotional features of speech, and can effectively overcome the problem of low recognition rate caused by emotional ambiguity. In addition, the model uses a key-value pair attention mechanism to fuse the features, which makes the fusion of speech and text features preferable. In experiments on two benchmark corpora IEMOCAP and EMO-DB, the recognition rates of 74.3% and 72.5% were obtained under respectively, which show that the proposed model can significantly improve the accuracy of emotion recognition. [ABSTRACT FROM AUTHOR]

The Fuzzy C-means (FCM) algorithm is one of the most widely used algorithms in unsupervised pattern recognition. As the intensity of observation noise increases, FCM tends to produce large center deviations and even overlap clustering problems. The relative entropy fuzzy C-means algorithm (REFCM) adds relative entropy as a regularization function to the fuzzy C-means algorithm, which has a good ability for noise detection and membership assignment to observed values. However, REFCM still tends to generate overlapping clusters as the size of the cluster increases and becomes imbalanced. Moreover, the convergence speed of this algorithm is slow. To solve this problem, modified suppressed relative entropy fuzzy c-means clustering (MSREFCM) is proposed. Specifically, the MSREFCM algorithm improves the convergence speed of the algorithm while maintaining the accuracy and anti-noise capability of the REFCM algorithm by adding a suppression strategy based on the intra-class average distance measurement. In addition, to further improve the clustering performance of MSREFCM for multidimensional imbalanced data, the center overlapping problem and the center offset problem of elliptical data are solved by replacing the Euclidean distance in REFCM with the Mahalanobis distance. Experiments on several synthetic and UCI datasets indicate that MSREFCM can improve the convergence speed and classification performance of the REFCM for spherical and ellipsoidal datasets with imbalanced sizes. In particular, for the Statlog dataset, the running time of MSREFCM is nearly one second less than that of REFCM, and the accuracy of MSREFCM is 0.034 higher than that of REFCM. [ABSTRACT FROM AUTHOR]

This study presents a new solution to address challenges encountered in additive manufacturing, specifically in the context of 3D printing, where failures can occur due to complications associated with the nozzle or filament. The proposed solution in this research involves using a time-domain feature extraction method that leverages sound and vibration patterns. By implementing sensors to capture these signals in a controlled and noise-free environment, and then utilizing a Multi-Layer Perceptron (MLP) model trained accurately to predict upcoming signals and vibrations, proactive anticipation of printing outcomes is facilitated, including potential failures. Simulation results obtained using MATLAB for the MLP showcase the effectiveness of this approach, demonstrating remarkably low error rates. Furthermore, through rigorous data validation, the proposed method's ability to accurately identify sound and vibration signals is confirmed. As a result, the likelihood of failures is significantly reduced, thereby preventing defects in the filament. The implications of this solution hold great promise in substantially enhancing the reliability and efficiency of additive manufacturing processes. [ABSTRACT FROM AUTHOR]

Rare antinuclear antibody (ANA) pattern recognition has been a widely applied technology for routine ANA screening in clinical laboratories. In recent years, the application of deep learning methods in recognizing ANA patterns has witnessed remarkable advancements. However, the majority of studies in this field have primarily focused on the classification of the most common ANA patterns, while another subset has concentrated on the detection of mitotic metaphase cells. To date, no prior research has been specifically dedicated to the identification of rare ANA patterns. In the present paper, we introduce a novel attention-based enhancement framework, which was designed for the recognition of rare ANA patterns in ANA-indirect immunofluorescence images. More specifically, we selected the algorithm with the best performance as our target detection network by conducting comparative experiments. We then further developed and enhanced the chosen algorithm through a series of optimizations. Then, attention mechanism was introduced to facilitate neural networks in expediting the learning process, extracting more essential and distinctive features for the target features that belong to the specific patterns. The proposed approach has helped to obtained high precision rate of 86.40%, 82.75% recall, 84.24% F1 score and 84.64% mean average precision for a 9-category rare ANA pattern detection task on our dataset. Finally, we evaluated the potential of the model as medical technologist assistant and observed that the technologist's performance improved after referring to the results of the model prediction. These promising results highlighted its potential as an efficient and reliable tool to assist medical technologists in their clinical practice. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DNNs) have achieved outstanding performance in this field. Meanwhile, handwritten Chinese character recognition (HCCR) is a challenging area of research in the field of computer vision. DNNs require a large number of parameters and high memory consumption. To address these issues, this paper proposes an approach based on an attention mechanism and knowledge distillation. The attention mechanism improves the feature extraction and the knowledge distillation reduces the number of parameters. The experimental results show that ResNet18 achieves a recognition accuracy of 97.63% on the HCCR dataset with 11.25 million parameters. Compared with other methods, this study improves the performance for HCCR. [ABSTRACT FROM AUTHOR]

