Identification Of Features Research Articles

Thermal imaging-based identification of facial features in noisy environment

<p class="Abstract">Face identification is amongst the most efficacious and extensive applications in biometrics involving extraction and locating facial features. With identification being monotonous task attributable to reliance on parameters like varied cameras, fluctuating backgrounds, and exposure to the environment in which an individual is present. Thermal imaging is endeavoring to resolve the accuracy issue of apparent imaging, such as lighting and brightness intensity, among all biometric variables. This paper presents a study of thermal imaging and effective methods involved in the feature extraction process for facial features with thermal imaging under the influence of varied noise. A novel face dataset is created TID comprising 27 thermal images and its corresponding visual band image using Fluke 480 Ti Pro camera. The study analyses detection efficiency of six feature extraction techniques in visible and thermal bands in facial features identification. Also, the influence of noise in the thermal band within the region of interest using feature points FIN, FOUT has been estimated. Throughout TID dataset, ORB extraction technique has been able to identify strongest inlier features FIN to a maximum extent with detection around the nose, eyes, and mouth. Further, results indicate feature detection in thermal images being invariant to effect of noise for detecting facial features.</p>

CO-Net++: A Cohesive Network for Multiple Point Cloud Tasks at Once With Two-Stage Feature Rectification.

We present CO-Net++, a cohesive framework that optimizes multiple point cloud tasks collectively across heterogeneous dataset domains with a two-stage feature rectification strategy. The core of CO-Net++ lies in optimizing task-shared parameters to capture universal features across various tasks while discerning task-specific parameters tailored to encapsulate the unique characteristics of each task. Specifically, CO-Net++ develops a two-stage feature rectification strategy (TFRS) that distinctly separates the optimization processes for task-shared and task-specific parameters. At the first stage, TFRS configures all parameters in backbone as task-shared, which encourages CO-Net++ to thoroughly assimilate universal attributes pertinent to all tasks. In addition, TFRS introduces a sign-based gradient surgery to facilitate the optimization of task-shared parameters, thus alleviating conflicting gradients induced by various dataset domains. In the second stage, TFRS freezes task-shared parameters and flexibly integrates task-specific parameters into the network for encoding specific characteristics of each dataset domain. CO-Net++ prominently mitigates conflicting optimization caused by parameter entanglement, ensuring the sufficient identification of universal and specific features. Extensive experiments reveal that CO-Net++ realizes exceptional performances on both 3D object detection and 3D semantic segmentation tasks. Moreover, CO-Net++ delivers an impressive incremental learning capability and prevents catastrophic amnesia when generalizing to new point cloud tasks.

Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features.

Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of molecular features. These approaches distill cancer histologic images into high-level features, which are used in predictions, but understanding the biologic meaning of such features remains challenging. We present and validate a custom generative adversarial network-HistoXGAN-capable of reconstructing representative histology using feature vectors produced by common feature extractors. We evaluate HistoXGAN across 29 cancer subtypes and demonstrate that reconstructed images retain information regarding tumor grade, histologic subtype, and gene expression patterns. We leverage HistoXGAN to illustrate the underlying histologic features for deep learning models for actionable mutations, identify model reliance on histologic batch effect in predictions, and demonstrate accurate reconstruction of tumor histology from radiographic imaging for a "virtual biopsy."

Diagnostic challenges in the assessment of thyroid neoplasms using nuclear features and vascular and capsular invasion: a multi-center interobserver agreement study.

