Autonomous Vehicles
Blind Spot Detection and Warning System for Vehicles
AP054 »
In this project, we aim to develop a blind spot detection system. In scenarios where vehicles are navigating sharp turns or bends, the area alongside and just behind the vehicle is a constant source of danger and often the cause of serious accidents. To make navigation easier in such scenarios, blind spot detection can be used to warn drivers of impending collisions. In the proposed system, ultrasonic sensors or lidars will be used for blind spot detection.
Sensor data will be acquired by a custom data acquisition system implemented on the FPGA. Sensor noise filtering and basic sensor data fusion will be performed using the ARM Cortex 9 Processor provided on the FPGA. A behaviour control model will be designed and implemented on the FPGA which will be interfaced with the display system on the vehicle and the speakers. If the driver fails to spot objects or vehicles in the blind spot while turning, or fails to detect adjacent vehicle lane changes in its vicinity, this behaviour model will trigger visual and audio warnings.
Sensor data will be uploaded to the cloud using Microsoft Azure. On the cloud, a learning algorithm will be developed which generates the possible trajectories of vehicles in the blind spot and provides the necessary warning signals back to the FPGA mounted in the vehicle.
Other: Sustainable Agriculture
FPGA based Agriculture Monitoring system with BLE mesh and IoT
AP055 »
The proposal is to develop a BLE (Bluetooth Low Energy) mesh system for network of sensors which monitors various parts of agricultural lands with FPGA (DE10 nano) being the processing unit. The land under monitoring is split into a grid of smaller areas. Each of these areas have a remote Bluetooth node with a sensor system in it. This sensor system monitors several factors like moisture in the soil, temperature, pH level which are essential for agriculture. The nodes which are connected in a Bluetooth mesh sends the sensor data to the master (Bluetooth) which is connected to FPGA (DE10 nano) and ESP8266 Gateway. With the data from the nodes, particular areas can be monitored for proper field conditions. All these data are then processed in the FPGA and transmitted to a web-based platform through IoT where the farmer can see the data.
Health
FPGA Accelerated Real time Medical Data Analysis
AP056 »
Real-time monitoring of patient data during medical operations can provide important diagnostic input that substantially improves the chances of success. With the growing speed of sensors, frameworks like Deep Neural Networks must execute computations under stringent time restrictions for real-time operation. The conventional computing platforms like CPU/GPU for running Deep learning algorithms incur a large overhead due to fixed architecture, communication protocols and memory accesses methods. However, the FPGA-based design can directly interface sensors, storage devices, display devices and even actuators, thus reducing the delays of data movement between ports and compute pipelines. In order to minimize human involvement and respond at an appropriate time, An FPGA accelerated healthcare monitoring system is proposed which can either monitor or measure three vital signs i.e. heart rate, respiratory rate and body temperature of human body. It will give the health report, health status and alerts to the concerned when required.
Autonomous Vehicles
Collision Avoidance and Traffic Management
AP058 »
The National Highway Traffic Safety Administration (NHTSA) estimates that about 9 per cent of all motor vehicle accidents occur due to some kind of lane changing or merging collision. These types of crashes are often dangerous because the other vehicle involved in the accident is caught off-guard and left in a very vulnerable position. Thus, changing lanes abruptly leaves the accident victim unable to react to avoid the crash.
The main cause of this is human error in recognition and decision making. Active safety systems have thus great potential for increasing vehicle safety at turns and intersections. It can issue warnings to the driver to take control of the vehicle in critical situations.
Some premium cars have already implemented some features for the vehicle collision avoidance system, but it's not yet scaled to every segment. Collision avoidance systems will act as a great boon to mankind in solving problems of navigation, road accidents and will help in better traffic management.
In the proposed system, wheel speed encoders, accelerometers and gyroscopes will be used as sensors for collision avoidance. These sensors are placed on all four corners of the vehicle, and the data will be acquired by a custom data acquisition system implemented on the FPGA. Sensor noise filtering and basic sensor data fusion will be performed using the ARM CORTEX 9 Processor provided on the FPGA. This information is then transferred to the cloud using the Azure cloud connectivity board.
The trajectory of all the vehicles in a window of interest around each vehicle will be estimated by a regression-based machine learning algorithm and if the estimated trajectories point to a possible collision, the driver will be alerted with visual and audio warnings. On the cloud, a learning algorithm will be developed which will be trained using predefined datasets to send the necessary warning signals to the vehicle thereby alerting the driver about the forthcoming collision.
