Smart City
Energy efficient mixed-precision algorithm optimization for edge applications
AS024 »
Embedded intelligence applications must optimize the energy efficiency of their computational differentiation. IEEE-754 floating-point has been the workhorse for robust numerical computing, but is notoriously energy inefficient. Next-generation arithmetic solutions, such as posits and cfloats combined with user-defined rounding have demonstrated a 2-4x power benefit over IEEE-754. We have developed a mixed-precision algorithm design and optimization environment to deliver this technology to the marketplace in the form of custom applications and hardware accelerators. We'll use this platform for key SmartCity application such as collective intelligence and congestion optimization.
Smart City
Design
AP032 »
Cloud & azure
Smart City
FPGA BASED SMART TRAFFIC SYSTEM FOR SMART CITIES
AP033 »
Modern world having the issue to controlling the traffic at major cities for rapid increase in automobiles and also large time delays between traffic lights. So, in order to rectify this problem, we will go for different modes of traffic light control system. In this article, we have three modes i.e., Normal mode, Density mode, Emergency mode, IOT Mode.
Smart City
Coprocessor-based Antivirus in Order to Detect Malware Preventively
AS025 »
Distinct metamodels of Smart Cities have been developed in order to solve the problems of cities in relation to different socioeconomic indicators. In view of the foregoing, information security plays an important role in guaranteeing human rights in contemporary society. From an infection by malware (malicious + software), a person or institution can suffer irrecoverable losses. As for a person, bank passwords, social networks, intimate photos or videos can be shared across the world wide web, which will affect finances, dignity and mental health. On the other side, an institution may have its vital data inaccessible and/or information from its respective customers and employees stolen. In synthesis, the theft of intimate information can lead to cases of depression, suicide and other mental disorders.
The proposed work investigates 86 commercial antiviruses. About 17% of the antiviruses did not recognize the existence of the malicious samples analyzed. Commercial antiviruses, which, even with billionaire revenue, have low effectiveness and have been criticized by incident researchers for more than a decade. Commercial antiviruses performance is based on signatures when the suspect executable is compared to a blacklist made from previous reports (and this requires that there have already had victims). Blacklists are assumed to be effectively null by the current worldwide rate of creation of virtual pests, that is 8 (eight) new malwares per second. We concluded that malwares have the ability to deceive antiviruses and other cyber-surveillance mechanisms
In order to overcome the limitations of commercial antiviruses, this project creates a core processor-based antivirus able to identify the modus operandi of a malware application before it is even executed by the user. So, our goal is to propose an antivirus, endowed with artificial intelligence, able to identify malwares through models based on fast training and high-performance neural networks. Our core processor-based antivirus is equipped with an authorial Extreme Learning Machine.
Our processor achieves an average accuracy of 99.80% in the discrimination between benign and malware executables. Preliminary results indicate that the authorial Coprocessor, built on FPGA, can speed up the response time of the proposed antivirus by about 4765 times compared to the CPU implementation employing the same FPGA. Thus, the malicious intent of the malware is preemptively detected even when executed on a slow (low processing power) device. Our antivirus enables high performance, large capacity of parallelism, and simple, low-power architecture with low power consumption. We concluded that our solution assists the main requirements for the proper operation and confection of an antivirus in hardware.
Smart City
桥梁裂缝动态监测系统
PR019 »
桥梁作为当今重要的交通枢纽，与行车的安全与畅通息息相关，它的正常运行能极大地推动经济的发展，促进社会的进步。随着时间的推移，外力的反复碾压及雨雪、洪水、冰冻、地震等这些自然因素的影响，必然会严重影响桥梁的安全和寿命，导致脱层和混凝土开裂，甚至会引发桥梁坍塌，造成不可挽回的经济损失和人员伤亡。
因此，及时检测并修复桥梁的已有损伤，保证服役桥梁在工作时的健康安全的状态，已经成为广大桥梁工程界研究者亟需解决的热门问题之一。由于裂缝是混凝土桥梁病害中破坏较为严重、威胁较大的一种，所以必须对桥梁裂缝进行监测，及时发现并修补，控制裂缝的产生、扩展，将其控制在合理的允许范围内，从而避免桥梁倒塌事故。
本系统针对桥梁裂缝的动态监测系统进行研究，采集桥梁上裂缝的微距图像并实时计算裂缝的宽度，同时监控桥梁上的行车情况，采用多传感器融合的方法，分析桥梁在各种荷载作用下，裂缝的开展和恢复状态，将其控制在安全合理范围内，为桥梁的安全运行服务。其意义有三：1）监测裂缝是否发生变化；2）监测桥上车辆行车状态；3）分析荷载对裂缝的影响。
Smart City
Automatic recycling system for residential units
AP034 »
Our team introduces an automatic classification system for wastes.
