Principle Component Analysis Technique Research Articles

Exploring Bounded Component Analysis Using an ℓ∞ Norm Criterion

In this paper we propose a new criterion for the Blind Source Separation (BSS) of antisparse bounded sources, based on the sum of the ℓ∞-norm of the sources. Based on the observation that the mixing process of bounded sources with any mixing matrix with unitary Frobenius norm will increase the ℓ∞-norm of the sources, unless it is the identity matrix, the minimization of the sum of the ℓ∞-norm of the sources can be used for the estimation of a separation matrix. To that, a Principle Component Analysis technique followed by a Givens Rotations based optimization method can be used for the separation of independent bounded sources. Also, the Givens Rotations based optimization method can be used for the separation of correlated bounded sources mixed by a rotation matrix. We theoretically analyze the proposed criterion and assess its performance through numerical simulations involving three distinct types of bounded signals. Our theoretical and experimental findings underscore the efficacy of the ℓ∞ norm as a suitable contrast function for antisparse bounded sources, showcasing its superior performance relative to a state-of-the-art algorithm.

Investigating the effect of changing the substrate material analyzed by laser-induced breakdown spectroscopy on the antenna performance

Miniaturized microstrip antennas are efficiently utilized in MICS band wearable and implantable medical applications. However, the properties of the materials employed for antenna fabrication influence its resultant parameters and play a vital role in its performance. Rogers have been widely used as a substrate material in various antenna designs. In this work, a proof of concept study has been conducted to determine how altering the substrate used in antenna construction affects antenna performance. Using the laser-induced breakdown spectroscopy (LIBS) approach, the elements present in the two distinct substrate raw materials were compared to investigate potential effects on the antenna’s performance. Given their accessibility and widespread use, two types of Rogers’ substrates, RO 3210 and RO 4003, were selected. Furthermore, two identical antenna designs were modeled and fabricated using the two substrate materials. The reflection coefficient (S11) and other antenna parameters were determined and compared. Moreover, the recorded LIBS spectra were evaluated using principle component analysis and partial least square regression techniques. The LIBS spectra showed different copper and iron contents between the two Rogers (i.e., other dielectric properties), leading to a frequency shift. Additionally, impurities in the fabricated material increase the possible losses. Consequently, the elemental contents of the utilized Rogers control the antenna’s performance and can ensure its safety in wearable and implant applications.

Early diagnosis of Parkinson’s disease: A combined method using deep learning and neuro-fuzzy techniques

Predicting Unified Parkinson's Disease Rating Scale (UPDRS) in Total- UPDRS and Motor-UPDRS clinical scales is an important part of controlling PD. Computational intelligence approaches have been used effectively in the early diagnosis of PD by predicting UPDRS. In this research, we target to present a combined approach for PD diagnosis using an ensemble learning approach with the ability of online learning from clinical large datasets. The method is developed using Deep Belief Network (DBN) and Neuro-Fuzzy approaches. A clustering approach, Expectation-Maximization (EM), is used to handle large datasets. The Principle Component Analysis (PCA) technique is employed for noise removal from the data. The UPDRS prediction models are constructed for PD diagnosis. To handle the missing data, K-NN is used in the proposed method. We use incremental machine learning approaches to improve the efficiency of the proposed method. We assess our approach on a real-world PD dataset and the findings are assessed compared to other PD diagnosis approaches developed by machine learning techniques. The findings revealed that the approach can improve the UPDRS prediction accuracy and the time complexity of previous methods in handling large datasets.

PCA-Based Adaptive Training-Feedback Scheme in Time-Varying FDD Massive MIMO Systems

For massive multiple-input multiple-output (MIMO) systems, the real-time channel state information (CSI) acquisition is crucial but difficult in fast time-varying scenarios, especially for downlink (DL) channels in frequency division duplex (FDD) systems. This paper proposes an adaptive training-feedback scheme to estimate the CSI of DL channels. Specifically, first, base station (BS) determines a subspace containing uplink (UL) channel and an orthogonal normal basis of this subspace by using received signals in UL channel and the principle component analysis (PCA) technique. Second, by using the spatial reciprocity, it is found that the obtained subspace above also contains the DL channel vector. Thus, regarding the basis as pilots, the BS transmits pilots to mobile user (MU), and the user estimates received signals, feeds back them to the BS. Finally, according to information of the feedback, the BS can construct the DL channel vector. In this scheme, the pilots are adaptive to the change of the UL channel. Furthermore, the times of training is the dimension of the subspace, and feedback overhead is the coefficients of linear combination of DL channel vector under the basis only. Thus, cost of training and feedback can be greatly reduced. The simulation results show that the performance of the proposed scheme can approach the optimal scheme with very few training times and feedback overhead at high speed.