In recent years, there has been a notable rise in the number of patients afflicted with laryngeal diseases, including cancer, trauma, and other ailments leading to voice loss. Currently, the market is witnessing a pressing demand for medical and healthcare products designed to assist individuals with voice defects, prompting the invention of the artificial throat (AT). This user-friendly device eliminates the need for complex procedures like phonation reconstruction surgery. Therefore, in this review, we will initially give a careful introduction to the intelligent AT, which can act not only as a sound sensor but also as a thin-film sound emitter. Then, the sensing principle to detect sound will be discussed carefully, including capacitive, piezoelectric, electromagnetic, and piezoresistive components employed in the realm of sound sensing. Following this, the development of thermoacoustic theory and different materials made of sound emitters will also be analyzed. After that, various algorithms utilized by the intelligent AT for speech pattern recognition will be reviewed, including some classical algorithms and neural network algorithms. Finally, the outlook, challenge, and conclusion of the intelligent AT will be stated. The intelligent AT presents clear advantages for patients with voice impairments, demonstrating significant social values. [ABSTRACT FROM AUTHOR]

The Bio-Radar is herein presented as a non-contact radar system able to capture vital signs remotely without requiring any physical contact with the subject. In this work, the ability to use the proposed system for emotion recognition is verified by comparing its performance on identifying fear, happiness and a neutral condition, with certified measuring equipment. For this purpose, machine learning algorithms were applied to the respiratory and cardiac signals captured simultaneously by the radar and the referenced contact-based system. Following a multiclass identification strategy, one could conclude that both systems present a comparable performance, where the radar might even outperform under specific conditions. Emotion recognition is possible using a radar system, with an accuracy equal to 99.7% and an F1-score of 99.9%. Thus, we demonstrated that it is perfectly possible to use the Bio-Radar system for this purpose, which is able to be operated remotely, avoiding the subject awareness of being monitored and thus providing more authentic reactions. [ABSTRACT FROM AUTHOR]

Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting changes in the Earth's surface, finding wide applications in urban planning, disaster management, and national security. Recently, deep learning (DL) has experienced explosive growth and, with its superior capabilities in feature learning and pattern recognition, it has introduced innovative approaches to CD. This review explores the latest techniques, applications, and challenges in DL-based CD, examining them through the lens of various learning paradigms, including fully supervised, semi-supervised, weakly supervised, and unsupervised. Initially, the review introduces the basic network architectures for CD methods using DL. Then, it provides a comprehensive analysis of CD methods under different learning paradigms, summarizing commonly used frameworks. Additionally, an overview of publicly available datasets for CD is offered. Finally, the review addresses the opportunities and challenges in the field, including: (a) incomplete supervised CD, encompassing semi-supervised and weakly supervised methods, which is still in its infancy and requires further in-depth investigation; (b) the potential of self-supervised learning, offering significant opportunities for Few-shot and One-shot Learning of CD; (c) the development of Foundation Models, with their multi-task adaptability, providing new perspectives and tools for CD; and (d) the expansion of data sources, presenting both opportunities and challenges for multimodal CD. These areas suggest promising directions for future research in CD. In conclusion, this review aims to assist researchers in gaining a comprehensive understanding of the CD field. [ABSTRACT FROM AUTHOR]