The diagnosis of thyroid neoplasms necessitates the identification of distinct histological features. Various education/hospital centers located in cities across Indonesia likely result in discordances among pathologists when diagnosing thyroid neoplasms. This study examined the concordance among Indonesian pathologists in assessing nuclear features and capsular and vascular invasion of thyroid tumors. Fifteen pathologists from different centers independently assessed the same 14 digital slides of thyroid tumor specimens. All the specimens were thyroid neoplasms with known BRAFV600E and RAS mutational status, from a single center. We evaluated the pre- and post-training agreement using the Fleiss kappa. The significance of the training was evaluated using a paired T-test. Baseline agreement on nuclear features was slight to fair based on a 3-point scoring system (k = 0.14 to 0.28) and poor to fair based on an eight-point system (k = -0.02 to 0.24). Agreements on vascular (κ = 0.35) and capsular invasion (κ = 0.27) were fair, whereas the estimated molecular type showed substantial agreement (κ = 0.74). Following the training, agreement using the eight-point system significantly improved (p = 0.001). The level of concordance among Indonesian pathologists in diagnosing thyroid neoplasm was relatively poor. Consensus in pathology assessment requires ongoing collaboration and education to refine diagnostic criteria.

Applications and Challenges of AI in PCB X-ray Inspection: A Comprehensive Study

As printed circuit boards (PCBs) continue to evolve in complexity and miniaturization, the demand for robust and efficient inspection techniques has become paramount in ensuring the quality and reliability of electronic devices. The application of machine learning and deep learning techniques has revolutionized PCB inspection in recent years, enabling the ability to automate and improve numerous elements of the process. In this paper, a comprehensive analysis is performed on the applications and challenges of artificial intelligence (AI), encompassing techniques of deep learning and machine learning, in the domain of PCB X-ray scrutiny. The main focus of this research centers around defect detection, identification of components and layers, deep learning algorithms for image reconstruction, as well as the identification of defects and features in advanced packaging. This study examines the current cutting-edge advancements in each of these areas, closely examining the existing methodologies and technologies employed. Furthermore, it delves into the limitations and challenges inherent in PCB X-ray inspection, such as the unavailability of data, computational demands, and the interpretability of models. In addition, this article offers prospective insights and presents promising avenues like application of generative adversarial networks and deep learning reconstruction methods for future exploration.

Selecting relevant structural features for glassy dynamics by information imbalance.

We numerically investigate the identification of relevant structural features that contribute to the dynamical heterogeneity in a model glass-forming liquid. By employing the recently proposed information imbalance technique, we select these features from a range of physically motivated descriptors. This selection process is performed in a supervised manner (using both dynamical and structural data) and an unsupervised manner (using only structural data). We then apply the selected features to predict future dynamics using a machine learning technique. One of the advantages of the information imbalance technique is that it does not assume any model a priori, i.e., it is a non-parametric method. Finally, we discuss the potential applications of this approach in identifying the dominant mechanisms governing the glassy slow dynamics.

Development of a model-based modular building kit for sensor-integrating machine elements—Theory and application

AbstractIn the Priority Program 2305 of the German Research Foundation, so-called Sensor-integrating Machine Elements (SiME) are to be developed. These are essentially highly standardized components with integrated microelectronics. The present article presents the development of a model-based construction kit to support the design of integrated sensor systems for these new machine elements. A methodical procedure for collecting the development data required for modeling the modular building kit for SiME is presented and applied to four different cases within the project. Use cases, product structure and module diagrams were recorded and modeled for the machine elements screw, gear, coupling and feather key. These are then linked in SysML models to enable sensor systems for SiME to be configured in line with requirements. The modeling of the system architectures deepens the understanding of the underlying mechatronic system architecture and supports the identification of differentiation features as well as synergy potentials.

Decoding Spatial Tissue Architecture: A Scalable Bayesian Topic Model for Multiplexed Imaging Analysis.