This information can be extrapolated and integrated into a traffic management system in which the trajectories can be used to prevent congestion of vehicles at traffic intersections by advising the drivers of the speed with which they need to drive while approaching the intersection.
Smart City
Smart Energy Meter
AP059 »
Energy and environmental problems are closely related, since it is nearly impossible to produce, transport, or consume energy without significant environmental impact. The environmental problems directly related to energy production and consumption include air pollution, climate change, water pollution, thermal pollution, and solid waste disposal. The emission of air pollutants from fossil fuel combustion is the major cause of urban air pollution. Burning fossil fuels is also the main contributor to the emission of greenhouse gases.
Wasted energy still means that it was produced. Therefore, we burned a ton of fossil fuels for no reason. That means there were both carbon and methane emissions, for electricity that was never even used. As a nation, we are not the most efficient with our appliances, which has a cumulatively negative effect.
Let’s take lights for example. How often have you left the lights on while heading out for the night? I’m sure plenty of times. We’ve all been guilty of leaving the lights on. The problem is that since it is such a common habit, it easily adds up, contributing to the 66.7 percent of wasted energy.
Wasting electricity creates the ultimate domino effect that can one day leave us with a country with insufficient room for all of its citizens.
To solve this issue and problem, the Authors have proposed the idea of Smart Energy Meters. In idea, Smart means it will be an IoT Edge Device connected with Appliances/Switchboards and monitor the consumption at regular intervals. This device will transfer the data from Edge to the cloud for analysis and generate reports for consumption. On the server-side Author will be developing an ML model which will analyze this data and keep giving suggestions to change old devices, alarms while the waste of energy is detected when no one is using, servicing the appliances, etc., all reports and data are available via application at end user as per their requirements and needs.
Marine Related
E-Matsya: FPGA Implemented Data Acquisition System
AP060 »
India has a coastline of 7516.6 km-- 5422.6 km of mainland coastline and 2094 km of island territories. India has nine coastal states: Gujarat, Maharashtra, Goa, Karnataka, Kerala, Tamil Nadu, Andhra Pradesh, Odisha, West Bengal. This addresses the biodiversity in the marine environment possible. Since the sea is beside the Western and Eastern Ghats, the diversity in the both regions can also be seen in sea. The sea has been a home for many marine animals and plants, along with helping many humans to depend on them. Thus, it is very essential to give technological assistance to maintain and make the marine environment more sustainable. Here calls the need for the observation of physical properties in the coastal region.
The E-Matsya is expected to serve, assess and monitor the environmental condition of the said regions. To do so, E-Matsya needs to be entirely autonomous in nature i.e., it needs to be piloted by an onboard computer to complete pre-programmed mission objectives. To attain this objective the use of the FPGA can be exploited completely, as it is reprogrammable and plays an important role for offering parallelism to perform various data collection from sensors and their analysis.
Our project aims at profiling the sea in 2 dimensions i.e., vertical and horizontal, which helps in conservation of the marine environment. The E-Matsya is expected to operate at an optimum depth of 10 meters and it can move up to 30 meters horizontally. Thus, the expected sweep area is 3000 sq. meters. Scalability, Cost effectiveness and robustness were the guiding design principles in this project.
Autonomous Vehicles
SMART SHIP
AP061 »
As per the UNCTAD calculations, the total cargo unloaded through maritime trade in the year 2000 is about 5984 million tons and the same has become 11076 million tons in the year 2019. But this marine ecosystem is quite challenging when it comes to transportation such as accidents, loss of crew, oil spills, Suez canal blockage, effect on marine life etc.,
To bring in efficiency in the above, autonomous vehicles “Ships” may help the maritime ecosystem by minimizing human errors and handling the situations of weather uncertainty conditions. Currently the work on the “Autonomous ship” is in the development of remote-operated vessels in Rolls-Royce. The MV Yara Birkeland is an autonomous 120 TEU container ship that is under construction and is due to be launched in 2021.
Now, if the above autonomous ships use the FPGA platform, There may be better performance in the operational speed of decision making and response mechanisms due to the special features of the FPGA platforms.
We are hereby proposing the design of a prototype of autonomous ships using the FPGA module (along with Azure Cloud) which will have the following features:
1.Ship movement with Global Positioning System.
2.Real-time monitoring of ship (using Azure Cloud).
3.Object detection and lane detection.
4.Route planning without collision.
