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

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Regional Final
📁High Performance Computing
👤Al Schneider (Schneider Software Systems)
📅Jun 30, 2019
2019 Innovate
FPGA Project Proposal
3D Stethoscope
By Al Schneider
als@alschneider.com

This entry to the contest presents a new computer concept. The purpose of this entry is to show the concept is feasible.

Background

The architect of this concept grew up in software in the seventies working on Univac multi-processor systems. Then, the power of such systems was apparent but shortcomings were also apparent. Years after leaving Univac this person discovered solutions to these shortcomings. The cost of implementing these solutions was high and prevented a serious attempt at demonstrating them. However, with the advent of FPGA technology, the solutions can be implemented in the real world at a reasonable cost.

Project Overview

In essence, this system enables large scale multiprocessing with all processors accessing a common memory without conflict. A system is planed with 256 processors to demonstrate the concept.

The method utilizes an approach somewhat opposite from traditional computer technology. Traditionally, computers move data to a hardware CPU. This system, on the other hand, utilizes virtual processor units (VPUs) as opposed to a central processor unit. They move within the system traveling to the data.

Experiments performed on a Max 10 FPGA indicate that the switching time for a LUT gate is 1.6 nanoseconds. Based on that, the aggregate throughput of the suggested system would be 200 Mega IPS.

A simple C like language will be provided to program the many processors.

Proposed Entry

The proposed entry would use a Terasic Open VINO Starter Kit to analyze the frequencies from an audio input into 256 sub bands and translate the sound into moving images on a display screen. This is to be implemented as a stethoscope that displays a visual representation of heart sounds as well as audio. An important point here is that the device will do this in real time. This requires analysis of audio input, image rendering, and image display within 33 milliseconds to produce 30 frames per second.

This link visualizing music illustrates how the display might appear:

https://youtu.be/bElku6DaY5U

FPGA Virtues Realized in this Entry

Using FPGA’s to develop concepts.
Demonstrates the value of having memory and logic on the same chip.
Demonstrates the ability to eliminate unneeded functionality.
Demonstrates the ability to add custom desired functionality.
Demonstrates performing many tasks simultaneously.
There may be a need to demonstrate the Cyclone V GX I/O capability and the PCIe interface.
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43
votes

👀 298   💬 1
Regional Final
📁Machine Learning
👤David Castells (Universitat Autonoma de Barcelona)
📅Oct 06, 2019
Collecting Mushrooms for human consumption is a very popular activity in Catalonia. It is so popular that the goverment is often discussing methods to control access to the forests of the country, including tolls. There are hundreds of different mushroom especies, some are very appreciated, and some are poisonous, and even can cause death. There are a number of fatalities every day because of mushroom intoxication.
The exact identification of mushroom especies is an important challenge that can save lives. The aim of this project is to build a machine learning system to identify each mushroom especies from photos taken with mobile phones.
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1
votes

👀 278   💬 5
Regional Final
📁Machine Learning
👤Elmira Tavakkoli (university of Guilan)
📅Oct 08, 2019
Todays, because of importance of performance, efficiency, cost and product presentation time, the influence of artificial intelligence (AI) in smart manufacturing is rapidly growing. Thus, we attempt to present a new type of smart glasses for the blind people by using AI with machine learning to improve their quality of life. In fact, our proposal can help visually-impaired people, specifically, completely blind people to identify person, detect objects, cross the street and generally, help them to navigate and find orientation without any assistance.
Our smart assistive device is very high efficient because of using FPGA implementation. In this system, we use a DE10-Nano SoC FPGA kit and a camera as main hardware. Also, we use an audio system and other software/hardware peripherals to complete the requirements of the target device.
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1
votes

👀 592   💬 10
Regional Final
📁Machine Learning
👤Mohamad OUSSAYRAN (N/A)
📅Jul 18, 2019
Introduction: Each year an enormous number of people die from skin diseases.
Based on BSD Work Group and the American Academy of Dermatology (AAD) there is 85 million US patient were diagnosed with at least one skin disease in 2013 (25% of the US population).
According to a study in 2016 from the “French society of dermatology” 16 million French suffer from a skin disease, and 80% of those suffer from more than 1 pathology which makes 24% of the French population.
Referring to the article “The burden of skin disease in the United States” in the journal of the AAD, 24 different categories of skin pathologies can be identified each one including at least 4 diseases.

