Field oriented control dataset of a 3-phase permanent magnet synchronous motor.

This paper presents a dataset of a 3-phase Permanent Magnet Synchronous Motor (PMSM) controlled by a Field Oriented Control (FOC) scheme. The data set was generated from a simulated FOC motor control environment developed in Simulink; the model is available in the public GitHub repository. The dataset includes the motor response to various input signal shapes that are fed to the control scheme to verify the control capabilities when the motor is subjected to real life scenarios and corner conditions. Motor control is one of the most widespread fields in control engineering as it is widely used in machine tools and robots, the FOC scheme is one of the most used control approaches thanks to its performance in speed and torque control, with the drawback of having to handcraft the Proportional-Integrative-Derivative (PID) regulators using Look Up Tables (LUT). The test conditions are designed by setting a motor desired speed. Different input speed variations shapes are proposed as well as extreme scenarios where the linear behaviour of the PID regulator is challenged by applying fast and high magnitude speed variations so that the PID controller is not able to correctly follow the reference. The measured data includes both the outer and inner-loop signals of the FOC, which opens the possibility to develop non-linear control approaches such as Machine Learning (ML) and Neural Networks (NN) with different topologies to replace the linear controllers in the FOC scheme.

A CNN-based image detector for plant leaf diseases classification.

Identifying diseases from images of plant leaves is one of the most important research areas in precision agriculture. The aim of this paper is to propose an image detector embedding a resource constrained convolutional neural network (CNN) implemented in a low cost, low power platform, named OpenMV Cam H7 Plus, to perform a real-time classification of plant disease. The CNN network so obtained has been trained on two specific datasets for plant diseases detection, the ESCA-dataset and the PlantVillage-augmented dataset, and implemented in a low-power, low-cost Python programmable machine vision camera for real-time image acquisition and classification, equipped with a LCD display showing to the user the classification response in real-time. Experimental results show that this CNN-based image detector can be effectively implemented on the chosen constrained-resource system, achieving an accuracy of about 98.10%/95.24% with a very low memory cost (718.961 KB/735.727 KB) and inference time (122.969 ms/125.630 ms) tested on board for the ESCA and the PlantVillage-augmented datasets respectively, allowing the design of a portable embedded system for plant leaf diseases classification. Source files are available at https://doi.org/10.17605/OSF.IO/UCM8D.

Dataset for Bluetooth 5.1 Direction of Arrival with non Uniform Rectangular Arrays.

This paper presents a dataset for Bluetooth 5.1 direction of arrival (DoA). The dataset was generated with a specifically designed mathematical model of a non-uniform rectangular antenna array. The Python source files that generated the dataset are also provided. The dataset was conceived as a starting point for developing and validating DoA algorithms for real-life scenarios. Unlike other datasets, it contains Bluetooth signals with not only varying intensity of additive white Gaussian noise, but also coherent interfering signals with random DoA coordinates. The dataset is divided into two branches, one consisting of pure sinusoidal tones and the second comprised of baseband Bluetooth signals. Since the codebase which generates the data is included, this dataset has a high reuse potential, and it can be modified to suit also other types of signals or different array topologies.

Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks.

This paper presents a dataset of images generated via 3D graphics rendering. The dataset is composed by pictures of the junction between the high-speed shaft and the external bracket of the power generator inside a wind turbine cabin, in presence and absence of oil leaks. Oil leak occurrence is an anomaly that can verify in a zone of interest of the junction. Since the wind turbines industry is becoming more and more important, turbines maintenance is growing in importance accordingly. In this context a dataset, as we propose, can be used, for example, to design machine learning algorithms for predictive maintenance. The renderings have been produced, from various framings and various leaks shapes and colors, using the rendering engine Keyshot9. Subsequent preprocessing has been performed with Matlab, including images grayscale conversion and image binarization. Finally, data augmentation has been implemented in Python, and it can be easily extended/customized for realizing any further processing. The Matlab and Python source codes are also provided. To the authors' knowledge, there are no other public available datasets on this topic.

Dataset of sodium chloride sterile liquid in bottles for intravenous administration and fill level monitoring.

We propose a dataset to investigate the relationship between the fill level of bottles and tiny machine learning algorithms. Tiny machine learning is represented by any Artificial Intelligence algorithm (spanning from conventional decision tree classifiers to artificial neural networks) that can be deployed into a resource constrained micro controller unit (MCU). The data presented has been originally collected for a joint research project by STMicroelectronics and Sesovera.ai. This article describes the recorded image data of bottles with 4 levels of filling. The bottles contain sodium chloride sterile liquid for intravenous administration. One subject of investigation using this dataset could be the classification of the liquid fill level, for example, to ease continuous human visual monitoring which may represent an onerous time-consuming task. Automating the task can help to increase the human work productivity thus saving time. Under normal circumstances, human visual monitoring of the saline level in the bottle is required from time to time. When the saline liquid in the bottle is fully consumed, and the bottle is not replaced or the infusion process stopped immediately, the difference between the patient's blood pressure and the empty saline bottle could cause an outward rush of blood into the saline.

A complexity-bounded motion estimation algorithm.

The full search motion estimation algorithm for video coding is a procedure of high computational cost. For this reason, in real-time low-power applications, low-cost motion estimation algorithms are viable solutions. A novel reduced complexity motion estimation algorithm is presented. It conjugates the reduction of computational load with good encoding efficiency. It exploits the past history of the motion field to predict the current motion field. A successive refinement phase gives the final motion field. This approach leads to a sensible reduction in the number of motion vector that have to be tested. The complexity is lower than any other algorithm algorithms known to the authors, in the literature, it is constant as there is no recursivity in the algorithm and independent of any search window area size. Experimental evaluations have shown the robustness of the algorithm when applied on a wide set of video sequences--a good performance compared to other reduced complexity algorithms and negligible loss of efficiency versus the full search algorithm.