Matrix Algorithm Research Articles

Numerical investigation of a novel method of laser assisted cryopreservation of biological tissue considering non-fourier heat conduction

The present article proposes a numerical model for a novel laser assisted cryopreservation through vitrification of biological tissue. A two-dimensional numerical model is developed considering the non-Fourier heat conduction. The Finite Volume Method is used for discretization of the governing differential equation while the Tri-diagonal Matrix Algorithm (TDMA) is used for solving the resulting discretized algebraic equation in order to obtain the temperature distribution inside the tissue domain. The existing enthalpy method is modified considering the thermal relaxation time to capture the freezing front. With the increase in thermal relaxation time value, rate of heat transfer and rise in temperature during laser heating decreases and rate of heat loss during freezing also decreases. This reduces the length up to which vitrification is achieved. So, a proper size of the tissue is to be chosen to achieve the desired freezing rate. This length may vary based on the laser parameters and the thermal relaxation time. However, the validity of the present study may be examined experimentally in real ambient conditions before application in tissue preservation.

Epitope1D: accurate taxonomy-aware B-cell linear epitope prediction.

The ability to identify B-cell epitopes is an essential step in vaccine design, immunodiagnostic tests and antibody production. Several computational approaches have been proposed to identify, from an antigen protein or peptide sequence, which residues are more likely to be part of an epitope, but have limited performance on relatively homogeneous data sets and lack interpretability, limiting biological insights that could otherwise be obtained. To address these limitations, we have developed epitope1D, an explainable machine learning method capable of accurately identifying linear B-cell epitopes, leveraging two new descriptors: a graph-based signature representation of protein sequences, based on our well-established Cutoff Scanning Matrix algorithm and Organism Ontology information. Our model achieved Areas Under the ROC curve of up to 0.935 on cross-validation and blind tests, demonstrating robust performance. A comprehensive comparison to alternative methods using distinct benchmark data sets was also employed, with our model outperforming state-of-the-art tools. epitope1D represents not only a significant advance in predictive performance, but also allows biologically meaningful features to be combined and used for model interpretation. epitope1D has been made available as a user-friendly web server interface and application programming interface at https://biosig.lab.uq.edu.au/epitope1d/.

A Low SNR Signal-Sensing Method Based on a Multiple-Antenna Covariance Matrix

In this paper, we propose a promising signal-sensing method based on both single- and multiple-antenna covariance matrices of the received signal. In comparison with microwave diagnostics or energy detection, the proposed method does not need prior information of signal and noise. Therefore, it can be widely used in noise power uncertainty scenarios, such as signal transmission in hypersonic plasma sheath, with symmetry to the signal-sensing method in cognitive radio. Designing an efficient signal-sensing method that supports both the application for the field of hypersonic plasma sheath communication or cognitive radio is still an attractive subject deserving of study in symmetry journals. Theoretical analysis and the implementation algorithm of a single-antenna covariance matrix are carried out for the proposed method, with a possible extension to multiple-antenna situations. Simulations show the proposed method has excellent performance, even for very low signal-noise-ratio (SNR) signals, and is competitive with state-of-the-art algorithms.

Compressed Sensing Σ-Δ Modulators and a Recovery Algorithm for Multi-Channel Wireless Bio-Signal Acquisition

Compressed sensing (CS) exploits signal sparsity in some domain to enable sub-Nyquist sampling which increases the energy efficiency of analog-to-digital conversion (ADC) and downstream data processing circuits–the sampling frequency is determined by the information rate, not the usual Nyquist rate. CS techniques for wireless multi-channel bio-signal recording applications based on sigma-delta modulation (SDM) are detailed and used to validate a multi-channel recovery algorithm. The SDM topology allows the required dot product calculations between the measurement and signal vectors to be performed in conjunction with its inherent integration using minimal additional circuitry. It eliminates opamp output saturation concerns and benefits directly from still ongoing Moore’s Law CMOS technology scaling. Finally, a sparse sensing matrix and recovery algorithm are described that exploit similar sparse signatures across multiple channels to improve both signal recovery accuracy and chip area efficiency. Simulation results validate the concepts.