By automating every task and resolving every challenge we can expect that AUTONOMOUS SHIP can revolutionize the maritime industry. It will allow the ship owners to manage their fleet to optimize operations and maximize profit. Implicitly, as the accidents get reduced (such as oil spilling etc) the pollution in the marine system can also be reduced.
Other: Energy
Multi Material Steam Turbine
AP063 »
According to the European Environmental Agency, waste is not only an environmental problem, but it is also an economic loss. An example would be, Europeans produce 481 kilograms per person of municipal waste per year and this continent generates a large amount of waste in the form of food, garden compose, construction, mining, industrial, sludge, electronics, cars, plastic, sanitary, clothes and furniture.
The amount of waste generated in this continent is highly linked to the consumption and production patterns and the sheer number of products entering the market poses another challenge. For example, the changes in demographic such as the increase in the number of one-person households, also affects the amount of waste generated.
This situation is also no different in the APJ (Asia Pacific and Japan) region as waste are a problem here too.
Therefore, our team, the Turbine Dwellers consisting of three people have decided to come up with the Multi Material Steam Turbine project to tackle the waste issue. We do this by combining waste materials to build a turbine that can rotate upon powered by steam.
As there are two major challenges in this project which are heat and vibration, we plan to make use of the DE10-Nano Cyclone V SoC FPGA Board together with Microsoft Azure IoT coupled with 2 QuickEval Boards from Analog Devices namely, LT6203 and LTC2984.
The idea behind this is that since our turbine would be made out of multi material from recycled waste such as plastics, wood chips and others, it is crucial to monitor vibration and heat generated from the turbine in order to operate the turbine in a useful manner to generate electricity.
Later, and if the turbine fails during its operation, the temperature and vibration reading would be logged and pushed to the cloud so that, the community could learn from the different types and combination of recycled materials and how they can be fused optimally into a turbine to generate electricity.
Hence, many of these data could be translated into useful information for communities whom wishes to use their waste to generate electricity via this method, thus fulfilling our team’s vision to a green and sustainable future.
Our vision through this project, is expected to reach the goal of reducing the cost of waste management significantly by 85 % and conserving valuable lands from becoming landfills since the waste would be made into turbines to generate electricity.
Block Diagram could be found here:
https://1drv.ms/b/s!As2N6ZPEYC1opVAsSXWk2siqEoWJ?e=lAUwGd
Water Related
Sustainability and Productivity Enhancement IoT system for Agriculture
AP064 »
The project vision is to create a modulo IoT network that could be easily modified and applied onto various types of farms and greenhouses to help optimize water use, give insights, and impact the health of crops and the surrounding environment. Hence, increase the quality of the product going to the market and reduce the wastes that will contribute to long-term sustainability.
An IoT network will include a master node made of the FPGA Cloud Connectivity Kit, which will be responsible for collecting data from the sensor nodes, communicating with the cloud along with controlling the actuators that will affect a specific crop on the field.
Each Sensor Node will have its own MCU connected with a Soil Moisture and pH Measurement sensor (CN0398), a Volatile Organic Compound (VOC) Detector (CN0395), the Light Recognition System (CN0397), and potentially any other sensors that the customer requests. The data collected from these sensors will be transmitted to the Master node through wired RS485, wireless LoRa, LoRaWAN or other local transmitting methods (depends on specific setup). The moisture data collected will be used to control a watering pump through an isolated Contactor. pH or other soil qualities data can be statistically displayed to give the user insight into what should be adjusted to the fertilizer. Moreover, the VOC and Light data can be used to conditioning airflow in the greenhouse and changing lighting components to provide the best environment for the crops to thrive.
The cloud-based service will be in contact with the master node to connect the user from anywhere around the world to monitoring and controlling their farm. Besides, the cloud is also responsible for updating the system's firmware Over-The-Air (OTA) and store the processed data from the FPGA for further analysis and future improvement.
If time permits, edge computing Machine Learning will also be applied to the master node to automatically control the watering, lighting and air ventilating based on the crop that is being monitored, hence reduce the network bandwidth, contribute to the long-term energy efficiency compared to applications that do most calculating on the cloud. This network can be scaled up with many Master Nodes, each responsible for specific crops in a greenhouse or a farm, connected all together and monitored by the cloud or by a local Grant Master Node. We are expected to test the system on a greenhouse facility in Vietnam after finishing the prototype, which will give us a better insight into how the system operates.
