Search Results (59)

Mechanical defects and sensor failures can substantially undermine the reliability of low-cost sensors, especially in applications where measurement inaccuracies or malfunctions may lead to critical outcomes, including system control disruptions, emergency scenarios, or safety hazards. To overcome these challenges, this paper presents a novel Self-X architecture with sensor redundancy, which incorporates dynamic calibration based on multidimensional mapping. By extracting reliable sensor readings from imperfect or defective sensors, the system utilizes Self-X principles to dynamically adapt and optimize performance. The approach is initially validated on synthetic data from tunnel magnetoresistance (TMR) sensors to facilitate method analysis and comparison. Additionally, a physical measurement setup capable of controlled fault injection is described, highlighting practical validation scenarios and ensuring the realism of synthesized fault conditions. The study highlights a wide range of potential TMR sensor failures that compromise long-term system reliability and demonstrates how multidimensional mapping effectively mitigates both static and dynamic errors, including offset, amplitude imbalance, phase shift, mechanical misalignments, and other issues. Initially, four individual TMR sensors exhibited mean absolute error (MAE) of 4.709°, 5.632°, 2.956°, and 1.749°, respectively. To rigorously evaluate various dimensionality reduction (DR) methods, benchmark criteria were introduced, offering insights into the relative improvements in sensor array accuracy. On average, MAE was reduced by more than 80% across sensor combinations. A clear quantitative trend was observed: for instance, the MAE decreases from 4.7°–5.6° for single sensors to 0.111° when the factor analysis method was applied to four sensors. This demonstrates the concrete benefit of sensor redundancy and DR algorithms for creating robust, fault-tolerant measurement systems. Full article

This paper introduces a comprehensive framework for evaluating rural water access efficiency using Data Envelopment Analysis (DEA). Despite significant investment in water infrastructure, disparities in access efficiency across regions remain a critical challenge for developing countries. We apply an input-oriented Banker-Charnes-Cooper (BCC) DEA model with bootstrap bias correction to assess the relative efficiency of 16 regions in rural Morocco. Our approach incorporates multiple inputs (infrastructure investment, operational costs) and outputs (access coverage, water quality) to evaluate each region’s efficiency in converting resources into water access outcomes. The results reveal substantial efficiency variations (mean bias-corrected efficiency: 0.906, SD: 0.071) with seven regions identified as globally efficient under variable returns to scale. We introduce a novel Water Access-Efficiency Matrix that enables targeted policy interventions across four strategic quadrants. The analysis demonstrates that inefficient regions have an average input reduction potential of 16.4%, with six regions requiring improvements exceeding 10%. Furthermore, the identification of returns to scale characteristics (5 IRS, 6 CRS, 5 DRS) provides crucial guidance for scaling strategies. This framework offers policymakers a robust, multi-dimensional decision support tool for optimizing resource allocation, benchmarking performance, and developing tailored strategies that address both technical efficiency and access equity in water infrastructure development. Full article

Precision agriculture is necessary for dealing with problems like pest outbreaks, a lack of water, and declining crop health. Manual inspections and broad-spectrum pesticide application are inefficient, time-consuming, and dangerous. New drone photography and IoT sensors offer quick, high-resolution, multimodal agricultural data collecting. Regional diversity, data heterogeneity, and privacy problems make it hard to conclude these data. This study proposes a lightweight, hybrid deep learning architecture called federated LeViT-ResUNet that combines the spatial efficiency of LeViT transformers with ResUNet’s exact pixel-level segmentation to address these issues. The system uses multispectral drone footage and IoT sensor data to identify real-time insect hotspots, crop health, and yield prediction. The dynamic relevance and sparsity-based feature selector (DRS-FS) improves feature ranking and reduces redundancy. Spectral normalization, spatial–temporal alignment, and dimensionality reduction provide reliable input representation. Unlike centralized models, our platform trains over-dispersed client datasets using federated learning to preserve privacy and capture regional trends. A huge, open-access agricultural dataset from varied environmental circumstances was used for simulation experiments. The suggested approach improves on conventional models like ResNet, DenseNet, and the vision transformer with a 98.9% classification accuracy and 99.3% AUC. The LeViT-ResUNet system is scalable and sustainable for privacy-preserving precision agriculture because of its high generalization, low latency, and communication efficiency. This study lays the groundwork for real-time, intelligent agricultural monitoring systems in diverse, resource-constrained farming situations. Full article

