On the Search for Potentially Anomalous Traces of Cosmic Ray Particles in Images Acquired by Cmos Detectors for a Continuous Stream of Emerging Observational Data.

In this paper we propose the method for detecting potential anomalous cosmic ray particle tracks in big data image dataset acquired by Complementary Metal-Oxide-Semiconductors (CMOS). Those sensors are part of scientific infrastructure of Cosmic Ray Extremely Distributed Observatory (CREDO). The use of Incremental PCA (Principal Components Analysis) allowed approximation of loadings which might be updated at runtime. Incremental PCA with Sequential Karhunen-Loeve Transform results with almost identical embedding as basic PCA. Depending on image preprocessing method the weighted distance between coordinate frame and its approximation was at the level from 0.01 to 0.02 radian for batches with size of 10,000 images. This significantly reduces the necessary calculations in terms of memory complexity so that our method can be used for big data. The use of intuitive parameters of the potential anomalies detection algorithm based on object density in embedding space makes our method intuitive to use. The sets of anomalies returned by our proposed algorithm do not contain any typical morphologies of particle tracks shapes. Thus, one can conclude that our proposed method effectively filter-off typical (in terms of analysis of variance) shapes of particle tracks by searching for those that can be treated as significantly different from the others in the dataset. We also proposed method that can be used to find similar objects, which gives it the potential, for example, to be used in minimal distance-based classification and CREDO image database querying. The proposed algorithm was tested on more than half a million (570,000+) images that contains various morphologies of cosmic particle tracks. To our knowledge, this is the first study of this kind based on data collected using a distributed network of CMOS sensors embedded in the cell phones of participants collaborating within the citizen science paradigm.

The Practice of Detecting Potential Cosmic Rays Using CMOS Cameras: Hardware and Algorithms.

In this paper, we discuss a practice of potential cosmic ray detection using off-the-shelves CMOS cameras. We discuss and presents the limitations of up-to-date hardware and software approaches to this task. We also present a hardware solution that we made for long-term testing of algorithms for potential cosmic ray detection. We have also proposed, implemented and tested a novel algorithm that enables real-time processing of image frames acquired by CMOS cameras in order to detect tracks of potential particles. We have compared our results with already published results and obtained acceptable results overcoming some limitation of already existing algorithms. Both source codes and data are available to download.

Zernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors.

Reliable tools for artefact rejection and signal classification are a must for cosmic ray detection experiments based on CMOS technology. In this paper, we analyse the fitness of several feature-based statistical classifiers for the classification of particle candidate hits in four categories: spots, tracks, worms and artefacts. We use Zernike moments of the image function as feature carriers and propose a preprocessing and denoising scheme to make the feature extraction more efficient. As opposed to convolution neural network classifiers, the feature-based classifiers allow for establishing a connection between features and geometrical properties of candidate hits. Apart from basic classifiers we also consider their ensemble extensions and find these extensions generally better performing than basic versions, with an average recognition accuracy of 88%.

CNN-Based Classifier as an Offline Trigger for the CREDO Experiment.

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.

Recognition of Cosmic Ray Images Obtained from CMOS Sensors Used in Mobile Phones by Approximation of Uncertain Class Assignment with Deep Convolutional Neural Network.

In this paper, we describe the convolutional neural network (CNN)-based approach to the problems of categorization and artefact reduction of cosmic ray images obtained from CMOS sensors used in mobile phones. As artefacts, we understand all images that cannot be attributed to particles' passage through sensor but rather result from the deficiencies of the registration procedure. The proposed deep neural network is composed of a pretrained CNN and neural-network-based approximator, which models the uncertainty of image class assignment. The network was trained using a transfer learning approach with a mean squared error loss function. We evaluated our approach on a data set containing 2350 images labelled by five judges. The most accurate results were obtained using the VGG16 CNN architecture; the recognition rate (RR) was 85.79% ± 2.24% with a mean squared error (MSE) of 0.03 ± 0.00. After applying the proposed threshold scheme to eliminate less probable class assignments, we obtained a RR of 96.95% ± 1.38% for a threshold of 0.9, which left about 62.60% ± 2.88% of the overall data. Importantly, the research and results presented in this paper are part of the pioneering field of the application of citizen science in the recognition of cosmic rays and, to the best of our knowledge, this analysis is performed on the largest freely available cosmic ray hit dataset.