Recent progress in multiplexed tissue imaging is advancing the study of tumor microenvironments to enhance our understanding of treatment response and disease progression. Cellular neighborhood analysis is a popular computational approach for these complex image data. Despite its popularity, there are significant challenges, including high computational demands that limit feasibility for largescale applications and the lack of a principled strategy for integrative analysis across images. This absence hampers the precise and consistent identification of spatial features and tracking of their dynamics over disease progression. To overcome these challenges, we introduce SpatialTopic , a spatial topic model designed to decode high-level spatial architecture across multiplexed tissue images. SpatialTopic integrates both cell type and spatial information within a topic modelling framework, originally developed for natural language processing and adapted for computer vision. Spatial information is incorporated into the flexible design of documents, representing densely overlapping regions in images. We employ an efficient collapsed Gibbs sampling algorithm for model inference. We benchmarked the performance against five state-of-the-art algorithms through various case studies using different single-cell spatial transcriptomic and proteomic imaging platforms across different tissue types. We show that SpatialTopic is highly scalable on large-scale image datasets with millions of cells, along with high precision and interpretability. Our findings demonstrate that SpatialTopic consistently identifies biologically and clinically significant spatial "topics" such as tertiary lymphoid structures (TLSs) and tracks dynamic changes in spatial features over disease progression. Its computational efficiency and broad applicability across various molecular imaging platforms will enhance the analysis of large-scale tissue imaging datasets.

Unveiling Urban River Visual Features Through Immersive Virtual Reality: Analyzing Youth Perceptions with UAV Panoramic Imagery

The visual evaluation and characteristic analysis of urban rivers are pivotal for advancing our understanding of urban waterscapes and their surrounding environments. Unmanned aerial vehicles (UAVs) offer significant advantages over traditional satellite remote sensing, including flexible aerial surveying, diverse perspectives, and high-resolution imagery. This study centers on the Haihe River, South Canal, and North Canal in Tianjin China, employing UAVs to capture continuous panoramic image data. Through immersive virtual reality (VR) technology, visual evaluations of these panoramic images were obtained from a cohort of young participants. These evaluations encompassed assessments of scenic beauty, color richness, vitality, and historical sense. Subsequently, computer vision techniques were utilized to quantitatively analyze the proportions of various landscape elements (e.g., trees, grass, buildings) within the images. Clustering analysis of visual evaluation results and semantic segmentation outcomes from different study points facilitated the effective identification and grouping of river visual features. The findings reveal significant differences in scenic beauty, color richness, and vitality among the Haihe River, South Canal, and North Canal, whereas the South and North Canals exhibited a limited sense of history. Six landscape elements—water bodies, buildings, trees, etc.—comprised over 90% of the images, forming the primary visual characteristics of the three rivers. Nonetheless, the uneven spatial distribution of these elements resulted in notable variations in the visual features of the rivers. This study demonstrates that the visual feature analysis method based on UAV panoramic images can achieve a quantitative evaluation of multi-scene urban 3D landscapes, thereby providing a robust scientific foundation for the optimization of urban river environments.

Learning Collective Cell Migratory Dynamics from a Static Snapshot with Graph Neural Networks

Multicellular self-assembly into functional structures is a dynamic process that is critical in the development of biological structures and diseases, including embryo development, organ formation, tumor invasion, and other processes. Being able to infer collective cell migratory dynamics from their static configuration is valuable for both understanding and predicting these complex behaviors. However, the identification of structural features that can indicate multicellular motion has been difficult, and existing metrics largely rely on physical instincts. Here we show that, through the use of a graph neural network, the motion of multicellular collectives can be inferred from a static snapshot of cell positions, in both experimental and synthetic datasets. Published by the American Physical Society 2024

Short-term forecast of solar irradiance components using an alternative mathematical approach for the identification of cloud features

Solar energy technologies require precise solar forecasting to reduce power generation losses and protect equipment from irradiance fluctuations. This study introduces an alternative methodology for short-term forecasting of direct normal irradiance (DNI) and global horizontal irradiance (GHI) utilizing ground-based sky images captured by a single device. A low-cost all-sky imager (ASI) was developed, which implements an angular transformation and an optical flow technique to extract cloud features such as shape and velocity. A mathematical model calculates cloud transmittance based on pixel intensity, eliminating complex training steps. Results from a 30-day experimental campaign, incorporating diverse meteorological conditions, were compared against a secondary standard solarimetric station, a smart persistence model, and state-of-the-art approaches. The DNI forecast achieved an RMSE (relative error) of 46.79 W/m2 (11.99%) for 1-min intervals and 90.21 W/m2 (17.54%) for 10-min intervals, while GHI ranged from 31.73 W/m2 (4.68%) to 75.02 W/m2 (13.63%). Pearson correlation coefficients exceeded 0.9 overall, reaching 0.98 and 0.99 for the 1-min DNI and GHI forecasts, and 0.91 and 0.96 for the 10-min DNI and GHI forecasts, respectively, underscoring the system’s accuracy and robustness in complex meteorological scenarios.

