Slovakia

With the arrival of the New Coronavirus 2020 pandemic, dif-ferent realities had to adapt in the face of the difficulties that plaguedworld society. creening and pre-diagnosis measures are extremely impor-tant to contain the growing advance with peaks of alarming contagions.Within this perspective of designing efficient solutions and quick resultsfor rapid pre-diagnosis, screening is extremely important to contain thespread of contagion. This study proposes an effective IoT-based solutionfor  the  classification  and  detection  of  coughs,  with  the  aim  of  identi-fying  COVID-19  and  non-COVID-19  cough  patterns  in  a  patient  withsymptoms  of  the  disease.  The  cloud  system  uses  a  framework  capableof  capturing  the  previously  acquired  coughing  sounds  and  classifyingthem on the spectrograms through different deep learning networks us-ing transfer learning techniques. The proposed study was able to obtain 96.16% Accuracy in its best MobileNet + MLP model, with a Precisionof 97.47%, demonstrating an excellent performance for pre-diagnosis ofCOVID-19 in patients for possible screening through invasive tests.

IWSSIP 2021

New Approach Detection of COVID-19 by Cough Spectrogram Using IoT and Learning Transfer Technique for Classification

covid-19 cough detection

iot system

deep learning

transfer learning


IWSSIP is an international conference that brings together researchers and developers from both academia and industry reporting the latest scientific and theoretical advances to discuss and debate major issues and state-of-the-art aspects on systems, signals and image processing.

###### For the session discussion, each author will be seated at individual breakout tables.  Please find which table they will be at here:
###### [QA table assignments in Gather.town](https://drive.google.com/file/d/1hBHOD_KF7NSsaTcpYcHCZslXJvQVxJQe/view?usp=sharing)

technical paper

We create a compact 3D model-based gait feature out of

CNN based speech commands recognition

In this paper, a novel feature extraction approach for suddendeath prediction in patients with Chagas Disease (ChD) using electro-cardiogram  (ECG)  signals  is  proposed.  The  Structural  Co-OccurrenceMatrix (SCM) configuration is modified to extract the main frequenciesof ECG signals. Also, our approach uses only the QRS complex as a pa-rameter. This is the first time, to our knowledge, that SCM is been usedto solve this problem. F1-Score, precision, recall, accuracy, and compu-tational cost are employed as metrics for evaluating SCM performance.SCM with four well-known classifiers used in the literature are assessedand  the  results  are  compared  with  two  traditional  extractors  (Higher-Order Statistics (HOS) and Fourier Transform). Moreover, a dataset forfuture  studies  is  developed.  The  results  confirm  that  SCM,  combinedwith  frequency  domain  filtering,  can  extract  discriminative  structuralinformation on the task of interest.

Sudden Death Classification Using Electrocardiogram Signals and Structural Co-occurrence Matrix in Patients with Chagas Disease

In the presented contribution we propose motor imagery based BCI system for device control. Based on the EEG datasets we perform a two and three class classification of selected features for real motor movements and motor imagery. Features are created using complex wavelet transform. The classification is based on convolution neural networks. The results show that the method has a similar performance as the known reference method for given datasets.

An Approach for BCI using Motor Imagery Based on Wavelet Transform, and Convolutional Neural Network

Fingerprints are widely used for biometric validation worldwide. Since human fingerprints are unique and remain constant over time, it provides an easy-to-use, reliable, and economical authentication method. In addition to that, fingerprint recognition systems are of great importance because of their applicability in our lives. This work presents a classification methodology based on Henry Classification System using Convolutional Neural Networks (CNNs) models, such as Darknet, Alexnet, Resnet, VGG16, and Deep Belief Network. Besides that, we evaluate our proposal by carrying out experiments using grayscale images and pre-processed images as input on the classification step with the combination of the Gabor filter and the morphological thinning operation. We have obtained the highest result accuracy of 95.1% in the NIST Special Database 4, a widespread fingerprint dataset, using the Resnet 34 model in grayscale images. The proposed approach was evaluated with extraction strategies of classic attributes and based on convolutional networks. According to the results, the proposed methodology presents promising results, surpassing the traditional approaches present in the literature.

Fingerprint Classification based on the Henry System via ResNet

"The paper presents a multi-tool environment with an online assistant for Direct Digital Synthesis (DDS). The environment is integrated into the Online CADCOM platform and serves the Moodle-based Computer-Aided Design in Communication course in the Faculty of Telecommunications at the Technical University of Sofia. The design of DDS, as an all-digital signal generator, is one of the topics in the category “Oscillators design”. 
The environment integrates the online tools which can be used for DDS and the MATLAB (Simulink) -Vivado (FPGA) combination for creating an experimental setup.  The online assistant offers multimedia and interactive digital content on DDS, leading students in the implementation of the set of tools integrated for solving DDS design tasks. By combining teaching materials with tasks, quizzes, and forums the online assistant helps students develop critical thinking and encourages collaboration between them. Learning Management Systems, Moodle, and collaborative learning with an open web-based blog enhance students’ analytical skills as they share knowledge and discuss in a group. The new environment is probated with a group of Erasmus+ students on a mobility in TU-Sofia, following the CADCOM course.
"

Multi-tool environment with an online assistant for teaching Direct Digital Synthesis

"The main aim of this article is to compare U-Net models
with different methods of medical image segmentation in ophthalmology when searching for the optic disc and vessels on the retina. We
used freely available databases to analyze the results. DRIVE, STARE
and MESSIDOR were used for blood vessel segmentation and DRION
database was used for optic disc segmentation. In vascular segmentation,
we compared the method based on morphological operations with U-Net
convolutional network. The best results were obtained by neural network, where we achieved accuracy of 96.7% on DRIVE dataset. Methods
based on morphological operations, Attentional U-Net network and Fast
Radial Symmetry Transform were used for optic disc localization, where
the U-net network achieved the best sensitivity of 91.6%. We show that
in our tests U-Net architectures achieve better results in vessel and optic disc segmentation even compared to standard methods, that do not
depend on the amount of available data."

Segmentation of signiﬁcant regions in retinal images: perspective of U-Net network through a comparative approach

In the presentation we show the main aspects of the work. We made a brief introduction to the research and related works. We explain the methodology step by step and briefly discuss the results and conclusion.

A New Approach for Facial Recognition based on Three-dimensional Point Cloud and Kinect Sensor using Machine Learning

This work presents the computer vision and machine learn-

Simultaneous localization and mapping in 3Dspace

ing techniques used in an implementation of a prototype system to aid

Advanced Scene Sensing for Virtual Teleconference

"Coronavirus (COVID-19) has been causing severe pneumonia worldwide since its emergence in 2019. Considering the scarcity of efficient resources for performing pre-diagnostic imaging, the computed tomography (CT) exam is used as an auxiliary resource for medical diagnosis by means of specialist doctors. Aiming to add solutions for the Computer Assisted Diagnosis (CAD), this work proposes an approach that uses the Internet of Things concept to dispose an API that provides the diagnosis of COVID-19 through Artificial Intelligence (AIoT) methods. The methodology was developed and divided into three stages, which are: 1) segmentation of the region of interest (lung); 2) deep extraction of attributes from the segmented region; and 3) patient classification using machine learning techniques. The Clean-CC-CCII dataset was used in the proposed approach of the experiment to validate the results. The experiment achieved excellent results with Accuracy of (91.12%), Sensitivity (93.45%), Precision (92.13%), Specificity (87.45%), F1-Score (92.79%), and AUC (0.910) in its best model thus surpassing works of
literature."

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