33,902 Results Found

Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. Th...

The massive adaptation of reverse transcriptase-polymerase chain reaction (RT-PCR) has facilitated efforts to battle against the COVID-19 pandemic that has inflicted millions of individuals around the world. Besides RT-PCR, radiography imaging examin...

Thickness quantification of conductive ferromagnetic materials has become a common necessity in present-day structural health monitoring and infrastructure maintenance. Recent research has found Pulsed Eddy Current (PEC) sensing, especially the detec...

Wearable sensors have become increasingly popular for assessing athletic performance, but the optimal methods for processing and analyzing the data remain unclear. This study investigates the efficacy of discrete and continuous feature-extraction met...

In order to improve image recognition technologies in an IoT environment, we propose a data reduction scheme for a feature-extractable CMOS image sensor and present simulation results for object recognition using feature data. We evaluated the accura...

The degradation of visual quality in remote sensing images caused by haze presents significant challenges in interpreting and extracting essential information. To effectively mitigate the impact of haze on image quality, we propose an unsupervised ge...

For the purpose of improving the accuracy of underwater acoustic target recognition with only a small number of labeled data, we proposed a novel recognition method, including 4 steps: pre-processing, pre-training, fine-tuning and recognition. The 4...

Phishing remains one of the most pervasive social engineering threats, exploiting human vulnerabilities and continuously evolving to bypass static detection mechanisms. Existing machine learning models achieve high accuracy but often act as opaque sy...

Deep-learning methods have exhibited exceptional performance in numerous target-detection domains, and their application is steadily expanding to include infrared small-target detection as well. However, the effect of existing deep-learning methods i...

Transformers play a crucial role in power systems. In this respect, fault diagnosis and aging state assessment have garnered significant attention from researchers. Herein, accelerated thermal aging and Raman scattering experiments are conducted on o...

The MVB is a kind of widely used vehicle-level bus, which is crucial for the normal operation of trains. However, the MVB bus contains device node terminals and the topology is complex, which makes fault location difficult. The voltage waveform of th...

Effective feature extraction (FE) has always been the focus of hyperspectral images (HSIs). For aerial remote-sensing HSIs processing and its land cover classification, in this article, an efficient two-staged hyperspectral FE method based on total v...

The fractal dimension (FD) is a classical nonlinear dynamic index that can effectively reflect the dynamic transformation of a signal. However, FD can only reflect signal information of a single scale in the whole frequency band. To solve this proble...

Hyperspectral imagery (HSI) integrates many continuous and narrow bands that cover different regions of the electromagnetic spectrum. However, the main challenge is the high dimensionality of HSI data due to the ’Hughes’ phenomenon. Thus, dimensional...

Brain tumors are pathologies characterized by a high degree of mortality. An early diagnosis of these pathologies could reduce mortality and limit the adverse effects of brain surgery. Computer-aided tomography (CT), and magnetic resonance imaging (M...

Bearing is one of the most important parts of rotating machinery with high failure rate, and its working state directly affects the performance of the entire equipment. Hence, it is of great significance to diagnose bearing faults, which can contribu...

In recent years, great progress has been made in the field of super-resolution (SR) reconstruction based on deep learning techniques. Although image SR techniques show strong potential in image reconstruction, the effective application of these techn...

Many research groups in academia and industry are focusing on the performance improvement of electronic nose (E-nose) systems mainly involving three optimizations, which are sensitive material selection and sensor array optimization, enhanced feature...

To address the difficulty of extracting the features of composite-fault signals under a low signal-to-noise ratio and complex noise conditions, a feature-extraction method based on phase-space reconstruction and maximum correlation Re’nyi entro...

The extraction of buildings from remote sensing images is of crucial significance in urban management and planning, but it remains difficult to automatically extract buildings with precise boundaries from remote sensing images. In this paper, we prop...

Marine background noise (MBN) is the background noise of the marine environment, which can be used to invert the parameters of the marine environment. However, due to the complexity of the marine environment, it is difficult to extract the features o...

In this paper, we propose a new algorithm for data extraction from time-series data, and furthermore automatic calculation of highly informative deep features to be used in fault detection. In data extraction, elevator start and stop events are extra...

Heart disease is currently the leading cause of death in the world. The electrocardiogram (ECG) is the recording of the electrical activity generated by the heart. Its low cost and simplicity have made it an essential test for monitoring heart diseas...

As a biological feature with strong spatio-temporal correlation, the current difficulty of gait recognition lies in the interference of covariates (viewpoint, clothing, etc.) in feature extraction. In order to weaken the influence of extrinsic variab...

The hyperspectral feature extraction technique is one of the most popular topics in the remote sensing community. However, most hyperspectral feature extraction methods are based on region-based local information descriptors while neglecting the corr...