The accurate classification of broad-absorption line (BAL) quasars and non-broad-absorption line (non-BAL) quasars is key in understanding active galactic nuclei (AGN) and the evolution of the universe. With the rapid accumulation of data from large-scale spectroscopic survey projects (e.g., LAMOST, SDSS, and DESI), traditional manual classification methods face limitations. In this study, we propose a new method based on deep learning techniques to achieve an accurate distinction between BAL quasars and non-BAL quasars. We use a convolutional neural network (CNN) as the core model, in combination with various dimensionality reduction techniques, including principal component analysis (PCA), t-distributed stochastic neighborhood embedding (t-SNE), and isometric mapping (ISOMAP). These dimensionality reduction methods help extract meaningful features from high-dimensional spectral data while reducing model complexity. We employ quasar spectra from the 16th data release (DR16) of the Sloan Digital Sky Survey (SDSS) and obtain classification labels from the DR16Q quasar catalogues to train and evaluate our model. Through extensive experiments and comparisons, the combination of PCA and CNN achieve a test accuracy of 99.11%, demonstrating the effectiveness of deep learning for classifying the spectral data. Additionally, we explore other dimensionality reduction methods and machine learning models, providing valuable insights for future research in this field. Full article

Recently, the application of deep neural networks to detect anomalies on medical images has been facing the appearance of noisy labels, including overlapping objects and similar classes. Therefore, this study aims to address this challenge by proposing a unique attention module that can assist deep neural networks in focusing on important object features in noisy medical image conditions. This module integrates global context modeling to create long-range dependencies and local interactions to enable channel attention ability by using 1D convolution that not only performs well with noisy labels but also consumes significantly less resources without any dimensionality reduction. The module is then named Global Context and Local Interaction (GCLI). We have further experimented and proposed a partial attention strategy for the proposed GCLI module, aiming to efficiently reduce weighted redundancies. This strategy utilizes a subset of channels for GCLI to produce attention weights instead of considering every single channel. As a result, this strategy can greatly reduce the risk of introducing weighted redundancies caused by modeling global context. For classification, our proposed method is able to assist ResNet34 in achieving up to 82.5% accuracy on the Chaoyang test set, which is the highest figure among the other SOTA attention modules without using any processing filter to reduce the effect of noisy labels. For object detection, the GCLI is able to boost the capability of YOLOv8 up to 52.1% mAP50 on the GRAZPEDWRI-DX test set, demonstrating the highest performance among other attention modules and ranking second in the mAP50 metric on the VinDR-CXR test set. In terms of model complexity, our proposed GCLI module can consume fewer extra parameters up to 225 times and has inference speed faster than 30% compared to the other attention modules. Full article

This paper introduces a robust bi-orthogonal projection (RBOP) learning method for dimensionality reduction (DR). The proposed RBOP enhances the flexibility, robustness, and sparsity of the embedding framework, extending beyond traditional DR methods such as principal component analysis (PCA), neighborhood preserving embedding (NPE), and locality preserving projection (LPP). Unlike conventional approaches that rely on a single type of projection, RBOP innovates by employing two types of projections: the “true” projection and the “counterfeit” projection. These projections are crafted to be orthogonal, offering enhanced flexibility for the “true” projection and facilitating more precise data transformation in the process of subspace learning. By utilizing sparse reconstruction, the acquired true projection has the capability to map the data into a low-dimensional subspace while efficiently maintaining sparsity. Observing that the two projections share many similar data structures, the method aims to maintain the similarity structure of the data through distinct reconstruction processes. Additionally, the incorporation of a sparse component allows the method to address noise-corrupted data, compensating for noise during the DR process. Within this framework, a number of new unsupervised DR techniques have been developed, such as RBOP_PCA, RBOP_NPE, and RBO_LPP. Experimental results from both natural and synthetic datasets indicate that these proposed methods surpass existing, well-established DR techniques. Full article

Due to the hydraulic sorting effect in the hydraulic filling process, a fine-grained aggregate layer dominated by silty fine sand with uneven distribution is easily formed in reclamation projects, which triggers issues with the bearing capacity and nonuniform settlement of calcareous sand foundations. In this study, a series of one-dimensional compression tests were conducted to investigate the effect of different fines contents (f_c) on the compression behavior of calcareous sand. The results show that at the same relative density (medium-density, D_r = 50%), the addition of fine particles leads to a reduction in the initial void ratio (for f_c ≤ 40%). Furthermore, while the compressibility of the soil samples increases with the rising of fines content, it begins to decrease with further addition of fine particles beyond a threshold value of fines content (f_c-th). Additionally, particle crushing contributes to the compressive deformation of calcareous sand, and the particle relative breakage of calcareous sand increases at the initial stage of adding fine particles. Moreover, a comparison of the compression test results between calcareous silty sand (f_c = 10%) and clean sand reveals that the addition of fine particles accentuates the compressibility differences among calcareous sands with different relative densities. These findings provide valuable insights for addressing the challenges posed by fine-grained layers in calcareous sand foundations. Full article

