Slovakia

The aim of this paper is to present a method for source detection within unknown mixtures using several measurement modalities. Contrary to the well studied case of single source detection, this approach enables simultaneous detection of multiple sources by exploiting the mixing coefficients resulting from supervised linear unmixing and thresholded non-negative least squares. The first contribution of this work is to propose an automated procedure to compute an optimized binary classifier rule for each source independently using a database of known mixtures. The second contribution is to propose a global decision rule based on the fusion of the multimodal decisions using weighting schemes such as those used in multiple classifier systems (MCS). A real database of Ion Mobiliy Mass Spectrometry (IMMS) data is used to evaluate the detection performance. An increase of the detection accuracy is reached using the multiple thresholds within the independent classifiers approach as compared to single modality detection.

IWSSIP 2021

Supervised Mixture Analysis and Source Detection from Multimodal Measurements

chemical mixture analysis

spectral unmixing

signal detection

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CAP Virtual Congress 2021

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)

The goal of this paper is to study the impact of
elevation mask angle and observation geometry on the position
accuracy of Global Navigation Satellite Systems (GNSS). The
precision of the user's location measurement relies on the dynamic
interaction of many factors. In the last decade, with the upgrade
and deployment of new satellite systems, there is an increase
interest in positioning techniques based on GNSS. This study
provides a performance assessment for different elevation mask
angles validated with real data. Through experimental results is
shown that with higher masking angles, a decrease in the number
of visible satellites is obvious which causes an increase in dilution
of precision values. The correlation between the low number of
visible satellites and a weak user – satellite geometry causes a
deterioration in the users’ position accuracy.

A Study on Cut-Off Elevation Angle Impact on GNSS Position Accuracy

We propose a system for digital documentation and presentation of museum collection objects that goes beyond the object appearance and can capture the object function and tell a story associated with the object. Museum cura-tors can prepare, without programming, interactive digital storytelling expe-riences for visitors about the object creation, restoration or to learn about the cultural history and societal development.

Digital storytelling for learning with heritage artefacts

An event-based looming objects detection algorithm for asynchronous event-based cameras is presented. The algorithm is fast and accurate both in the detecting the correct number of objects as well as whether the objects are looming.

Event-based Looming Objects Detection

Particle colliders are machines built to probe fundamental questions in physics. The properties of the produced particles are measured by complex experiments which use a wide variety of devices, such as the calorimeter system. In the ATLAS experiment at LHC, the Tile Calorimeter (TileCal) comprises about 10,000 readout channels that amplify, shape, and sample each signal every 25 ns. Since LHC collisions occur every 25 ns, and due to the increase of the luminosity level, signals from adjacent collisions may be read out within the same TileCal readout window, deforming the expected signal, and degrading the energy estimation efficiency. Therefore, this work compares the performance of the currently available methods for TileCal energy estimation using LHC collision data. Different pile-up conditions are considered. The results show that the performance in terms of the uncertainty of the energy estimation can be improved up to 35% in high occupancy readout channels.

Content-Based Multimodal Retrieval: Using Image and Audio Descriptors for Retrieving Cases from a Database of Cardiac Examinations

The COVID-19 (Coronavirus Disease 2019), caused by the SARS-CoV-2 virus, can cause severe pneumonia. The chest X-ray (CXR) is the preliminary radiographic exam to investigate pneumonia and other lung abnormalities. However, the identification of opacities in the CXR is challenging due to its overall low quality. In the literature, several works have proposed automated approaches to improve the quality of CXR images in general. However, as far as we know, few studies explored them in the context of COVID-19 identification in a thorough manner. In this work, we applied a series of digital image processing techniques, focusing on contrast enhancement. The impact of each method was evaluated using traditional digital image quality metrics, specifically peak signal-to-noise ratio (PSNR) and universal image quality (UQI), and a deep learning model for COVID-19 detection. We also investigated which regions were most frequently used by the model after each transformation using an Explainable Artificial Intelligence (XAI) approach called LIME. The rationale is to evaluate if any transformation improves the lung region used by the recognition model. The enhancement strategies that presented the overall best results considering PSNR and UQI were Gamma Correction (GC) and Adaptive Gamma Correction With Weighting Distribution (AGCWD). However, no method improved the identification of COVID-19 in a significant matter.

Static and Dynamic Point Cloud Compression by TSPLVQ

More than three quarters of world network data traffic is already video-related. This content does not only consist of professional and user generated clips, video from streamed and downloaded services and media pre-recorded or generated on the fly, but also content with different degrees of interactivity. Rich media also includes multi-sensorial media, omnidirectional video and mixed reality content. This content can be consumed at home, at work, in public places by a heterogeneous user population, while static or on the move. A wide range of devices contributes to generation and use of this content is being distributed over a network environment which is also highly heterogeneous. Challenges of transmitting this rich media content are highlighted and several solutions to support high quality, performance and/or energy efficiency of rich media delivery in such a complex context are presented.

Delivering High Quality Rich Media Content in Current Network Environment: Challenges and Solutions

JPEG AI: Towards the First Learning based Image Compression Standard

Detection and Localization of Objects within Images Using Computer Vision and Machine Learning

Chest X-ray image enhancement: a COVID-19 identification case study

The paper focuses on three basic topics: an online environment for sharing of in-novative teaching materials; design and implementation of modern teaching mate-rials; and description of advanced types of learning objects used in such materi-als.
In these days we are about to conclude a set of projects that deal with develop-ment of highly innovative teaching materials and the environments needed for their delivery to the target groups. The most visible output of these projects is an online portal that provides access to hundreds of completely new teaching ma-terials containing a large number of learning objects.
In the paper we explain the technical specifics of the online environment, teaching materials and individual learning objects, which have a significant added value compared to conventional teaching aids.

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Supervised Mixture Analysis and Source Detection from Multimodal Measurements

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