Research

ISTC RESEARCH PROJECTS

The FPGA – Accelerated Time-Series (key-value) Database is a dedicated hardware for IoT (Internet of Things) data storage. Smart sensors’ data passes directly into FPGA from Ethernet adapter bypassing any type of operating systems and software layers. All data processing takes place under FPGA on fly and streams into SSD for storage. Artificial Intelligence algorithms can be used to continuously evaluate interconnections between channels, classify and predict data. Device provides general functionality of software enabled time-series (key-value) database systems while implemented on hardware layer. FPGA’s native parallel feature is heavily used to achieve data throughputs greater then all available software enabled time series database systems can offer, while keeping server power consumption 3-4 times lower compared to CPU based solutions.

Team Members

  • Artavazd Khachatryan/YSU
  • Khachik Sahakyan/YSU

The main purpose of this project is to apply the latest developments in machine learning to medical databases. Although large databases of medical time-series data are freely available, they are largely ignored by the machine learning community. The main reason is that lots of medical expertise is required to understand and process these databases.During this project we will define several standardized benchmarks on MIMIC-III clinical database1 that are known to be useful for doctors (e.g. in-hospital mortality prediction, phenotype classification, length of stay prediction etc.), will apply various state-of-the-art machine learning models on these benchmarks and will develop new models.

Team Members

The brain is integrity of billions of computational units from the point of view of information processing. The communication between these units is realized via electrical signals and each unit shows the ability of experience integration, i.e. the abilityof learning. The study of the mechanisms of information storage, encoding and conducting in this system is of great importance for application in brain machine interfaces and neuroprosthetics. The recording of the electrical activity of a brain is an irreplaceable method for studying the behavior of neural populations and athematical or physical patterns underlying this behavior. Recording of this signal makes it possible to detect the behavior of neural populations with high temporal and spatial resolution. An additional reason of interest in LFPs is that they provide stable signal for a longer period and therefore, are useful for long‐term chronic experiments and for clinical applicationssuch as Brain Machine Interfaces (BMI). However, as LFP captures a multitude of neural processes, the extraction of essential features is among the main problems of Computational euroscience.The investigation of neural activity underlying cognitive functions requires recording of activity in several brain regions and analytical methods that will give an opportunity to ombine multiple features of spatially and temporally separated complex signals to achieve a robust classification of brain states. The extraction of cognitive functions requires complex processing of information. We will continuously execute these steps in a cycle and at each iteration, we are going to improve further the placement methods and positioning of the electrodes, the LFP signal conditioning circuit and signal analyzing algorithms. So, one of the outcomes of the project will be the developed complete methodology for LFP recording and analysis. Another expected outcomeof this project is an analysis tool for LFP data which can also be used in the animal models of neurological and neurodegenerative disorders. As a result of our project, we will have a large amount of data of LFP recordings and video recordings, both in raw and processed versions. We are going to make this data available in the Internet. This community-driven data sharing may offer cross-validation of findings and refinement of interpretations of the data. After the achievement of solid results with LFP recording, we are planning to couple it with simultaneous recording of EEG in the same animal. This can lead to translation of our methods to human EEG data analysis, which is relevant as a non-invasive method.

Team Members

  • Serob Muradyan/RAU
  • Lusine Hovhannisyan/YSMU
  • Amalya Hakobyan/YSMU
  • Ani Alaverdyan/YSMU
DRAA project excels at managing complex and changing information, which is important for automating many kinds of analysis and policies. It provides new methods for rapidly authoring structured logical information (i.e., encoded knowledge) starting from user-supplied English documents, and for powerfully reasoning with that knowledge to answer queries and present the conclusions in English. In other words, users assert and query knowledge in DRAA. Automated decisions can then be made based on the results of the analysis. For example, compliance alerts can be generated automatically based on reasoning about policies and regulations that have been encoded into a DRAA knowledge base.The meaning of an English sentence is captured deeply and precisely. Unlike existing systems, almostanything one can say in English can be encoded and operationalized, relatively easily, largely by the subject matter experts themselves. In this connection, DRAA can perform truly deep reasoning based on the encoded knowledge statements. This makes the automation of many aspects of analysis and decision making become much more fordable and practical.

