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

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Regional Final
📁Machine Learning
👤Corey Lammie (James Cook University)
📅Jun 18, 2019
Deep Neural Networks (DNNs) have recently achieved remarkable performance in a myriad of applications, ranging from image recognition to language processing. Training such networks on Graphics Processing Units (GPUs) currently offers unmatched levels of performance; however, GPUs are subject to large power requirements. With recent advancements in High Level Synthesis (HLS) techniques, new methods for accelerating deep networks using Field Programmable Gate Arrays (FPGAs) are emerging. FPGA-based DNNs present substantial advantages in energy efficiency over conventional CPU- and GPU-accelerated networks. Using the Intel FPGA Software Development Kit (SDK) for OpenCL development environment, networks described using the high-level OpenCL framework can be accelerated targeting heterogeneous platforms including CPUs, GPUs, and FPGAs. These networks, if properly customized on GPUs and FPGAs, can be ideal candidates for learning and inference in resource-constrained portable devices such as robots and the Internet of Things (IoT) edge devices, where power is limited and performance is critical. Here, we propose a project using a novel FPGA-accelerated deterministically binarized DNN, tailored toward weed species classification for robotic weed control. We intend to train and benchmark our network using our publicly available weed species dataset, named DeepWeedsX, which includes close to 18,000 weed images. This project acts as a significant step toward enabling deep inference and learning on IoT edge devices, and smart portable machines such as an agricultural robot, which is the target application of this project.
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5
votes

👀 289   💬 2
Regional Final
📁Machine Learning
👤Youki Sada (Tokyo Institute of Technology)
📅Oct 15, 2019
This project is an FPGA implementation of the accurate monocular depth estimator with realtime. The monocular depth estimation estimates the depth from single RGB images. Estimating depth is important to understand the scene and it improves the performance of 3D object detections and semantic segmentations. Also, there is many applications requiring depth estimation such as robotics, 3D modeling and driving automation systems. The monocular depth estimation is extremely effective in these applications where the stereo images, optical flow, or point clouds cannot be used. Moreover, there is the possibility to replace an expensive radar sensor into the general RGB camera.

We choose the CNN (Convolutional Neural Network)-based monocular depth estimation since the stereo monocular estimation requires larger resource and CNN schemes are able to realize accurate and dense estimation. Estimating the depth from 2D images is easy for human but it is difficult to implement accurate system under limited device resources. Because CNN schemes require massive amount of multiplications. To handle this, we adapt 4 and 8-bit quantizations for the CNN and weight pruning for the FPGA implementation.

Our CNN-based estimation is demonstrated on OpenVINO Starter Kit and Jetson-TX2 GPU board to compare the performance, inference speed and energy efficiency.
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2
votes

👀 447   💬 5
Regional Final
📁High Performance Computing
👤TZE KIAN OOI (Universiti Teknologi Malaysia)
📅Oct 10, 2019
Heart disease is known as the top silent killer of the world. The ratio of patients to cardiologists is not balanced especially in developing countries. This lead to heavy workload to the existing cardiologists. Advance ECG equipment are mostly found in urban's hospitals. Hence, long travel time and queuing time are needed for heart monitoring especially for rural community. Current ECG devices lack state-of-the-art classifications and primarily used in ECG signal delineation. All these factors indirectly lead to increasing public mortality due to cardiovascular diseases (CVD).

To solve the increasing mortality, this project presents a smart heart monitoring device which able to support real-time self-classification of few life-threatening arrhythmia such as premature atrial contraction (PAC), premature ventricular contractions (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), ventricular tachycardia (VTach) and atrial fibrillation (AFib) for layman home user to enable frequent monitoring of the heart conditions. This device is an enhancement of previous in-house design by deploying SoC-FPGA technology on DE10 nano FPGA development platform, as well as developing a complete system equips with Internet-of-Thing (IoT) features. The complete system comprised of three main components, which are DE10 nano kit, Android-based mobile app, and Google Firebase technology. The DE10 nano kit combined with Olimex ECG shield will acquire single lead electrocardiograph (ECG) as input analog biosignal, perform analog to digital conversion, ECG signal preprocessing, features extraction and machine learning classification to detect multiple life-threatening arrhythmia using hardware/software co-design technique. The raw ECG signal and classification result will transmit from DE10 nano kit to Android-based mobile application through Bluetooth wireless communication for real-time ECG graph plotting and classification result display. The mobile phone will then upload the data to Google firebase server to give access to professional clinician for further validation and medical actions for early prevention of heart disease. The system functionality verification and computation timing performance evaluation are carried out using Fluke ProSim3 vital sign simulator and LeCroy HDO6104 mixed-signal oscilloscope.
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17
votes

👀 397   💬 4
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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13
votes

👀 427   💬 6
Regional Final
📁Digital Design
👤Vladislav Sharshin (Independent SP-Team)
📅Oct 08, 2019
The aim of our project is to develop the real-time video frame depth reconstruction device using FPGA.
There are a lot of classical algorithms of depth map reconstructing, but even the finest of them do it slowly, it takes a few seconds to process even a single frame.
These approaches don’t work in real-time.
Within the project, we are intended to develop the device, which can speed up a frame depth map reconstruction process, dealing with that task in real time without reduction in the processing result quality.
To construct a real-time depth map we use a special architecture deep neural network, implemented in FPGA, which processes two images from stereo-pare simultaneously.
FPGA enables the user to make this process more efficient than CPU thanks to implementation of both the parallel architecture and pipelines, so we achieve a great speed up of this process with help of parallel data processing and pipelining in data flow.
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62
votes

👀 684   💬 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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17
votes