Improved Design of Feedback Matrix of Adaptive Observer for Induction Motor

In order to address the issue of the induction motor speed sensorless vector control system’s poor dynamic performance at low speed, this research suggests a brand-new feedback matrix algorithm for the full-order adaptive observer. Firstly, the open-loop transfer function based on the back-EMF error is analyzed, the stability conditions of the induction motor operation are deduced, and a preliminary framework is established for designing the feedback gain. Secondly, in order to maximize the system’s dynamic performance, the damping ratio of the speed identification system is chosen as the subject of study. To achieve this, the pole of the second-order characteristic equation of the observer’s complex number system is set up to satisfy the premise of stable operation at low speed and inverted state. The outcomes of the simulations confirm the method’s efficacy.

POMEGRANATE DISEASE DETECTION AND PESTICIDES SUGGESTION

Abstract: Pomegranate is a valuable fruit crop that is widely cultivated in various regions. However, its production is often threatened by various diseases that can significantly affect its yield and quality. In this project, we propose a method for pomegranate disease detection and pesticides suggestion using the gray-level co-occurrence matrix (GLCM) algorithm. The proposed approach utilizes GLCM to extract texture features from the pomegranate leaf images and classify them into healthy or diseased categories. Moreover, we suggest the most effective pesticides to control the detected diseases by using the GLCM algorithm. The performance of the proposed method is evaluated using a dataset of pomegranate leaf images with different disease symptoms. The experimental results demonstrate the effectiveness of our approach in detecting and classifying pomegranate diseases with high accuracy. Furthermore, the suggested pesticides show promising results in controlling the diseases. Overall, the proposed method can assist farmers in identifying pomegranate diseases at an early stage and provide appropriate suggestions for pesticide selection, which can reduce crop losses and enhance crop productivity. Keywords: classification, disease detection, GLCM algorithm.

The Application of Modified K-Nearest Neighbor Algorithm for Classification of Groundwater Quality Based on Image Processing and pH, TDS, and Temperature Sensors

The limited availability of water in remote areas makes rural communities pay less attention to the water quality they use. Water quality analysis is needed to determine the level of groundwater quality used using the Modified K-Nearest Neighbor Algorithm to minimize exposure to a disease. The data used in this study was images combined with sensor data obtained from pH (Potential of Hydrogen), TDS (Total Dissolved Solids) sensors and Temperature Sensors. The test used the Weight voting value as the highest class majority determination and was evaluated using the K-Fold Cross Validation and Multi Class Confusion Matrix algorithms, obtaining the highest accuracy value of 78% at K-Fold = 2, K-Fold = 9, and K- Fold = 10. Meanwhile, the results of testing the effect of the K value obtained the highest accuracy value at K = 5 of 67.90% with a precision value of 0.32, 0.37 recall, and 0.33 F1-Score. From the results of the tests carried out, it can be concluded that most of the water conditions are suitable for use.

Sequential color ghost imaging based on compressed sensing algorithm of post-processing measurement matrix

Compressed sensing algorithm can be used in color ghost imaging to restore the image of object at the less demand of measurements times. However, the quality of the reconstructed color image is usually not satisfactory. In this paper, we propose a sequential color ghost imaging method that can complete color ghost imaging in a simple architecture and improve the quality of color image, which is optimize compressed sensing that can get better result of compressed sensing algorithm by post-processing the measurement matrix and establishing a new compressed sensing process. Under the condition of the same measurement times and reconstruction algorithm, compared with the unpost-processing one, the quality and detail of reconstructed image by the post-processing measurement matrix is quite improved especially with the increase of measurement times. Discussions on factors affecting the quality of the new compressed sensing process, such as the number of measurement and the detection noise intensity, are also conducted. Numerical simulation and physical experiment verified our proposed method.

Stratified multi-density spectral clustering using Gaussian mixture model

Spectral clustering aims to minimise inter-cluster similarity by constructing graph model, which possesses a significant effect in data of arbitrary shape. Nonetheless, there are still two limitations in the existing algorithms. First, spectral methods perform poorly if the densities of instances vary greatly. Second, they are challenging to handle the complex distribution that different objects of densities are mixed. To address the two limitations, in this paper, a novel spectral clustering algorithm is constructed to accommodate more complex multi-density data. The idea of density stratification is proposed through the probability density of Gaussian mixture model, and the number of layers can be automatically determined by the defined scatter index. Then, a new density ratio is established to effectively reduce the density imbalance of diverse instances based on mutual nearest-neighbour and density stratification, which is more advantageous in the clusters with less clear boundaries. Finally, the optimised adjacency matrix and multi-density spectral clustering algorithm are induced to improve the effect of multi-density data. As per experimental results, the proposed algorithm generally outperforms the popular representative algorithms for both synthetic and benchmark datasets, and works more effectively on multi-density as well as single-density data, which illustrates the superiority of the proposed algorithm.