Machine Learning Methodology in a System Applying the Adaptive Strategy for Teaching Human Motions.

The teaching of motion activities in rehabilitation, sports, and professional work has great social significance. However, the automatic teaching of these activities, particularly those involving fast motions, requires the use of an adaptive system that can adequately react to the changing stages and conditions of the teaching process. This paper describes a prototype of an automatic system that utilizes the online classification of motion signals to select the proper teaching algorithm. The knowledge necessary to perform the classification process is acquired from experts by the use of the machine learning methodology. The system utilizes multidimensional motion signals that are captured using MEMS (Micro-Electro-Mechanical Systems) sensors. Moreover, an array of vibrotactile actuators is used to provide feedback to the learner. The main goal of the presented article is to prove that the effectiveness of the described teaching system is higher than the system that controls the learning process without the use of signal classification. Statistical tests carried out by the use of a prototype system confirmed that thesis. This is the main outcome of the presented study. An important contribution is also a proposal to standardize the system structure. The standardization facilitates the system configuration and implementation of individual, specialized teaching algorithms.

Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor.

The motivation of this paper is to examine the effectiveness of state-of-the-art and newly proposed motion capture pattern recognition methods in the task of head gesture classifications. The head gestures are designed for a user interface that utilizes a virtual reality helmet equipped with an.

Radial Basis Functions Intended to Determine the Upper Bound of Absolute Dynamic Error at the Output of Voltage-Mode Accelerometers.

In this paper, we propose using the radial basis functions (RBF) to determine the upper bound of absolute dynamic error (UAE) at the output of a voltage-mode accelerometer. Such functions can be obtained as a result of approximating the error values determined for the assumed-in-advance parameter variability associated with the mathematical model of an accelerometer. This approximation was carried out using the radial basis function neural network (RBF-NN) procedure for a given number of the radial neurons. The Monte Carlo (MC) method was also applied to determine the related error when considering the uncertainties associated with the parameters of an accelerometer mathematical model. The upper bound of absolute dynamic error can be a quality ratio for comparing the errors produced by different types of voltage-mode accelerometers that have the same operational frequency bandwidth. Determination of the RBFs was performed by applying the Python-related scientific packages, while the calculations related both to the UAE and the MC method were carried out using the MathCad program. Application of the RBFs represent a new approach for determining the UAE. These functions allow for the easy and quick determination of the value of such errors.

Human Actions Analysis: Templates Generation, Matching and Visualization Applied to Motion Capture of Highly-Skilled Karate Athletes.

The aim of this paper is to propose and evaluate the novel method of template generation, matching, comparing and visualization applied to motion capture (kinematic) analysis. To evaluate our approach, we have used motion capture recordings (MoCap) of two highly-skilled black belt karate athletes consisting of 560 recordings of various karate techniques acquired with wearable sensors. We have evaluated the quality of generated templates; we have validated the matching algorithm that calculates similarities and differences between various MoCap data; and we have examined visualizations of important differences and similarities between MoCap data. We have concluded that our algorithms works the best when we are dealing with relatively short (2-4 s) actions that might be averaged and aligned with the dynamic time warping framework. In practice, the methodology is designed to optimize the performance of some full body techniques performed in various sport disciplines, for example combat sports and martial arts. We can also use this approach to generate templates or to compare the correct performance of techniques between various top sportsmen in order to generate a knowledge base of reference MoCap videos. The motion template generated by our method can be used for action recognition purposes. We have used the DTW classifier with angle-based features to classify various karate kicks. We have performed leave-one-out action recognition for the Shorin-ryu and Oyama karate master separately. In this case, 100 % actions were correctly classified. In another experiment, we used templates generated from Oyama master recordings to classify Shorin-ryu master recordings and vice versa. In this experiment, the overall recognition rate was 94.2 % , which is a very good result for this type of complex action.