Team Members

  • Kristina Sargsyan/MSUIECS
  • Arman Grigoryan/ERIICTA
  • Eduard Khachatryan/ERIICTA

Main purpose of our project is to deliver a cloud-based service for white-hat penetration testers and security professionals for an advanced and automated shellcode obfuscation techniques, thus helping them to easily conduct social engineering based penetration vectors.The core of the problem and what are we trying to solve is that during social engineering vectors security professionals use no or very poor obfuscation for theirshellcodes and payloads, because this would take big amount of time, effort and research. In result – very low percentage of successful conducted vectors due to high risk for shellcodes and payloads to be caught by an end-point security system. This totally confronts with the point of penetration testing audits because they are ment to simulate real-world scenarions of cyberthreat and in real-world scenarios companies\organisations are being attacked not by white-hat security professionals but by black-hat hackers who do have access to illegal obfuscating programs. Summarizing all this we get that the black-hat hackers have serious advantages over legitimate white-hat professionals who help improve security.

Team Members

  • Vahe Karapetyan
  • Sipan Vardanyan

Electronic Health Information Management System (e-HIMS) is an essential element of healthcare improvement and a vital component of strategies for healthcare system reform. A health information system that provides reliable, timely, high-quality information is a key prerequisite for healthcare system improvement in now days. Since 2010 Armenian government is working on development and implementation of integrational Integrated Health Information System of Armenia (IHISA). Currently the government is highlighting the need of e-HIMS implementation in the healthcare facilities. This fact enforcing and expanding the market for e-HIMS in Armenia. Having this vision and perspectives we developed Electronic Health Information Management System (e-HIMS) for the healthcare facilities which is intelligent instrument that supports health care services to compile and use health information for better clinical and financial performance. It has multiple modules that allows computerizing clinical workflow throughout the health care facility and adapt the system to reflect the way healthcare facility works. Moreover, one of the key components of our e-HIMS product is electronic quality control management system. The Quality of healthcare is comparatively new concept in healthcare and it is defined as level of value delivered by any healthcare resources. According to the US Institute of Medicine it is a degree to which health services for people increase the probability of targeted health outcomes and are consistent with existing medical knowledge. In other words, quality of health care services could be defined as doing the right thing, at the right time, in the right way, for the right person – and having the best possible results. Effective and efficient quality systems can promote good practice. The studies and scientific publications are showing that is very difficult to implement described e-Health projects. For doctors, patients, and the health care managers the implications of Electronic Health Information Management Systems are usually hard to understand, as are the likely pace and extent of adoption of e-HIMS.

Team Members

  • Karapet Davtyan/AUA
  • Artashes Tadevosyan/YSMU
  • Hayk Davtyan/AUA

The Project aims to make a deep analysis of three business processes in elecommunication sector companies of Armenia and to define efficiency growth measures via application of IBM® products with sufficient automation and control. The following business processes shall be considered: Training & Development, Compensation & Benefits (Reward Management), and Performance Management. The projects aim to link science with practice via connecting theoretic research with practical applications and output analyses.Purpose 1: Analyze telecommunication sector development trends and BPM challenges from customer excellence and shareholder expectations perspective. Purpose 2: Integrate IBM® products into the Training & Development, Reward Management, and Performance Management processes. Purpose 4: Test and prove efficiency of IBM® products application in telecommunication sector from BPM perspective.

Team Members

  • Marine Ghahramanyan/UJML
  • Lilit Poghosyan/NUACA
  • Irina Poghossian/AUA

Research based decision making is a rare procedure for Armenian regional development. Technology based research and innovative solutions also are far from the development strategy. In this context the proposed interdisciplinary research (including statistics, economics, regional development and public administration) cloud integrate academic approach, business needs and community development through new technological solutions through development of computer-human interaction and knowledge-rich intervention. Economics, statistics and public administration are core components of the research that will be developed through SPSS modeling, GIS spatial managment and Cloud infrastructure managment tools. The objectives of the project are regional economic modeling and spatial development strategy based on inter-municipal cooperation and social entrepreneurship for the target region, as well as knowledge transfer and education. The expected results of the project will be (1) a developed course for higer education, (2) a five person research group, (3) a manual, (4) economic models and relevant academic papers, (5) a spatial development strategy and relevant academic paper.

Team Members

  • Sos Khachikyan/ASUE
  • Arthur Dolmajian/École Polytechnique de Montréal
  • Armen Ktoyan/ASUE
  • Davit Shahnazaryan/YSU