Deadline to register is Jun 30, 2019.
Teams can still edit your proposals during judging period.

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Regional Final
📁Internet of Things
👤Ishkhan Balgudanian (National Research University Higher School of Economics)
📅Oct 07, 2019
Purpose: to create a 3D printer that helps to prevent failures and problems during a printing progress, to have a user-friendly control interface (LCD display), an isolated body and a large printing area.

Applications: The scope of 3D printers application is wide. We use SoC to provide functioning of the printer with advanced features. SoC is used to generate signals for the mechanical parts of the printer, to realize high-level information processing algorithms, and to provide the interaction interface to users.

Target user: This product is a prototype of future 3D printers on SoC. At present, there are no similar products in the middle price segment. Our team presents a new concept of a printer control system which provides a convenient user interface with the possibility of further connection to smart home systems using IoT technology.
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👀 3008   💬 6
Regional Final
📁Machine Learning
👤Alexander Molnar (Uzhhorod National University)
📅Oct 07, 2019
When studying new materials for electronics, one of the most important characteristics is the dielectric constant of the material and its dependence on the frequency and amplitude of the measuring field, as well as environmental parameters such as temperature, lighting, humidity, etc. Therefore, when studying the physical properties of substances, dielectric spectroscopy is often used, which gives a fairly complete information of the polarization mechanisms in a given material. However, when analyzing the obtained frequency dependences, rather great difficulties arise associated with the interpretation of the results obtained. The idea of our project is to develop a smart dielectric spectrometer using machine learning elements to interpret Havriliak–Negami and Cole–Cole relaxation diagrams. Unfortunately, to date, there are practically no automated methods for analyzing impedance spectra, and manual decoding is very laborious and slow.
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👀 5284   💬 9
Regional Final
📁Other: Robotic
👤Edward Rzayev (National Research University Higher School of Economics)
📅Oct 08, 2019
The goal of this project is to obtain the final result in the form of a fully working and debugged anthropomorphic robot that performs the stated basic and additional functions such as helping people with visual limitations to navigate in space (to warn about the nearest obstacles); a robot guide who will tell the information about objects near him; robot nanny who can take care of the sleep and daytime activities of the child during the absence of parents; companion robot, which can become a friend of man and a good conversationalist.

In the process of development it will be made an application for smartphones that allows you to contact with the robot using Wi-Fi or Bluetooth connections. It was already made a developed neural network that can determine the boundaries of objects based on the received images from the camera.
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👀 2539   💬 4
Regional Final
📁Digital Design
👤Viktoriya Kosolapova (National Research University Higher School of Economics)
📅Oct 17, 2019
This project is aimed at the development of hardware cryptosystem based on TPM (Tree Parity Machine). TPM is a particular multi-layer feedforward neural network structure employing the mutual learning concept for neural cryptography. Two TPMs synchronization is used as a secret key exchange protocol. Sent information is encrypted with PRESENT block cipher. FPGA implementation advantages of TPM and PRESENT are high speed and low power and resources consumption.
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👀 2357   💬 4
Regional Final
📁Machine Learning
👤Alexander Kustov (IPPM RAS)
📅Oct 06, 2019
Neural networks have become a popular means of solving problems that are difficult to access for ordinary algorithms. Neural networks are used in robotics, medicine, Internet, business, geological exploration, and other fields. The present work is dedicated to the use of complex neural networks in FPGA for solving the complex task of processing a video stream and detecting objects of various kinds in the frame. As an example, it is planned to use a neural network based on MobileNet_v1. The prototype will recognize the presence of people in the frame. It is also planned to add several alternative models with high detection accuracy (recognition of cars and animals in the frame).
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👀 4249   💬 2
Regional Final
📁Internet of Things
👤Maximilian Schrapel (Leibniz University of Hannover)
📅Oct 11, 2019
In human-computer interaction, haptic feedback is one of the most important modalities. In particular, modern devices such as virtual reality headsets use vibratory feedback to increase immersion in virtual environments. So far, the main problem has been that with an increasing number of actuators, signal generation is limited to a few possibilities. As a result, various effects cannot be precisely represented at the expense of immersion.

