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ISTC Research Grant Program

Innovative Solutions and Technologies Center (ISTC) with the support of Enterprise Incubator Foundation and USAID Armenia have conducted ISTC Research Grant Program (IRGP) which was intended to encourage collaboration between scientific community and industry, R&D promotion and commercialization.
ISTC supported projects undertaking innovation from the demonstration stage through to piloting, test-beds, systems validation in real-world/working conditions. We targeted relatively mature new technologies, concepts, processes and business models that need the last development step to reach the market and achieve wider deployment.
In the scope of IRGP we provided:
  • Funding – 6000 USD NET for 1-year research work
  • Access to ISTC infrastructure and resources;
  • Funding for travel to US, in case of matching with IBM partner University in USA, and invitation letter from the partner;
  • Support in the development of a commercialization strategy;

Projects of Call 1

  • Artavazd Khachatryan/YSU
  • Khachik Sahakyan/YSU
GrovfDB is a FPGA-based key-value database to efficiently store and retrieve cached data. TCP/IP and UDP stacks are implemented on FPGA to receive data from 10G Ethernet bypassing any type of operating systems and software layers. Key-value store algorithms are implemented completely on FPGA to handle Memcached compatible operations. Direct connection with DRAM from FPGA is used to stream data to memory for storage and retrieve it on demand
  • Hrayr Harutyunyan/YerevaNN
  • Hrant Khachatryan/YerevaNN
  • Serob Muradyan/RAU
  • Lusine Hovhannisyan/YSMU
  • Amalya Hakobyan/YSMU
  • Ani Alaverdyan/YSM
The project “Machine Learning for Data Analysis of Local Field Potentials with Feedback Regulation” is aimed to study neural coding that underlies certain behavior or cognitive functions by recording and analyzing extracellular signals of the brain.
The realization of the project assumed improvement and modernization of the research methods, the adaptation of electrophysiological recording system to freely behavioral recording environment, customization of neural probe production and finally the development of a recording and data analysis tool.
In the frame of the research grant, an Intan RHD2000 series digital electrophysiology amplifier board was acquired. This board gives us the opportunity to record in freely behavioral condition and perform recording with high signal to noise ratio
  • Kristina Sargsyan/MSUIECS
  • Arman Grigoryan/ERIICTA
  • Eduard Khachatryan/ERIICTA
Deep Reasoning for Advanced Analytics (DRAA) project excels at managing complex and changing information, which is important for automating many kinds of analysis and policies. DRAA project uses an advanced form of logic that is uniquely close to natural language, to represent the encoded knowledge and to generate conclusions. These conclusions are highly accurate and precise – much more, so that question answering techniques based on the fuzzy processing of natural language.
During the project are developed and implemented beta versions of the “DRAA Knowledge Factory” and the “DRAA Knowledge Reasoner”, integrated application available for registered Users and Experts. DRAA research outputs were presented during the RAU annual conference and in two scientific papers publications.
Latest progress of the DRAA project: project SLAP based on the main of outputs of the DRAA project is selected for the semi-final of “Eurasian Digital Platforms” Innovative Projects Competition. /The semi-final will be held in the framework at DigiTec Expo 2018
  • Karapet Davtyan/AUA
  • Artashes Tadevosyan/YSMU
  • Hayk Davtyan/AUA
  • Marine Ghahramanyan/UJML
  • Lilit Poghosyan/NUACA
  • Irina Poghossian/AUA/