Modeling Dynamic Urban Growth Using Cellular Automata and Geospatial Technique: Case of Casablanca in Morocco

The rapid urbanisation contribute to the spatial expansion in cities.However, the rapid and unmanaged urban growth, degraded the urban environment. In casablanca the economic engine of Morocco, the rapid urbanization was a result of demographic explosion, rural exodus, and the introduction of new urban projects. Understanding the interdependencies between urban growth patterns, infrastructure, and socioeconomic indicators is a critical step in achieving a sustainable urban development. In order to help Casablanca's sustainable growth, this study used remote sensing data to evaluate past urban land use changes. This study was conducted to examine past urban land cover change on the basis of remote sensing data collected between 1989 and 2019. To forecast the city's expansion for the years 2019e2029, an integrated Cellular Automata urban growth model was used. The research looked into the CA algorithm's capacity to work independently for urban development modeling satellite data from four time periods at equal intervals, as well as population density, distance to the city center, slope, and distance to roadways, are used for this purpose. Between 1989 and 2019, the satellite-based LULC reported an increase of 61.77Km2 (an 88 percent increase). The principle component analysis (PCA) technique was used to analyze geographic variation and found good classification similarity ranging from 87 to 90%. Based on the anticipated LULC, the built-up area will grow to 131.88 km2 in 2019, mostly in the west and southwest. Urbanization will replace and transform other LULC (net loss of 15.348Km2) between 2019 and 2029, followed by plant cover (net loss of 1.608Km2).

Do Culture and Religion Matter to Economic Growth?

This study offers a different empirical approach to examining the e?ects of culture and religion on economic growth using time series data for the period 1994-2020. I constructed a cultural index through the Principle Component Analysis (PCA) technique to achieve this goal. This study employs seven leading indicators from the World Values Survey (WVS) and the European Values Study (EVS) for cultural development, such as control, trust, respect, obedience, and identity. Further, the Generalized Method of Moments (GMM) system estimation was adopted. The findings confirm a significant positive impact of culture and religion on economic growth.

MULTIVARIATE ANALYSIS OF GENETIC DIVERSITY AMONG THIRTY-SEVEN Chenopodium quinoa GENOTYPES UNDER ORGANIC AND MINERAL FERTILIZATION

Assessing the genetic diversity among quinoa germplasm is of prime importance for its effective utilization in breeding programs. The aims of the present investigation were to evaluate the magnitude of genetic diversity, based on phenotypic data, among 37 quinoa genotypes, under organic and/or inorganic fertilizer conditions and assess interrelationships between seed yield and its related traits under both environments. Two experiments were carried out in two seasons; the 1st experiment under organic fertilization and the 2nd under mineral fertilization conditions. A randomized complete blocks design with three replications was used. Principle component analysis (PCA) and GT-Biplot technique were used. Quinoa genotypes recorded significant differences (P≤0.01) for all studied traits under each environment. The promising genotype(s) for each trait were identified. Results of GT-biplot indicated that the traits, branches/plant, seed diameter, seed yield/plant, seed nitrogen content, biological yield/plant, 1000-seed weight, seed oil content, plant height, inflorescence diameter, inflorescences/plant and chlorophyll concentration index were strongly correlated with seed yield/ha, had high estimates of heritability and genetic advance and thus could be considered as secondary traits for high seed yield either under organic or inorganic fertilization. The clustering analysis assigned the 37 quinoa genotypes into three groups. The highest genetic dissimilarity Euclidean coefficients were recorded between G23 and each of G8, G34, G4, G9, G24 and G5; they are the most unrelated genotypes, but the lowest dissimilarity was between G13 and G26, they are the most related genotypes. The identified promising genotypes and secondary traits could be offered to quinoa breeders for use in future breeding programs to improve seed yield.

Characterization Studies of a GC based SAW Sensor System for Potential Detection and Identification of Toxic Environmental Gases/Vapors

The characterization of a custom-designed GC-based SAW e-Nose sensor system is presented here to study the sensing ability of the sensor system to detect and identify low medium and high toxic vapors. A semi-automated multi-vapor generator generates vapors of chemical compounds that are then exposed to the sensing system to examine its performance under various concentrations. Time-domain verses frequency response of GC-SAW Sensor is noted for repeated cycles against different chemical compounds like xylene, 1,2 dibromoethane, dimethyl sulfate, triethyl phosphate, nitrobenzene, phosphorous trichloride being tested. The generated data is examined using a principle component analysis (PCA) technique to detect a unique response for an individual chemical compound. Experimental results are reported.

