Self‐Adaptive Quantum Kernel Principal Component Analysis for Compact Readout of Chemiresistive Sensor Arrays
Sign Up to like & getrecommendations! Published in 2024 at "Advanced Science"
DOI: 10.1002/advs.202411573
Abstract: The rapid growth of Internet of Things (IoT) devices necessitates efficient data compression techniques to manage the vast amounts of data they generate. Chemiresistive sensor arrays (CSAs), a simple yet essential component in IoT systems,… read more here.
Keywords: principal component; sensor; component analysis; sensor arrays ... See more keywords
Self‐Adaptive Quantum Kernel Principal Component Analysis for Compact Readout of Chemiresistive Sensor Arrays (Adv. Sci. 15/2025)
Sign Up to like & getrecommendations! Published in 2025 at "Advanced Science"
DOI: 10.1002/advs.202570102
Abstract: Quantum Machine Learning In article number 2411573, Zeheng Wang, Timothy van der Laan, and Muhammad Usman introduce a quantum algorithm‐driven framework to compress chemiresistive sensor data. By employing a self‐adaptive quantum kernel (SAQK), classical data… read more here.
Keywords: principal component; self adaptive; adaptive quantum; chemiresistive sensor ... See more keywords
Fault detection and diagnosis strategy based on a weighted and combined index in the residual subspace associated with PCA
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Chemometrics"
DOI: 10.1002/cem.2981
Abstract: Process monitoring and diagnosis are crucial for efficient and optimal operation of a chemical plant. Most multivariate statistical process monitoring strategies, such as principal component analysis, kernel principal component analysis, and dynamic principal component analysis,… read more here.
Keywords: detection; index; diagnosis; principal component ... See more keywords
Principal component regression that minimizes the sum of the squares of the relative errors: Application in multivariate calibration models
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Chemometrics"
DOI: 10.1002/cem.3341
Abstract: Relative errors are typically used in chemometrics to evaluate the performance of a multivariate predictive model. However, these models are not obtained through the criterion of minimizing relative errors, as would be expected in a… read more here.
Keywords: regression minimizes; principal component; minimizes sum; relative errors ... See more keywords
Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity
Sign Up to like & getrecommendations! Published in 2024 at "Human Brain Mapping"
DOI: 10.1002/hbm.26682
Abstract: Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA)… read more here.
Keywords: principal component; brain; pca; component analysis ... See more keywords
Evaluation of principal component analysis image denoising on multi‐exponential MRI relaxometry
Sign Up to like & getrecommendations! Published in 2019 at "Magnetic Resonance in Medicine"
DOI: 10.1002/mrm.27658
Abstract: Multi‐exponential relaxometry is a powerful tool for characterizing tissue, but generally requires high image signal‐to‐noise ratio (SNR). This work evaluates the use of principal‐component‐analysis (PCA) denoising to mitigate these SNR demands and improve the precision… read more here.
Keywords: multi exponential; image; principal component; relaxometry ... See more keywords
Principal component analysis of hybrid functional and vector data.
Sign Up to like & getrecommendations! Published in 2021 at "Statistics in medicine"
DOI: 10.1002/sim.9117
Abstract: We propose a practical principal component analysis (PCA) framework that provides a nonparametric means of simultaneously reducing the dimensions of and modeling functional and vector (multivariate) data. We first introduce a Hilbert space that combines… read more here.
Keywords: vector; hybrid functional; principal component; vector data ... See more keywords
A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis
Sign Up to like & getrecommendations! Published in 2018 at "Pure and Applied Geophysics"
DOI: 10.1007/s00024-018-1856-3
Abstract: For the first time, we introduced the probabilistic principal component analysis (pPCA) regarding the spatio-temporal filtering of Global Navigation Satellite System (GNSS) position time series to estimate and remove Common Mode Error (CME) without the… read more here.
Keywords: time series; principal component; analysis; time ... See more keywords
Data-driven topology design based on principal component analysis for 3D structural design problems
Sign Up to like & getrecommendations! Published in 2024 at "Structural and Multidisciplinary Optimization"
DOI: 10.1007/s00158-025-04036-5
Abstract: Topology optimization is a structural design methodology widely utilized to address engineering challenges. However, sensitivity-based topology optimization methods struggle to solve optimization problems characterized by strong non-linearity. Leveraging the sensitivity-free nature and high capacity of… read more here.
Keywords: principal component; methodology; structural design; data driven ... See more keywords
Qualitative evaluation of ferritin in serum samples by Raman spectroscopy and principal component analysis
Sign Up to like & getrecommendations! Published in 2018 at "Lasers in Medical Science"
DOI: 10.1007/s10103-018-2576-8
Abstract: Iron molecule is of great importance in the synthesis of hemoglobin which is essential for oxygen transport. Iron levels are quantified by accurately high sensitivity tests, such as serum ferritin (SF). However, common studies to… read more here.
Keywords: serum samples; principal component; analysis; spectroscopy ... See more keywords
Random permutation principal component analysis for cancelable biometric recognition
Sign Up to like & getrecommendations! Published in 2017 at "Applied Intelligence"
DOI: 10.1007/s10489-017-1117-7
Abstract: Although biometrics is being increasingly used across the world, it also raises concerns over privacy and security of the enrolled identities. This is due to the fact that biometrics are not cancelable and if compromised… read more here.
Keywords: principal component; cancelable biometric; biometrics; random permutation ... See more keywords