This document explores the significance of remote sensing in monitoring maritime activities and identifying vessels. It emphasizes the need for surveillance to ensure safety, security, and emergency management, given the increasing number of vessels worldwide. The document highlights the use of technologies like the Automatic Identification System (AIS) and remote sensing in situations where collaborative systems are not reliable. It also discusses the integration of data from different sensors and the application of data science techniques for a comprehensive assessment of maritime traffic. The document concludes by summarizing research papers on ship detection, tracking, and classification using various sensors and data processing techniques. [Extracted from the article]

Accurate large-scale crop yield estimation under climate variability is essential to understanding the dynamics of global food security. The deep learning method has shown well performance for crop yield estimation because of its high capacity for temporal pattern recognition. However, most existing deep learning models were usually based on multi-source time series with weekly or coarse temporal resolutions, which simplified the dynamics of crop growth progress. How to effectively learn the long-term and short-term crop response from the remotely sensed and agro-meteorological time series at daily intervals remains challenging. In this study, a hierarchical deep learning model, named Daily DeepCropNet (DDCN), has been developed to process daily time series of satellite-derived vegetation indices and climatic variables for corn yield estimation. The DDCN model was a three-level pyramid structure that incorporated the corn phenology durations to extract crop growth patterns from the daily inputs. The Transformer and Long Short-Term Memory (LSTM) were implemented as a temporal filter in each level to capture the short-term crop growth patterns. The temporal patterns were hierarchically transferred from weekly to growing season aggregation to learn long-term dependency. The experiment was conducted at county level over the US Corn Belt from 2006 to 2020. LSTM, Transformer, and Random Forest (RF) models were built for comparison. The input analysis showed that the daily time series of climatic variables provided more temporal fluctuations than weekly aggregated inputs. The DDCN model provided the highest accuracy for end-of-season yield estimation under normal climate conditions in 2018–2020 (RMSE = 0.86 Mg ha−1) and extreme heat and drought conditions in 2012 (RMSE = 1.17 Mg ha−1). Further analyses indicated that the DDCN model had the potential for capturing the short-duration stresses. We also found that the DDCN model identified the silking phase as the key growth stage and achieved higher accuracy than other models for in-season yield estimation. The results showed that features extracted by the DDCN model had higher correlation coefficients with corn yield for the upper levels. By evaluating the contributions of different variables, we found that vegetation indices yield higher impact than climatic variables for yield estimation under both normal and stressful climate conditions. This study provided a promising approach to extracting the temporal pattern of crop growth at daily time steps. The DDCN model could be further integrated with the process-based model to achieve the fusion of knowledge and data for explainable AI in agricultural system modeling. [ABSTRACT FROM AUTHOR]

Dimension reduction techniques are an essential part of the climate analyst's toolkit. Due to the enormous scale of climate data, dimension reduction methods are used to identify major patterns of variability within climate dynamics, to create compelling and informative visualizations, and to quantify major named modes such as El Niño–Southern Oscillation. Principal components analysis (PCA), also known as the method of empirical orthogonal functions (EOFs), is the most commonly used form of dimension reduction, characterized by a remarkable confluence of attractive mathematical, statistical, and computational properties. Despite its ubiquity, PCA suffers from several difficulties relevant to climate science: high computational burden with large datasets, decreased statistical accuracy in high dimensions, and difficulties comparing across multiple datasets. In this paper, we introduce several variants of PCA that are likely to be of use in climate sciences and address these problems. Specifically, we introduce non-negative, sparse, and tensor PCA and demonstrate how each approach provides superior pattern recognition in climate data. We also discuss approaches to comparing PCA-family results within and across datasets in a domain-relevant manner. We demonstrate these approaches through an analysis of several runs of the E3SM climate model from 1991 to 1995, focusing on the simulated response to the Mt. Pinatubo eruption; our findings are consistent with a recently identified stratospheric warming fingerprint associated with this type of stratospheric aerosol injection. [ABSTRACT FROM AUTHOR]