If garbage is not classified, it can cause environmental problems, and if it is classified well, resources can be saved through recycling.
Our system can classify not only good-conditioned garbage (plastic, cans, bottles, etc.), but also bad-conditioned garbage.
Data Management
Image and Video Upscaling and Downscaling using FPGA
AP035 »
With time the rate at which we are producing data is increasing at a very tremendous rate. And it seems that this trend will continue with our advancements in technologies and user requirements. One of the big portions of the world’s overall data transmission and data storage is our Videos and Images. Security and surveillance, entertainment, streaming, and roughly every other industry use this kind of data for their applications and the demand for data is increasing than ever. But this increasing demand for data causes two major (global) issues: first, during transmission, they can take a lot of bandwidth of our network, and second that they tend to take a lot of storage space since we need a lot of data points to effectively utilize them for our needs.
Here, this project will be showing the downscaling and upscaling algorithms that are already used in many applications for quite a few years and implementing these algorithms on FPGA and cloud effectively to get the best possible results. Initially, data can be taken from a low-resolution input(or can be downscaled) and then transmitted/stored. When we have to use it, upscaling can be done. These algorithms show very promising results while using fewer resources as to if directly high-resolution input be used from the beginning.
This will give us more bandwidth and storage to work with for our applications, and as computation will be done on FPGA, it will be easily scalable. Edge computing like this will effectively increase productivity and will help in a sustainable advancement in technology. For the continuous advancement in technology and sustainable growth, we have to use our resources efficiently and intelligently and I hope that this project can play a small part in this.
Health
Field Programmable Health
AS026 »
FPGAs have the ability to enhance electromedical applications, including patient monitoring, ventilation, and other life sustainment applications. By using FPGAs and association analog connectivity devices, the provision of such electromedical care can be distributed and reduce the need for concentrated services and their associated transportation and other costs. This project seeks to apply FPGAs to these applications by exploiting integrated sensors and compute capability.
Health
FPGA based Covid-19 detection using Lung Ultrasound Image
AP037 »
With the onset of the Covid-19 pandemic, there has been a tremendous impact on the lives of people globally. The global tally of the no. of infected cases is 229,293,200 and the total death toll is 4,705,498 as of September 20, 2021, and this is just the no. of accounted cases. Apart from this, the covid-19 genome sequence is continuously mutating which resulted in the generation of different and more dangerous types of variants like the delta and delta plus to name a few. The globe witnessed the horrific scenes caused due to the shortage of resources in terms of healthcare during the unprecedented first and second waves. The leading scientists have already predicted the onset of the third wave which is expected to start in the last quarter of the year 2021.
To tackle the third wave, the governments have started the vaccination campaign for the people. But still, as per the earlier predictions the third wave is inevitable and the third world countries that are lagging in terms of medical infrastructure would be affected the most in the oncoming third wave. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is considered as the standard reference for the Covid-19 based testing but it has certain disadvantages like higher testing costs and longer processing time for the collected samples. Considering the factors like lack of essential infrastructure required for the testing and the capital required, we are hereby proposing a novel approach as a preparatory measure for detecting the presence of Covid-19 using Lung Ultrasound Imaging which in turn can be further deployed on a real-time basis with the help of Intel FPGA Cloud Connectivity Kit and Azure IoT Suite.