With our project we want to develop a mobile haptic full body feedback device, which we call MultiWave.
Due to the almost unlimited possibilities of FPGAs, we will develop a high-precision independent multichannel function generator that can produce any periodic signal for haptic feedback on every output. All possible signal parameters such as frequency, function, amplitude and phase between the outputs will be freely configurable in real time. An expansion shield for the DE10 nano platform is to be built, which supplies the system with power and has Bluetooth and wifi connection as well as a wired USB/UART interface for controlling the FPGA, which generates the output signals.

The aim is to simulate the collision with objects in virtual environments.
In a demonstrator VR game, various objects such as bushes, water, explosions, etc. are to be correctly replicated by the built full body suit and the user experience will be maximized.

Furthermore we will create a python API to control the FPGA. In a graphical user interface made with our API, all parameters and the number of outputs should be freely configurable even without prior knowledge of the system. In the style of a simple drawing program, the grid of actuators are to be controllable almost in real time in order to explore haptic effects on the skin even without programming knowledge.
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👀 2524   💬 1
Regional Final
📁Digital Design
👤Jan Snoeijs (Swiss Federal Institute of Technology Lausanne (EPFL))
📅Aug 31, 2019
The project implements an artificial neural network based on Gated Recurrent Units (GRU) as an accelerator for the classification of epileptic seizures on FPGA. Full precision training phase and quantized re-training are done off-line with software tools on a PC, while the inference phase is realised on a CycloneV System-on Chip. The trained neural network classifies sequences extracted from pre-recorded signals by non-invasive scalp Electroencephalography (sEEG) simulating real-time conditions. The system performing detection and prediction of seizures could at term be part of a closed-loop neuro-stimulation device to control epilepsy in cases where medication or surgery are not effective.
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👀 2120   💬 3
Regional Final
📁Internet of Things
👤Lidia Izmailova (National Research University Higher School of Economics)
📅Oct 13, 2019
In this project, automated care for plants that grow in the greenhouse is provided. The robot moving along the rails irrigates the soil and monitors the climatic conditions, such as light, humidity, temperature, etc. Using the website in the local network, users can set up parameters for crops and monitor plants in real time. The introduction of FPGA with Arduino in the project allows expanding the capabilities of the robot and improving the care of plants.
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👀 2182   💬 0
Regional Final
📁Machine Learning
👤Amr Adel (Cairo university, Faculty of Engineering)
📅Jul 06, 2019
Our project is about deep neural networks hardware accelerators. The power consumption of current GPU implementations restricts the usage of neural nets in small devices such as cell phones. Our design is about developing a digital design specific to CNNs, such design can speed up the inference time and reduce the area and power thus providing a tiny but efficient deep learning processor for mobile devices or critical applications

We will use the proposed design to detect distracted car drivers (more details below)
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👀 1954   💬 2
Regional Final
📁Machine Learning
👤Nazia Gillani (University of Central Punjab)
📅Oct 07, 2019
Accurate, non-intrusive gait phase detection is a prerequisite for quantification of gait abnormalities, in-situ monitoring of fall-prone patients (such as frail population), and synergistic control of orthoses and prostheses. This project aims to develop a prediction engine for human gait phase detection using Convolution Neural Networks (CNN) implemented on Altera Cyclone V FGPA SoC.
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👀 4739   💬 8
Regional Final
📁Internet of Things
👤Tatyana Chibisova (National Research University Higher School of Economic)
📅Oct 18, 2019
The development object is a hardware and software complex for the device management and access system management for the smart home system. The project is divided into components: a neural network for facial recognition, a server, a mobile application for Android, a control system for DE10-Nano modules.

Implemented a mobile application that supports communication with external devices de10-Nano. For communication applications and de10-Nano created the server, dispatching requests to switch on the devices. Added face registration to provide additional functionality – "smart lock". Developed and trained neural network that allows you to determine the registered user.

Further development of the work is to improve the quality of user recognition, increase the range of signal transmission, expanding the list of managed modules and the functionality of the application.
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👀 14286   💬 0
Regional Final
📁Other: Robotics
👤Evgeny Lezhnev (National Research University Higher School of Economics)
📅Oct 17, 2019
The development object is the hardware and software to implement the spherical robot. The project consists of two main parts. The hardware consists of motors, feedback sensors to control motors, distance sensors, gyroscope, accelerometer. The software part is based on the FPGA DE10-Nano plate, on which calculations are performed to control motors and sensors. Also implemented a mobile application for the possibility of manual control of the device.
The advantage of our development is to improve the quality of element management of the device, in particular improving the quality of motor control for stable movement.
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👀 2137   💬 1

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