Soil nutrients, including soil available potassium (SAK), soil available phosphorous (SAP), and soil organic matter (SOM), play an important role in farmland soil productivity, food security, and agricultural management. Spectroscopic analysis has pr...
A vehicle-mounted solar occultation flux–Fourier transform infrared spectrometer uses the sun as an infrared light source to quantify molecular absorption in the atmosphere. It can be used for the rapid three-dimensional monitoring of pollutant...
The development of fibre optic sensors for measuring the refractive index is related to the creation of new periodic structures and demodulation algorithms for the measured spectrum. Recently, we proposed a double-comb Tilted fibre Bragg grating (DCT...
Fourier transform infrared spectroscopy (FT-IR) is widely used in the analysis of the chemical composition of biological materials and has the potential to reveal new aspects of the molecular basis of diseases, including different types of cancer. Th...
To address the challenges in processing and identifying mine acoustic emission signals, as well as the inefficiency and inaccuracy issues prevalent in existing methods, an enhanced CELMD approach is adopted for preprocessing the acoustic emission sig...
Tone mapping (TM) aims to display high dynamic range scenes on media with limited visual information reproduction. Logarithmic transformation is a widely used preprocessing method in TM algorithms. However, the conventional logarithmic transformation...
Over the last decade, supervised denoising models, trained on extensive datasets, have exhibited remarkable performance in image denoising, owing to their superior denoising effects. However, these models exhibit limited flexibility and manifest vary...
The intra-pulse modulation of radar emitter signals is a key feature for analyzing radar systems. Traditional methods which require a tremendous amount of prior knowledge are insufficient to accurately classify the intra-pulse modulations. Recently,...
The state of charge (SoC) is one of the critical parameters in battery management systems, as it directly determines the operational safety and reliability of batteries. To accurately predict the SoC of an electric forklift under varying operating co...
The state of health (SOH) of a lithium-ion battery (LIB) should be accurately estimated to ensure its safe operation under various driving conditions for electric vehicles. To estimate the SOH of LIBs, it is necessary to develop a technique that can...
This manuscript addresses the problem of technical state assessment of power transformers based on data preprocessing and machine learning. The initial dataset contains diagnostics results of the power transformers, which were collected from a variet...
(1) Background: Arrhythmias, or irregular heart rhythms, are a prevalent cardiovascular condition and are diagnosed using electrocardiogram (ECG) signals. Advances in deep learning have enabled automated analysis of these signals. However, the effect...
With the Fourth Industrial Revolution unfolding worldwide, technologies including the Internet of Things, sensors, and artificial intelligence are undergoing rapid development. These technological advancements have played a significant role in the dr...
Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been used to identify microorganisms and predict antibiotic resistance. The preprocessing method for the MS spectrum is key to extracting critical infor...
Frequent and severe icing on transmission lines poses a serious threat to the stability and safe operation of the power system. Meteorological data, inherently stochastic and uncertain, requires effective preprocessing and feature extraction to ensur...
Partial discharge (PD) is a common issue in power transformers that can lead to catastrophic failures if left undetected. Time reversal (TR) is a well-known technique in signal processing that can reconstruct signals by reversing the direction of tim...
This paper presents a pre-processing method to remove multiple scattering artifacts in spectroscopic optical coherence tomography (SOCT) using time–frequency analysis approaches. The method uses a multiple multipole expansion approach to model the li...
The prices of walnuts vary according to their geographical origin and, therefore, offer a financial incentive for adulteration. A reliable analysis method is required to quickly detect possible misdeclarations and thus prevent food fraud. In this stu...
In the practical reality of face recognition applications, the human face can have only a limited number of training images. However, it is known that, in general, increasing the number of training images also increases the performance of face recogn...
Rapid and non-destructive acquisition of soil nutrient information is crucial for precision fertilization and soil quality monitoring. This study aims to establish a Raman spectroscopy-based framework for predicting key soil fertility indicators, inc...
The huge quantity of information and the high spectral resolution of hyperspectral imagery present a challenge when performing traditional processing techniques such as classification. Dimensionality and noise reduction improves both efficiency and a...
Minor elements significantly influence the properties of stainless steel. In this study, a laser-induced breakdown spectroscopy (LIBS) technique combined with a back-propagation artificial intelligence network (BP-ANN) was used to detect nickel (Ni),...
In this paper, a model based on discrete wavelet transform and convolutional neural network for brain MR image classification has been proposed. The proposed model is comprised of three main stages, namely preprocessing, feature extraction, and class...
Due to image reconstruction process of all image capturing methods, image data is inherently affected by uncertainty. This is caused by the underlying image reconstruction model, that is not capable to map all physical properties in its entirety. In...
Untargeted metabolomic studies generate information-rich, high-dimensional, and complex datasets that remain challenging to handle and fully exploit. Despite the remarkable progress in the development of tools and algorithms, the “exhaustive” extract...
