Background and Objective: New technology enables constant boost to the powers of mobile devices, which in the previous years have transformed from simple mobile phones to smart phones. Computational powers of these electronics enable actions that previously were possible only for computers. By the use of special applications we may benefit from sensors and multimedia capabilities of operating systems. Therefore a new era for devoted implementations opens, in which a smart application can take a role of computing system to estimate the symptoms of diseases by evaluating signals coming from a human body. Methods: We propose a model of an application implemented for mobile Android systems, which can be used for examination of central nervous system motor disorders occurring in patients suffering from Huntington (HD), Alzheimer (AD) or Parkinson (PD) diseases. In particular, the model tracks tremors (involuntary movements), and cognitive (memory loss or dementia) impairments using touch and visual stimulus modalities. The proposed model interprets the symptoms from human bodies that indicate one of the diseases of the nervous system. Pre-processing of collected data for feature extraction is executed on a mobile device by using core functionality and methods provided in Android's Application Programming Interface (API). The information is evaluated by a back-propagation neural network (BPNN) classifier and the result is presented to the end user. The system is able to contact medical supervision and provide an assistance from the clinic. Results: The system uses a collected dataset of 1928 records, taken from 11 Huntington disease patients and 11 healthy persons in Lithuania, to gather statistics about examinations and presents the results as medical evaluation with prediction on the state of health. The accuracy of recognition of early, prodromal symptoms for central nervous system motor disorders is 86.4% (F-measure 0.859). The app (available on Google Play) is easy to use and is efficient tool for decision support in medical examinations. Conclusions: The use of intelligent apps which can help to evaluate neurodegenerative disorders is an important enhancement to medical diagnosis. The developed smartphone app supports the doctor with additional results, which are easy to compare to other examinations. For patients this kind of examination is a nice change from classic stereotypes, especially for younger age patients, who are used to various aspects of information technology.

We introduce an approach to predict deterioration of reaction state for people having neurological movement disorders such as hand tremors and nonvoluntary movements. These involuntary motor features are closely related to the symptoms occurring in patients suffering from Huntington’s disease (HD). We propose a hybrid (neurofuzzy) model that combines an artificial neural network (ANN) to predict the functional capacity level (FCL) of a person and a fuzzy logic system (FLS) to determine a stage of reaction. We analyzed our own dataset of 3032 records collected from 20 test subjects (both healthy and HD patients) using smart phones or tablets by asking a patient to locate circular objects on the device’s screen. We describe the preparation and labelling of data for the neural network, selection of training algorithms, modelling of the fuzzy logic controller, and construction and implementation of the hybrid model. The feed-forward backpropagation (FFBP) neural network achieved the regression R value of 0.98 and mean squared error (MSE) values of 0.08, while the FLS provides a final evaluation of subject’s reaction condition in terms of FCL.

We present a model for digital neural impairment screening and self-assessment, which can evaluate cognitive and motor deficits for patients with symptoms of central nervous system (CNS) disorders, such as mild cognitive impairment (MCI), Parkinson’s disease (PD), Huntington’s disease (HD), or dementia. The data was collected with an Android mobile application that can track cognitive, hand tremor, energy expenditure, and speech features of subjects. We extracted 238 features as the model inputs using 16 tasks, 12 of them were based on a self-administered cognitive testing (SAGE) methodology and others used finger tapping and voice features acquired from the sensors of a smart mobile device (smartphone or tablet). Fifteen subjects were involved in the investigation: 7 patients with neurological disorders (1 with Parkinson’s disease, 3 with Huntington’s disease, 1 with early dementia, 1 with cerebral palsy, 1 post-stroke) and 8 healthy subjects. The finger tapping, SAGE, energy expenditure, and speech analysis features were used for neural impairment evaluations. The best results were achieved using a fusion of 13 classifiers for combined finger tapping and SAGE features (96.12% accuracy), and using bidirectional long short-term memory (BiLSTM) (94.29% accuracy) for speech analysis features.