Problematic: Using this approximation, there is a minimum of 100 diseases. With this huge number of diseases, it implies prognostic more difficult to establish for doctors. Furthermore, patients who have these types of disease, they feel shy. Furthermore, patients who have these types of disease, they feel shy. As well as, psychological problems and stress.
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61
votes

👀 488   💬 13
Regional Final
📁Machine Learning
👤Paulo Alonso (Phirmup Systems)
📅Oct 15, 2019
Real time streaming analytics Machine Learning by FPGA accelerator card using FPGA AI Engine OpenVINO/DLA. The approach get through cloud based on open source for multispectrum, multi-RAT Spectrum sharing between M2M and LTE-A in 5G networks. The "streaming" works on a secure framework to provide firmware updates on Network devices code bases by Coreboot and EFI Development Kit (tianocore) for the rapidly evolving RTOS embedded system with detailed coverage of requirements and optimization of Boot Setting File (BSF). The software access multiple connections through protocols for secure remote access and learns from the "behavior" of your network, then activates the most probable event scene based on the Java-based web interface usage pattern with a multi-cloud hybrid platform using the Python framework to automate data analytics, including edge-based gateway decision, improves connection, monitoring, authentication of intelligent sensor efficiency for automatic adaptation of usage. Enhancing the learning capabilities for qualification of components based for specific tasks allocated in gateways that could act as instruments distributed by a platform have a dedicated neural network processing unit and AI function API for performance the FPGA tuning module SIM card data with modular network topology Data Center (AS, peering, links) and Edge Firewall. With a smart layer for rules and automation, uses "admin" link data centers networks and Gateways controls allowing for addition to performer "node to gateway" communications such as (BACnet). Slicing data for 5G evolution bridge our physical and virtual world for operators tap into based on our experience on development ready to turn technical concepts into 5G transformations. The ability to carry out processes, like profiling data sources algorithms can comb through all of the different data sources include the data log and select the ones that fall within a certain category have a clearer "frame" of the sources available, AI technology can tag them automatically, eliminating considerable manual work. In addition the links among different sources for detecting anomalies on data traficc. Select switch protocols to explore Link-Aggregation (802.3ad) or VPC like and always think about stacking and VLAN (802.1q). VLAN Extension in L2, VPLS / VPWS, OTV like E-VPN and PBB-EVPN. Define QoS, Jumbo Frame and SAN Network. Framework programmatic ones being 5G security sensors automated firmware update by FOTA. The software can connect industrial SCADA or DCS directly to the cloud using industrial protocols such as NB-IoT, CAT-M1, MODBUS, OPC, ISA100 wireless technology, PROFIBUS for connectivity and CoAP / MQTT for gateway solves the problem of interoperability and M2M communication through of internal flash memory FPGA, with this feature in synchronization of private files from open cloud storage service to a safe and upgraded service. It has differential correction parameters implemented through the very useful radio module when there is no reliable network coverage structure to reduce the high cost of the DTLS handshake in the WSN and provides reduced latency when compared to a standard DTLS use case without require changes to the final hosts in a multiple output multiplex network (MIMO) through the RTM module in parallel through many more antennas.
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43
votes

👀 584   💬 2
Regional Final
📁Internet of Things
👤Kuba Kowalczykowski (Poznan University of Technology)
📅Oct 06, 2019
In the era of constantly growing traffic we are challenged to find the best solutions not only for car movement organization but also for adequate parked car management. Therefore, we have to develop bigger and more complicated parking spaces than ever before. Facing these infrastructures can be tricky for many drivers and they can feel overwhelmed by car parks complexity. Innovative IoT solutions come to help these drivers who are straying in the darkness of underground car parks.

ParkMe is a simple idea for managing car park traffic based on video processing, routing, guiding and tracking of free parking places. ParkMe’s main functionality is to guide every single car individually from the car park’s entrance to a park place most suitable to driver’s needs and then all the way back to the least occupied exit.

We value the time above everything else. ParkMe is a time-saving solution for all complex parking infrastructures. With our project we would like to put an end to: - crawling around a parking & searching for a parking place, - traffic jams in the parking areas, - that irritating feeling when someone takes a place that you just spotted. Our solution will save a lot of driver’s time, which can be spent on doing something more constructive than being stuck in a traffic jam.

ParkMe’s infrastructure is based on sophisticated nodes called GuideNests, which contain Terasic FPGA boards with Intel OpenVINO Toolkit for image processing, sensors, indicators and connectivity with central server and other existing systems. GuideNests let the system individually identify every car in the car park. With the support of traffic monitoring and free parking places indicators the system can calculate the route for every driver, considering changeable environment of the car park. The system is scalable and can be expanded due to specific implementation case.

With enough time a human being is capable to do anything. Don’t waste it for parking traffic!
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0
votes

👀 343   💬 3
Regional Final
📁Internet of Things
👤Paul DeCarlo (Microsoft)
📅Oct 17, 2019
Azure IoT Edge enables developers to deploy containerized modules to internet connected devices which allows for maintaining a desired state of running services through cloud-configured deployment configurations. This mechanism also offers the ability to securely update running modules at runtime on remote devices via changes to this configuration.