Spectral features cannot effectively reflect the differences among the ground objects and distinguish their boundaries in hyperspectral image (HSI) classification. Multi-scale feature extraction can solve this problem and improve the accuracy of HSI...

We propose a local feature descriptor based on moment. Although conventional scale invariant feature transform (SIFT)-based algorithms generally use difference of Gaussian (DoG) for feature extraction, they remain sensitive to more complicated deform...

This paper aims to provide a brief review of the feature extraction methods applied for finger vein recognition. The presented study is designed in a systematic way in order to bring light to the scientific interest for biometric systems based on fin...

This paper describes an image enhancement method for reliable image feature matching. Image features such as SIFT and SURF have been widely used in various computer vision tasks such as image registration and object recognition. However, the reliable...

Hyperspectral images (HSIs) contain subtle spectral details and rich spatial contextures of land cover that benefit from developments in spectral imaging and space technology. The classification of HSIs, which aims to allocate an optimal label for ea...

Plant growth is closely related to the structure of its stem. The ultrasonic echo signal of the plant stem carries much information on the stem structure, providing an effective means for analyzing stem structure characteristics. In this paper, we pr...

Semantic segmentation is one of the most active research topics in computer vision with the goal to assign dense semantic labels for all pixels in a given image. In this paper, we introduce HFEN (Hierarchical Feature Extraction Network), a lightweigh...

Extracting diverse spectral features from hyperspectral images has become a hot topic in recent years. However, these models are time consuming for training and test and suffer from a poor discriminative ability, resulting in low classification accur...

Roller bearings are the most widely used and easily damaged mechanical parts in rotating machinery. Their running state directly affects rotating machinery performance. Empirical mode decomposition (EMD) easily occurs illusive component and mode mixi...

In a player-versus-player game such as StarCraft II, it is important to match players with others with similar skills. Studies modeling player skills were conducted, with 47.3% and 61.3% performance. In order to improve the performance, we collected...

Background: Electrocardiography (ECG) signals are commonly used to detect cardiac disorders, with 12-lead ECGs being the standard method for acquiring these signals. The primary objective of this research is to propose a new feature engineering model...

Mining the inherent emotional feature of life is of great significance, and the method to extract the inherent emotional feature with a small number of samples is explored based on neonatal cry in this study. The minimum embedding dimension is taken...

This paper investigates weak fault feature extraction in AUV thrusters under multi-input signal conditions. Conventional methods often rely on insufficient input signals, leading to a non-monotonic mapping between fault features and fault severity. T...

In this paper, we develop a hyperspectral feature extraction method called sparse and smooth low-rank analysis (SSLRA). First, we propose a new low-rank model for hyperspectral images (HSIs) where we decompose the HSI into smooth and sparse component...

Metal components such as rolling bearings are prone to wear, cracks, and defects in harsh environments and long-term use, leading to performance degradation and potential equipment failures. Therefore, detecting surface cracks and other defects in ro...

The feature point method is the mainstream method to accomplish inter-frame estimation in visual Simultaneous Localization and Mapping (SLAM) methods, among which the Oriented FAST and Rotated BRIEF (ORB) feature-based method provides an equilibrium...

Background: At the time of cancer diagnosis, it is crucial to accurately classify malignant gastric tumors and the possibility that patients will survive. Objective: This study aims to investigate the feasibility of identifying and applying a new fea...

Facial occlusion represents a significant challenge in the domain of facial expression recognition (FER). The absence of feature information due to occlusion has been demonstrated to result in a reduction in recognition accuracy and model robustness....

Lung cancer is a global health challenge, hindered by delayed diagnosis and the disease’s complex molecular landscape. Accurate patient survival prediction is critical, motivating the exploration of various -omics datasets using machine learnin...

Airborne LiDAR (Light Detection and Ranging) is an active Earth observing system, which can directly acquire high-accuracy and dense building roof data. Thus, airborne LiDAR has become one of the mainstream source data for building detection and reco...

This paper reports on a new feature extraction method for detection of applied stress using magnetic Barkhausen noise (MBN). Some previous methods for extracting MBN features need to choose a suitable threshold so that these features can have good li...

To tackle the challenges of relative attitude adaptability and limited sample availability in underwater moving target recognition for active sonar, this study focuses on key aspects such as feature extraction, network model design, and information f...

Landcover classification is an important application in remote sensing, but it is always a challenge to distinguish different features with similar characteristics or large-scale differences. Some deep learning networks, such as UperNet, PSPNet, and...

We have developed a novel algorithm for sEMG feature extraction and classification. It is based on a hybrid network composed of spiking and artificial neurons. The spiking neuron layer with mutual inhibition was assigned as feature extractor. We demo...

In order to accurately extract the characteristics of debris flow caused by group rainstorms, effectively identify the on-site information of debris flow, and provide a scientific basis for debris flow monitoring, early warning and disaster control,...