Optical Detection of SARS-CoV-2 Utilizing Antigen-Antibody Binding Interactions.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the coronavirus disease (COVID-19) pandemic, is sweeping the world today. This study investigates the optical detection of SARS-CoV-2, utilizing the antigen-antibody binding interactions utilizing a light source from a smart phone and a portable spectrophotometer. The proof-of-concept is shown by detecting soluble preparations of spike protein subunits from SARS-CoV-2, followed by detection of the actual binding potential of the SARS-CoV-2 proteins with their corresponding antigens. The measured binding interactions for RBD and NCP proteins with their corresponding antibodies under different conditions have been measured and analyzed. Based on these observations, a “hump or spike” in light intensity is observed when a specific molecular interaction takes place between two proteins. The optical responses could further be analyzed using the principle component analysis technique to enhance and allows precise detection of the specific target in a multi-protein mixture.

To Determine the Relationship between Differentiation Strategy and Performance of Dairy Cottage Industries in Kiambu County, Kenya

The dairy cottage industries in Kiambu County in Kenya have not achieved high performance of their businesses with respect to profit; in terms of market share, customer retention and sales volume amongst others. As such, t he main purpose of the study was to determine the relationship between differentiation strategy and performance of dairy cottage industries in Kiambu County, Kenya. The study utilized a descriptive cross sectional survey research design, using both quantitative and qualitative research approach. The target population comprised of 162 study sample and a research sample size of 114 dairy cottage industries in Kiambu County. Stratified sampling was used to divide Kiambu County into 12 Sub-Counties (or strata). Structured and unstructured questionnaire was used for data collection from the target population. The instrument was pre-tested using the Cronbach’s alpha value to determine the validity and reliability of the tests. Data collected from the field, both quantitative and qualitative in nature, was analyzed using the Statistical Package for Social Science (SPSS) Version 21. Quantitative data was analysed using inferential and simple descriptive statistics. Qualitative data analysed was presented using frequency distribution tables and histograms. The study utilized Exploratory Factor Analysis (EFA) statistical procedure, a technique within Factor Analysis (FA) to determine the number of latent variables that are needed to elucidate the correlations between latent variables and observed variables. The descriptive statistical technique of Principle Component Analysis (PCA) was utilized in identifying patterns in data to highlight their similarities and differences. Further, PCA was utilized to reduce the dimensionality of huge data sets in an attempt to compress the masses of data into fewer factors for ease of analysis. Correlation analysis as well as regression model were also applied to determine the relationship between differentiation strategy and performance of dairy cottage industries in Kiambu County, Kenya. The study revealed that differentiation strategy is an integral aspect of firm management. The study concluded there was significant relationship between differentiation strategy and performance of dairy cottage industries in Kiambu County. This study recommends that the proprietors of the dairy cottage industries in Kiambu County, Kenya should put some efforts on differentiation dimensions that is; brand image and products features, and service differentiation to improve on performance of their dairy cottage industries in Kiambu County, Kenya.

Targeting iron prospective within the Kabul Block (SE Afghanistan) via hydrothermal alteration mapping using remote sensing techniques

The application of remote sensing has significant time- and cost-saving benefits as a technological tool for the delineation of iron prospects. Afghanistan is a prime target for these methods owing to the presence of several metallic and nonmetallic mineral deposits, most of which remain undiscovered. This study aims to target iron prospective areas within the Kabul Block (SE Afghanistan) by detecting hydrothermal alteration zones and iron-bearing minerals, using Landsat 8 OLI/TIRS. With this objective, we applied four different techniques, including false-color composite (FCC), band ratioing (BR), principle component analysis (PCA) via the Crosta technique, and the spectral angle mapper (SAM). The results of each algorithm are in high correlation with the conventional data. FCC was successful in highlighting the hydrothermal alteration zones, while the BR and PCA techniques were able to detect iron oxide and ferrous and ferric minerals. The SAM algorithm, as expected, detected iron-bearing minerals. The obtained results were validated by previously limited iron occurrences, and favorable/permissible areas are marked by USSR and USGS, presenting high accuracy. Under the findings of this study, 18 iron prospective areas in total were detected throughout the northern, eastern, southern, and central regions of the Kabul Block, including 6 previously verified areas and 12 newly proposed favorable regions.