A rolling bearing fault signal denoising algorithm that combines a new adaptive information entropy with a new wavelet threshold function

Abstract Mechanical fault diagnosis is of great significance to industrial automation, and extracting vibration fault signals is one of the important tasks in mechanical health monitoring and fault diagnosis. However, due to the complex working environment of rolling bearings, a large amount of noise makes it difficult to extract vibration fault signals. Denoising the vibration signal of bearings can remove interference noise, simplify the early identification of signal features, and thus improve diagnostic accuracy and mechanical maintenance efficiency. This paper proposes a rolling bearing fault signal denoising algorithm, which constructs a new feature extraction and denoising function. This method first decomposes the noisy signal into Intrinsic Mode Functions (IMFs) by Intrinsic Computing Expressive Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). Secondly, a new adaptive information entropy threshold function is constructed to extract the noisy IMFs from it. Then, the noisy IMF signal is denoised by a new wavelet threshold function. Finally, the noise-free IMFs are reconstructed to reconstruct the denoised signal. To verify the actual performance of the algorithm, comparative experiments were conducted on a self-collected dataset and a public dataset, the results show that this method improves the continuity of signal reconstruction and can remove various types of vibration fault noise signals more effectively and accurately, thereby improving the fault detection accuracy by 2%–9%.

Diagnosis of incipient faults in wind turbine bearings based on ICEEMDAN–IMCKD

AbstractTo address the difficulty in extracting early fault feature signals of rolling bearings, this paper proposes a novel weak fault diagnosis method for rolling bearings. This method combines the Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and the Improved Maximum Correlated Kurtosis Deconvolution (IMCKD). Utilizing the kurtosis criterion, the intrinsic mode functions obtained through ICEEMDAN are reconstructed and denoised using IMCKD, which significantly reduces noise in the measured signal. This approach maximizes the energy amplitude at the fault characteristic frequency, facilitating fault feature identification. Experimental studies on two test benches demonstrate that this method effectively reduces noise interference and highlights the fault frequency components. Compared with traditional methods, it significantly improves the signal‐to‐noise ratio and more accurately identifies fault features, meeting the requirements for discriminating rolling bearing faults. The method proposed in this study was applied to the measured vibration signals of the gearbox bearings in the new high‐speed wire department of a Long Products Mill. It successfully extracted weak characteristic information of early bearing faults, achieving the expected diagnostic results. This further validates the effectiveness of the ICEEMDAN–IMCKD method in practical engineering applications, demonstrating significant engineering value for detecting and extracting weak impact characteristics in rolling bearings.

Developing a 10-Layer Retinal Segmentation for MacTel Using Semi-Supervised Learning.

Automated segmentation software in optical coherence tomography (OCT) devices is usually developed for and primarily tested on common diseases. Therefore segmentation accuracy of automated software can be limited in eyes with rare pathologies. We sought to develop a semisupervised deep learning segmentation model that segments 10 retinal layers and four retinal features in eyes with Macular Telangiectasia Type II (MacTel) using a small labeled dataset by leveraging unlabeled images. We compared our model against popular supervised and semisupervised models, as well as conducted ablation studies on the model itself. Our model significantly outperformed all other models in terms of intersection over union on the 10 retinal layers and two retinal features in the test dataset. For the remaining two features, the pre-retinal space above the internal limiting membrane and the background below the retinal pigment epithelium, all of the models performed similarly. Furthermore, we showed that using more unlabeled images improved the performance of our semisupervised model. Our model improves segmentation performance over supervised models by leveraging unlabeled data. This approach has the potential to improve segmentation performance for other diseases, where labeled data is limited but unlabeled data abundant. Improving automated segmentation of MacTel pathology on OCT imaging by leveraging unlabeled data may enable more accurate assessment of disease progression, and this approach may be useful for improving feature identification and location on OCT in other rare diseases as well.