Rotating machine fault diagnosis plays a vital role in reducing maintenance costs and preventing accidents. Machine learning (ML) methods and Internet of things (IoT) technologies have been recently introduced into machine fault diagnosis and have generated inspiring results. An ML model with more trainable parameters can typically generate a higher fault diagnostic accuracy. However, the IoT nodes have limited computation and storage resources. How to design an ML model with high accuracy and computational efficiency is still a difficulty and challenge. This work develops an enhanced sparse filtering (ESF) method for mining and fusing the features of the machine signals for fault diagnosis. First, a dimension reduction algorithm is utilized for obtaining the principal components of the vibration signals that are hindered by noises. The distinct features of the principal components are then exploited by using sparse filtering (SF). To reduce the overfitting of the SF model, the L 1/2 norm is applied to regularize the objective function. Finally, the obtained features are combined as the inputs of a softmax classifier for machine fault pattern recognition. The effectiveness, superiority, and robustness of the proposed ESF method are validated by the simulated signals and the practical bearing and motor fault signals compared with the other conventional methods. The lightweight and intelligent ESF algorithm is also deployed onto an edge computing node to realize online motor fault diagnosis. The designed model and the proposed method show great potential in highly accurate and efficient rotation machine fault diagnosis. [ABSTRACT FROM AUTHOR]

With the continuous development of computer technology and the deepening of mathematical theory research, the combination of mathematics and computer pattern recognition technology has penetrated into various fields of scientific research and social production. In order to accurately detect and analyze students' movements in dance teaching, a new optical detection method is proposed in this study, which uses dynamic time distortion algorithm to accurately identify and analyze dance movements. The dynamic time warping algorithm is used to compare two time series and is applied to the detection of dance movements to realize the accurate identification and analysis of students' movements. The dance video data is collected and pre-processed into time series data, and then each time series is compared and analyzed using a dynamic time warping algorithm, and key movement features are extracted. Finally, the machine learning model is trained to classify and evaluate the students' movements. The experimental results show that the optical detection method based on dynamic time distortion algorithm can achieve high accuracy and precision in dance teaching. When applied to dance teaching, it has high accuracy and precision in detecting dance movements, which is expected to provide effective tools and support for dance teaching. [ABSTRACT FROM AUTHOR]

Recent advancements in ubiquitous computing have emphasized the need for privacy-preserving occupancy detection in smart environments to enhance security. This work presents a novel occupancy detection solution utilizing privacy-aware sensing technologies. The solution analyzes time-series data to detect not only occupancy as a binary problem, but also determines whether one or multiple individuals are present in an indoor environment. On three real-world datasets, our models outperformed various state-of-the-art algorithms, achieving F1-scores up to 94.91% in single-occupancy detection and a macro F1-score of 91.55% in multi-occupancy detection. This makes our approach a promising solution for improving security in smart environments. [ABSTRACT FROM AUTHOR]

User adaptation is important in conversational robots to enhance the user experience and engagement. One way of user adaptation is to change the dialogue behavior. A character (e.g. extrovert or agreeable) can be defined to achieve human-like behaviors and user adaptation, and the appropriate character differs depending on each user. In this study, we investigate user adaptation by character expression of conversational robots. Our character expression model adopts the Big Five traits for controlling four dialogue behaviors: amount of utterance, backchannel frequency, filler frequency, and switching pause length. We cluster the system character and user personality into four classes based on an analysis of a speed dating dialogue corpus, and we found the best combinations between the system character and user personality. We implemented the character expression model into a laboratory guide and a chit-chat robot and conducted subjective experiments, where the subjects talked with robots with four different characters and evaluated their impressions of each character. The results shows that the Role model character was preferred for the task-oriented dialogue of the laboratory guide, but a robot character complementary to the subject personality was preferred for the non-task-oriented dialogue of the speed dating and chit-chat. [ABSTRACT FROM AUTHOR]

