Smart City
smart home
PR015 »
智能家居控制系统，是以智能家居系统为平台，家居电器及家电设备为主要控制对象， 利用综合布线技术、网络通信技术、自动控制系统、音频技术等将家居生活有关的设施进行高效集成，提升家居智能、安全、便利、舒适，为我们的生活带来很的方便性。
Transportation
Renewable Energy Mover
EM011 »
Renewable energy Mover :- this project utilizes three Renewable Energy Source ( electricity, solar and wind energy ) to create a robust smart energy distribution system. The project focus on design of drone based vehicle which utilizes the three energy alternately to store energy which can be transported to any region in the world regardless of energy distribution barrier. It is of great excellent as it does not depend on a single energy source ,this made it to be in energy service regardless of environmental factor as a certain energy source (either wind, sun or electricity) will always be maximally efficient at a particular period of time in a season. This project is design to create a vehicle that is smart enough to use the maximal energy source at a given period , store the energy and as well as transporting them. the with use of IOT to self driving can be implemented, AI to make use of weather forecast to locate and trap where renewable energy is maximally present.
Data Management
Hydroponic Harvest Information Infrastructure
AS020 »
A controlled environment minimizes the weather crop dependency, consequently, the hydroponic greenhouses increase the harvest quality and water management. Despite the benefits, this kind of crops requires a better knowledge in physiology and vegetal nutrition to understand the nutritional balance in order to implement chemical corrections in short-term periods. In addition, it is possible compare similar crops in a distributed way, but this fact does not allow and effective work from the field engineer. If the field engineer has access to crop information and its environment, he can apply preventive and corrective protocols to reduce toxicity damage from an element or improve the plant features for best product obtaining. Researchers would contrast between their results and farmers crops to develop action protocols and enhance vegetal genes. The data handling is the main task in this proposal. To get better workflow, an infrastructure that allow share information for crop analysis while engineer arrive and act will be implemented. In one hand, the main station, composed by DE10-Nano and signal conditioners, brings and interface between the user and cloud services to upload crop data. In the other hand, the cloud services allow remote interaction between the engineer and the crop without presential stand.
The station would use the FPGA to control the data acquisition and flow while the HPS monitors and interfaces the data with cloud services. The signal conditioners reduce the acquisition challenges for the pH, conductivity, relative humidity, light intensity, etc. From the sensors, the cloud services allow the storage, interpretation and provides Machine Learning tools to improve the information meaning.
Food Related
Development of Intelligent Food Supply Chain Management System
AP028 »
This project proposal presents an Intelligent Food Supply Chain Management System for covering from field to end-user. This project is based principally on THREE (3) different application areas such as Food Plantation, Food Warehouse, and Food Transportation using a smart intelligent system. This system seeks to support and to reduce food waste by improving the reliability of the technology from manual detection to automated detection system designed especially for real-time monitoring using IoT implementation on growth and maturity level of fruits in the Food Plantation, classification of fruits grading in the Food Warehouse and also fruits controlling and tracking during Food Transportation operation. The system is implemented on a FPGA-SoC Intel Cyclone V SoC available on DE10-Nano Kit, Microsoft Azure, and Analogue Devices module to acquire the sensors reading and control the actuators to maintain the suitable environment in three different application areas. The proposed system has high-performance requirements covered by FPGA-SoC since it has concurrency and low power consumption, making it suitable for this intelligent food supply chain management system in smart agriculture applications.
Industrial
AI IOT Driven CNC Machine optimisation
AP029 »
Using an FPGA to improve the motion control of CNC machines under dynamic loading.
Smart City
智能安防中的异常行为检测
PR016 »
基于FPGA和云端计算的智能安防系统。
在数据端利用FPGA实时对摄像头采集到的图像数据预处理，然后将处理后的信息传入云端，利用云端训练好的AI模型中进行异常行为判断，然后将判断结果返回到FPGA数据端进行警示，报警等相关操作。
Smart City
基于FPGA的视觉SLAM实现及部署
PR017 »
采用FPGA计算SLAM算法中计算量最大耗时最多的图像特征点提取模块，对图像特征点的提取进行加速，然后将计算后的图像数据传入视觉SLAM算法中，使其具有更好的实时性和更低的能耗
Food Related
鲜茶叶分级系统设计
PR018 »
针对目前茶叶产业中的质量筛选问题，为了提高实际生产中的工作效率和准确率等，通过图像处理技术结合FPGA来设计一个能实现自动进行茶叶分级分拣的系统。
Food Related
Dregs Reloaded
AS021 »
Good food is the foundation of genuine happiness. But once discarded, its also the source of 8-10% of greenhouse gas emissions of our earth. According to the latest UNEP (United Nations Environment Program ) food waste index report, the true scale of food waste and the opportunities related to it, is largely untapped and under-exploited.
