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

Category: Sort By:
Regional Final
📁Other: Controls and Dashboards
👤Luis Fernando Aljure Munoz (MSWinTools)
📅Sep 14, 2019
This is a project development based on 'Inverse Thermodynamics Technology' taking advantage of Hilsch Tube efficiency to design a levitation vehicle control. According to this technology, a few small pressure vessels (air tanks) are enough to produce high rotating kinethic energy that makes the vehicle levitate and move forward.

The use of a high speed FPGA is imperative to keep track of all physical varibles involved in the process to synchronize and control the mechanism of the vehicle. A dashboard display is implemented to gather all sensor variables and control information.
details »
42
votes

👀 1294   💬 2
Regional Final
📁Machine Learning
👤Hong Lin (University of Houston-Downtown)
📅Jun 28, 2019
Existing physiological reading systems, e.g., those used in patient monitoring, are ineffective for daily practices. This project aims to simulate an environment for daily physiological tracking using a FPGA DE10 board with physiological sensors. On a single board computer, we will an affordable reusable, expandable, and wireless, machine that can monitor a user’s temperature, ECG heart activity and EEG brain waves. With the integrated sensors and coding, the device should be capable of live streaming and exporting collected data on a local web server for rendering.
For the collection of EEG brain waves signals, a hand-made headset will be connected to the FPGA board via a Bluetooth module. Data will be sent and collected from the EEG headset using a Python Library.
The hosting microcomputer will be made capable of configuring and programming the required html, PHP, and python files. Overall, the data rendering software simulates a professional medical interface and is available to both the mobile devices and internet browser. The user should be able to connect the device to a remote server via the internet wirelessly, attach the reusable sensors to his/her body, and download the information gathered.
This project aims to challenge the affordability and accessibility of existing healthcare oriented monitoring equipment. From this point on, a system with the ability to collect data, and perform machine learning tasks based on the collected data is desirable. Ideally, with the computational power provided by the remote server, such a system will be able to diagnose the user’s mental states based on the knowledge gathered with the machine learning power and the organized data collection and processing.
A virtual reality system on mobile phone will be connected to the DE10 board to render mind intervention activities based on the diagnosis of the user’s mental state. Continual diagnosis and intervention will be studied to find the best routine for certain type of mental health problems.
The FPGA DE10 board will exert its power in this project, especially in the stages of machine learning for brain state recognition and rendering of virtual reality scenes. Those machine learning and virtual reality tasks will be handled using packages that run on full-fledged operating systems supported by FPGA DE10.
details »
41
votes

👀 448   💬 4
Regional Final
📁Digital Design
👤Daryna Dyka (National University of Kyiv)
📅Jul 05, 2019
In our work we will study the possibility of replacing monolithic ADC with a combination of Comparator and DAC. Adding FPGA resources (memory, hardware multipliers, configurable logic, etc.) to the Comparator+DAC structure will allow us to obtain flexible data acquisition system for various applications.
We plan to use this technology to create Time-domain reflectometer (TDR) and Sampling Oscilloscope with an equivalent sampling rate of up to 3 GS/s.
details »
3
votes

👀 917   💬 11
Regional Final
📁High Performance Computing
👤Hossam Omar (Ain Shams University)
📅Sep 14, 2019
Many regions, all over the world nowadays, are suffering severely from several contemporary issues that without a doubt are hindering the overall world progressions toward reaching a more prosperous economic status. Moreover, most of these issues are related directly to the failure of monitoring the borders thoroughly.
The main goal of this project is to propose an appropriate Artificial Intelligence (AI) based solution that could substantially increase the efficiency of border monitoring by creating a wireless network using ultra-low power nodes that could capable to detect any suspicious intruding behaviors. The design of the proposed project depends on three main stages: The sensory data fusion interface; The biological inspired Fuzzy Logic (FL) system controller; and the Wireless Sensor Network (WSN) interface stage.
details »
4
votes

👀 903   💬 9
Regional Final
Community Award
📁Machine Learning
👤Abarajithan Gnaneswaran (University of Moratuwa)
📅Jul 11, 2019
Traffic congestion is a widespread problem that results in the loss of billions of dollars annually, valuable time of citizens and in some cases: invaluable human lives. By utilizing our custom designed CNN accelerator, we propose an edge-computing solution for this problem, that is both cost-effective and scalable. For developing countries like Sri Lanka, our vision-based traffic control on FPGA would be an ideal solution as described below.

In most countries, traffic flow is controlled by traffic lights with pre-set timers. In Sri Lanka, this often causes congestion during peak hours as the system is not sensitive to the traffic levels in each lane of an intersection. To solve this, the traffic policemen usually turn off the lights and manually control the traffic during peak hours. However, the policemen are unable to visually judge the level of traffic in each lane from their vantage point close to the ground.