Artificial synapses have gained great interest in the past few years because of their importance in deep learning and image recognition. To fulfill device miniaturization and reduce the energy consumption of device operation, a series of silane‐based photoactive/conjugated self‐assembled molecules (CSAMs), including isoindigo (IID), diketopyrrolopyrrole, and benzodithiophene, are used as the charge‐trapping layers in synaptic phototransistors. The devices comprising CSAM demonstrate excellent short‐term/long‐term memory behaviors and can emulate the paired‐pulse facilitation (PPF) function. Notably, the IID‐based device shows the highest photoresponse, and this performance is highly related to the charge transfer efficiency and the photophysics lifetimes derived from the time‐resolved photoluminescence and the transient absorption characterizations. Therefore, IID produces the highest PPF ratios of 139% to blue light and 144% to green light. In addition, the energy consumption of 0.029 fJ at an operating voltage of −0.1 mV is achieved, which is the lowest value in synaptic phototransistors so far. Notably, neural networks of supervised and unsupervised learning algorithms are demonstrated in the device studied to process a pattern recognition system. Collectively, using conjugated self‐assembled materials as a charge‐trapping layer is a promising way for synaptic phototransistor applications to reduce energy consumption and fulfill the device miniaturization. [ABSTRACT FROM AUTHOR]

Recognition of partial discharge (PD) patterns is essential for insulation diagnosis of covered conductors in overhead lines. Current research has not sufficiently addressed the complex background noise in real environments, and most detection methods depend primarily on feature engineering or deep learning, suggesting potential for improvement in accuracy and efficiency. This has led the authors to propose a PD pattern recognition algorithm that integrates feature selection and deep learning. This algorithm incorporates the design of a discrete wavelet denoising function specifically tailored to the characteristics of PD for data preprocessing. It employs Bayesian optimization algorithms and light gradient boosting machines for characterizing corona discharge features. Furthermore, it develops multi‐scale clustering features and phase‐resolved features for feature fusion, and constructs insightful features based on the light gradient boosting machine. Finally, a novel deep learning model is formulated, demonstrating exceptional detection performance for early faults in covered conductors. Experimental results show that this algorithm attains an Matthews correlation coefficient score of 0.814, a 13.2% improvement over the baseline algorithm's 0.719, and a speed increase of 39.18%. The final accuracy amounts to 97.85%. This algorithm demonstrates exceptional performance in detecting early insulation faults in conductors. [ABSTRACT FROM AUTHOR]

In this paper, a novel strategy based on a hybrid algorithm of Elephant Herding Optimization Big Bang–Big Crunch is provided to address the English character recognition problem with interference from external noise. A model used to recognize handwritten Hindi (Devanagari) characters is called Hindi OCR (optical character recognition). Despite the fact that only a few nature-inspired algorithms are used for OCR, none are used for the characters seen in natural scenes due to the difficulties presented by the various fonts, lighting, orientation, and texture variations. Also, Hindi's curved and complicated architecture makes it challenging to separate and analyze the text or character. Better feature databases are created using the proposed Hybrid EHO–B3C model to facilitate categorization. Two standard datasets of English characters, Char74k and ICDAR03, are used to assess the performance of the novel Hybrid EHO–B3C on natural scene characters. The proposed model is also evaluated on the Devanagari handwritten character dataset, which is a dataset consisting of characters from the Hindi language. Results show 83% results on Char74k and 86.4% on ICDAR, comparable to most available techniques. We have also examined our algorithm for face recognition by examining it on five different datasets, namely MUCT, Faces94, Extended Yale B, Georgia Tech, and Grimace, and attained high recognition results, even though the data sets differ in size, image size, brightness and ethnicity of people. [ABSTRACT FROM AUTHOR]