High-Accuracy Spectral Measurement of Stimulated-Brillouin-Scattering Lidar Based on Hessian Matrix and Steger Algorithm

The measurement accuracy of Brillouin scattering spectra is crucial for ocean remote sensing by Brillouin scattering lidar. Due to the limited resolution of ICCD cameras, the traditional processing methods remain at the pixel or partial sub-pixel level, which cannot meet the requirements of high-performance lidar. In this paper, to extract the frequency shift with high precision from stimulated Brillouin scattering (SBS) lidar, a novel spectral processing method with sub-pixel recognition accuracy is proposed based on the Hessian matrix and Steger algorithm combined with the least square fitting method. Firstly, the Hessian matrix and Frangi filter are used for signal denoising. Then, the center points of SBS spectra at the sub-pixel level are extracted using the Steger algorithm and are connected and classified according to the signal type. On that basis, the frequency shifts of Brillouin scattering are calculated by using the center and radii of interference spectra after through fitting by the least squares method. Finally, the water temperatures are inverted by using the frequency shifts of Brillouin scattering. The results show that the processing method proposed in this paper can accurately calculate the frequency shift of Brillouin scattering. The measured errors of frequency shift are generally at an order of MHz, and the inversion accuracy of water temperature can be as low as 0.14 °C. This work is essential to the application for remote sensing the seawater parameters by using the Brillouin lidar technique.

Numerical Analysis of Unsteady Heat Transfer in the Chamber in the Piston Hybrid Compressor with Regenerative Heat Exchange

The adjoint thermodynamic and heat exchange processes in the new class piston compressor with regenerative heat exchange are considered in the paper. The implicit tridiagonal matrix algorithm is implemented to study the unsteady thermal conductivity in the cylinder–piston group. After the formulation of proper initial and boundary conditions, the optimal relationship between temporal and spatial discretization steps is determined. Two different time steps are used in the numerical solution of the two-way coupled fast thermodynamic and slow heat exchange models. The relationship between those time steps is determined as well. The conducted numerical experiments allow the analysis of the dynamics of heat exchange in the cylinder–piston group, temperature variation in different parts of the cylinder–piston group, the impact of the heat transfer processes on isothermal and adiabatic efficiency, the impact of heat exchange dynamics on the thermodynamic cycle, as well as other thermodynamic and energetic effects.

A new semianalytical algorithm for rapid simulation of triaxial electromagnetic logging responses in multilayered biaxial anisotropic formations

A set of compact and stable formulas are developed for the computation of triaxial electromagnetic (EM) logging responses in multilayered formations with biaxial anisotropy. These formulas are based on a new global amplitude propagator matrix (GAPM) algorithm, which derives the amplitudes of upgoing/downgoing waves at the boundaries rather than the reflection/transmission coefficients of each layer. The kernel of the GAPM algorithm is assembling the local continuity equations into a global linear system, which has addressed the notorious overflow problems commonly encountered by the traditional coefficient propagator matrix approach. To fast and accurately convert the EM fields from the spectral domain into the spatial domain, an efficient twofold inverse Fourier transform (IFT) technique is established for the computation of triaxial EM logging responses. Two main techniques are developed to significantly improve the computation accuracy and speed of IFT. On the one hand, the spectral fields at the first integral quadrant can be used to express the fields at the other three quadrants. Consequently, the original twofold infinite integral is analytically simplified to a semiinfinite one and the computation cost can be reduced by three-fourths. On the other hand, a two-level subtraction scheme is developed to reduce the integral singularity. The first-level subtraction is achieved by separating the primary fields from the total fields. The second-level process includes subtracting the spectral scattered fields of an equivalent transverse isotropic (TI) layered medium, integrating the residual fields, and computing the spatial fields of the TI model. The subtractions on the primary and scattered fields can make the integral convergence increase by more than 10 orders of magnitude in favorable conditions. Numerical experiments demonstrate that the new semianalytical formulas are well suited for any dipping angle and an arbitrary number of layers, as well as for thin and thick formations. The modeling method is efficient and robust, which can provide a fast simulator for the interpretation of triaxial EM logging data.

