Health
高精度多路人体生理参数测量系统设计
PR024 »
本研究项目核心在于生理参数采集传感器的选择、信号调理电路设计、电源模块设计以及高速通信接口设计等关键技术环节，为此，提出如下技术方案；整个研究过程的技术路线如下：
1、针对市场上现有生理参数测量仪产品的功能特点，以及当前人体生理参数测量的相关研究成果，针对嵌入式技术初步确定高精度多路人体生理参数测量系统实现方案；
2、对基于FPGA技术的高精度生理参数测量系统进行硬件开发，包括具体的测量数量（包括心率、皮肤电反应、胸/腹呼吸、血压、动作和环境温湿度）的确定，相关生理参数采集传感器的选择，并针对各传感器进行模拟微弱信号调理电路的设计，电源电路的设计，高速通信电路的设计等；
3、对基于嵌入式技术的高精度生理参数测量系统的软件设计和实现；
4、设计用于显示、存储数据的上位机软件，以及高精度生理参数测量系统硬件和上位机软件联合调试。
Smart City
Realtime energy monitoring and reporting
AP043 »
This project aims to develop an always-on device that measures raw energy usage at the ingress power line (current, voltage), processing of this realtime data (PF etc.) and transmitting the processed data via WiFi to the internet or locally.
Scope is as below,
FPGA:
- Code that collects and process raw values from ADC.
- Code that interfaces with the ESP32
ESP32:
- Code that interfaces with the cloud
Server backend:
- Code that runs on the cloud that collects data from these ESP32s.
Frontend:
- Simple dashboard that displays current and historical energy usage.
Hardware scope is as bellow:
1. Analog interfacing circuit that interfaces the FPGA to the powerline.
Autonomous Vehicles
Orthogonal Recurrent Neural Network on DE10-Nano (ORNN Nano)
EM014 »
In this project, we aim to train a quantized orthogonal Recurrent Neural Network (ORNN) model for speech recognition and implement its inference accelerator on DE-10 Nano using intel HLS. With an era of increased use of AI to facilitate our lives speech recognition at the edge has numerous advantages. From a tiny personal assistant in our phone to the autopilots of self-driving cars, speech recognition has shown impressive performances and has contributed to assisting our lives and making it much easier. Similarly on the other hand having a speech recognition model (which is compute and memory expensive) on an edge device like DE-10 Nano allows us to explore the potential of the Altera FPGAs and to be able to deploy such technologies (e.g., bigger speech recognition model such as conversation bots) offline with a huge power savings (thanks to the configurable architecture of FPGAs). This allows us to integrate and explore the potential usage of edge devices (like DE-10 Nano) in autopilots. Moreover, the orthogonal RNNs are the choice of our project because they have the capacity to learn long-term dependencies like LSTMs but have a much lower parameter count.
What potential expertise do I have to complete this project successfully?
I (Ussama) have 4+ years of experience in working with efficient quantized training of deep learning models i.e., MLPs, CNNs, GANs, RNNs, etc., with a research internship at Xilinx on the same topic. Currently, I am an MS/Ph.D. student at KAUST working at the intersection of Deep Learning and embedded systems i.e., “Edge AI”. Have a detailed look at my LinkedIn (https://www.linkedin.com/in/ussamazahid96/) and Hackster.io profile (https://www.hackster.io/ussamazahid96) which also features my winning project i.e., “Quad96”.
Other: Internet of Things : Home/Industrial Automation and Security system on FPGA with hardware acceleration
AI based Smart Home Assistant and Security System
AP045 »
With the rapid emergence of IoT-based technologies in the recent era, the need for high-performance edge computing to handle data and run AI-based algorithms has increased. The objective of moving the computation from the cloud to the edge devices for compute-intensive tasks, provides increased reconfigurability, low latency, and scalability. In this project, we use Intel’s DE-10 Nano SoC FPGA, by leveraging the ARM Processor to run Linux Server, Docker containers, etc., required to host the IoT system for the premise, we offload the AI-based object detection task from the HPC to the FPGA to accelerate it. The system design uses the ARM Processor to run the Linux Server and connect to the home network hosting the Home-assistant framework, NodeRED, Grafana, and other services. On the other hand, every switch box in the house is equipped with a Wi-Fi-based Custom ESP8266 based Relay switches running Tasmota firmware which is connected to the same network. Similar edge deployments are made for getting temperature, water level in tanks, etc., All these data are communicated to the Home Assistant server running on the HPC. Using Azure Event hub integration we send the data to the Azure IoT Hub. The security system consists of CCTV Cameras which are connected to DVR and using ONVIF the stream is captured in our application for object detection and processing. We use the FPGA for Accelerating the YOLOv2 object detection algorithm. We process the results and in the event of an intruder/vehicle/motion detection, the necessary alarms/lightings/or any other actions like email/SMS/mobile alerts, etc. can be set up according to the end-user requirements. The use of FPGA in this kind of security system allows to train new data sets for uses cases like but not limited to automatic distinguished detection of house members to perform actions like opening the garage doors/preventing false-positive security alarms/ lighting based on motion detection etc., We believe that by using SoC FPGAs especially DE-10 Nano, along with Microsoft Azure IoT, we would be able to accomplish a reconfigurable, scalable and low-cost IoT setup that can be deployed in real-time.
