Synthetic Cats and Dogs Dataset
A synthetic dataset of 20,000 cat and dog images generated using Domain Randomization.
Posts by Collection
portfolio
Automated Oral Dental Exam
A convolutional based model capable of identifing common dental problems from an image.
Automated Exercise Logger
A workout repetitions counter and exercises classifier based on pose estimation.
Synthetic Light Field Dataset
A synthetic dataset of 20,000 light fields generated using Domain Randomization.
Sneaker SKU Number Scanner
A realtime label scanner module utilizing text recognition to identify product SKUs
publications
Convolutional Autoencoders for Lossy Light Field Compression
Expansion and reduction of a neural network’s width has well known properties in terms of the entropy of the propagating information. When carefully stacked on top of one another, an encoder network and a decoder network produce an autoencoder, often used in compression. Using this architecture, we develop an efficient method of encoding and decoding 4D Light Field data, with a substantial compression factor at a minimal loss in quality. Our best results managed to achieve a compression of 48.6x, with a PSNR of 29.46 dB and a SSIM of 0.8104. Computations of the encoder and decoder can be run in real time, with average computation times of 1.62s and 1.81s respectively, and the entire network occupies a reasonable 584MB by today’s storage standards.
Valtchez, S.Z. and Wu, J. (2019). "Convolutional Autoencoders for Lossy Light Field Compression", IEEE Journal of Selected Topics in Signal Processing, *in revision* http://zarkonium.github.io/files/Convolutional_Autoencoders_for_Light_Field_Compression.pdf
Identifying Volatility Regimes in Bitcoin Prices
We apply clustering techniques to volatility and market sentiment measurements of historical Bitcoin prices in the aim of identifying hidden structural patterns separating different regions of our data. Using these regimes should help a fund allocate between trading strategies depending on market conditions, based on relatively simple market measurements. K-means, Ward, complete-linkage Agglomerative and Birch Clustering was used to separate the data. The resulting clusters were used as state vectors in a Markov Chain model, and used to predict the market’s state in the short-term horizon. Lastly, we provide an improvement to our one time-step-ahead forecast using historical results and the conditional probability framework.
Valtchev, S.Z., Sadiku, J. and Wu, J. (2019). "Identifying Volatility Regimes in Bitcoin Prices", Manuscript. http://zarkonium.github.io/files/Identifying_Volatility_Regimes_in_Bitcoin_Prices.pdf
A Correlation Measure for Multi-Dimensional Signals
Similarity measures are largely needed for a variety of tasks such as anomaly detection, classification and forecasting. In this paper we explore the shortcomings of current multi-dimensional correlations measures, namely the RV coefficient and the normalized RV coefficient. When a particular dimension is positively correlated between 2 samples, while another is negatively correlated, these methods can provide undesired results. We propose a new measure, the DwC (Deviation-weighted Correlation) coefficient, which overcomes these limitations. Our measure correctly incorporates positive and negative correlation on a dimension-by-dimension basis, ultimately providing a more intuitive and useful measure that generalizes to higher dimensions for the comparison of arbitrary matrices. The measure also holds some scaling properties which become useful in the presence of noise. Lastly we provide an example using accelerometer data, to classify common human activities based on maximum DwC between predefined templates and the data.
Valtchev, S.Z. and Wu, J. (2019). "A Correlation Measure for Multi-Dimensional Signals", Manuscript. http://zarkonium.github.io/files/A_Similarity_Measure_for_Multi_Dimensional_Signals.pdf
Artificial Intelligence Model of Drive-Through Vaccination Simulation
Published in MDPI International Journal of Environmental and Public Health, 2020
Planning for mass vaccination against SARS-Cov-2 is ongoing in many countries considering that vaccine will be available for the general public in the near future. Rapid mass vaccination while a pandemic is ongoing requires the use of traditional and new temporary vaccination clinics. Use of drive-through has been suggested as one of the possible effective temporary mass vaccinations among other methods. In this study, we present a machine learning model that has been developed based on a big dataset derived from 125K runs of a drive-through mass vaccination simulation tool. The results show that the model is able to reasonably well predict the key outputs of the simulation tool. Therefore, the model has been turned to an online application that can help mass vaccination planners to assess the outcomes of different types of drive-through mass vaccination facilities much faster.
Asgary, A., Valtchev, S.Z., Chen, M., Najafabadi, M. and Wu, J. (2020). "Artificial Intelligence Model of Drive-Through Vaccination Simulation", MDPI International Journal of Environmental and Public Health. 18(1):268. http://zarkonium.github.io/files/Artificial_Intelligence_Model_of_Drive-Through_Vac.pdf
Domain Randomization for Neural Network Classification Permalink
Published in SpringerOpen Journal of Big Data, 2021
Large data requirements are often the main hurdle in training neural networks. Synthetic data is a cheap and efficient solution to assemble such large datasets. Using domain randomization, we show that a sufficiently well generated synthetic image dataset can be used to train a neural network classifier, achieving accuracy levels as high as 88% on 2 category classification. We show that the most important domain randomization parameter is a large variety of subjects, while secondary parameters such as lighting and textures are not. Based on our results, there is reason to believe that models trained on domain randomized images transfer to new domains better than those trained on real photos. Model performance seems to diminish slightly as the number of categories increases.