Multi-Techniques for Analyzing X-ray Images for Early Detection and Differentiation of Pneumonia and Tuberculosis Based on Hybrid Features.

An infectious disease called tuberculosis (TB) exhibits pneumonia-like symptoms and traits. One of the most important methods for identifying and diagnosing pneumonia and tuberculosis is X-ray imaging. However, early discrimination is difficult for radiologists and doctors because of the similarities between pneumonia and tuberculosis. As a result, patients do not receive the proper care, which in turn does not prevent the disease from spreading. The goal of this study is to extract hybrid features using a variety of techniques in order to achieve promising results in differentiating between pneumonia and tuberculosis. In this study, several approaches for early identification and distinguishing tuberculosis from pneumonia were suggested. The first proposed system for differentiating between pneumonia and tuberculosis uses hybrid techniques, VGG16 + support vector machine (SVM) and ResNet18 + SVM. The second proposed system for distinguishing between pneumonia and tuberculosis uses an artificial neural network (ANN) based on integrating features of VGG16 and ResNet18, before and after reducing the high dimensions using the principal component analysis (PCA) method. The third proposed system for distinguishing between pneumonia and tuberculosis uses ANN based on integrating features of VGG16 and ResNet18 separately with handcrafted features extracted by local binary pattern (LBP), discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) algorithms. All the proposed systems have achieved superior results in the early differentiation between pneumonia and tuberculosis. An ANN based on the features of VGG16 with LBP, DWT and GLCM (LDG) reached an accuracy of 99.6%, sensitivity of 99.17%, specificity of 99.42%, precision of 99.63%, and an AUC of 99.58%.

Grey level co-occurrence matrix and learning algorithms to quantify and classify use-wear on experimental flint tools

Today, the analysis of use-wear traces of archaeological artefacts uses both qualitative (traditional) and quantitative (innovative) techniques. However, quantitative analyses require time and huge economic resources. It is important to find quantitative solutions that do not have high costs and that allow qualitative analysis to be more robust. Furthermore, quantitative analysis with 2D techniques would facilitate the standardization of the terms used to discriminate and describe the different types of polish.The Gray-Level Co-occurrence Matrix is a 2D image analysis technique that provides quantitative data on image texture. Although quite common in other research field, this technique has not been systematically applied in the archaeological research. Unlike other quantitative approaches this technique takes little time and has no cost, as it uses traditional 2D images. Futhermore, this method acts as a bridge between qualitative and quantitative analyses. In this first approach, four use-wear traces (unused, butchering, shell, wood, antler) on flint experimental samples were analysed with selections of square images of 300x300 and 600x600 pixels (30 for each contact material).This technique together with a learning algorithm called Support Vector Machine has proved effective in distinguishing and quantifying four types of use-wear traces.

Active power balance capability of security and stability control system (SSCS) against outage of UHVDC

The aim of the security and stability control system (SSCS) is to maintain active power balance and frequency stability. For large SSCS, it is difficult to find the state space and the transition rates of the paths. The flat design yields more complex topology. Considering the random outages, the ultra-high-voltage dc (UHVDC) has more capacity levels and states. The existing evaluation cannot quantify the power balance capability of the SSCS. This paper newly quantifies active power balance capability of the multi-level SSCS with partial or complete outage of the UHVDC. An incidence matrix is proposed to describe the topology of the SSCS. A recursive solution to calculate the transition rates is proposed. The equivalent downward transition rate is newly defined. The increasable generation and the transition rates of the UHVDC at any initial state are newly included. The numerical analysis shows that the incidence matrix and recursive algorithm reduces the calculation effort for the practical SSCS with multi-level, redundancy component, and flat design. The active power balance capability of the SSCS is decided by the increasable power, the reduced generation and the downward transition rate.

