Analysis of Precoder Decomposition Algorithms for MIMO System Design

Markkandan, S.; Logeshwaran, R.; Venkateswaran, N.

doi:10.1080/03772063.2021.1920848

Cited by 19 publications

(6 citation statements)

References 21 publications

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“…ese are the consequences which result in filtering error over space [33]. e satellite communication is the latest emerging field and it helps in agriculture, whether monitoring and cultivation etc.…”

Section: Related Workmentioning

confidence: 99%

Onboard Pointing Error Detection and Estimation of Observation Satellite Data Using Extended Kalman Filter

Dhanalakshmi¹,

Bhavani

Raju

et al. 2022

Computational Intelligence and Neuroscience

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The satellite communication is embellished constantly by providing information, ensuring security, and enables the communication among huge at a particular time efficiently. The satellite navigation helps in determining the people’s location. Global development, natural disasters, change in climatic conditions, agriculture crop growth, etc., are monitored using satellite observation. Hence, the satellite includes detailed information data, and it must be protected confidentially. The field of the satellite is enhanced at an astonishing pace. Satellite data play an important role in this modern world; hence, the onboard-satellite data must secure through the proper selection of error detection and estimation schema. Lightweight deep learning algorithm based on Extended Kalman Filter (KFK) is proposed to detect and estimate onboard pointing error such as an error in attitude and orbit. The Extended Kalman Filter (EKF) is widely used in the satellite system. EKF is utilized in this proposed model to detect the onboard pointing error such as attitude and orbit determination. An autonomous estimation of orbit position is possible through space-borne gravity. The information obtained through the observation of satellite data is compared with the accurate gravity model in detecting the error. The utilization of EKF reduces the dependence of the ground tracking system in satellite determination. The orbital altitude and orbital position are the most important challenges faced in the satellite determination system. The satellite model using the Extended Kalman Filter is an optimum method in estimating the orbital parameters. The errors in the linearization process are detected, and this can be overcome through the proper selection of linear expansion point with the EKF algorithmic model with the Jacobian matrix calculation. The results show that the EKF implementation helps in attaining better accuracy than other methodologies. Its contribution is enormous to many space missions, autonomous rendezvous and docking for manned and unmanned missions (e.g., ISS operations and beyond, in-orbit servicing, and in-orbit refueling), routine satellite OD operations, orbital debris removal systems, Space Situational Awareness (SSA) operations, and others.

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Section: Related Workmentioning

confidence: 99%

Onboard Pointing Error Detection and Estimation of Observation Satellite Data Using Extended Kalman Filter

Dhanalakshmi¹,

Bhavani

Raju

et al. 2022

Computational Intelligence and Neuroscience

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“…The benchmark steps up in the many organizations is attempted with the support of many technologies. The Transaction Processing Performance Council had developed two types, namely TPC-H [1] and TPC-DS [2], to create a benchmark in the big data system model. TPC-H had been set in Hadoop, Pig, and Hive ( [3] and [4]).…”

Section: Literature Surveymentioning

confidence: 99%

Integrated benchmarking standard and decision support system for structured, semi structured, unstructured retail data

Kumar

2021

Wireless Netw

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Technological development in the organization made society entirely dependent on the internet for their needs, leading to further growth in the variety of data. Initially, the prominent organization faces difficulties due to the exponential development of data. The benchmarking is required to allow data mining tools such as Rapid Miner and Big Data in the environment. Structured, unstructured, and semi-structured data are the types of Big Data. Structured data are used to develop a page by giving enough information. Unstructured data will not have a predefined model to arrange the data in a particular way. Semi-structured data has some structure, but it will not have any data model. The extensive data system is the more apt method in evaluating the benchmark. The proper research and analysis must be carried out to provide a standardized benchmark. The organization has to develop a clear-cut idea about the strategy to overcome the consequence effectively. The benchmark is usually classified into two types internal and external benchmarking. Internal benchmarking includes SWOT, performance metrics, functional values, a practice carried out, financial information, and external benchmarking, including collaborative benchmarking of process, product, corporate, strategic, and global. The primary purpose of the internal benchmark is to compare the performance of an organization internally. The external benchmark gives information about the organization's performance with its competitor companies in this industrial world. The primary big bench model for the product retailer includes a synthetic data maker with variety, speed, and extensive data systems, which contains structured and unstructured data. The significant bench workload is performed with a set of queries. The queries occupy various departments of comprehensive data analysis based on a business perspective. Data sources, processing types of queries, and analytic techniques are included in designing the queries from a technical perspective. Transaction Processing Performance Council decision support (TPC-DS) is adapted to develop a standard benchmark for a big bench data model.

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“…It reduces the cost of acquiring new guests while also aiding in client retention. It takes more marketing time and plutocrat to develop a new customer than to keep an living consumer [5]. guests who are reluctant to make a purchase or are prepared to switch shopping spots due to fiscal enterprises can be converted and gripped.…”

Section: Introductionmentioning

confidence: 99%

Customer Churn Prediction Using Machine Learning

Patil

2024

IJSREM

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Customer churn, the phenomenon where customers cease their relationship with a business, is a critical concern for e-commerce platforms striving for sustained growth and profitability. Predicting churn in advance can empower businesses to implement proactive retention strategies, thereby mitigating revenue loss and enhancing customer satisfaction. In this study, we propose a machine learning-based approach to predict customer churn in e-commerce settings. We begin by collecting extensive data encompassing various customer attributes, transactional history, browsing behavior, and engagement metrics. Leveraging this rich dataset, we employ state- of-the-art machine learning algorithms such as logistic regression, random forests, gradient boosting machines, and neural networks for predictive modeling. Feature engineering techniques are applied to extract meaningful patterns and insights from the raw data, enhancing the predictive performance of the models. By deploying the developed predictive model into production environments, businesses can proactively identify at-risk customers and tailor targeted retention strategies to mitigate churn. The results demonstrate the effectiveness of machine learning in accurately predicting customer churn in e-commerce, enabling businesses to proactively implement retention strategies and enhance customer engagement. Index Terms— Machine learning, E-commerce, Logistic re- gression, Decision tree, Random forest algorithm.

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Analysis of Precoder Decomposition Algorithms for MIMO System Design

Cited by 19 publications

References 21 publications

Onboard Pointing Error Detection and Estimation of Observation Satellite Data Using Extended Kalman Filter

Onboard Pointing Error Detection and Estimation of Observation Satellite Data Using Extended Kalman Filter

Integrated benchmarking standard and decision support system for structured, semi structured, unstructured retail data

Customer Churn Prediction Using Machine Learning

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