Vol. 28, No. 1

Browse

Now showing 1 - 5 of 7

Improving Initial Aerofoil Geometry Using Aerofoil Particle Swarm Optimisation
(Institute of Automation and Computer Science, Brno University of Technology, 2022-06-30) Muller, Jan
Advanced optimisation of the aerofoil wing of a general aircraft is the main subject of this paper. Meta-heuristic optimisation techniques, especially swarm algorithms, were used. Subsequently, a new variant denoted as aerofoil particle swarm optimisation (aPSO) was developed from the original particle swarm optimisation (PSO). A parametric model based on B-spline was used to optimise the initial aerofoil. The simulation software Xfoil was calculating basic aerodynamic features (lift, drag, moment).
The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images
(Institute of Automation and Computer Science, Brno University of Technology, 2022-06-30) Amami, Rimah; Saif, Suleiman Ali Al; Amami, Rim; Eleraky, Hassan Ahmed; Melouli, Fatma; Baazaoui, Mariem
The new Coronavirus or simply Covid-19 causes an acute deadly disease. It has spread rapidly across the world, which has caused serious consequences for health professionals and researchers. This is due to many reasons including the lack of vaccine, shortage of testing kits and resources. Therefore, the main purpose of this study is to present an inexpensive alternative diagnostic tool for the detection of Covid-19 infection by using chest radiographs and Deep Convolutional Neural Network (DCNN) technique. In this paper, we have proposed a reliable and economical solution to detect COVID-19. This will be achieved by using X-rays of patients and an Incremental-DCNN (I-DCNN) based on ResNet-101 architecture. The datasets used in this study were collected from publicly available chest radiographs on medical repositories. The proposed I-DCNN method will help in diagnosing the positive Covid-19 patient by utilising three chest X-ray imagery groups, these will be: Covid-19, viral pneumonia, and healthy cases. Furthermore, the main contribution of this paper resides on the use of incremental learning in order to accommodate the detection system. This has high computational energy requirements, time consuming challenges, while working with large-scale and regularly evolving images. The incremental learning process will allow the recognition system to learn new datasets, while keeping the convolutional layers learned previously. The overall Covid-19 detection rate obtained using the proposed I-DCNN was of 98.70\% which undeniably can contribute effectively to the detection of COVID-19 infection.
Grouped Pointwise Convolutions Reduce Parameters in Convolutional Neural Networks
(Institute of Automation and Computer Science, Brno University of Technology, 2022-06-30) Schwarz Schuler, Joao Paulo; Romani, Santiago; Abdel-Nasser, Mohamed; Rashwan, Hatem; Puig, Domenec
In DCNNs, the number of parameters in pointwise convolutions rapidly grows due to the multiplication of the number of filters by the number of input channels that come from the previous layer. Our proposal makes pointwise convolutions parameter efficient via grouping filters into parallel branches or groups, where each branch processes a fraction of the input channels. However, by doing so, the learning capability of the DCNN is degraded. To avoid this effect, we suggest interleaving the output of filters from different branches at intermediate layers of consecutive pointwise convolutions. We applied our improvement to the EfficientNet, DenseNet-BC L100, MobileNet and MobileNet V3 Large architectures. We trained these architectures with the CIFAR-10, CIFAR-100, Cropped-PlantDoc and The Oxford-IIIT Pet datasets. When training from scratch, we obtained similar test accuracies to the original EfficientNet and MobileNet V3 Large architectures while saving up to 90% of the parameters and 63% of the flops.
Intelligent Sampling of Anterior Human Nasal Swabs using a Collaborative Robotic Arm
(Institute of Automation and Computer Science, Brno University of Technology, 2022-06-30) Parak, Roman; Juricek, Martin
Advanced robotics does not always have to be associated with Industry 4.0, but can also be applied, for example, in the Smart Hospital concept. Developments in this field have been driven by the coronavirus disease (COVID-19), and any improvement in the work of medical staff is welcome. In this paper, an experimental robotic platform was designed and implemented whose main function is the swabbing samples from the nasal vestibule. The robotic platform represents a complete integration of software and hardware, where the operator has access to a web-based application and can control a number of functions. The increased safety and collaborative approach cannot be overlooked. The result of this work is a functional prototype of the robotic platform that can be further extended, for example, by using alternative technologies, extending patient safety, or clinical tests and studies. Code is available at https://github.com/Steigner/Robo_Medicinae_I
Color-Aware Two-Branch DCNN for Efficient Plant Disease Classification
(Institute of Automation and Computer Science, Brno University of Technology, 2022-06-30) Schwarz Schuler, Joao Paulo; Romani, Santiago; Abdel-Nasser, Mohamed; Rashwan, Hatem; Puig, Domenec
Deep convolutional neural networks (DCNNs) have been successfully applied to plant disease detection. Unlike most existing studies, we propose feeding a DCNN CIE Lab instead of RGB color coordinates. We modified an Inception V3 architecture to include one branch specific for achromatic data (L channel) and another branch specific for chromatic data (AB channels). This modification takes advantage of the decoupling of chromatic and achromatic information. Besides, splitting branches reduces the number of trainable parameters and computation load by up to 50% of the original figures using modified layers. We achieved a state-of-the-art classification accuracy of 99.48% on the Plant Village dataset and 76.91% on the Cropped-PlantDoc dataset.

Browse

Recent Submissions