Projects of Call 2

  • Yevgeniy Mamasakhlisov/ YSU
  • Shushanik Tonoyan/ YSU
  • Vardges Mambreyan/ YSU
Intrinsically disordered proteins (IDPs) are associated with various diseases and have been proposed as
promising drug targets. However, conventional structure-based approaches cannot be applied directly to
IDPs, due to their lack of ordered structures. Nowadays the processes of designing new drugs by using
bioinformatics tools have opened a new area of drug research and development. Computational
techniques assist in searching drug target and in designing a drug in silico. As many protein-protein and
ligand-protein interactions can be used as key targets for drug design, one of the solutions is to design
protein drugs based directly on the protein complexes or the target structure. Recently the several
strategies for structure-based protein drug design have been developed.
The project is dedicated to the development of the neural networked algorithms and software for the intrinsically disordered proteins under the different conditions of the environment. The project will be focused mainly on the coarse-grained models of the intrinsically disordered proteins and their improvement with neural – network based algorithms. The planned results will be demanded in bioinformatics and related areas.
  • Hayk Sargsyan/ University of Zurich
  • Sona Hunanyan/ EPFL Lausanne, Switzerland
  • Arsen Yeghiazaryan/ Yerevan Physics Institute
Due to its importance in many application areas such as video surveillance, content-driven retrieval, robotics, and human-computer interaction research, human action recognition in videos is an important task in computer vision research. Despite the rapid development of the research in this field, the human action recognition still remains a very challenging task, due to the complexness of capturing information from still frames and motion between frames on the one hand, and the combination of these two streams in a way that optimizes class prediction, on the other. The research proposal implies a two-fold contribution. Firstly, the team is working on the promotion in Armenia the computer vision research in general and the video classification research in particular. Secondly, the team determines development to a condition of contributing to the international research on this topic.
  • Hrant Davtyan/ AUA
  • Nare Banduryan/ YSU
There are many existing standards in the financial sector, yet the documents available in the industry and in its different areas are usually inconsistent. Therefore, the available data is unstructured and cannot easily be analyzed. The vision of this project is to develop a product that will ensure the uninterrupted realization of the whole process of matching, extraction, transformation, analysis, and reporting of financial data.
  • Hayk Ishkhanyan/ Moscow Institute of Physics and Technology
  • Sergey Kovalenko/ Moscow Institute of Physics and Technology
An original logistics optimization algorithm is being developed. It considers the whole vehicles & orders the team have shown results in qualitative optimization for Chicago taxi historical data (up to 46%). A further validation of real-time data is required. The proposed research project addresses that gap.
  • Lusine Hovhannisyan/ YSMU
  • Marina Davtyan/ YSMU
  • Sose Berberyan/ AUA
The purpose of this research project is to design and develop a bionic hand which can be controlled by simultaneous EEG data analysis using Python machine learning algorithms.
Projects of Call 3
  • Hrant Khachatryan/YerevaNN
  • Marat Yavrumyan/ University of Bayreuth, Germany
The main purpose of this project is to build high quality and open source part-of-speech taggers, morphological analyzers and sentence parsers for multiple languages. Part-of-speech tagging and dependency parsing of sentences are the two fundamental building blocks of NLP systems. Morphological analyzers are more important for morphologically-rich languages (including Russian and Armenian). The task is motivated by the success of the CoNLL 2017 Shared Task which was about creating POS taggers and parsers for 60 languages. This project follows two goals. First is the participation in the CoNLL 2018 Shared Task, which is expected to start in April 2018 and last for six months. It will be the continuation of the CoNLL 2017 Shared Task and will bring researchers from all over the world to build the next generation of POS taggers, parsers, lemmatizers and morphological analyzers based on UD corpora. The second goal is to continue building Eastern Armenian UD Treebank and make it available for CoNLL 2018 Shared Task.
  • Maria Nikoghosyan/ RAU
  • Ani Sakhlyan/ AUA
  • Hovhannes Haroyan/ Radiophysics Research Institute
  • Gor Hovsepyan/ The Institute for Physical Researches Ashtarak, Armenia
  • Sargis Sargsyan/ Institute of Radiophysics and Microelectronics
The main goal of this project is investigating the possibility of applying non-orthogonal multiple access technologies with the compound modulation scheme for future 5G networks to improve the overall spectral and spatial efficiency. Develop calculation method of channel budget and BER estimation for NOMA with combined SM and amplitude phase modulation schemes. Carry out numerical calculations and realize a simulation model of NOMA for 5G networks. Implementation and testing of the proposed scheme in existing standards (for instance for WI-FI with MIMO).
  • Senik Matinyan/ Candle Scientific Research Institute
  • Liana Hayrapetyan/ YSMU
  • Lusine Hovhannisyan/ YSMU
The goal of this project is to develop a system for closed-loop deep brain stimulation in vascular dementia animal model using machine-learning packages. We are going to design and implant microelectrode arrays within subcortical brain regions, which are the most
vulnerable areas seen in VD. The array concept consists of three logical parts: the sensing part will give us the ability to record the electrical activity of neural cells in a relatively large spatiotemporal pattern [5]. Stimulation will be made with the same microelectrode array. Recovery time will be calculated to remove noise after stimulation. As for the third part, drug delivery microcannula will be attached to this system for injection of a neuroprotective compound as a stimulation positive control. The custom-made program will
be developed in the LabVIEW environment to analyze gained data. Recordings will be done from both anesthetized and behaving animals to validate “proof of concept”. Behavioral patterns will be correlated with gained data to implement effective machine learning algorithms for this system. Two-photon microscopy station of Candle SRI will be used for Ca2+ imaging of selected sites. Image processing algorithms will be used to detect the spikes. This will be a two-fold validation of electrophysiological studies.
  • Lilit Nersisyan/ Institute of Molecular Biology NAS RA