Through the project, besides expecting to learn a heap of new useful knowledge, our team hopes to create a foundation for improving the efficiency of watering and energy usage in agriculture, especially in developing countries such as our hometown Vietnam.
Transportation
Project Vanguard
AP065 »
We propose to develop an IoT-based device that alerts nearby emergency services and primary contacts when an accident happens. We propose to develop a device using FPGA along with peripherals like an accelerometer, GPS module, and two cameras to alert the officials thereby mitigating the loss.
When a collision occurs the accelerometer sensors detect the drastic malfunction of an accelerometer. As soon as a collision is detected, the recording is stored in memory so the footage can be retrieved and used by the police for further investigation if necessary. Two memory devices will be used to store the recorded video, as soon as 60 minutes are elapsed, the recording continues in another memory device, while the other one gets cleared so that recording shall not be delayed due to the clearing process.
The FPGA along with the sensors comes in a single package(entity) along with two external cameras. The user should plug the device into the battery socket which is made available in every car which will be a power supply for the FPGA device. The user has to connect the cameras with the device and mount the cameras at the apt position which might require a technician to care of the wiring.
On top of this, the data collected from series and video will be relayed to the cloud on which we plan to implement different algorithms to further process data like average speed during the entire course, etc. Also, the video will be further processed using the algorithms on the cloud to determine various aspects which include but are not limited to the severity of accident, possible recognition of number plates.
Disclaimer: The above information describes the basic functioning of the device as a whole. The final product may include extra features and possible optimizations.
Health
Design and Implementation of FPGA Accelerator for Computed Tomography based 3D reconstruction
AP066 »
COMPUTED tomography (CT) is a commonly used methodology that produces 3D images of patients. It allows doctors to non-invasively diagnose various medical problems such as tumors, internal bleeding, and complex fractures. However, high radiation exposure from CT scans raises serious concerns about safety. This has triggered the development of low-dose compressive sensing-based CT algorithms. Instead of traditional algorithms such as the filtered back projection (FBP) , iterative algorithms such as expectation maximization (EM) are used to obtain a quality image with considerably less radiation exposure.
In this proposed work we present a complete and working CT reconstruction system implemented on a server-class node with FPGA coprocessors. It incorporates several FPGA-friendly techniques for acceleration. The contributions of the proposed work includes:
• Ray-driven voxel-tile parallel approach: This approach exploits the computational simplicity of the ray-driven approach, while taking advantage of both ray and voxel data reuse. Both the race condition and the bank conflict problems are completely removed. Also easily increase the degree of parallelism with adjustment of tile shape.
• Variable throughput matching optimization: Strategies to increase the performance for designs that have a variable and disproportionate throughput rate between processing elements. In particular, the logic consumption is reduced by exploiting the low complexity of frequently computed parts of the ray-driven approach. The logic usage is further reduced by using small granularity PEs and module reuse.
• Offline memory analysis for irregular access patterns in the ray-driven approach: To efficiently tile the voxel for irregular memory access, an offline memory analysis technique is proposed. It exploits the input data independence property of the CT machine and also a compact storage format is presented.
• Customized PE architecture:
We present a design that achieves high throughput and logic reuse for each PE.
• Design flow for rapid hardware-software design:
A flow is presented to generate hardware and software for various CT parameters. In this work we adapt the parallelization scheme, offline memory analysis technique, variable throughput optimization, and the automated design flow. Hence the new optimization would be expected to be much faster than earlier model with same dataset.
Health
DEVELOPMENT OF MULTIPLE MOBILE ROBOTS FOR HEALTHCARE APPLICATIONS USING FPGA
AP068 »
The purpose of our project is to describe the implementation of a “Multiple Mobile Robots” (MMR) that plans and controls the execution of logistics tasks by a set of mobile robots in a real-world hospital environment. The MMR is developed upon an architecture that hosts a routing engine, a supervisor module, controllers and a cloud service. The routing engine handles the geo-referenced data and the calculation of routes; the supervisor module implements algorithms to solve the task allocation problem and the trolley loading problem a temporal estimation of the robot’s positions at any given time hence the robot’s movements are synchronized. Cloud service provides a messaging system to exchange information with the robotic fleet, while the controller implements the control rules to ensure the execution of the work plan on individual robots. The proposed MMR has been developed to have a safe, efficient, and integrated indoor robotic fleets for logistic applications in healthcare and commercial spaces. Moreover, a computational analysis is performed using a virtual hospital floor-plant.