Hybrid features prediction model of movie quality using Multi-machine learning techniques for effective business resource planning

Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc.

Blind deconvolution of sources of opportunity in ocean waveguides using bilinear channel models.

A general blind deconvolution algorithmic framework is developed for sources of opportunity (e.g., ships at known locations) in an ocean waveguide. Here, both channel impulse responses (CIRs) and unknown source signals need to be simultaneously estimated from only the recorded signals on a receiver array using blind deconvolution, which is generally an ill-posed problem without any a priori information or additional assumptions about the underlying structure of the CIRs. By exploiting the typical ray-like arrival-time structure of the CIRs between a surface source and the elements of a vertical line array (VLA) in ocean waveguides, a principle component analysis technique is applied to build a bilinear parametric model linking the amplitudes and arrival-times of the CIRs across all channels for a variety of admissible ocean environments. The bilinear channel representation further reduces the dimension of the channel parametric model compared to linear models. A truncated power interaction deconvolution algorithm is then developed by applying the bilinear channel model to the traditional subspace deconvolution method. Numerical and experimental results demonstrate the robustness of this blind deconvolution methodology.

Location Mapping of Hydrothermal Alteration Using Landsat 8 Data: A Case of Study in Prestea Huni Valley District, Ghana

Utilization of multispectral satellite images is an excellent approach in the reconnaissance stage of gold prospecting due to its high effectiveness and low cost. This research used Landsat 8 Remote Sensing data and Geographical Information System to identify, delineate and map hydrothermal alteration zone relating to gold pattern mineralization in Prestea Huni Valley District in the Western Region of Ghana. Principle Component Analysis (PCA), three band combinations, spectral rationing and Crosta techniques are used in this study. For three band combinations, bands 5, 4, 3 and 5, 6, 7 are used to map location of hydrothermal alterations. Band ratios (7/5and6/7), (6/5) and (4/2) are used to identify presence of clay, ferrous and iron oxide minerals respectively. Sabin's ratio 4/2, 6/7 and 6/5, Kaufmann ratio 7/5, 5/4 and 6/7, and 4/2, 6/7 and 5 are used to identify vegetation, outcrop and hydrothermal alterations respectively. Crosta and PCA techniques were used to suppress the interference of vegetation in delineating the alteration zones. Results revealed that the three band combinations and spectral ratioing clearly depicts hydrothermal deposit of ferrous minerals, clay and iron oxide minerals. PCA identifies presence of iron-oxide and hydroxyl minerals as bright pixels. The first three high order principal components (PC1, PC2 and PC3) of input spectral bands gave more than 98% of the spectral information. Thus, the results from satellite images an effective and efficient way of mapping hydrothermal alteration zones at regional scale.

Smart Classifiers Based Classification and Condition Monitoring of Induction Motor Faults

With the advancements in the field of automation in the industries the use of machines is very high and if the machine which require some rotatory action for the load, the Induction motor comes in to play because of the advantages such as robustness, low maintenance, low cost etc. But with the increase in the dependency over the motors it becomes highly recommended to have machines with reliability because break in the work can lead to huge amount of loss. In order to increase the reliability of the motors predictive maintenance comes into play which requires fault classification or detection which is easily and accurately possible using the Machine Learning algorithms. With the requirements of the present scenario for predictive maintenance, this paper presents the fault classification of induction motor using Support Vector Machine SVM) and K- Nearest Neighbour (KNN) technique of classification. Here in this paper the bearing fault (BF) and broken rotor bar (BRB) fault is considered. The results collected are on the basis of validation and Principle Component Analysis (PCA) technique. And it is found that the SVM technique is better than the KNN for fault classification of Induction motor.

Adaptive Neuro-Fuzzy Inference System for Prediction of Surgery Time for Ischemic Stroke Patients

With the advent of machine learning techniques, creation and utilization of prediction models for different medical procedures including prediction of diagnosis, treatment and recovery of different medical conditions has become the norm. Recent studies focus on the automation of infarction volume growth rate prediction by the utilization of machine learning techniques. These techniques when effectively applied, could significantly help in reducing the time needed to attend to stroke patients. We propose, in this proposal, a Fuzzy Inference System that can determine when a stroke patient should undergo Decompressive Hemicraniectomy. The second infarction volume growth rate and the decision whether a patient needs to undergo this procedure, both predicted outputs of two trained models, act as inputs to this system. While the initial prediction model, that which predicts the second infarction volume growth rate is adopted from an earlier model, we propose the later model in this paper. Three Machine Learning techniques - Support Vector Machine, Artificial Neural Network and Adaptive Neuro Fuzzy Inference System with and without the feature reduction technique of Principle Component Analysis were modelled and evaluated, the best of which was selected to model the proposed prediction model. We also defined the structure of Fuzzy Inference System along with its rules and obtained an overall accuracy of 95.7% with a precision of 1 showing promising results from the use of fuzzy logic.