In this study, we propose the CVMFCC-DR (Complex-Valued Mel-Frequency Cepstral Coefficients Dimensionality Reduction) algorithm as an efficient method for reducing the dimensionality of speech signals. By utilizing the complex-valued MFCC technique, which considers both real and imaginary components, our algorithm enables dimensionality reduction without information loss while decreasing computational costs. The efficacy of the proposed algorithm is validated through experiments which demonstrate its effectiveness in building a speech recognition model using a complex-valued neural network. Additionally, a complex-valued softmax interpretation method for complex numbers is introduced. The experimental results indicate that the approach yields enhanced performance compared to traditional MFCC-based techniques, thereby highlighting its potential in the field of speech recognition. Full article

Multivariate pattern analysis (MVPA) has played an extensive role in interpreting brain activity, which has been applied in studies with modalities such as functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) and Electroencephalography (EEG). The advent of wearable MEG systems based on optically pumped magnetometers (OPMs), i.e., OP-MEG, has broadened the application of bio-magnetism in the realm of neuroscience. Nonetheless, it also raises challenges in temporal decoding analysis due to the unique attributes of OP-MEG itself. The efficacy of decoding performance utilizing multimodal fusion, such as MEG-EEG, also remains to be elucidated. In this regard, we investigated the impact of several factors, such as processing methods, models and modalities, on the decoding outcomes of OP-MEG. Our findings indicate that the number of averaged trials, dimensionality reduction (DR) methods, and the number of cross-validation folds significantly affect the decoding performance of OP-MEG data. Additionally, decoding results vary across modalities and fusion strategy. In contrast, decoder type, resampling frequency, and sliding window length exert marginal effects. Furthermore, we introduced mutual information (MI) to investigate how information loss due to OP-MEG data processing affect decoding accuracy. Our study offers insights for linear decoding research using OP-MEG and expand its application in the fields of cognitive neuroscience. Full article

The high dimensionality of hyperspectral images (HSIs) brings significant redundancy to data processing. Band selection (BS) is one of the most commonly used dimensionality reduction (DR) techniques, which eliminates redundant information between bands while retaining a subset of bands with a high information content and low noise. The wild horse optimizer (WHO) is a novel metaheuristic algorithm widely used for its efficient search performance, yet it tends to become trapped in local optima during later iterations. To address these issues, an enhanced wild horse optimizer (IBSWHO) is proposed for HSI band selection in this paper. IBSWHO utilizes Sobol sequences to initialize the population, thereby increasing population diversity. It incorporates Cauchy mutation to perturb the population with a certain probability, enhancing the global search capability and avoiding local optima. Additionally, dynamic random search techniques are introduced to improve the algorithm search efficiency and expand the search space. The convergence of IBSWHO is verified on commonly used nonlinear test functions and compared with state-of-the-art optimization algorithms. Finally, experiments on three classic HSI datasets are conducted for HSI classification. The experimental results demonstrate that the band subset selected by IBSWHO achieves the best classification accuracy compared to conventional and state-of-the-art band selection methods, confirming the superiority of the proposed BS method. Full article

