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Author Dennis G. Romero; A. F. Neto; T. F. Bastos; Boris X. Vintimilla pdf  url
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  Title An approach to automatic assistance in physiotherapy based on on-line movement identification. Type Conference Article
  Year 2012 Publication (up) VI Andean Region International Conference – ANDESCON 2012 Abbreviated Journal  
  Volume Issue Pages  
  Keywords patient rehabilitation, patient treatment, statistical analysis  
  Abstract This paper describes a method for on-line movement identification, oriented to patient’s movement evaluation during physiotherapy. An analysis based on Mahalanobis distance between temporal windows is performed to identify the “idle/motion” state, which defines the beginning and end of the patient’s movement, for posterior patterns extraction based on Relative Wavelet Energy from sequences of invariant moments.  
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  Publisher IEEE Place of Publication Andean Region International Conference (ANDESCON), 2012 VI Editor  
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  Call Number cidis @ cidis @ Serial 24  
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Author Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L. url  openurl
  Title Time series in sensor data using state of the art deep learning approaches: A systematic literature review. Type Conference Article
  Year 2022 Publication (up) VII International Conference on Science, Technology and Innovation for Society (CITIS 2021), mayo 26-28.  Smart Innovation, Systems and Technologies. Abbreviated Journal  
  Volume Vol. 252 Issue Pages 503-514  
  Keywords time series, deep learning, recurrent networks, sensor data, IoT.  
  Abstract IoT (Internet of Things) and AI (Artificial Intelligence) are becoming

support tools for several current technological solutions due to significant advancements of these areas. The development of the IoT in various technological fields has contributed to predicting the behavior of various systems such as mechanical, electronic, and control using sensor networks. On the other hand, deep learning architectures have achieved excellent results in complex tasks, where patterns have been extracted in time series. This study has reviewed the most efficient deep learning architectures for forecasting and obtaining trends over time, together with data produced by IoT sensors. In this way, it is proposed to contribute to applications in fields in which IoT is contributing a technological advance such as smart cities, industry 4.0, sustainable agriculture, or robotics. Among the architectures studied in this article related to the process of time series data we have: LSTM (Long Short-Term Memory) for its high precision in prediction and the ability to automatically process input sequences; CNN (Convolutional Neural Networks) mainly in human activity

recognition; hybrid architectures in which there is a convolutional layer for data pre-processing and RNN (Recurrent Neural Networks) for data fusion from different sensors and their subsequent classification; and stacked LSTM Autoencoders that extract the variables from time series in an unsupervised way without the need of manual data pre-processing.Finally, well-known technologies in natural language processing are also used in time series data prediction, such as the attention mechanism and embeddings obtaining promising results.
 
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  Call Number cidis @ cidis @ Serial 152  
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Author Velesaca, Henry O.; Suárez, Patricia L.; Sappa, Angel D.; Carpio, Dario; Rivadeneira, Rafael E.; Sanchez, Angel url  openurl
  Title Review on Common Techniques for Urban Environment Video Analytics. Type Conference Article
  Year 2022 Publication (up) WORKSHOP BRASILEIRO DE CIDADES INTELIGENTES (WBCI 2022) Abbreviated Journal  
  Volume Issue Porto Alegre: Sociedade Brasileira de Computação Pages 107-118  
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  Call Number cidis @ cidis @ Serial 192  
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Author Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa pdf  openurl
  Title Fine-tuning based deep covolutional networks for lepidopterous genus recognition Type Conference Article
  Year 2016 Publication (up) XXI IberoAmerican Congress on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 1-9  
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  Abstract This paper describes an image classi cation approach ori- ented to identify specimens of lepidopterous insects recognized at Ecuado- rian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butter ies and also to facilitate the reg- istration of unrecognized specimens. The proposed approach is based on the ne-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists, is presented|a recognition accuracy above 92% is reached. 1 Introductio  
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  Call Number cidis @ cidis @ Serial 53  
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