Robust Principal Component Analysis-Based Infrared Small Target Detection

A method based on Robust Principle Component Analysis (RPCA) technique is proposed to detect small targets in infrared images. Using the low rank characteristic of background and the sparse characteristic of target, the observed image is regarded as the sum of a low-rank background matrix and a sparse outlier matrix, and then the decomposition is solved by the RPCA. The infrared small target is extracted from the single-frame image or multi-frame sequence. In order to get more efficient algorithm, the iteration process in the augmented Lagrange multiplier method is improved. The simulation results show that the method can detect out the small target precisely and efficiently.

Retrieving Atmospheric and Land Surface Parameters From At-Sensor Thermal Infrared Hyperspectral Data With Artificial Neural Network

The radiances observed by satellites are influenced by both land surface and atmospheric parameters, and it is difficult to retrieve these parameters simultaneously from multispectral measurements with high accuracies. Even though several methods have been proposed, they focus on the retrieval of land surface or atmospheric parameters separately. Generally, these atmospheric parameters are atmospheric water vapor and temperature profiles. Thus, this study aims to establish a back propagation (BP) artificial neural network (ANN) to retrieve land surface emissivity (LSE), land surface temperature (LST), atmospheric transmittance, upward radiance, and downward radiance simultaneously from the hyperspectral thermal infrared (TIR) data, suitable for various air mass types and surface conditions. The principle component analysis technique is first used to compress and remove noise from the data. The evaluation of the ANN using the simulated data without noise indicated that the root mean square error (RMSE) of LST is approximately 0.643 K; the RMSEs of emissivity, transmittance, upward, and downward radiance are approximately 0.0046, 0.005, 0.72, and 2.95 K, respectively. When applied on the simulated data containing noise, the errors of LST, LSE, transmittance, upward, and downward radiance are 1.26, 0.01, 0.01, 1.54, and 4.57 K, respectively. When applied on the real atmospheric infrared sounder data, the retrieved accuracies become worse because of various unstudied reasons. However, the results show that the proposed ANN is promising in retrieving the land surface and atmospheric parameters simultaneously. Because of its simplicity, the proposed ANN can be used to produce preliminary results employed as the first estimates for physics-based retrieval methods.

Performance Analysis of CT Scan and X-Ray Medical Images Compression Using PCA and WDR techniques

Image compression which is a subset of data compression plays a crucial task in medical field. The medical images like CT, MRI, PET scan and X-Ray imagery which is a huge data, should be compressed to facilitate storage capacity without losing its details to diagnose the patient correctly. Now a days artificial neural network is being extensively researched in the domain of image processing. This paper examines the performance of two techniques namely Principle Component Analysis (PCA) and Wavelet difference reduction (WDR). Wavelet difference reduction method is a wavelet coding technique. The potential of the techniques to compress the medical image and achieving good quality is measured by MSE and PSNR quality metrics. The investigation is carried on CT scan of lower abdomen and X-ray scan of Rib Cage medical images.

Structural damage identification incorporating uncertain boundary flexibility by ensemble couple sparse coding classification method

Transfer function (TF) represents the ratio of structure’s response to the loading and can be utilised for the identification of damage in a structure. The capability of distinguishing between changes of the TF due to damage in the existing structure and due to idealised boundary conditions of the analytical model (such as soil-foundation effects or spring bearings) is noteworthy. However, in many cases, having precise knowledge of boundary characteristics is infeasible. In this regard, a number of couple sparse coding (CSC) learnings are utilised with TFs of the structure with various probable boundary conditions as input parameters to reduce the uncertainty of idealised boundary conditions. The system of CSCs termed ensemble to enhance efficiency of the method. Principle component analysis (PCA) technique is applied to avoid over fitting problem due to the large size of TF data in machine learning training. In numerical verifications, the damage scenarios are considered different from the trained data. A 2 D frame structure is presented as numerical example to demonstrate the adequacy of the proposed method for damage detection under complicated effects of soil foundation. A beam which is suspended using soft rubber bands with uncertain stiffness is considered for experimental validation of the method. Results indicate that the proposed method is capable of identifying damage properties with acceptable precision.