Water Related
Smart Water Conservation System
AP046 »
The project is aimed to improve water distributor's capacity so everyone can use water even during dry and drought season.
Food Related
Smart and Safe Livestock Farm Monitoring System
AP047 »
The project aims to provide real time counting and monitoring of poultry or livestock which are freed to graze on the farm's grass. Using FPGAs parallel execution, the system will be able to automate the farm operations. With image processing, the farm will also identify and alarm for any predators coming inside the farm such as snakes, lizards or wolves. The system will connect to Microsoft Azure where it sends those head count for real time information to the owner. The system will also connect to cloud services to get the weather conditions and keep the farm animals safe.
Smart City
基于FPGA的智慧教室系统
PR027 »
随着无线通信、云计算技术、人工智能技术的不断发展和成熟，物联网被广泛应用于各个产业领域，建设面向智慧校园、智慧教室的物联网云平台在高校迅速发展起来。为此我们设计并制作了一款利用Intel DE10-nano现场可编程逻辑平台的智慧教室系统。本系统充分利用了Intel DE10-nano中FPGA与ARM的高速并行运算的优势，通过在Intel DE10-nano现场可编程逻辑平台上移植linux操作系统，在其中运用人工智能技术，将摄像头对课堂的实时监测画面进行AI算法处理。处理后将识别出当前课堂学生数量，每位学生信息，并通过识别学生动作，判断出学生是否举手，是否在睡觉，是否存在在玩手机等行为，最后将所识别信息通过intel FPGA Cloud Connectivity Kit即英特尔FPGA云连接套件上传到云端，帮助教师对学生进行云端监控，实现了教师维持课堂秩序的便捷管理功能。
Industrial
UAV Intelligent Recognition System Based on Deep Convolutional Neural Network
PR028 »
随着无人机地快速发展与普及，在带来便利的同时，也出现了越来越多的“黑飞”事件，给国家和公众都带来了安全隐患和经济损失。如何反制无人机非法入侵的问题越来越受到各国的重视，而快速精确识别无人机是实现反制的关键。与通过图像识别的方法识别无人机相比，通过无人机辐射的电磁信号进行无人机的识别具有更宽泛的应用范围和更高的灵敏度。无人机所辐射的信号中，无人机与操作手之间通信的飞控信号必然存在，因此识别无人机遥控信号谱图是一个有效的解决方案。民用无人机的飞控信号一般采用跳频信号进行通信。跳扩频信号属于非平稳信号，可通过短时傅里叶变换 STFT 方法，将天线、接收机采集到的多个时刻的信号变换成信号谱图。然后利用卷积神经网络（CNN）在图像识别方面的优势，实现精准识别。
本项目基于以上分析，考虑将信号多特征提取与识别的思想适用于工程实现的无人机检测与识别算法。采用基于神经网络的无人机遥控信号识别监测算法，通过联合自适应信号检测阈值计算改进现有的阈值计算方法，并通过预处理操作来对抗窄带与宽带干扰，最后利用神经网络识别是否存在及其机型。借助 FPGA 高速处理性能，对算法进行流水线设计，完成了一套具有实际工程意义的无人机检测与识别系统，该系统具有库内多架无人机检测及型号识别，以及库外无人机检测功能。
Water Related
Sustainable Aquaphonics
AP048 »
Singapore is a highly industrialized country which is one of the top countries with high population density. Trees and vegetation can be found in every block but is limited in every multi-storey residential building like HDBs and Condominiums. The projects aims to promote local hdb/condo residents to implement and improve their existing aquariums into a sustainable aquaponics which helps to reduce the carbon footprint and reduce the water usage.
