Experience
Customized Solutions for research
OVER 20 years of scientific software development
Projects listed below were developed in research collaborations and most of the solutions are not for sale. However we have the experience in building similar systems from scratch. Contact us for a free consultation. Describe your problem, we get back to you if we are interested.
We are also available for algorithm or research reproduction, or research of alternative approaches to patented and protected solutions.
Explore the world of physics-based crowd simulations with our crowd simulation platform.
Expertly designed to compute isovist and visibility factors, our software provides valuable insights for architecture and design applications, helping you achieve an optimal spatial configuration. We create bespoke tools to explore design elements in both 2D and 3D. This dynamic approach allows architects, designers, and planners alike to visualize, analyze, and perfect their designs in a comprehensive and interactive way, ensuring a more effective and aesthetically pleasing realization of their vision.
Modular Psychophysics (mPsy)
(https://github.com/wisions/mPsy3)
mPsy3 is a modular approach to design of psychophysical experiments for Python 3. Experiments can be built out of independent programs and modules. Programs can be in different languages. It offers a library of standard stimuli and also tools for building new stimuli, static and dynamic.
mPsy3 is free, open-source, and extremely compact. It embodies a minimalist approach to programming and psychophysics. The core of mPsy3 is written in Python. mPsy3 is a Python 3 compatible upgrade of the first mPsy version written for Python 2 in 2015.
The 'pymdfa' module
(https://github.com/juricap/pymdfa)
A minimalistic and fast implementation of MFDFA in Python, described in Jurica P. Multifractal analysis for all. Frontiers in Physiology. 2015;6:27.
Multifractal analysis is a statistical methodology that is employed to analyze patterns in data sets that exhibit complex, fractal-like scaling behavior across multiple scales. It aims to characterize and quantify the irregularities and heterogeneities present in such data sets, which are often observed in diverse fields such as physics, geology, finance, and biology. This method uses mathematical functions called multifractal spectra to identify and measure the different scaling behaviors within a data set, which can provide insights into the underlying processes or structures that produced the data. Multifractal analysis allows for a deeper understanding of the multi-scale, nonlinear characteristics of complex systems.
CogExTools
(https://github.com/juricap/cogextools)
CogExTools is a platform for design and execution of cognitive examination on tablets and personal computers. CogExTools is a toolbox written in Python, built around vector graphics editor Inkscape and the SVG standard.
Screening for cognitive impairment is becoming more important as the world’s population grows older. Most of mental fitness examinations are performed by humans, which instruct and subsequently grade the execution of a cognitive exercise task. By involving computers in both roles, instructing and scoring, we aim to increase the precision and replicability of the examination.
Jurica P, Valenzi S, Struzik Z, Cichocki A (2014) - Methods for Transition Toward Computer Assisted Cognitive Examination. Methods of Information in Medicine, 54(3), 256-261
Sensory Optimization by Stochastic Tuning
(http://evolver.juricap.com/)
Stochastic tuning of receptive fields across their entire parameter space. The figure shows how independent stochastic updates of tuning in multiple cells add up to a distribution of tuning consistent with the distribution of sensitivity in human vision (Kelly, 1979) and with prescriptions of a normative theory of neural resource allocation (Gepshtein, et al. 2007). Left panel: Receptive fields of individual cells are represented by red dots. Here there are 5,000 independent cells across the entire range of plausible spatial and temporal extents of receptive fields. Right panel: The spatio-temporal contrast sensitivity function calculated from the distribution of receptive fields in the left panel. The oblique lines are "constant-speed lines", parallel to one another in the logarithmic coordinates.
Jurica P, Gepstein S, Tyukin I, van Leeuwen C (2013) - Sensory Optimization by Stochastic Tuning. Psychological Review, 120(4), 798-816.
Cattle Tag Tracking
Classical computer vision techniques to automate the detection, reading, and tracking of cattle tags.
It began with image acquisition where low quality images of cattle were captured by random users and their phones. Automatic pre-processing steps were to enhance visibility of tags. An edge detection algorithm to highlight the tag boundaries and chroma and geometric shape analysis were used. The identified tag region was then processed by Optical Character Recognition (OCR) to read the tag information.
OMPC allows running MATLAB®'s m-files using Python interpreter.
(http://ompc.juricap.com/)
OMPC reads m-files and translates them into Python compatible code.
OMPC aims to enable reuse of the huge open and free code base of MATLAB® on a free and faster growing Python platform. Running Python and MATLAB® in a single interpreter avoids issues with running two separate applications. Python adds general purpose programming libraries to the convenient syntax of the language of technical computing.
Patents
Illumination apparatus for special purpose having improved color rendering index, and method for designing same - P Jurica, S Valenzi, WO/2022/230855 https://patents.google.com/patent/WO2022230855A1
Biological information measurement device - S Valenzi, P Jurica, US Patent App. 16/332,481 https://patents.google.com/patent/US20210298622A1
Publications
(complete list at https://scholar.google.com/citations?user=YK7rJOMAAAAJ&hl=en)
Takahara Y, Ota T, Nakanishi Y, Ueda S, Jurica P, Struzik ZR, et al. (2023) Exploration of electroencephalogram response to MPH treatment in ADHD patients. Psychiatry Research: Neuroimaging, 111631.