Valtchev, S.Z. and Wu, J. (2021). "Domain Randomization for Neural Network Classification", SpringerOpen Journal of Big Data. 8:94. http://zarkonium.github.io/files/Domain_Randomization_for_Neural_Network_Classification_Published.pdf
Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data
Published in MDPI Electronics, 2021
Research on SARS-CoV-2 and its social implications have become a major focus to interdisciplinary teams worldwide. As interest in more direct solutions, such as mass testing and vaccination grows, several studies appear to be dedicated to the operationalization of those solutions, leveraging both traditional and new methodologies, and, increasingly, the combination of both. This research examines the challenges anticipated for preventative testing of SARS-CoV-2 in schools and proposes an artificial intelligence (AI)-powered agent-based model crafted specifically for school scenarios. This research shows that in the absence of real data, simulation-based data can be used to develop an artificial intelligence model for the application of rapid assessment of school testing policies.
Valtchev, S.Z., Asgary, A, Chen, M., Cronemberger, F.B., Najafabadi, M., Cojocaru, M.G. and Wu, J. (2021). "Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data", MDPI Electronics. 10(14). http://zarkonium.github.io/files/Managing_SARS-CoV-2_Testing_in_Schools_with_an_Art.pdf
Lossy Light Field Compression Using Modern Deep Learning and Domain Randomization Techniques Permalink
Published in YorkSpace, 2022
Lossy data compression is a particular type of informational encoding utilizing approximations in order to efficiently tradeoff accuracy in favour of smaller file sizes. The transmission and storage of images is a typical example of this in the modern digital world. However the reconstructed images often suffer from degradation and display observable visual artifacts. Convolutional Neural Networks (CNNs) have garnered much attention in all corners of Computer Vision (CV), including the tasks of image compression and artifact reduction. We study how lossy compression can be extended to higher dimensional images with varying viewpoints, known as light fields. Domain Randomization (DR) is explored in detail, and used to generate the largest light field dataset we are aware of, to be used as training data. We formulate the task of compression under the frameworks of neural networks and calculate a quantization tensor for the 4-D Discrete Cosine Transform (DCT) coefficients of the light fields. In order to accurately train the network, a high degree approximation to the rounding operation is introduced. In addition, we present a multi-resolution convolutional-based light field enhancer, producing average gains of 0.854 db in Peak Signal-to-Noise Ratio (PSNR), and 0.0338 in Structual Similarity Index Measure (SSIM) over the base model, across a wide range of bitrates.
Valtchev, S.Z. and Wu, J. (2022). "Lossy Light Field Compression Using Modern Deep Learning and Domain Randomization Techniques", YorkSpace. 2022-12-14. http://zarkonium.github.io/files/Lossy_Light_Field_Compression_Using_Modern_Deep_Learning_and_Domain_Randomization_Techniques.pdf
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
MATH269 - Statistics for Computer Systems
Undergraduate course, Centennial College, Math Department, 2017
The course covers descriptive and inferential statistics and probability. It addresses discrete and continuous types of distributions, including the normal distribution. Statistical software is used in the course to organize, summarize, analyze and present conclusions about sets of data.
PHYS100 - Applied Physics
Undergraduate course, Centennial College, Math Department, 2017
Physics 100 is a first semester basic physics course in kinematics and dynamics. It is a problem-solving course utilizing the concepts of force, equilibrium, friction and torque. Work, energy and conservation law are introduced and used for solving varied applied engineering problems. The course also incorporates rotational motion and gears.
MATH231 - Differential Calculus with Analytic Geometry
Undergraduate course, Centennial College, Math Department, 2018
Topics include straight line equations, conic sections, the limit concept, average and instantaneous rates of change, the derivative as a limit, higher order derivatives, differentials and approximate values, curve sketching and maximum-minimum problems, logarithmic and exponential functions, trigonometric, hyperbolic and inverse functions.
MATH169 - Mathematics for Computer Systems 2
Undergraduate course, Centennial College, Math Department, 2018
This is a first-year course in mathematics for computer systems. Through classroom instruction, you will study the fundamental mathematics required to solve related problems encountered in computer systems. As well, you will be introduced to set theory. You will also be familiarized with various concepts relating to modular systems.
MATH180 - Technology Mathematics 3
Undergraduate course, Centennial College, Math Department, 2019