Bulk and single‐cell spatial transcriptomic analysis reveals cuproptosis‐related molecular subtypes characterized by distinct tumour microenvironment infiltrates in colorectal cancer

AbstractBackgroundThe association between tumour microenvironment (TME) in colorectal cancer (CRC) and cell death patterns requires further exploration. Cuproptosis may provide new insight into analyzing CRC's TME.MethodsWe used cuproptosis‐related genes (CRGs) to stratify the meta‐Gene‐Expression Omnibus cohort by the “non‐negative matrix factorization” consistency matrix algorithm. To clarify the relative abundance of different cells in TME, CIBERSORT and single‐sample gene set enrichment methods were performed. Then, CRGs’ transcription in different cell types and the spatial position of CRGs’ enrichment were demonstrated through single‐cell spatial transcriptomics (STs) analysis. Prediction of clinical outcomes and response rate of immunotherapy was also conducted by constructing CRG_score.ResultsThree cuproptosis‐related TME phenotypes were figured out in CRC with distinctive clinicopathological and prognostic features. Three phenotypes were identical to immune‐inflamed, immune‐desert and immune‐excluded profiles, respectively. Interestingly, cuproptosis‐related cluster 3 (CPRC3) phenotype‐related gene_score was predominantly upregulated in the fibroblast region, consisting of stromal cells. This result was in line with CPRC3's immune‐excluded profile. Based on the robust cuproptosis‐related risk_score and nomogram, CRC patients’ clinical outcome and response to PD1/PD‐L1 immunotherapy can be well predicted.ConclusionsThis study characterized three cuproptosis‐related TME patterns with unique immune infiltration and prognosis in both bulk and single‐cell ST profiling. Risk_score was also built to help clinicians decide on personalized anticancer treatment and immunotherapy applications for CRC patients.

Editorial: Tensor numerical methods and their application in scientific computing and data science

Editorial: Tensor numerical methods and their application in scientific computing and data science

A Secure Mutual Authentication and Key Agreement Protocol that Preserves Internet of Things (Iot) Modules

IoT systems need a high security to protect information exchanged between devices and users. Securing the information and providing authentication are the most significant features of end-user devices on vulnerable and open network infrastructure. Thus, it is more often and significant to reinforce and modernize the security protocol for IoT devices headed for security perception. The security concern of IoT devices can be fulfilled with effective cryptography algorithm techniques. Several private and public cryptography techniques provide an extensive choice of services to protect users’ information in the form of scrambled text, that can be authenticated only by authorized users or devices. Particularly, end user-device authentication is essential for the Internet of Things (IoT). IoT devices are linked via the internet and they are deployed in open and public places, which creates them susceptible activity by unauthorized agents. Thus, it is significant to design an effective and strong authentication protocol to protect the end user-device with affordable cost and rapid communication. In the proposed work, the linear matrix-based, block cipher cryptography protocol scheme is used. The protocol encrypts confidential data using cryptography to generate a different scenario of the same text. In contrast to simple block cipher algorithms based on linear matrix algorithms, symmetric cryptography reduces encryption time but is not concerned about security. The objective of the research paper is to establish secure links for endto-end communication with a proper and high speed authentication scheme.

Joint optimization methods for Gaussian random measurement matrix based on column coherence in compressed sensing

In compressed sensing, a measurement matrix Φ having low coherence with sparse dictionary Ψ can achieve better signal reconstruction performance. To improve the signal reconstruction performance, this paper proposes two joint optimization algorithms for the Gaussian random measurement matrix to minimize the coherence between the measurement matrix Φ and the sparse dictionary Ψ. First, a joint optimization algorithm is proposed that can simultaneously reduce the average mutual coherence μg and the mutual coherence μ based on an alternating projection strategy. Then, to further decrease the coherence between Φ and Ψ, an improved shrinkage method based on K-order cumulative coherence μK is proposed. Furthermore, another joint optimization algorithm is proposed by fusing this improved shrinkage method, which can simultaneously decrease the average mutual coherence μg and the K-order cumulative coherence μK. Simulation results show that the two proposed joint optimization algorithms outperform existing algorithms in reducing coherence and improving reconstruction performance.

An improved adaptive periodical segment matrix algorithm for ECG denoising based on singular value decomposition.

Wearable devices that monitor heart health of cardiac disease patients in real time are in great demand. We propose an algorithm of improved segment periodical matrix construction for irregular electrocardiogram (ECG) signal denoising. While splitting the heartbeat based on each RR interval for periodical segments matrix construction, the as-filtered ECG signal is reconstructed by the maximum singular value after a singular value decomposition. The results demonstrate a higher noise reduction effect with lower signal distortions of our methods compared to several singular value decomposition counterpart approaches. Our method has great potential to enhance wearable devices diagnosis accuracy by denoising the complex noises such as electromyography artifacts in real-time ECG sensing.