At present, it is critical to accurately monitor wheat crops to help decision-making processes in precision agriculture. This research aims to retrieve various wheat crop traits from hyperspectral data using machine learning regression algorithms (MLRAs) and dimensionality reduction (DR) techniques. This experiment was conducted in an agricultural field in Arborea, Oristano-Sardinia, Italy, with different factors such as cultivars, N-treatments, and soil ploughing conditions. Hyperspectral data were acquired on the ground using a full-range Spectral Evolution spectrometer (350–2500 nm). Four DR techniques, including (i) variable influence on projection (VIP), (ii) principal component analysis (PCA), (iii) vegetation indices (VIs), and (iv) spectroscopic feature (SF) calculation, were undertaken to reduce the dimension of the hyperspectral data while maintaining the information content. We used five MLRA models, including (i) partial least squares regression (PLSR), (ii) random forest (RF), (iii) support vector regression (SVR), (iv) Gaussian process regression (GPR), and (v) neural network (NN), to retrieve wheat traits at either leaf and canopy levels. The studied traits were leaf area index (LAI), leaf and canopy water content (LWC and CWC), leaf and canopy chlorophyll content (LCC and CCC), and leaf and canopy nitrogen content (LNC and CNC). MLRA models were able to accurately retrieve wheat traits at the canopy level with PLSR and NN indicating the highest modelling performance. On the contrary, MLRA models indicated less accurate retrievals of the leaf-level traits. DR techniques were found to notably improve the retrieval accuracy of crop traits. Furthermore, the generated models were re-calibrated using soil spectra and then transferred to an airborne dataset collected using a CASI-SASI hyperspectral sensor, allowing the estimation of wheat traits across the entire field. The predicted crop trait maps illustrated consistent patterns while also preserving the real-field characteristics well. Lastly, a statistical paired t-test was undertaken to conduct a proof of concept of wheat phenotyping analysis considering the different agricultural variables across the study site. N-treatment caused significant differences in wheat crop traits in many instances, whereas the observed differences were less pronounced between the cultivars. No particular impact of soil ploughing conditions on wheat crop characteristics was found. Using such combinations of MLRA and DR techniques based on hyperspectral data can help to effectively monitor crop traits throughout the cropping seasons and can also be readily applied to other agricultural settings to help both precision farming applications and the implementation of high-throughput phenotyping solutions. Full article

The goal of Feature Extraction Algorithms (FEAs) is to combat the dimensionality curse, which renders machine learning algorithms ineffective. The most representative FEAs are investigated conceptually and experimentally in our work. First, we discuss the theoretical foundation of a variety of FEAs from various categories like supervised vs. unsupervised, linear vs. nonlinear and random-projection-based vs. manifold-based, show their algorithms and compare these methods conceptually. Second, we determine the finest sets of new features for various datasets, as well as in terms of statistical significance, evaluate the eminence of the different types of transformed feature spaces and power analysis, and also determine the FEA efficacy in terms of speed and classification accuracy. Full article

Enhancing the generalization capability of time-series models for streamflow prediction using dimensionality reduction (DR) techniques remains a major challenge in water resources management (WRM). In this study, we investigated eight DR techniques and their effectiveness in mitigating the curse of dimensionality, which hinders the performance of machine learning (ML) algorithms in the field of WRM. Our study delves into the most non-linear unsupervised representative DR techniques, including principal component analysis (PCA), kernel PCA (KPCA), multi-dimensional scaling (MDS), isometric mapping (ISOMAP), locally linear embedding (LLE), t-distributed stochastic neighbor embedding (t-SNE), Laplacian eigenmaps (LE), and autoencoder (AE), examining their effectiveness in multi-step ahead (MSA) streamflow prediction. In this study, we conducted a conceptual comparison of these techniques. Subsequently, we focused on their performance in four different case studies in the USA. Moreover, we assessed the quality of the transformed feature spaces in terms of the MSA streamflow prediction improvement. Through our investigation, we gained valuable insights into the performance of different DR techniques within linear/dense/convolutional neural network (CNN)/long short-term memory neural network (LSTM) and autoregressive LSTM (AR-LSTM) architectures. This study contributes to a deeper understanding of suitable feature extraction techniques for enhancing the capabilities of the LSTM model in tackling high-dimensional datasets in the realm of WRM. Full article

In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier’s performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier’s performance emphasizes the role of feature selection methods. Full article

In the present study, various combinations of dimensionality reduction methods with data clustering methods for the analysis of biopsy samples of intracranial tumors were investigated. Fresh biopsies of intracranial tumors were studied in the Laboratory of Neurosurgical Anatomy and Preservation of Biological Materials of N.N. Burdenko Neurosurgery Medical Center no later than 4 h after surgery. The spectra of Protoporphyrin IX (Pp IX) fluorescence, diffuse reflectance (DR) and Raman scattering (RS) of biopsy samples were recorded. Diffuse reflectance studies were carried out using a white light source in the visible region. Raman scattering spectra were obtained using a 785 nm laser. Patients diagnosed with meningioma, glioblastoma, oligodendroglioma, and astrocytoma were studied. We used the cluster analysis method to detect natural clusters in the data sample presented in the feature space formed based on the spectrum analysis. For data analysis, four clustering algorithms with eight dimensionality reduction algorithms were considered. Full article