The automated traffic control system is designed to regulate the flow of vehicles in the city. Tasks solved by such an automated system: 1. Detection of emergencies; 2. Measurement of cargo weight; 3. Determining the speed of the car; 4. Determining the level of air pollution; 5. Detection of traffic jams; 6. Adjusting the green light time for the traffic light.
The project will be about creating an Autonomous Agriculture Assitant using IoT.
Designe special version of RISC-V MCU with three cores and special supervisor logic. The cores will work in parallel and independent with same as programm. The special logic part controls result of calculations and outputs for detect broken core/result. The best result is do working device with broken some parts by any reason and restore working all cores if possible.
With climate change and water scarcity, arid countries' policies aim to conserve dams’ water for domestic and industrial use only. Due to a lack of budget, having no other alternative, Small-farms crop production turns to the use of innovative low-cost solutions for irrigation and livestock. The proposed project outlines a new approach supporting agricultural agencies and policies at all levels, livestock professionals, smallholding farmers, and local populations, to stabilize the ecologically unsustainable exploitation of the water on dry-lands. The proposed approaches aim to implement Edge Artificial Intelligence on Intel FPGA and Microsoft Azure cloud computing for the prediction of water quality and its evolution, manage an innovative irrigation process, livestock watering points, as well as artisan activities (pottery...).
We try to build a FPGA based solution to stimulate retinal tissue phase dependently of the overlaying local field potentials. By this we want to increase the stimulation efficiency and thus decrease the power consumption of the device.
Prototype of FPGA-based automatic irrigation system for soft fruit farms in Perthshire, Scotland, UK
This project is about building a Smart City Lightning System: Specifically streetlights for this particular project. Operating street lights are very necessary for security and safety in our communities and very costly as well. Sustainability when incorporated would improve efficiencies of the system in relation to its key objectives (security and safety) whilst cutting down cost as well. We focus on improving the efficiencies of streetlights for security and safety of our roads and cities in a smart approach. Explained below are some aspects of this project.
City cameras are almost found everywhere in our cities. The quality of these cameras is useless without proper street lighting. To do this, we include analog light sensors to read the brightness of the environment and allow the street light to give a certain brightness to improve illumination. The duration of day and night are also incorporated as well to deliver the right amount of lighting.
A sound reception device with trained machine learning models for different kinds of sound that depict a car crash, gun shot(s), intense noise (from human or not) indicating a variety of responses from a bad event would be incoporated as well. This concept would be applied, especially in areas where city cameras are around. Should in case a gunshot is heard, within a considerable radius in the surrounding, streetlights would be brightened to allow the city cameras have a better recording of the incident with and see how the shotter moves around when spotted in the city camera: This would be applied to other scenarios as well. In so doing, security agencies are alerted and this system would help them track the armed robbers or unwanted events as well (could be an accident, riot, etc).
All of the streetlights would be connected and inditcated on app with a map. This would help to indicate various conditions or states of the streetlights such as those that are down in real-time and need repairs, amongst others.
With regards to the above, the cost of operating streetlights would be controlled sustainably whilst improving efficiencies of streetlights for security and safety on our roads and cities.
An FPGA is ideal for this case since it is very fast and embraces parallel computing.
Our project is a smart system to track traffic in city streets to track traffic densities, and it can also detect driving violations.
The Mobile Precision Agriculture Platform is a battery-powered, lightweight 4x4 vehichle, used for probing the soil on the field and for applying mixtures of fertilizer and pH regulators, precisely synthesized in real-time, based on the measured parameters and the requirements submitted to the cloud platform. A camera is used to identify objects in the field and record images for recognizing pest.
A probe mounted at the front of the vehicle tests the soil, before the vehicle crosses over the measured ground to apply the required substances. In addition, it can be equipped with pesticide sprayers. The system is autonomous, using GPS navigation and following way-points uploaded to Azure. Spatial and temporal sensory data, together with device telemetry (temperature, battery status, etc.) are transmitted in real-time to Azure for analysis.
GIS is used visualizing spatial sensor data and perhaps also for navigation. The accelerometer provides stability control and ultrasonic sensors are used for collision detection.
The HPS is mainly used for connectivity, vision and GPS, while the FPGA is used for sensor data acquisition, vehichle control and fertilizer mixing.
The Azure Cloud architecture makes use of Azure IoT, Cosmos DB, Azure Functions and if time permits, machine learning algorithms to predict crop quality based on the parameters measured repeatedly, crop type, weather data and geographical location.
It will not be easy to quantify the crop yield results empirically, without a full season with an already working device - though research material in precision agriculture can be used for these purposes. It will however be straight-forward to quantify the equipment, labor and energy cost, as well as the environmental impact, compared with other methodologies used in (precision) agriculture.
The design will make full use of FPGA I/O and parallelism, while also outlining the solid advantages provided by the HPS and the convenience of utilizing Microsoft Azure.
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 also 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 we as transporting them. the with use of IOT to self driving will be implemented, AI to make use of weather forecast to locate and trap where renewable energy is maximally present.
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
This project recommends Internet of Things based sensors network for cultivation use. This Sensor System Consist of sensor to detect wateriness of sand, sensor to detect temperature of soil, and ph. sensor for soil. This all sensor connected to each other FPGA kit that will be communicate with Azure cloud . After uploaded to Azure IoT, The agriculturist can access all the
data on his mobile phone and tablet. This system also
controls water necessity and fertilizer necessity for these
sensor data for different type of crop in different time of
year. The saved data of sensors can be further inspected and
used for future uses. This IoT based methodology provides
atomized irrigation and fertilizer usage in actual time to
farmer which is very useful.