Arata Y, Shiga I, Ikeda Y, Jurica P, Kimura H, Kiyono K, Sako Y (2022) Insulin signaling shapes fractal scaling of C. elegans behavior. Scientific Reports 12 (1), 10481.
Arata Y, Shiga I, Ikeda Y, Jurica P, Kimura H, Kiyono K, Sako Y (2021) Fractal scaling of C. elegans behavior is shaped by insulin signaling. bioRxiv, 2021.12.02.471007.
Valenzi S, Jurica P (2021) Biological information measurement device. US Patent App. 16/332,481.
Miao Y, Jurica P, Struzik ZR, Hitomi T, Kinoshita A, Takahara Y, Ogawa K, et al. (2021) Dynamic theta/beta ratio of clinical EEG in Alzheimer's disease. Journal of Neuroscience Methods 359, 109219.
Ikeda Y, Jurica P, Kimura H, Takagi H, Struzik ZR, Kiyono K, Arata Y (2020) C. elegans episodic swimming is driven by multifractal kinetics. Scientific reports 10 (1), 1-17.
Yokota T, Struzik ZR, Jurica P, Horiuchi M, Hiroyama S, Li J, Takahara Y, Ogawa K, Nishitomi K, Hasegawa M, Cichocki A (2018) - Semi-Automated Biomarker Discovery from Pharmacodynamic Effects on EEG in ADHD Rodent Models. Scientific Reports 8 (1), 5202.
Jurica P, Struzik ZR, Li J, Horiuchi M, Hiroyama S, Takahara Y, Nishitomi K , Ogawa K, Cichocki A (2018) Combining behavior and EEG analysis for exploration of dynamic effects of ADHD treatment in animal models. Journal of Neuroscience Methods 298, 24-32.
Alexander DM, Nikolaev AR, Jurica P, Zvyagintsev M, Mathiak K, van Leeuwen C (2016) - Global Neuromagnetic Cortical Fields Have Non-Zero Velocity. PLoS ONE, March 8, 2016, http://dx.doi.org/10.1371/journal.pone.0148413
Jurica P (2015) - Multifractal analysis for all. Frontiers in Physiology, 6(27). (https://github.com/juricap/pymdfa - IPython notebooks)
Jurica P, Gepstein S, Tyukin I, van Leeuwen C (2013) - Sensory Optimization by Stochastic Tuning. Psychological Review, 120(4), 798-816.
Jurica P, Valenzi S, Struzik Z, Cichocki A (2014) - Methods for Transition Toward Computer Assisted Cognitive Examination. Methods of Information in Medicine, 54(3), 256-261. (CogExTools available at https://github.com/juricap/cogextools)
Valenzi S, Islam T, Jurica P, Cichocki A (2014) - Individual Classification of Emotions Using EEG. Journal of Biomedical Science and Engineering, 7, 604-620.
Nikolaev AR, Jurica P, Nakatani C, Plomp G, van Leeuwen C (2013) - Visual encoding and fixation target selection in free viewing: presaccadic brain potentials. Front. Syst. Neurosci., 27 June 2013.
Alexander DM, Jurica P, Trengove C, Nikolaev AR, Gepshtein S, Zvyagintsev M, Mathiak K, Schulze-Bonhage A, Rueschere J, Ball T, van Leeuwen C (2013) - Traveling waves and trial averaging: The nature of single-trial and averaged brain responses in large-scale cortical signals. NeuroImage 73, 95–-112.
Nikolaev AR, Nakatani C, Plomp G, Jurica P, van Leeuwen C (2011) - Eye fixation-related potentials in free viewing identify encoding failures in change detection. NeuroImage, 56 (3), 1598--1607.
Jurica P, van Leeuwen C (2009) - OMPC: an open-source MATLAB(R)-to-Python compiler. Frontiers in Neuroinformatics, 3 (5), 1--9. (hosted at http://ompc.juricap.com)
Jurica P, Gepshtein S, Tyukin I, Prokhorov D, van Leeuwen C (2007) - Unsupervised adaptive optimization of motion-sensitive systems guided by measurement uncertainty. 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 179--184.
Kwok HF, Jurica P, Raffone A, van Leeuwen C (2007) - Robust emergence of small-world structure in networks of spiking neurons. Cognitive Neurodynamics, 1, 39--51.
Jurica P, Yang S (2003) - Predictions on Trajectory and Velocity of Visually Guided Saccades Based on Gravitational Field Model of Occulomotor Movements. Proceedings of IEEE International Conference on Computational Cybernetics, ICCC2003.
Jurica P, Brinkers M, van Leeuwen C (2002) - Component-based Framework for Design and Simulation of Neural Networks, Proceedings of KES2002, September, 16th - 18th 2002.