Cryo-electron tomographic investigation of native hippocampal glutamatergic synapses

Chemical synapses are the major sites of communication between neurons in the nervous system and mediate either excitatory or inhibitory signaling. At excitatory synapses, glutamate is the primary neurotransmitter and upon release from presynaptic vesicles, is detected by postsynaptic glutamate receptors, which include ionotropic AMPA and NMDA receptors. Here, we have developed methods to identify glutamatergic synapses in brain tissue slices, label AMPA receptors with small gold nanoparticles (AuNPs), and prepare lamella for cryo-electron tomography studies. The targeted imaging of glutamatergic synapses in the lamella is facilitated by fluorescent pre- and postsynaptic signatures, and the subsequent tomograms allow for the identification of key features of chemical synapses, including synaptic vesicles, the synaptic cleft, and AuNP-labeled AMPA receptors. These methods pave the way for imaging brain regions at high resolution, using unstained, unfixed samples preserved under near-native conditions.

Cryo-electron tomographic investigation of native hippocampal glutamatergic synapses.

Chemical synapses are the major sites of communication between neurons in the nervous system and mediate either excitatory or inhibitory signaling. At excitatory synapses, glutamate is the primary neurotransmitter and upon release from presynaptic vesicles, is detected by postsynaptic glutamate receptors, which include ionotropic AMPA and NMDA receptors. Here, we have developed methods to identify glutamatergic synapses in brain tissue slices, label AMPA receptors with small gold nanoparticles (AuNPs), and prepare lamella for cryo-electron tomography studies. The targeted imaging of glutamatergic synapses in the lamella is facilitated by fluorescent pre- and postsynaptic signatures, and the subsequent tomograms allow for the identification of key features of chemical synapses, including synaptic vesicles, the synaptic cleft, and AuNP-labeled AMPA receptors. These methods pave the way for imaging brain regions at high resolution, using unstained, unfixed samples preserved under near-native conditions.

FRENCH ARGOTOLOGY IN RUSSIA IN THE FIRST QUARTER OF THE 21st CENTURY

In the anniversary year of Eda Moiseevna Beregovskaya, it is important to note the continued interest in the study of both the features of social variability of the national language and the expressive capabilities of elements of a specific lexical continuum in literary and journalistic texts. The method of systematic description of French argotisms, which has no analogues in the world practice, proposed by the founder of the national school of argotology, has been repeatedly applied both to the characteristics of a certain sublanguage and to the comparative analysis of several social variants of the national language. Moreover, the scientific heritage of E.M. Beregovskaya contains an array of information on the development and testing of an algorithm for analyzing argotic units in the fabric of a literary text. The analysis allows the addressee to understand words of connivance in context and adequately assess the stylistic effect arising from their presence. The article also describes a convergent approach to the selection of areas of research (lexicological, linguostylistic, field research), which contributes to the inventory of linguistic facts and the identification of universal and specific features of the functioning of elements of argotic vocabulary. Special attention is paid to the followers of E.M. Beregovskaya, who implement individual projects for the study of substandard lexemes as part of the work of the International Research Laboratory of Orel State University named after I.S. Turgenev “Problems of Social and Territorial Heteroglossia”.