Artificial intelligence applications are becoming increasingly popular and are producing better results in many areas of research. The quality of the results depends on the quantity of data and its information content. In recent years, the amount of...
The leaf water content (LWC) of citrus is a pivotal indicator for assessing citrus water status. Addressing the limitations of traditional hyperspectral modelling methods, which rely on single preprocessing techniques and struggle to fully exploit th...
Condition diagnosis of power transformers using acoustic signals is a nonstop, contactless method of equipment maintenance that can diagnose the transformer’s type of abnormal condition. To heighten the accuracy and efficiency of the abnormal m...
Black soil in northeast China is gradually degraded and soil organic matter (SOM) content decreases at a rate of 0.5% per year because of the long-term cultivation. SOM content can be obtained rapidly by visible and near-infrared (Vis–NIR) spec...
Medicinal plants have incredibly high economic value, and a practical evaluation of their quality is the key to promoting industry development. The deep learning model based on residual convolutional neural network (ResNet) has the advantage of autom...
This paper proposes a method of automatically detecting and classifying low frequency noise generated by power transformers using sensors and dedicated machine learning algorithms. The method applies the frequency spectra of sound pressure levels gen...
In today’s data-driven world, where information is one of the most valuable resources, forecasting the behavior of time series, collected by modern sensor networks and IoT systems, is crucial across various fields, including finance, climatolog...
Because lithium-ion batteries are widely used for various purposes, it is important to estimate their state of health (SOH) to ensure their efficiency and safety. Despite the usefulness of model-based methods for SOH estimation, the difficulties of b...
Bearing fault diagnosis is important to ensure safe operation and reduce loss for most rotating machinery. In recent years, deep learning (DL) has been widely used for bearing fault diagnosis and has achieved excellent results. Continuous wavelet tra...
Rainfall prediction is a fundamental process in providing inputs for climate impact studies and hydrological process assessments. Rainfall events are, however, a complicated phenomenon and continues to be a challenge in forecasting. This paper introd...
The potential of a voltametric E-tongue coupled with a custom data pre-processing stage to improve the performance of machine learning techniques for rapid discrimination of tomato purées between cultivars of different economic value has been...
The widespread use of mobile devices has popularized the idea of indoor navigation. The Wi-Fi fingerprint method is emerging as an important alternative indoor positioning method for GPS usage difficulties. This study utilizes RSSI signals with three...
According to the Industry 4.0 paradigm, all objects in a factory, including people, are equipped with communication capabilities and integrated into cyber-physical systems (CPS). Human activity recognition (HAR) based on wearable sensors provides a m...
As the field of fault diagnosis in electrical machines has significantly attracted the interest of the research community in recent years, several methods have arisen in the literature. Also, raw data signals can be acquired easily nowadays, and, thu...
Characterization of Unintended Conducted Emissions (UCE) from electronic devices is important when diagnosing electromagnetic interference, performing nonintrusive load monitoring (NILM) of power systems, and monitoring electronic device health, amon...
Currently, invasive and external radio frequency identification (RFID) devices and pet tags are widely used for dog identification. However, social problems such as abandoning and losing dogs are constantly increasing. A more effective alternative to...
In this paper, a novel approach to facial expression recognition based on the discrete separable shearlet transform (DSST) and normalized mutual information feature selection is proposed. The approach can be divided into five steps. First, all test a...
Brain tumors are abnormal cell growth in the brain tissues that can be cancerous or not. In any case, they could be a very aggressive disease that should be detected as early as possible. Usually, magnetic resonance imaging (MRI) is the main tool com...
The frequent occurrence of single-phase grounding faults affects the reliable operation of power systems. When a single-phase grounding fault occurs, it is difficult to accurately identify the fault type because of the weak characterization and subtl...
The emergence of anti-social behaviour in online environments presents a serious issue in today’s society. Automatic detection and identification of such behaviour are becoming increasingly important. Modern machine learning and natural languag...
Refineries are complex industrial systems that transform crude oil into more valuable subproducts. Due to the advances in sensors, easily measurable variables are continuously monitored and several data-driven soft-sensors are proposed to control the...
Language is that kind of expression by which effective communication with another can be well expressed. One may consider such as a connecting bridge for bridging communication gaps for the hearing- and speech-impaired, even though it remains as an a...
Conventional synchronization signal detection methods rely on linear correlation function analysis with fixed thresholds, which are insufficient for handling the nonlinear characteristics of practical wireless communication systems. In such environme...
This paper focuses on sorting out two backbone network variants’ development routes for CNNs and transformers based on deep learning methods for side-channel analysis. Combining parametric and quantitative structural analyses, it is preferred t...
Background/Objectives: The automatic identification of coronary artery dominance holds critical importance for clinical decision-making in cardiovascular medicine, influencing diagnosis, treatment planning, and risk stratification. Traditional classi...
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