👀 773   💬 13
Regional Final
📁Machine Learning
👤SHIVANSHU SRIVASTAVA (University of Allahabad, Prayagraj, India)
📅Jun 30, 2019
There has been a long history of studying Altered States of consciousness(ASC) to better understand the phenomenological properties of conscious visual perception. ASC can be defined as the qualitative alternation in the overall pattern of mental functioning such that experiencer feels that their consciousness being very different from the normal. One of the qualitative properties of ASC is visual hallucination (Tart C. T.,1972).
Hallucination Machine(HM) is a combination of Virtual Reality and Machine learning developed by Keisuke Suzuki and his team at Sackler Centre for Consciousness Science, University of Sussex, United Kingdom. This can be used to isolate and simulate one specific aspect of psychedelic phenomenology i.e. visual hallucination. HM uses panoramic videos modified by Deep Dream algorithm and are presented through Virtual Reality head set with head tracking facility allowing to view videos in naturalistic manner. The immersive nature of the paradigm, the close correspondence in representational levels between layers of Deep Convolutional Neural Network(DCNN) and the primate visual hierarchy along with the informal similarities between DCNN and biological visual systems, together suggest that the Hallucination Machine is capable of simulating biologically plausible and ecologically valid visual hallucinations (Keisuke et al. 2017).
Deep Dream is the algorithm developed by Mordvintsev, Tyka (2015) et al. at Google. When an input image is fed into a neural network using Deep Dream algorithm, and the user chooses a layer, the network enhances whatever it detects at the user defined layer. For example, if we choose higher level layer, complex features or even whole images tends to appear. So if a cloud in an image looks like a bird, neural network will make it look more like a bird and enhancement of the bird image in the output image will depend on the number of iterations computed for.
Due to the physiological effects of psychedelic drugs which are known to induce ASC, scientific community is in need of some alternative tool to study consciousness. Study done by Keisuke et al.(2017) provides no information whether the Hallucination Machine can be used to study the neural underpinnings behind the conscious perception of emotional visual processing. It is still a very hot topic in scientific community whether there is any role of top-down signalling or predictive processing theories of perception (Bayesian Inference) in the formation of perceptual content. We even don’t have any clear answers regarding whether the emotional visual processing is a late or early process.
So to answer these questions our team is developing a DCNN using Deep Dream and Deep Dream Anim algorithms and it will be trained on large data set of emotional images prepared by Dr. Narayanan Srinivasan at CBCS, University of Allahabad, India. Then test images will be evaluated by tweaking the lower and higher level layers, number of iterations and other parameters. Based on the analysis of results the above mentioned questions can be answered.
So, it will be an exploratory research to decipher the science of conscious perception that can be used in advancement of vision science and technologies around it.
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1
votes

👀 274   💬 2
Regional Final
📁Other: High-speed Video Processing/Artificial Intelligence/IoT
👤Neeloy Chakraborty (University of Illinois at Urbana-Champaign)
📅Oct 12, 2019
The purpose of our project is to build a smart home security camera system. On a high level, the system would focus its sights on particular objects in the room that the user wants to ensure doesnt get handled by intruders. If an intruder enters the room and begins to handle the object, then the user will be notified. Aside from the notification, there will be an hdmi display that shows the camera feed with detected objects. That feed will also be transferred to the user pc or phone over wifi.

We aim to build this system using the DE10-nano development kit. We would run OpenCV on the ARM Cortex A9 chip to interface with the camera and take care of the frame differencing and Kalman filtering to identify and track moving objects. The Cyclone V SE would be used to run an Artificial Intelligence that classifies the moving object detected by the ARM chip. There would be a live video stream via HDMI to a monitor, a data dump via ethernet, and a wireless status update via the Arduino header to the cloud.
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34
votes

👀 625   💬 2
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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9
votes

👀 364   💬 4
Regional Final
📁Digital Design
👤Longyu Ma (The University of Auckland)
📅Oct 06, 2019
In recent decades, error correction codes have been found to be not only capable of correcting received data conveyed via a communication channel, but also enhance the robustness of biometric systems, like iris recognition systems. The basic idea of error-correcting biometric information derives from an analogy between a communication channel and a biometric channel. To be more specific, as every capture of a certain slice of biometric information cannot be 100% identical, the received information in the first time or enrollment phase can be denoted as original data in a “sender” and the information captured in other time except the first time or verification phase can be denoted as received data in a “receiver”. Through a virtual communication, data in the “receiver’s side” is contaminated by noise that leads to the data difference in both sides. Enough differences between the enrolled and to-be-verified biometric may fail to fulfill the threshold for matching.
Our project aims to deploy an iris recognition system with an FPGA-friendly Forward Error Correction scheme that helps to increase the acceptance rate. Taking advantage of this Intel DE-10 board, the traditional image processing will be handled by HPS and FPGA part will be responsible for the error correction. Such collaboration can eliminate the penalty caused by the additional error correction to the maximum extent.
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2
votes

👀 238   💬 0
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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9
votes

👀 275   💬 4
Regional Final
📁Machine Learning
👤Narayan Raval D (LD college of engineering)
📅Oct 08, 2019
Lie detection is an evolving subject. Polygraph techniques is the most trending so far,but a physical contact has to be maintained.The project proposes the lie detection by extracting facial expressions using image processing. The captured images to be analyzed is broken into facial parts like eyes, eyebrows,nose etc. Each facial parts is then studied to determine various emotions like eyebrows raised and pulled together,raised upper eyelids,lips stretched horizontally back to ears signifies fear while eyebrows down and together, narrowing of the lip shows anger. All the emotions can be aggregated to determine wheather a person is lying or not. The interrogation video or live video is broke down into various facial images of the particular individual. Different emotions from the various images is collected and processed with the general face reading criteria to evaluate his truthfullness.
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415
votes

👀 948   💬 75

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