A Multilayer Hidden Markov Models-Based Method for Human-Robot Interaction

To achieve Human-Robot Interaction (HRI) by using gestures, a continuous gesture recognition approach based on Multilayer Hidden Markov Models (MHMMs) is proposed, which consists of two parts. One part is gesture spotting and segment module, the other part is continuous gesture recognition module. Firstly, a Kinect sensor is used to capture 3D acceleration and 3D angular velocity data of hand gestures. And then, a Feed-forward Neural Networks (FNNs) and a threshold criterion are used for gesture spotting and segment, respectively. Afterwards, the segmented gesture signals are respectively preprocessed and vector symbolized by a sliding window and a K-means clustering method. Finally, symbolized data are sent into Lower Hidden Markov Models (LHMMs) to identify individual gestures, and then, a Bayesian filter with sequential constraints among gestures in Upper Hidden Markov Models (UHMMs) is used to correct recognition errors created in LHMMs. Five predefined gestures are used to interact with a Kinect mobile robot in experiments. The experimental results show that the proposed method not only has good effectiveness and accuracy, but also has favorable real-time performance

Automatic Crack Detection Using Convolutional Neural Network

Manual inspection of cracks on concrete surfaces requires wholesome knowledge and depends entirely on the expertise and capabilities of the inspector. This study proposes the use of a simple Convolutional Neural Network (CNN) for automatic crack detection. A comparative approach for Automated Crack Detection is presented between Feed-Forward Fully Connected Neural Networks and CNN, focusing on the primary hyperparameters affecting the accuracy of both systems. An inclination towards CNN is concluded due to its simplicity and computational efficiency. For the purpose of this study, the input data is extracted from an open-source platform. In the second step, the images are pre-processed for obtaining low-pixel density images with the aim to get better accuracy at lower computer power. The CNN proposed uses Max Pooling and appropriate optimization techniques. The model is trained to detect and segregate cracked and non-cracked concrete surfaces through input images. The proposed model predicts and labels images with cracks on concrete surfaces and images with no cracks using pixel-level information. The final accuracy achieved is 97.8% by the proposed CNN model. The proposed model is a novel approach to detecting cracks on low pixel density images of concrete surfaces for its economic and processing efficiency and thus eliminates the need for high-cost digital image capturing devices. This study signifies and confirms the impact of Artificial Intelligence in the Civil Engineering field where using simple techniques like a simple four-layered Neural Network is capable of carrying automatic inspection of cracks which can be further developed for other applications

Clasificación de imágenes mediante algoritmos de Deep Learning: Mascarillas de COVID-19

La pandemia mundial causada por la Covid-19 ha provocado un antes y un después en nuestras vidas, tanto, que ahora llevar mascarilla con el fin de frenar su contagio es algo primordial e impensable en determinadas ocasiones. A raíz de la desesperación originada por este virus se ha incrementado el interés en métodos científicos que puedan ayudar a estabilizar y controlar la situación. Este proyecto gira en torno a este tema tan actual, ya que persigue alcanzar una eficiente clasificación de imágenes según se lleve mascarilla o no, así como diferenciando también si se lleva de forma incorrecta. Para desarrollarlo, se han empleado redes neuronales convolucionales basadas en Deep Learning, algunos populares paquetes básicos de aprendizaje automático como es Keras o TensorFlow y el lenguaje de programación Python 3.6. Los resultados obtenidos en este experimento, usando las herramientas presentadas y trabajando para lograr un ajuste de parámetros que optimice el resultado, terminan con una precisión del algoritmo máxima de un 95.31 % para el diseño final seleccionado.The global pandemic caused by Covid-19 has caused a before and after in our lives, so much that now wearing a mask in order to stop its contagion is essential and unthinkable in certain occasions. As a result of the desperation caused by this virus, there has been an increased interest in scientific methods that could help stabilize and control the situation. This project revolves around this very current topic, since it seeks to achieve an efficient images classification depending on whether a mask is worn or not, as well as differentiating whether it is worn incorrectly. To develop it, convolutional neural networks based on Deep Learning, some popular basic machine learning packages such as Keras or TensorFlow and the Python 3.6 programming language have been used. The results obtained in this experiment, using the tools presented and working to achieve a parameter adjustment that optimizes the result, ends with a maximum algorithm precision of 95.31%.Universidad de Sevilla. Grado en Ingeniería de las Tecnologías de Telecomunicació