With the rapid economic development and high concentration of urban population, people's income level and quality of life continue to improve, resulting in more and more crowded scenes caused by people going out. Especially in urban commercial centers, transportation hubs, sports venues during important events, tourist attractions, etc., crowd gatherings occur frequently. However, accidents involving crowd gatherings in public places occur frequently, causing heavy casualties and property losses. Therefore, for crowd recognition, this paper proposes a new method to accurately estimate the number of dense crowds. In this method, a density map with accurate pedestrian locations is first generated using the focal inverse distance transform and used as ground truth labels for network training. Then, a multi-scale feature fusion algorithm based on residual network is designed, combining spatial and channel attention mechanisms to improve the accuracy and stability of crowd density estimation. In dense crowds, the phenomenon of overlapping and occlusion of people is very common and serious, making it difficult for existing pedestrian detection methods to distinguish each individual and accurately count the flow of people. To solve this problem, this paper proposes a density map-based method that uses a local maximum detection strategy and a K-nearest neighbor algorithm to convert the density map into the corresponding dense head bounding box. This method can effectively reduce the impact of occlusion and improve the accuracy of people counting. In order to further improve the estimation accuracy, a pattern recognition density peak clustering algorithm is introduced to study the clustered crowds. By treating the head bounding box as an element point, the distance between each element point is calculated, and the density of each point is calculated. Then perform clustering to find the cluster center with the highest density in each class. Finally, by comparing the density of each cluster center with the corresponding density threshold and adopting the corresponding decision-making method, the accuracy of people counting is further improved. [ABSTRACT FROM AUTHOR]

Rolling bearings are a key component of rotating machinery and their health directly affects the safe operation of mechanical equipment. Therefore, fault diagnose for rolling bearings is very important. The fault diagnosis process of rolling bearings includes three stages: signal decomposition, feature extraction, and pattern recognition. Variational mode decomposition (VMD) can suppress end effects, but improper parameter settings will cause information losses or excessive decomposition. In this work, an improved whale optimization algorithm (IWOA) is applied to parameter settings of VMD. Correspondingly, an IWOA-VMD signal decomposition method is proposed. The decomposed signal is combined with a Laplace score method and classifier to remove the redundancy and noise in the feature set and obtain a low-dimensional sensitive feature subset. Then, aiming at the problem of the parameter settings of a least squares support vector machine (LSSVM) affecting the recognition performance and accuracy, a salp swarm algorithm (SSA) is used to globally optimize the penalty parameter and kernel width in the LSSVM to establish an SSA-LSSVM fault recognition model. This model is applied to the fault diagnosis of rolling bearings. In particular, rolling bearing fault samples at Case Western Reserve University are used to verify the method. The results indicate that the proposed method is effective and improves the speed and accuracy of fault diagnosis. [ABSTRACT FROM AUTHOR]

Script identification of text in scene images has attracted massive attention recently. However, the existing techniques primarily emphasize on scripts where data are available abundantly, such as English, European, or East Asian. Although these methods are robust in dealing with high‐resource data, how these techniques will work on low‐resource scripts has yet to be discovered. For example, in India, there is a disparity among the text scripts across the country's demographic. To bridge the research gap for resource‐constraint script identification, we present a few‐shot learning network called the TextScriptFSLNet. This network does not require huge training data while achieving state‐of‐the‐art performance on benchmark datasets. Our proposed method acts in accordance with a C$$ C $$‐way K$$ K $$‐shot paradigm by splitting the train set as support and query set, respectively. The support set learns representative knowledge of each class and creates its prototypes. We use multi‐kernel spatial attention fused 2‐layer convolutional neural network and averaging technique to generate the prototype of each class. This spatial attention aids in grasping important information in an image and enriches the feature representation. To the best of our knowledge, the proposed work is the first of its kind in the scene text understanding domain. Additionally, we created a dataset called Indic‐FSL2023 comprising 10 of the 22 officially recognized Indian scripts. The proposed method achieves the highest accuracy among the tested methods on the newly created Indic‐FSL2023. Experiments are also conducted on MLe2e to demonstrate its versatility. Furthermore, we also showed how our proposed model performed concerning illumination changes and blur on scene text script images. [ABSTRACT FROM AUTHOR]