Investigation of Lineament Extraction: Analysis and Comparison of Digital Elevation Models in the Ait Semgane Region, Morocco

An automated lineament extraction model was successfully applied in the mountainous region of Ait Semgane, Morocco, enabling the identification of geological and tectonic linear features. Our research implemented several approaches including Topographic Position Index (TPI), shading, and Digital Elevation Models (DEMs), as input for the lineament extraction algorithm and applied to various Dems. We aimed to compare all of these strategies to determine the optimal method and the most favorable input DEM. Results revealed variable performance among methods, emphasizing the importance of choosing the optimum method based on specific objectives. TPI and Hillshade methods showed high sensitivity in detecting lineaments, while ALOS PALSAR and Sentinel 1 InSAR datasets were effective for subtle features. Lineament density exhibited specific orientations for highly-dissected zones, with TPI highlighting NE-SW and E-W orientations. Lineament orientations demonstrated consistency with established geology, confirming pre-existing tectonic knowledge. Cartographic analysis of faults emphasized the success of the SRTM DEM model with the TPI method, highlighting significant faults. Results also revealed a concentration of faults in the NW and southern sectors, corroborating bibliographic references and the well-documented tectonic setting of the study area. This automated methodology facilitated lineament extraction in unmapped areas, reinforcing the validity of the undertaken cartographic analysis.

Research on BP neural network model-based data mining technique in KOL identification

Abstract. Accompanied by the development of artificial intelligence industry, to play the data value of web crawler technology has been the focus of research in the field of computer network and data science, and the data mining technology based on artificial neural network intelligent algorithm is widely used. In view of this, this paper takes the BP neural network data mining technology, which has excellent nonlinear mapping capability, parallel processing capability and fault tolerance and is widely used, as the basis, and integrates the methods and ideas of data mining into the mining of data laws in the field of KOL identification of key opinion leaders, with a view to finding valuable intrinsic laws and relationships between the mining of web crawler technology and the identification of KOL features. The research content of this paper mainly includes two aspects of research work, the design of high-performance data mining technology and the actual work in the field of KOL recognition. On the one hand, this paper comprehensively describes the basic theory and methods of data mining, and focuses on the in-depth analysis and elaboration of BP neural network-based data mining technology on the basis of understanding and analyzing a variety of data mining technologies. On the other hand, this paper aims to solve the problem of bias in the prediction of traditional KOL model and design the experimental method of KOL recognition by BP neural network algorithm.

Dual-path aggregation transformer network for super-resolution with images occlusions and variability

While Transformer-based approaches have recently achieved notable success in super-resolution, their extensive computational requirements impede widespread practical adoption. High-resolution meteorological satellite cloud imagery is essential for weather analysis and forecasting. Enhancing image resolution through super-resolution techniques facilitates the accurate identification and localization of geographic features by meteorological systems. However, current super-resolution methods fail to restore the intricacies of cloud formations and complex regions fully. This research introduces a novel dual-path aggregation Transformer network (DPAT) tailored to enhance the super-resolution of meteorological satellite cloud images. The DPAT network adeptly captures cloud imagery's subtle details and textures, effectively addressing occlusions and the variability inherent in satellite imagery. It bolsters the model's ability to manage the complex attributes of cloud images through the introduction of the Dual-path Aggregation Self-Attention (DASA) mechanism and the Multi-scale Feature Aggregation Block (MFAB), thereby enhancing performance in processing intricate cloud features. The DASA mechanism synthesizes features across spatial, depth, and channel dimensions via a dual-path approach, thoroughly exploiting feature correlations. The MFAB, designed to supplant the multilayer perceptron, incorporates shift convolution and a multi-scale interaction block to augment feature information, compensating for the deficiency in local information absorption due to fixed receptive fields. Experimental outcomes indicate that DPAT delivers superior super-resolution outcomes. With a parameter count of only 32% of the Enhanced Deep Residual Network (EDSR) or 77% of the Image Restoration using Shift Window Transformer (SwinIR), DPAT matches SwinIR's performance on the satellite cloud dataset. Moreover, DPAT balances accuracy and parameter economy across various datasets. This technology is expected to improve image super-resolution capabilities in multiple fields such as human action recognition and industrial recognition, and indirectly improve the accuracy of image perception tasks.