  • Vahe Momjyan/ AUA
  • Arpine Manukyan/ AUA
  •  Shushan Astanyan/ YSU
Biomolecular signaling cascades or pathways are represented as directed graphs with nodes corresponding to biomolecules and edges to physical interactions between them. These graphs have input nodes corresponding to signal receptors and output or sink nodes corresponding to final targets.
The latters usually represent realizations of biological processes (e.g. cell death, cell division, or migration). The set of all possible interactions within a cell forms one global pathway, which theoretically can reach a size of up to 109 interactions. However, for comprehension and analytical purposes, this global pathway is artificially divided into functional units or sub-pathways, which are
stored separately in pathway databases (KEGG, Reactome, Wikipathways, SMPDB, etc.). These databases have two major shortcomings: they do not account for interactions between sub-pathways (sharing of nodes, branches) and they do not cover all the information about the global pathway. On the other hand, databases storing protein-protein interaction (PPI) data are relatively rich and cover the vast
amount of accumulated knowledge about PP interactions. However, they also have disadvantages: first, they do not contain nodes of type “biological process” (BP) and, thus, do not link the networks of interactions to cellular responses, and second, they do not store directionality, which is important for recovery of signal flow from inputs to outputs. Owing to the mentioned limitations, currently available databases are not suitable for studies aimed at drug target identification or repositioning.
Overall, the PPI and pathway databases each store separate pieces of the puzzle. Our project’s objective is to combine these pieces together to create a model of the global cellular pathway. We, thus, aim at generating a distributed database that stores a graph representing the global pathway, which keeps not only PPI information, but also protein-BP links, and has directionality. We will develop a front-end tool that will allow for retrieving any arbitrarily defined path or sub-pathway between user-defined input and
output nodes, at the same time accounting for custom constraints, such as the tissue type, gene expression signature, etc. An example use case will assume a user to supply a drug or immediate drug targets, and our platform to retrieve all the biological processes potentially affected by the drug, and displaying the pathways linking the drug to predicted BPs in an interactive GUI. The user may thus
predict drug side effects, or identify potential targets for drug repositioning. Later, we plan to incorporate additional modules, allowing the user to run various types of analyses on retrieved pathways.
  • Davit Ghukasyan/ RAU
  • Tigran Ghukasyan/ RAU
  • Arpine Manukyan/ AUA
  • Shushan Astanyan/ YSU
A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The primary goal of BCI research is to give disabled people with different diseases such as amyotrophic lateral sclerosis (ALS), brainstem stroke, spinal cord injury etc. the ability to communicate using only cerebral activity. One of the reasons why BCI research is gaining popularity is ever growing number of above-mentioned diseases, but also more accessible and smaller technology. As an example we can mention, that the processors in nowadays smartphones can perform machine learning
tasks in an acceptable time, which wasn’t possible until recently. To get proper results standard BCI usually has to be able to perform different tasks at once such as signal acquisition, preprocessing or signal enhancement, feature extraction, and classification. Different BCI uses different methods for signal acquisition such as electroencephalography (EEG), electrocorticography (ECoG), magnetoencephalography (MEG), functional magnetic resonance (fMRI) and near-infrared spectroscopy. But as you can imagine EEG is the widest spread and most accessible method for brain activity measurement, thanks to its non-invasive application, relatively small size and easy to use interface. The EEG recording system consists of electrodes, amplifiers, A/D converter, and a recording device. The electrodes acquire the signal from the scalp, the amplifiers process the analog signal to enlarge the amplitude of the EEG signals so that the A/D converter can digitalize the signal in a more accurate way. Finally, the recording device, which may be a personal computer or similar, stores, and displays the data. We are also planning to research ways of making EEG devices more accessible(cheaper and
portable) to the public, which plays a key role to spread BCI.However, this is something for the future, for this research we will use commercial EEG devices.
  • Armen Allahverdyan/ Yerevan Physics Institute
  • Narek Martirosyan/ Yerevan Physics Institute
The team plans to develop methods for parameter learning in non-identifiable models, where the number of parameters is so large that their precise estimation is impossible even with infinitely large data (i.e. for a large number of samples). A straightforward example of a non-identifiable model is when the data (prior to observation) passed through a noise with unknown magnitude, e.g. Hidden Markov
Models. The team wants to develop parameter learning methods that generalize the maximum likelihood, provide an automatic Occam’s razor and deal with the overconfidence problem.
  • Gevorg Martirosyan/ RAU
  • Ashkhen Hovhannisyan/ YSU
Nowadays transportation and health-care companies face real optimization problems. They need to optimize asset usage (e.g. trucks, airlines), scheduling (e.g. surgeon, staff planning) etc. Many real-world optimization solutions use algorithms and techniques developed by Operation Researches discipline. These include Large-scale Neighborhood Search algorithm, Discrete Event Simulation, Mixed Integer Programming etc. In real-world solutions, these algorithms and techniques become a performance bottleneck. Also, Operation Research algorithms and techniques do not consider customizations and preferences (e.g. driver’s favorite routes).
The objective of this research is to solve real-world optimization problems with Machine Learning discipline.
  • Vardan Baghdasaryan/ AUA
  • Aleksandr Grigoryan/ AUA
  • Hrant Davtyan/ AUA
  • Knar Khachatryan/ AUA
Credit scoring remains one of the main tools used during the loan provision decision-making process. Yet, the traditional system used by local financial institutions is still anchored on the opinion of loan specialists or in some rare cases on a basic scoring system. This study aims to leverage machine learning techniques to automate evidence-based decision-making and allow for adaptive learning from new observations. At the same time, the econometric approach will be applied to compensate for the uninterpretability of artificial neural networks.