D'NIA: um sistema de tempo real orientado a objeto

D'nia é um ambiente de programayso orientado a objeto para sistemas de tempo real. Este ambiente inclui urna ferramenta para "cross-development" de sistemas dedicados, um núcleo de tempo real e um mecanismo de conftguração para definir o controle de sistemas (processos). O sistema de "crosso development" D'nia é baseado e tem a mesma fi1osofia do sistema aberon, um novo ambiente de programayao orientado a objeto desenvolvido no ETH Zurique, Suíya. a núcleo de tempo real é baseado em eventos e traz as dependéncias de tempo dos processos para um primeiro plano de importancia. D'nia é utilizado como software base para controladores de robos modulares e outros sistemas mecatronicos e de automação.Eje: Ingeniería de softwareRed de Universidades con Carreras en Informática (RedUNCI

D'NIA: um sistema de tempo real orientado a objeto

D'nia é um ambiente de programayso orientado a objeto para sistemas de tempo real. Este ambiente inclui urna ferramenta para "cross-development" de sistemas dedicados, um núcleo de tempo real e um mecanismo de conftguração para definir o controle de sistemas (processos). O sistema de "crosso development" D'nia é baseado e tem a mesma fi1osofia do sistema aberon, um novo ambiente de programayao orientado a objeto desenvolvido no ETH Zurique, Suíya. a núcleo de tempo real é baseado em eventos e traz as dependéncias de tempo dos processos para um primeiro plano de importancia. D'nia é utilizado como software base para controladores de robos modulares e outros sistemas mecatronicos e de automação.Eje: Ingeniería de softwareRed de Universidades con Carreras en Informática (RedUNCI

A set of metrics for characterizing simulink model comprehension

Simulink is a powerful tool for Embedded Systems, playing a key role in dynamic systems modeling. However, far too little attention has been paid to quality of Simulink models. In addition, no research has been found linking the relationship between model complexity and its impact in the comprehension quality of Simulink models. The aim of this paper is to define a set of metrics to support the characterization of Simulink models and to investigate their relationship with the model comprehension property. For this study, we performed a controlled experiment using two versions of a robotic Simulink model — one of them was constructed through the ad hoc development approach and the other one through the re-engineered development approach. The results of the experiment show that the re-engineered model is more comprehensible than the ad hoc model. In summary, the set of metrics collected from each version of the Simulink model suggests an inverse relationship with the model comprehension, i.e., the lower the metrics, the greater the model comprehension.Facultad de Informátic

A set of metrics for characterizing simulink model comprehension

Simulink is a powerful tool for Embedded Systems, playing a key role in dynamic systems modeling. However, far too little attention has been paid to quality of Simulink models. In addition, no research has been found linking the relationship between model complexity and its impact in the comprehension quality of Simulink models. The aim of this paper is to define a set of metrics to support the characterization of Simulink models and to investigate their relationship with the model comprehension property. For this study, we performed a controlled experiment using two versions of a robotic Simulink model — one of them was constructed through the ad hoc development approach and the other one through the re-engineered development approach. The results of the experiment show that the re-engineered model is more comprehensible than the ad hoc model. In summary, the set of metrics collected from each version of the Simulink model suggests an inverse relationship with the model comprehension, i.e., the lower the metrics, the greater the model comprehension.Facultad de Informátic

A set of metrics for characterizing simulink model comprehension

Simulink is a powerful tool for Embedded Systems, playing a key role in dynamic systems modeling. However, far too little attention has been paid to quality of Simulink models. In addition, no research has been found linking the relationship between model complexity and its impact in the comprehension quality of Simulink models. The aim of this paper is to define a set of metrics to support the characterization of Simulink models and to investigate their relationship with the model comprehension property. For this study, we performed a controlled experiment using two versions of a robotic Simulink model — one of them was constructed through the ad hoc development approach and the other one through the re-engineered development approach. The results of the experiment show that the re-engineered model is more comprehensible than the ad hoc model. In summary, the set of metrics collected from each version of the Simulink model suggests an inverse relationship with the model comprehension, i.e., the lower the metrics, the greater the model comprehension.Facultad de Informátic

Automatic Routing System for Intelligent Warehouses

Automation of logistic processes is essential to improve productivity and reduce costs. In this context, intelligent warehouses are becoming a key to logistic systems thanks to their ability of optimizing transportation tasks and, consequently, reducing costs. This paper initially presents briefly routing systems applied on intelligent warehouses. Then, we present the approach used to develop our router system. This router system is able to solve traffic jams and collisions, generate conflict-free and optimized paths before sending the final paths to the robotic forklifts. It also verifies the progress of all tasks. When a problem occurs, the router system can change the task priorities, routes, etc. in order to avoid new conflicts. In the routing simulations, each vehicle executes its tasks starting from a predefined initial pose, moving to the desired position. Our algorithm is based on Dijkstra's shortest path and the time window approaches and it was implemented in C language. Computer simulation tests were used to validate the algorithm efficiency under different working conditions. Several simulations were carried out using the Player/Stage Simulator to test the algorithms. Thanks to the simulations, we could solve many faults and refine the algorithms before embedding them in real robots.Comment: 2010 IEEE International Conference on Robotics and Automation, International workshop on Robotics and Intelligent Transportation System, Full Day Workshop, May 7th 2010, Anchorage, Alaska. Organizers,Christian Laugier (INRIA, France), Ming Lin (University of North Carolina, USA), Philippe Martinet IFMA and LASMEA, France),Urbano Nunes (ISR, Portugal

EEG-Based Epileptic Seizure Prediction Using Temporal Multi-Channel Transformers

Epilepsy is one of the most common neurological diseases, characterized by transient and unprovoked events called epileptic seizures. Electroencephalogram (EEG) is an auxiliary method used to perform both the diagnosis and the monitoring of epilepsy. Given the unexpected nature of an epileptic seizure, its prediction would improve patient care, optimizing the quality of life and the treatment of epilepsy. Predicting an epileptic seizure implies the identification of two distinct states of EEG in a patient with epilepsy: the preictal and the interictal. In this paper, we developed two deep learning models called Temporal Multi-Channel Transformer (TMC-T) and Vision Transformer (TMC-ViT), adaptations of Transformer-based architectures for multi-channel temporal signals. Moreover, we accessed the impact of choosing different preictal duration, since its length is not a consensus among experts, and also evaluated how the sample size benefits each model. Our models are compared with fully connected, convolutional, and recurrent networks. The algorithms were patient-specific trained and evaluated on raw EEG signals from the CHB-MIT database. Experimental results and statistical validation demonstrated that our TMC-ViT model surpassed the CNN architecture, state-of-the-art in seizure prediction.Comment: 15 pages, 10 figure

Knowledge Discovery strategy over patient performance data towards the extraction of hemiparesis-inherent features: A case study

Aiming to perform an extraction of features which are strongly related to hemiparesis, this work describes a case study involving the efforts of patients in upper-limb rehabilitation, diagnosed with such pathology. Expressed as data (kinematic and dynamic measures), patients' performance were sensed and stored by a single InMotion Arm robotic device for further analysis. It was applied a Knowledge Discovery roadmap over collected data in order to preprocess, transform and perform data mining through machine learning methods. Our efforts culminated in a pattern classification with the abilty to distinguish hemiparetic sides with an accuracy rate of 94%, having 8 features of rehabilitation performance feeding the input. Interpreting the obtained feature structure, it was observed that force-related attributes are more significant to the composition of the extracted pattern