Food Related
Farm Management System
AP053 »
The project aims at developing an efficient farm management system that would prove to be resourceful in identifying pests on crops, report the nutrient and moisture content of soil, and accordingly irrigate the farm. For an efficient implementation, the system is integrated in a moving bot whose brain is the DE-10 Nano Cyclone V SoC FPGA board. For the time being, we are implementing our design on kharif crops, which can be further expanded to include rabi crops as well after suitable changes. For an efficient implementation, our project has further sub divisions as controlling the wheels of the bot, path tracking, analyzing the nutrients of the soil, image processing for detecting pests and irrigation system.
Smart City
基于FPGA的3D人脸特征传输系统
PR034 »
随着社会的发展，人工智能在人们身边出现的越发频繁，智慧景区以及智慧城市也是当前的人们发展趋势，本设计顺应需求，针对部分场景，如景区中的游客流量、游客、性别等基础信息的统计，或无人超市中人物信息的识别以及保存，并将这些数据备份、发送到云端，并在app中对部分数据进行实时更新，在做数据统计与分析时，利用云端中存储的数据，可以很全面且客观的分析出现状，这些工作靠人力实践是非常耗时耗力的。在必要时，还可调用出对象的信息和图像，对图像进行伪3D显示，大大提高了人们对图像的感知力。基于此类功能以及特性，本设计可广泛推广于安保、有人员统计需求类的场景。
本设计以 DE10-Nano 开发板为平台，结合当前最新推出的 DE10 云套件 RFS 子卡搭建了一款物联网系统，在此基础之上加入摄像头、液晶显示屏以及四片半透明介质，组合构成对象人物信息提取系统和佩伯尔幻像伪全息人物显示系统，并通过在arm核中移植linux系统以及在此之上配置的opencv和openvino环境对获取的图像信息进行处理分析，所得到的数据传回fpga，通过云系统发送到app，实现实时对部分数据的监控，每隔一段时间会发送数据到数据库储存供以随时调用。并可通过调用信息，读取对象人物的信息，选择对象的照片进行全息显示。
Industrial
基于FPGA的多路JPEG-LS无损解压缩
PR035 »
随着相机技术的发展，像素分辨率与帧率都得到了大幅度提高，由此需要处理的图像数据量也随之越来越大。在一些如地理信息、医学影像、卫星侦察领域，因数据传输通道变化不大，所以需要对传输的图像数据进行压缩后传输，并在终端解压缩进行图像还原。而在图像压缩中，JPEG-LS具有显著的优势，它比JPEG2000的算法复杂度更低，比JPEG的压缩性能更强。所以JPEG-LS算法具有硬件易实现，复杂度低，同时具有较好的压缩性能。
基于JPEG-LS的压缩目前多采用硬件实现方案，但其解压缩却多采用软件解码方式，而软解存在CPU资源占用过多、功耗较大以及处理实时性等问题，尤其在多路软解下问题更突出。据此提出本项目“基于FPGA的多路JPEG-LS无损解压缩”以满足多路视频解码的实时性。
项目拟采用多路并行处理结构，对接读取的云端多路图像数据。系统由多路相机、服务器、FPGA DE10-Nano套件以及其他配套电路组成，拟实现对4路1080p@50Hz的视频压缩码流进行实时解码。其中压缩码流与解压码流均通过网络与服务器互联。
该方案在JPEG-LS解压缩的速度上有较大提高，尤其在多路图像数据（高帧率、高像素）的处理时，效果更为显著。通过该项目的实施可以较好体现FPGA的重构性、实时性、低能耗等性能。
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.
Smart City
Turkey: Smart Garbage Containers to Reduce Fuel Consumption Caused by The Frequency of Solid Waste Collection
EM018 »
The aim of this project is to reduce fuel consumption in the waste collection process, with smart garbage containers. Smart garbage containers will send the waste bin fill-level information to the cloud, and the garbage collection vehicles will reschedule and optimize the waste collection route. In this way, garbage collection vehicles will not enter the areas where unfilled containers are located, so unnecessary fuel consumption will be prevented. Smart garbage container also detects the temperature and gas emissions that are above normal by its sensors and send a warning so that threatening garbage can explosions can be detected and prevented in advance. The fill-level of the smart garbage containers is detected by the distance sensors in the garbage container. The sensor measures the distance between the cover and the wastes. When the measured distance falls below a certain level, it sends the information to the cloud that it is full. Volatile Organic Compound Gas detector will be used to measure methane gas emission, and a thermocouple system will be used to measure temperature.
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