Slovakia

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.

IWSSIP 2021

Fingerprint Classification based on the Henry System via ResNet

henry classification system

fingerprint

cnns

classification

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

technical paper


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)

In this paper we present our GNU Radio based Software-Defined Radio laboratory configured to leverage the practical benefits provided by vir-tualization for lab installation and operation. In the context of recent social dis-tancing-mandated remote operation requirements the procedure for connecting the laboratory online, while adhering to the security guidelines required by the institutes network policy, is described in detail. This setup, combining the virtu-alized configurations with remote access through Virtual Private Networking allows the staff and students to access the specialized laboratory equipment – the USRP signal processors, located on premises, conveniently from their homes, thus eliminating the need for otherwise necessary lab attendance.

A Remote-Access Software-Defined Radio Virtualized Wireless Communications Laboratory

In this contribution we focus on technical and economic aspects of using battery storage in microgrids for peak shaving. We perform feasibility anal-ysis of large battery storage usage in microgrid systems under the current Slovak legislative windows. We use multiple real-life consumption pro-files from various industry oriented microgrids. The primary usage of bat-tery storage in our models is peak shaving and possible reduction of re-quired reserved capacity. The main objective is to find the equilibrium be-tween technical innovations, acquisition costs and energy cost savings.

Peak shaving in microgrids using battery storage

rosurgeon and quantitatively based on statistical tests. These viewpoints

Comparison of TCP Receiver-based Loss Differentiation Algorithms in Wired/Wireless Networks

"The major contributions of our approach are:

1. Implementation of model with mathematical concepts for analyzing the overall delay results during handover.
2. Verification model is presented with necessary verification procedures involved in the sensitivity analysis including complexity using virtual data plane switches.
"

Sensitivity Analysis for SDN Based Wi-Fi Handover Delay Model

"Veterinary medicine emphasizes accidents caused by toxic plants with domestic animals as an extremely important topic, as the right diagnosis can be crucial for the affected animal. In this work, we propose the classification of toxic ornamental plants, according to nine different categories, using five widely-known CNN architectures, namely: DenseNet, ResNet, VGG16, VGG19 and Xception. The rationale behind it is that the automatic identification of these types of plant can be a useful tool to help in the prevention of those accidents. The authors have carefully curated a database to support the development of this work, collecting images available on the Pinterest website, and also performing some important data pre-processing. This database was also made available as a contribution of this work.
Transfer learning was employed by taking advantage of feature learned from the ImageNet dataset. We also analyzed the heat maps generated by the Layer-wise Relevant Propagation method, which allowed to observe the individual behavior of the best and worst architectures. The best performance was achieved using DenseNet, with an accuracy of 97.67%. That model managed to generalize very well, even to deal with noisy images, which are frequent in photos of decorative environments."

Classification of Toxic Ornamental Plants for Domestic Animals using CNN

The incidence of breast cancer has been increasing significantly worldwide. In 2020, more than 2.2 million new cases were registered. Breast cancer tends to alter the thermal pattern of the breasts. In this scenario, thermography appears as a functional examination capable of pointing out thermal changes. Several studies explore the use of this examination for cancer screening and diagnosis. However, few investigate its use for monitoring breast cancer treatment. This work aims at advancing these investigations using dynamic thermography. The data are clustered to obtain areas with higher temperatures and, from these, time series are built to compute different measures and follow up the tumor progress. The achieved results are promising and show that this approach could indicate the patient’s tumor evolution.

Clustering Thermography Data to Follow up Breast Cancer Treatment

"In recent years the native advertisement is becoming more and more prevalent in online spaces. Differentiating between genuine content and native advertisement using Natural Language Processing is therefore also becoming a very interesting research topic. In this paper, we examine the possibilities of using deep textual representation for the Slovak language to recognize the ""PR articles"" (that serve as a native advertisement in this context) from authentic news articles on popular Slovak news websites. We show that the BERT (Bidirectional Encoder Representations from Transformers) embeddings as a text representation are sufficient for this task (achieving accuracy over 80% even with a Logistic Regression model) and that the models generally perform better without prior lemmatization.
"

Automatic recognition of Native advertisements for the Slovak language

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.

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Next from IWSSIP 2021

A Remote-Access Software-Defined Radio Virtualized Wireless Communications Laboratory

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A Weak Coupling of Semi-Supervised Learning with Generative Adversarial Networks for Malware Classification

Stay up to date with the latest Underline news!

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COMPANY

RESOURCES

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