This IoT Edge module will allow the user to configure the FPGA portion of the Cyclone V SoC from Linux within an IoT Edge module, allowing for a robust deployment mechanism for shipping FPGA configurations to remote devices at scale.

This can allow for FPGA enabled devices to be dynamically reprogrammed in the field, without physical access. As such, we can now deliver hardware configurable updates over the air to enable a wide variety of never before seen use cases including fixing issues without need for a physical recall, updating configurations to be more performant or add new features, and allowing devices to be bootstrap with remotely delivered FPGA configurations on first run.
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0
votes

👀 279   💬 1
Regional Final
📁Machine Learning
👤Miguel Ángel Castillo-Martínez (National Polytechnic Institute of Mexico)
📅Sep 30, 2019
When the skin cancer is not detected in early stage can cause metastasis, consequently, the cancer scatters to overall body. Based in this fact, the proposal consists in image processing and machine learning approach to make a computer assists in cancer detection in acquired images according to existent patterns
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2
votes

👀 324   💬 2
Regional Final
📁High Performance Computing
👤Kristoffer Flores (Xavier University - Ateneo de Cagayan)
📅Oct 07, 2019
In photography, aperture is the opening within the lens through which light travels. It determines the cone angle of light from the image plane. One of the effects of aperture is depth of field. Depth of field is the amount of your photograph that appears sharp from front to back. The further an object from the focus plane, the more blurry it appears in the photo. Conversely, if we can find a way to measure sharpness of an object through digital image processing, we can determine the relative distance of all the objects in an image and even surfaces. With the computing ability of FPGA, it can easily produce an elevation map of an area which can be used for better image analysis, urban planning, and disaster risk assessment.
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4
votes

👀 18930   💬 3
Regional Final
📁Machine Learning
👤Masayuki Shimoda (Tokyo Institute of Technology)
📅Oct 08, 2019
This project presents an accurate, fast, and energy-efficient object detector with a thermal camera on an FPGA for surveillance systems. A thermal camera outputs pixel values which represent heat (temperature), and the output is gray-scale images. Since the thermal cameras do not depend on whether there is the light or not unlike other visible range cameras, object detection using the thermal camera is reliable without dependence on the ambient surrounding. Additionally, for a surveillance system, visible images are not suitable since they potentially violate user privacy. Thus, this topic is of a broad interest in object surveillance and action recognition. However, since it is challenging to extract informative features from the thermal images, the implementation challenges of the object detector with high accuracy remain. In recent works, convolutional neural networks (CNNs) outperform conventional techniques, and a variety of object detectors based on the CNNs have been proposed. The representative networks are single-shot detectors that consist of one CNN and infer locations and classes simultaneously (e.g., SSD and YOLOv2). Although the primary advantage of the type is that it enables to train detection and classification simultaneously, the resulting increased computation time and area requirements can cause problems of implementation on an FPGA. Also, as for the proposed networks on RGB three channel images, one of the problems is false positive; the realization of a more reliable object detector is required. This project demonstrates an FPGA implementation of such reliable YOLOv2-based object detector that meets high accuracy and real-time processing requirements with high energy-efficiency. We explore the best preprocessing among conventional ones for the YOLOv2 to extract more informative features. Also, well-known model compression techniques, both quantization and weight pruning are applied to our model without significant accuracy degradation, and thereby the reliable model can be implemented on an FPGA.
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41
votes

👀 592   💬 2
Regional Final
📁Machine Learning
👤Walter Gontijo (LINSE/EEL/CTC/UFSC)
📅Sep 30, 2019
This project is dedicated to the implementation of an FPGA-based acoustic keyword spotting (KWS) system for the portuguese language. Such system performs real-time processing using MFCC extraction as pre-processing and a convolutional neural network (CNN) as the classifier.
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1
votes

👀 372   💬 1
Regional Final
📁Machine Learning
👤Jose Francisco Sanchez Rosales (Independent Consultant)
📅Jun 17, 2019
The Sliding Desktop Robot is a modified PLA 3D printed CNC platform able to make complex task in a wide working area through deep learning using neural networks deployed in a FPGA. To train the model, a customized environment is created to adjust the precision of the movements. The autonomous of the robot is made through visual recognition over the environment with a camera. Intel OpenVINO toolkit will be used to process the tensorflow model resulting after training for two different kinds of neural networks: classification for visual recognition and reinforcement learning for the robot automatic movements.
Python programming is used for final inference simulation, so all the tasks can be simulated without any peripheral at development stage, before production. For demonstration purposes, some tasks will be implemented as a bakery decorator and the classic pick and place, but with shape recognition added.
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5
votes

👀 186   💬 1

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