Intelligence in Embedded Systems: Overview and Applications

Rosero-Montalvo, Paúl D.; Batista, Vivian F. López; Jaramillo, Edgar D.; Caraguay, Jorge; Pijal-Rojas, José; Peluffo-Ordóñez, Diego H.

doi:10.1007/978-3-030-02686-8_65

Cited by 11 publications

(10 citation statements)

References 27 publications

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“…Table 3 summarizes the results of the classifiers with cross-validation with ten random folds. To graphically appreciate the results of the whole data processing scheme, just as done in previous works [11,14], we use the principal component analysis conventional algorithm as a dimensionality reduction approach to represent the original data over a lower-dimensional domain. Figure 6 presents scatter plots regarding the two first principal components to depict the decision borders generated by every considered classifier.…”

Section: Classification Performancementioning

confidence: 99%

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Intelligent WSN System for Water Quality Analysis Using Machine Learning Algorithms: A Case Study (Tahuando River from Ecuador)

et al. 2020

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This work presents a wireless sensor network (WSN) system able to determine the water quality of rivers. Particularly, we consider the Tahuando River from Ibarra, Ecuador, as a case study. The main goal of this research is to determine the river’s status throughout its route, by generating data reports into an interactive user interface. To this end, we use an array of sensors collecting several measures such as: turbidity, temperature, water quality, pH, and temperature. Subsequently, from the information collected on an Internet-of-Things (IoT) server, we develop a data analysis scheme with both data representation and supervised classification. As an important result, our system outputs a map that shows the contamination levels of the river at different regions. Furthermore, in terms of data analysis performance, the proposed system reduces the data matrix by 97% from its original size, while it reaches a classification performance over 90%. Furthermore, as an additional remarkable result, we here introduce the so-called quantitative metric of balance (QMB), which measures the balance or ratio between performance and power consumption.

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Section: Classification Performancementioning

confidence: 99%

“…However, the large amount of data demands the implementation of machine learning algorithms to create systems that automatically can detect high levels of water pollution and make proper decisions. For that purpose, historical data (training data) become valuable to turn WSN nodes into intelligent systems [13,14].…”

Section: Introductionmentioning

confidence: 99%

Intelligent WSN System for Water Quality Analysis Using Machine Learning Algorithms: A Case Study (Tahuando River from Ecuador)

et al. 2020

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“…Generally, the data collection process of a studied phenomenon is carried out by means of sensors, given that these electronic elements are built with transducers capable of converting a physical signal into an electrical one. For the latter process to be carried out without loss of information, an adequate coupling between the sensor and the system is required [ 16 ]. After the data acquisition process, it is possible to use the information to adapt any system to make its own decisions when learning from external impulses.…”

Section: Introductionmentioning

confidence: 99%

Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities

Rosero-Montalvo

Fuentes-Hernández

Morocho-Cayamcela

et al. 2021

Sensors

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The analysis of plantar pressure through podometry has allowed analyzing and detecting different types of disorders and treatments in child patients. Early detection of an inadequate distribution of the patient’s weight can prevent serious injuries to the knees and lower spine. In this paper, an embedded system capable of detecting the presence of normal, flat, or arched footprints using resistive pressure sensors was proposed. For this purpose, both hardware- and software-related criteria were studied for an improved data acquisition through signal coupling and filtering processes. Subsequently, learning algorithms allowed us to estimate the type of footprint biomechanics in preschool and school children volunteers. As a result, the proposed algorithm achieved an overall classification accuracy of 97.2%. A flat feet share of 60% was encountered in a sample of 1000 preschool children. Similarly, flat feet were observed in 52% of a sample of 600 school children.

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“…Next, the WSN node will be able to collect data from the entire crop and by means of a GPS module, store said information at its respective location. As a result, a robust data analysis methodology can be implemented that can be compiled in each WSN node in order to make decisions, learn from external stimuli, and adapt to changes [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Environment Monitoring of Rose Crops Greenhouse Based on Autonomous Vehicles with a WSN and Data Analysis

Rosero-Montalvo

Erazo-Chamorro

Batista

et al. 2020

Sensors

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This work presents a monitoring system for the environmental conditions of rose flower-cultivation in greenhouses. Its main objective is to improve the quality of the crops while regulating the production time. To this end, a system consisting of autonomous quadruped vehicles connected with a wireless sensor network (WSN) is developed, which supports the decision-making on type of action to be carried out in a greenhouse to maintain the appropriate environmental conditions for rose cultivation. A data analysis process was carried out, aimed at designing an in-situ intelligent system able to make proper decisions regarding the cultivation process. This process involves stages for balancing data, prototype selection, and supervised classification. The proposed system produces a significant reduction of data in the training set obtained by the WSN while reaching a high classification performance in real conditions—amounting to 90 % and 97.5%, respectively. As a remarkable outcome, it is also provided an approach to ensure correct planning and selection of routes for the autonomous vehicle through the global positioning system.

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Intelligence in Embedded Systems: Overview and Applications

Cited by 11 publications

References 27 publications

Intelligent WSN System for Water Quality Analysis Using Machine Learning Algorithms: A Case Study (Tahuando River from Ecuador)

Intelligent WSN System for Water Quality Analysis Using Machine Learning Algorithms: A Case Study (Tahuando River from Ecuador)

Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities

Environment Monitoring of Rose Crops Greenhouse Based on Autonomous Vehicles with a WSN and Data Analysis

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