Food Related
TerraMon: Climate Change and Soil Health monitoring
AS014 »
Research has shown that climate change impacts soil PH but long term studies have been challenging to conduct at large scales. Terra-Mon hopes to leverage power efficient edge computing, analog device sensors and the cloud to create a solution that can be deployed in remote regions and relay data back via satellite (SWARM), long range low bandwidth (LORAWAN) terrestrial links as well as faster urban links (WIFI or Cellular) to monitor soil quality. Terra-Mon measures soil PH, moisture and temperature. Once the data is measured, the data is preprocessed at the edge to make sure the data stream is as compact as possible.
A neutral PH of 5.5 to 7.5 is ideal for growing food. Food security over time will be impacted by climate change as USDA zones change and the need to amend soils changes from our standard operational model. Researchers need to start gathering a healthy time series dataset today to create appropriate models for changes in soil health. For Farmers & Hobbyists can use Terra-Mon to monitor real time properties and see the how various soil products impact soil health and yields.
InnovateFPGA allows us to leverage the combination of Intel’s edge compute capabilities, Analog Devices daughter boards and the power of Azure’s IoT stack to create a solution that can be used to gather data that will allow for a comprehensive understanding of soil health and ultimately understand how to improve soil quality for improved harvests and food security.
Food Related
Dregs Reloaded
AS015 »
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 Environmnet Programme)
food waste index report, the true scale of food waste and the opprotunities related to it 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 an external reservoir/tank outside a restaurant or home
which is used to collect food waste. The tank includes a pulverizing and churning motor which is activated at regular intervals to accelerate the
decomosping process. It also monitors 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 restaurant cooking. The decomposed food
waste can be collected and used as fertilizer. Temperature,light & gas sensors can be monitored and analyzed , so that the entire system/feedback controls can be used efficiently.
Marine Related
Waterway cleanup
AS017 »
Using AI to recognize plastic or other waste present in waterways. The location will be recorded and information on the identified waste will be transmitted to a clean up crew.
Food Related
ManGO!
AS019 »
IOT system for mango preservation using Microsoft Azure and FPGAs.
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
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.
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.
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
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.
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.
Smart City
Smart City Traffic Analyzer
AS027 »
A small form factor device for analyzing vehicle and pedestrian traffic using a computer vision workload running on the DE-10 Nano with local sensor data provided by the Analog Device plug-in board. The computer vision workload will be delivered using Azure IoT Edge and allow for extrapolating analytics to the cloud.