In US restaurants alone, about 33 billion pounds of food is wasted each year. If these discarded food is collected in an inhouse reservoir, it could be reused to power up via biogas and the produce manure to grow in-house vegetables.
The Dregs Reloaded project involves setting up two external reservoirs/tanks outside a restaurant/home/community
which is used to collect organic food waste. The tank includes a pulverizing and churning motor which is activated at regular intervals to accelerate the decomposing process. It also monitors and collects data related to temperature, biogas, food level etc.,
which could be analyzed making use of cloud storage and computing. The biogas generated from the tank could be used for cooking or heating. The decomposed food waste can be collected and used as fertilizer. With the help of dual reservoirs, when one gets filled and being decomposed, the other one
can be opened up/enabled for the next collection of food waste.
Most people favor steps to reduce climate impact related to human behavior. Projects like Dregs Reloaded provide a way for every human being to be part of the problem solving method for a sustainable green earth.
Other: General Processor
General Purpose Neural Processing Unit
AP031 »
[The General Purpose Neural Processor Unit proposed in this project aims to simulate and cluster an SNN-based neural network that can solve at least one problem.]
There is a saying, butterfly effect. This means that the small wings of butterflies drive a big typhoon. Likewise, something happens under the influence of many things. For example, solar power generation is affected by wind, cloud, temperature, etc., and farming is affected by solar radiation, precipitation, and temperature. To solve these nonlinear problems, methods such as machine learning have emerged these days.
Machine learning is an algorithm that uses neural networks to analyze data and make decisions based on learned information. Since various types of data can be used as data for learning, it is suitable and widely used to solve nonlinear problems. However, it has only recently begun to be used because it requires a lot of computing power. And even now, big models take a lot of time.
As a way to solve this problem, a neural network model was designed and uploaded to the FPGA for use. But one neural network model had the disadvantage of being able to solve only one problem. To this end, accelerators such as NPUs equipped with many modules that perform repeated operations (mainly convolution operations) also came out.
These NPUs were created with neural networks such as CNN and RNN, which are second-generation neural networks. However, research on SNN, a third-generation neural network, is active these days, and NPU using it is being designed. A typical example is IBM's Truenorth. SNN is a neural network that mimics real neurons and has several advantages in terms of power and learning. In addition, SNN is completely bio-plausible, so it is an essential route in the future to implement artificial intelligence.
Several neurons gather to create a neural network system, and the system gathers to form a neural network network. As things happen under the influence of many things, data on many things are important. Each task is proposed as an idea to break away from not the basic way provided to the neural network as a variable and but solve it through network connection and cluster.
Smart City
Energy Waste Monitoring In The Home
AS022 »
Historical, realtime, and predictive measurement of the energy usage and waste within a home given acceptable ranges of temperature, lighting, etc. and correlating with the presence of, movement of, and absence of people and their respective needs for the environmental elements that draws power, uses resources, etc. From this, can determine the level of energy/resource efficiency of a given household. Can then be expanded to community, city, county, etc. scales.
Other: AI
Jaguar
AS023 »
"Softbank expects ARM to deliver 1 trillion IoT chips in the next 20 years." (Reuters, 2017)
Since AI is considered as an emerging technology by many companies and research institutes in the world because of its performance and accuracy, the number of AI chips and relevant systems are increased exponentially as Softbank already expected in 2017. With this kind of technical evaluation, it is expected that AI systems will help people to make their life richer than in the past because of its versatile functions and better performance than human-being.
However, people are also faced with side effects of AI systems' exponential increment because recently many researchers found that AI inference and training sequences can generate numerous CO2s. For example, training a single AI model can generate 626,000 pounds of carbon dioxide from relevant operations. This amount is five times bigger than a car. (MIT Technology Review, 2019)
For that reason, TinyML with lower-power and high-performance features is emerging as a replacement of ultra-scale AI systems.
To contribute to maintaining sustainability for the next generation, we will focus on designing low-power and high-performance TinyML accelerator with minimal hardware resources and energy consumption. To realize this goal, we will use Intel FPGA-based IoT device kits, self-developed hardware architecture and optimized software stacks for covering AI full stacks and contributing to CO2 reduction with low-power features for global sustainability.