This paper presents a novel stereo matching algorithm Cyclops2. The algorithm produces a disparity image, provided two rectified grayscale images. The matching is based on the concept of minimising a weight function calculated using the absolute difference of pixel intensities. We present three simple and easily parallelizable weight functions. Each presented function gives a different trade-off between algorithm processing time and reconstructed depth image accuracy. Detailed description of the algorithm implementation in CUDA is provided. The implementation was specifically optimised for embedded NVIDIA Jetson platform. NVIDIA Jetson TK1 and TX1 boards have been used to evaluate the algorithms. We evaluated seven algorithm variations with different parameter values. Each variation results in a different speed accuracy trade-off, demonstrating that our algorithm can be used in various situations. The presented algorithm achieves up to 70 FPS processing time on lower resolution images (750 × 500 pixels) and up to 23 FPS on high-resolution images (1500 × 1000 pixels). The use of optional post-processing stage (median filter) has also been investigated. We conclude that despite its limitations, our algorithm is relevant in the field of real-time obstacle avoidance.

Straipsnyje supažindinama su informacinių technologijų (toliau – IT) naudojimo mokyklose tyrimu, pateikiama priemonių ir būdų visuma informacijai apdoroti, kaupti bei sisteminti, naudojant kompiuterius ar kitą šiuolaikinę techniką. Remiantis informatikos, informacinių technologijų ugdymo 2015 metų strategijos gairėmis, IT dalykas mokykloje turi suteikti mokiniams kompiuterinio raštingumo pagrindus, išmokyti kryptingai derinti minimalias teorines technologines žinias ir maksimalius praktinius gebėjimus. Strategija nustato IT diegimo švietime numatytas tolesnes kryptis ir svarbiausius žingsnius, pateikia IT diegimo į bendrąjį lavinimą ir profesinį mokymą analizę, apibrėžia viziją, tikslus ir uždavinius, prioritetus ir diegimo principus. Taip siekiama užtikrinti IT prieinamumą kiekvienam asmeniui kaip vieną iš svarbiausių dalykų, gerinti ES švietimo ir profesinio rengimo sistemų kokybę ir veiksmingumą kintant žinių visuomenės poreikiams ir mokymo bei mokymosi pobūdžiui. Analizuojami 2012 metais atlikto IT prieinamumo, naudojimo bei poreikio Lietuvos mokyklose tyrimo rezultatai, pateikiamos išvados ir tolesnio technologijų naudojimo taikant mišrų mokymosi būdą rekomendacijos. Apibūdinama atvirųjų švietimo išteklių plėtros perspektyva Lietuvoje ir jų taikymas mišriam mokymui.Pagrindiniai žodžiai: informacinės technologijos, mišrus mokymas, atvirieji švietimo ištekliai, pedagogų profesinis rengimas.Advanced learning technologies in education Daina Gudonienė, Danguolė Rutkauskienė, Andrius Lauraitis SummaryThe paper presents a study on information technology (hereinafter IT) in schools, the tools and techniques to combine information, collect and organize it using computers and other modern technologies. According to computer science and information technology education, since 2015 the strategies for the IT subject in schools will provide students with computer literacy, minimum theoretical purposefully combined technological knowledge and maximum practical abilities. It provides IT implementation in education and further directions of the most important steps of IT implementation in the general education and training, defines the IT vision, goals, and objectives, priorities and implementation principles.The strategic aims are to ensure the availability of IT to each person as one of the most important things, and thus to improve the EU’s education and training systems’ quality and effectiveness, to meet the changing needs of the knowledge society and to improve the nature of teaching and learning. This paper presents the 2012 survey results on IT availability, use, and need for Lithuanian schools, conclusions and recommendations for their further use applying a blended learning approach.Another aspect covered in this paper is the role and developement of Open Educational Resources (OER) in Lithuania. An important factor is to apply OER in blended learning and improve its usage in Lithuanian education institutions. In this way, a more robust, modern, and attractive learning approach will be reached.i, sans-serif;">