Other: 智能农业
智能农业温室大棚监管系统研究
PR029 »
本设计基于物联网的三层架构：感知层、网络层、应用层，构建农业温室大棚监控管理系统，通过多种传感器实时感知农业温室大棚内部环境参数；通过WiFi将各个节点采集的数据上传到服务器；用户通过手机即可实时了解到大棚内部情況，并可进行远程设备控制。
Autonomous Vehicles
Enhancing Vehicular Safety Using Cloud Based IOT
AP051 »
In this project we propose to build smart vehicle monitoring and assistance systems using cloud computing in vehicular Ad Hoc networks. Increasing number of on road vehicles has become a major source of unintended accidents. Developing intelligent transportation system using cloud based connected vehicular networks can provide better estimate of time of arrival and localization matrix of vehicles in a given range of interest. We propose to build a safety enhancement feature to cater to accidents that occur due to random stoppage of vehicles and random opening of doors. Sensors will be used to detect and estimate events related to stoppage and door opening and create an event token. This event token will be correlated with the time of arrival and localization matrix of vehicles in a given radius of the vehicle from where the token was generated. Based upon this correlation a warning system and door enable disable system will be implemented to avoid collision. Additionally, temperature and humidity sensors will be deployed inside the vehicle to automatically or partially open the windows for maintaining proper air flow and ventilation.
Other: Agriculture
Pursuit Futurology in Smart Agriculture using FPGA
AP052 »
The proposed system is a system which will closely monitor the parameters of a field on a regular basis round the clock for cultivation of crops or specific plant species which could maximize their production over the whole crop growth season and to eliminate the difficulties involved in the system by reducing human intervention to the best possible extent.
We mainly have Two Blocks for this Project:
a. HPS (Hard Processor System):
Our system will take multi-inputs from multiple sensors such as humidity, light intensity, temperature and IR sensor and give those parameters as input to the HPS automatic training module (as initial values). For certain time the process of taking multiple input will be repeated over time. These will be an input to the HPS. Post processing the data will give as input to the FPGA part.
b. FPGA (Field Programmable Gate Array (Terasic DE10-Nano FPGA)):
By taking the input data in HPS as input to the FPGA part of the board, the system will pre-process the data. Then, it compares both the data and make the result. The result will be sent to the display as a message
Our main aim of the Digital Farming is to improve agricultural yield and reduce potential environmental risks, while benefits are:
1. Water level sensing.
2. Atmospheric temperature sensing.
3. Atmospheric humidity sensing.
4. Detection of pest in the field and automatically start the pest repeller if pest is detected using IR sensor.
5. Suggest best measures to be taken by the farmer.
1. Water Level Sensing:
An advanced water level sensing sensor will be fixed in the field for monitoring the water level. Based on the information given to the system, our system will alert the farmer along with the water level percentage through message.
2. Atmospheric Temperature sensing:
A temperature sensing device will be fixed at the field for checking the temperature of the present atmosphere. Our employed FPGA will decide whether the atmospheric temperature is sufficient for field growth or not and send the information through message.
3. Atmospheric Humidity sensing:
An atmospheric humidity sensing sensor will be placed at the field for checking the humidity present in the atmosphere. The output of the sensor will be given as input to FPGA for checking humidity is high or normal for crops growing. Our system will send this Atmospheric humidity level to the farmer through message.
4. Detection of pests such as rodents:
Here using IR sensors detects the entry and exit of unwanted pests such as rodents.
5. Diagnosis and Suggest some measure to be taken by farmer:
After detecting the sensor inputs from the field, our system will send the actions to be taken and suggest some precautionary measures.
This project is divided into four phases: -
Identifying the appropriate sensor for measuring temperature and relative humidity. Temperature sensor to be used is RTD so that low-cost aim can be successful with best stability.
Design of controller using FPGA, sensor interface card, isolation circuit for input and output, output relay card.
Identify if there any pests enter into the premisses/field and take the necessary action if so.
Development of a user interface and the controlling software.
Since a FPGA(Terasic DE10-Nano FPGA) is used as the heart of the system, it makes the set-up low-cost and effective nevertheless. As the system also employs an LCD display for continuously alerting the user about the condition inside the greenhouse, the entire set-up becomes user friendly.