The man-machine interface (MMI) is one of the most exciting areas of contemporary research. To make the MMI as convenient for a human as possible, it is desirable that efficient algorithms for recognizing body language are developed. This paper presents a system for quick and effective recognition of gestures of hand body language, based on data from a specialized glove equipped with ten sensors. In the experiment, 10 people performed 22 hand body language gestures. Each of the 22 gestures was executed 10 times. Collected data were preprocessed in multiple ways and three machine learning algorithms were designed based on classifiers (probabilistic neural network, support vector machine, and k-nearest neighbors algorithm) trained and tested by a tenfold cross-validation technique. The best designed classifiers gained effectiveness of gesture recognition at κ = 98.24% with a very short time of testing, below 1 ms. The experiments confirm that efficient and quick recognition of hand body language is possible.

The Cosmic-Ray Extremely Distributed Observatory (CREDO) is a newly formed, global collaboration dedicated to observing and studying cosmic rays (CR) and cosmic-ray ensembles (CRE): groups of at least two CR with a common primary interaction vertex or the same parent particle. The CREDO program embraces testing known CR and CRE scenarios, and preparing to observe unexpected physics, it is also suitable for multi-messenger and multi-mission applications. Perfectly matched to CREDO capabilities, CRE could be formed both within classical models (e.g., as products of photon–photon interactions), and exotic scenarios (e.g., as results of decay of Super-Heavy Dark Matter particles). Their fronts might be significantly extended in space and time, and they might include cosmic rays of energies spanning the whole cosmic-ray energy spectrum, with a footprint composed of at least two extensive air showers with correlated arrival directions and arrival times. As the CRE are predominantly expected to be spread over large areas and, due to the expected wide energy range of the contributing particles, such a CRE detection might only be feasible when using all available cosmic-ray infrastructure collectively, i.e., as a globally extended network of detectors. Thus, with this review article, the CREDO Collaboration invites the astroparticle physics community to actively join or to contribute to the research dedicated to CRE and, in particular, to pool together cosmic-ray data to support specific CRE detection strategies.

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.

We present the purpose, long-term development vision, basic design, detection algorithm and preliminary results obtained with the Cosmic Ray Extremely Distributed Observatory (CREDO) Detector mobile application. The CREDO Detector app and related infrastructure are unique in terms of their scale, targeting many form-factors and open-access philosophy. This philosophy translates to the open-source code of the app, open-access in terms of both data inflow as well as data consumption and above all, the citizen science philosophy that means that the infrastructure is open to all who wish to participate in the project. The CREDO infrastructure and CREDO Detector app are designed for the large-scale study of various radiation forms that continuously reach the Earth from space, but with the sensitivity to local radioactivity as well. Such study has great significance both scientifically and educationally as cosmic radiation has an impact on diverse research areas from life on Earth to the functioning of modern electronic devices. The CREDO Detector app is now working worldwide across phones, tablets, laptops, PCs and cheap dedicated registration stations. These diverse measurements contribute to the broader search for large-scale cosmic ray correlations, as well as the CREDO-specific proposed extensive air showers and incoherent secondary cosmic rays.

Gamification is known to enhance users’ participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various radiation forms that continuously reach the Earth from space, collectively known as cosmic rays. The CREDO Detector app relies on a network of involved users and is now working worldwide across phones and other CMOS sensor-equipped devices. To broaden the user base and activate current users, CREDO extensively uses the gamification solutions like the periodical Particle Hunters Competition. However, the adverse effect of gamification is that the number of artefacts, i.e., signals unrelated to cosmic ray detection or openly related to cheating, substantially increases. To tag the artefacts appearing in the CREDO database we propose the method based on machine learning. The approach involves training the Convolutional Neural Network (CNN) to recognise the morphological difference between signals and artefacts. As a result we obtain the CNN-based trigger which is able to mimic the signal vs. artefact assignments of human annotators as closely as possible. To enhance the method, the input image signal is adaptively thresholded and then transformed using Daubechies wavelets. In this exploratory study, we use wavelet transforms to amplify distinctive image features. As a result, we obtain a very good recognition ratio of almost 99% for both signal and artefacts. The proposed solution allows eliminating the manual supervision of the competition process.