Compared to CT scans and X-Ray based detections, Ultrasound Imaging is low cost and radiation-free detection method. In this proposed approach, we would conduct a thorough literature review on the existing deep learning models which has been developed for predicting the covid-19. Based on the earlier approaches, a new model would be proposed which will be extensively trained with available datasets for addressing the limitations offered by the earlier models. Upon the satisfactory performance of the model in terms of accuracy, precision, and computation time, it would be deployed onto the FPGA Cloud Connectivity kit for real-time application. With the help of Azure IoT support, the Ultrasound Images from different centers can be obtained and the test results can be again diverted back to the respective centers at a significantly less amount of time as compared to the RT-PCR test.
This proposed approach will help set up a nationwide/worldwide low-cost testing facility with a significantly less amount of capital being invested. With this approach, we are intending to address the two major challenges prevailing in the present scenario with the first one being high testing costs and improved timing for generating the test results. This approach would in turn be helpful to combat the oncoming third wave of this Covid-19 global pandemic.
Food Related
smart farm system
EM012 »
In this project, we will create an autonomous smart farm system by measuring the climatic conditions such as humidity temperature CO2 emissions then we will control the farm equipment such as water tank temperature inside greenhouse and Fire suppression system etc.
The system will be also controlled by the farmers using a website and a mobile app, the farmer can change the temperature, switch on and off the drip irrigation system etc. The website and the mobile app will provide a real time overview of the system so we can check the temperature, soil humidity… at any moment. In case of a failure in any part in the system the farmer will be informed by the website and mobile and an alarm is triggered.
The farm is also provided with a security system so when intruders try to enter the farm an alarm will be triggered and a SOS message will be sent to the farmer and the police
The energy to the system will be provided by a solar tracker panel
Other: 智慧农业
智能盆栽系统设计
PR020 »
智能盆栽系统以FPGA作为硬件平台并辅以各类传感器实时监测盆栽的环境温度、湿度、土壤水分等参数，然后实时做出反应——自动进行浇水、通风等养护动作，以ESP-WROOM-02 WIFI 模块接入Azure IoT Central平台，让用户通过手机进行远程的检测和养护管理。本设计由监测端和控制系统两部分组成。监测端对周围的环境进行监测并采集数据，然后分析温湿度传感器、土壤湿度传感器返回的参数，并在LCD并行24位RGB接口的触摸屏上实时显示各个参数，监测系统实现了对植物生长的实时监测，可以随时查看植物的生长状况。控制系统具有土壤湿度过低时自动浇水的功能、温度过高时自动调节风扇转速的功能、温度过低时自动调节加热板温度的功能、光照不足时自动调节LED植物生长灯亮度的功能。最后，通过 ESP-WROOM-02 WIFI模块将数据上传至网络，接入物联网云平台中，在云平台上实现移动端的功能和界面设计，并在移动端上实现监测和控制的功能，达到远程控制的目的。智能盆栽系统可以视作智能农业大棚的雏形，可以尝试将应用领域扩展至农业以创造更大的价值。
Smart City
ITHS, Interactive Temperature and Humidity System
PR021 »
這個專案預計製造了一個新的HVAC系統，此系統可針對每個人的特定喜好及感受來調整輸出之設定，使同一場域內不同位置之使用者都能感受到舒適的溫度，並保持能源成本最小化。
這個專案使用DE10-Nano Cyclone V SoC FPGA Boar作為主控制器，利用Humidity and temperature sensor獲取環境之資訊，如溫度與濕度，而太陽輻射資訊經由太陽能板或Ambient Light Sensor接收，將類比電壓透過Analog Devices plug-in boards的DC1012A-A轉換為數位訊號輸入至控制板，除此之外，此專案也會藉由感測器讀取人體之體溫、心跳速率等資訊並將其透過藍芽或Wifi module輸入至控制板，最終將獲得之資訊利用Microsoft Azure IoT儲存於雲端或是進行大數據的分析與運算，在使用者感受的方面，本專案預計設計一操作簡單之APP介面，供使用者選擇其目前對於溫度的感受以及自身的喜好設定。