An automated solution to this problem would be vision-based traffic sensing. However, the neural networks that excel in machine vision tasks require powerful GPUs or dedicated hardware. Laying cables along the road to transmit video feeds to control centers would require expensive infrastructure which is infeasible for a developing country like Sri Lanka.

Therefore, we present an implementation of a traffic sensing algorithm that is based on Object Detection on FPGA as a cost-effective, scalable, edge solution. We use YOLOv2, a state-of-the-art CNN for object detection accelerated through our custom CNN accelerator with post processing done on the ARM processor.

Custom CNN Accelerator Design:

A unique aspect of our project is, we design and implement a brand-new highly parallelized CNN accelerator whose single core at 100 Mhz can run a 384 x 384 RGB image through YOLOv2: (a 23-layer state-of-the-art object detection CNN with 2 billion floating point multiplications, 6 million comparisons, 8 billion additions) within 0.2 seconds. Multiple such cores can be implemented in parallel / series inside an FPGA to further improve throughput. The architecture can also be used to accelerate several other neural networks with slight modifications.
details »
630
votes

👀 1991   💬 165
Regional Final
📁Machine Learning
👤Gregor Schedlbauer (University of Applied Sciences Darmstadt)
📅Aug 29, 2019
For many visually disabled people it is challenging to identify other persons and classify objects correctly. A wearable infrared camera system with multiple, real-time convolutional neural network would be helpful for low vision affected and increases the confidence in rain, fog and darkness.

We would like to compare two established technology approaches and unite the individual advantages of the 110,000 reconfigurable logic elements and the sequential ARM Cortex-A9 hardcore processor on the Terasic DE10-Nano SoC.

Our project objective is a small, helpful bodycam for 217,000,000 visually handicapped people!

Index Terms: Field programmable gate arrays, Neural network hardware, Fixed-point arithmetic, 2D convolution
details »
6
votes

👀 1073   💬 4
Regional Final
📁Machine Learning
👤Badiss Djafar (Rhumel SA)
📅Jun 15, 2019
This project will be developed for Rhumel SA, a small european fintech company aiming at assisting decision making in finance.

Throughout the last eleven years since the '08 crisis, the industry has been relying heavily on standard algorithmic approach as well as low interest rate for its development. But those two approaches have their limits.

Although the latter has helped to provide an apparent sluggish recovery, non conventional economic stimuli can't last forever as they tend to create bubbles. As for the algorithmic approach (HFT,indexes), the flash crash of 2010 has proven that under particular conditions, this technology hasn't helped improving market liquidity when most needed, quite the opposite. An average of 90% of trades placed by machines are canceled ...

Machine learning on the other hand, could help processing charts (graphic chart analysis on particular companies going back to the '70s) and help active investors process the information that really matters.

The intent of that project, would be to use the openVINO starter kit to implement a Convolution Neural Network (CNN) for candle stick charts (Open High Low Close or more commonly OHLC).

1) Machine learning for chart analysis:
First focusing on one company, say Intel for example, and going back to its introduction in the market, openVINO could be used to graphically analysis on candle stick charts and give sensible insight in other it is a good idea or not to invest in the stock. This very long term approach is radically different from what exist at the moment in the market.

2) OpenVINO and the state of the art:
Intel website already provides examples and articles concerning dealing with CNN. The idea here is to start from those examples (Alex Net image classification, the R-CNN demo) and implement a sequence of Convolution filter and Pooling filters in order to replicate the human chart analysis but on a much bigger scale.

The final aim of Rhumel is to help money flowing back to the real economy using modern tools and being pragmatic.

Thus, contributing in financing small businesses, avoiding bubbles, keeping governments and institutions out of debt.

In other words, contributing to have a sound economy again.
details »
1
votes

👀 253   💬 2
Regional Final
📁Internet of Things
👤Belkacem BENADDA (University ABOU BEKR BELKAID of Tlemcen)
📅Sep 22, 2019
Currently, it is not possible to live without electricity. Our lifestyles influence this resource consumption. Indeed, it is obvious that each one of us with his own attitude and mores imposes a certain profile to electrical consumption. This project aims to develop a solution that allows smart remote measurements of electricity consumption. Artificial intelligence (AI) is used to properly analyze the consumption profile targeting individuals identification who certainly have different lifestyles. Moreover, adequate intelligence applied to consumption measurements makes it possible to guess the type of machines used. We also aim to associate with the developed system intelligence, Home automation control planning the residents' actions.
details »
3
votes

👀 284   💬 5
Regional Final
Community Award
📁Internet of Things
👤Enginnering Hope (N/A)
📅Jul 04, 2019
Internet of Things (IoT) network will allow huge number of unified entities to interconnect to each other through wireless communication technologies offering accessibility to remote user and open the door for modern innovative applications. IoT implementation faces challenges of working in dynamic environment conditions and cost to purchase RF spectrum. Therefore, IoTs are drifting toward finding a new diagram to interact with environment in intelligent way and using Cognitive Radio Networks CRNs to utilize Radio spectrum more efficiency.

Every task in IoT based CR demand various types of algorithms to implemented in one system and need flexible and rapid hardware. therefore, In this project we aim to investigate modern System on Chip SOC technology to design and implement IoT device can interactive, monitor and control user environment in intelligence way and has Cognitive Radio functionalities to connect devices wirelessly,with more efficiency RF spectrum utilization.

As tasks difference in IoT and Cognitive Radio due to different environments they are interacted with , and to accelerate and make our system more flexible we will use In our design, different AI algorithms and will be divided tasks to be implemented into hardware or software according to their flexibility and speed that needed. Indeed, our design will be able accelerate testing the whole and every part of such IoT based CR device designs by emulating real world scenarios in laboratory in addition With ease to interface to analog world and cloud storage.
details »
328
votes

👀 2224   💬 5
Regional Final
📁Machine Learning
👤Raul Valencia (University of Auckland)
📅Jul 09, 2019
With the explosive interest in the utilization of Neural Networks (NN), several approaches have taken place to make them faster, more accurate or power efficient; one technique used to simplify inference models is the utilization of binary representations for weights, activations, inputs and/or outputs. This competition entry will present a novel approach to train from scratch Binary Neural Networks (BNN) using neuroevolution as its base technique (gradient descent free) executed on Intel FPGA platforms to achieve better results than general purpose GPUs

Traditional NN uses different variants of gradient descent to train fixed topologies, as an extension to that optimization technique, BNN research has focused on the application of such algorithms to discrete environments, with weights and/or activations represented by binary values (-1,1). It has been identified by the authors that the most frequent obstacle of the approach taken by multiple BNN publications to date is the utilization of gradient descent, given that the procedure was originally designed to deal with continuous values, not with discrete spaces. Even when it has been shown that precision reduction (Float32 -> Float16 -> Int16) can train NN at a comparable precision [1], the problem resides in the adaptation of a method originally designed for continuous contexts into a different set of values that create instabilities at time of training.

In order to tackle that problem, it is imperative to take a completely different approach to how BNNs are trained, which is the main proposition of this project, in which we expose a new methodology to obtain neural networks that use binary values in weights, activations, operations and is completely gradient free; which brings us to the brief summary of the capabilities of this implementation:

• Use weights and activations as unsigned short int values (16 bits)
• Use only logic operations (AND, XOR, OR...), no need of Arithmetic Logic Units (ALU)
• Calculate distance between individuals with hamming distance
• Use evolutionary algorithms to drive the space search and network topology updates.

These substantial changes simplify the computing architecture needed to execute the algorithm, which match natively with the Logic Units in the FPGA, but also allows us to design processing elements that effectively adapt to the problem to be solved, while at the same time, remain power efficient in terms of the units needed to deploy because agents with un-optimized structures would automatically be disregarded.

The algorithm proposed, Binary SUNA (SUNA [2] with binary extensions ), will be used to solve standard reinforcement learning challenges, which are going to be connected to an FPGA to solve them more efficiently, given that the architecture will match the evolved network at multiple stages, specially during training and inference. Comparison of the performance gains between CPU, GPU and FPGA will be demonstrated.

[1] Michaela Blott et al. 2018. FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks. ACM Trans. Reconfigurable Technology
[2] Danilo Vargas et al. 2017. Spectrum-Diverse Neuroevolution With Unified Neural Models. IEEE Transactions on Neural Networks and Learning Systems 28
details »
43
votes

👀 382   💬 1
Regional Final
📁Machine Learning
👤Christopher Moran (Now Why Would You Do That)
📅Jun 28, 2019
This project intends to create a working prototype of an add-on solution for motor vehicles (including motor cycles) to actively detect pedestrian movements and warn of potential collision hazards.
The solution will project information onto a heads-up display to minimise distraction to the driver.
details »
43
votes

👀 332   💬 4
Regional Final
📁Digital Design
👤Parikshit Saha (Independent Researcher)
📅Jun 26, 2019
Effect of Diabetes on the eye is known as Diabetic Retinopathy which may lead to blindness in its furthermost stage. Highly skilled Doctors or Ophthalmologists use to inspect the patient's eye to detect the affectedness of this disease. But the amount of well-trained doctors is very less compare to drastically increasing amounts of patients. So proper treatment is nearly impossible. Plus also founding doctors in the remote locations is rarely possible. This Diabetic Retinopathy comes with no early symptoms. Only regular eye-checking need to be done to diagnosis it's starting affectedness. So we have planned to make an engineering-based solution making a standalone system which will do the primary diagnosis of the eye & suggest the patient the immediate precaution they need to take.
details »
55
votes

👀 761   💬 15

1 2 3 ... 22