Our innovation management has key strategic forces:
– We prioritize and nurture a culture of proactive innovation in which all our collaborators are involved.
– We innovate focusing on the challenges faced by our clients.
– We believe in open innovation, and therefore we innovate with the entire national and international innovation ecosystem and creatively incorporate the latest technologies and expertise in order to provide solutions to our customers.
– We generate innovation alliances with national & international partners in order to enhance and complement our capabilities.
At CSI, we are committed to a proactive innovation culture: Our Innovation Space within the company is a place for enhancing the creative exchange of innovative ideas and pilot implementations of the selected ones, with a customer-centric approach coupled with a clear understanding of world technological trends. Since its inception in 2015, key innovation tracks were developed with a broad participation of CSI-CIEMSA employees, representatives of the National Research and Innovation Agency (ANII), colleagues from technological centers, universities, and professionals from various fields.
IoT, Predictive Analysis, Big Data applied to the development of “Smart Cities”, “Water and Wastewater” “Industry” and “Energy” are currently the main topics covered in diverse activities, from idea generation to real world pilot implementations.
Pedro Mastrángelo (Head of Innovation at CSI-CIEMSA) and Mónica Almansa (Innovation Management at CSI-CIEMSA) are the people in charge of organize, curate and program the Space activities.
We investigate and analyze the technological macro trends and the global state of the art of selected disciplines for an early identification of opportunities seeking to add value to our products/services.
We are founding partners of the Technology Center of Information and Communication Technologies for Vertical Sectors (ICT4V) along with 15 other private companies, public institutions and universities. ICT4V is the first Technology Center supported by ANII, with strong links with diverse innovation centers in LATAM, USA and Europe.
Along with the National Agency of Innovation (ANII) we conducted diverse ground breaking activities: the first contest of innovative ideas for large private companies in Uruguay, the launching of the Innovation Space, inspirational talks and scientific conferences led by key experts, round tables and conferences in which our allies, clients, employees have the opportunity to speak and collaborate.
We actively collaborate in discover insights about innovation as a vector of competitiveness and development for our country/region. For example we co-chaired a conference “Technological research and innovation for development of the country”, part of the events celebrating the 100th anniversary of the Faculty of Engineering, UdelaR.
This project developed an innovative service for automatic counting of young and mature eucalyptus and pine trees using UAV NIR images.
An application with a client dashboard was developed. It consists of the selection, sequence and automatic application of the most suitable set of features and mathematical algorithms depending on the species, age, characteristics of forested land, topography, etc.
This method was verified against traditional field counting data, with accuracy results higher than 95%.
The increasing penetration of renewable energy sources (wind, solar) in the world and especially in Uruguay (2nd in the world) increased the demand for energy storage technologies to optimize the energy mix.
This CSI- ORT University project, incubated in ICT4V, is aimed to develop a software that optimizes design, operation and stability of energy storage facilities by convex and dynamic mathematical algorithms.
The project also will allow the evaluation of their economic impact, in different electricity market scenarios (spot market, others) and renewable energy production.
This project compares the levels of pedestrian particulate matter (PM) exposure before and after implementation of the CGM (Traffic Management Center of Montevideo), a recent and major innovation project that is improving the safety while reducing the traffic congestion in Montevideo.
Feasibility studies for scaling the project to large areas of Montevideo and other cities were conducted. The opportunity is to use massive low cost PM sensors, native to Internet of Things, in order to have an accurate data map coupled with air dynamics modelling.
This pioneer project in Uruguay is aimed to mitigate the impact of urban flooding due to overload of drainage and sanitation networks by generating predictive flood models that trigger a flooding alarm via VMS, apps, SMS, etc.
The following real time data is collected and integrated to different modules of the the smart city big data platform : Sea level, Level and flow sensors in wastewater networks, native IoT raingauges, key operation levels from O&M Montevideo sewage system (SCADA).
In this project an accurate UV Radiometer for the measurement of UVA and UVB irradiance was installed at an strategic spot, integrating the collated data to the Smart City platform, doing some experimental data analysis and correlations with other relevant data already in place, and finally execute a data push of relevant UV info to diverse VMS (Variable Message Signs) and apps in order to inform the citizen the actual and accurate UV levels.
This project is funded by the National Innovation Agency of Uruguay
This project demonstrated that automatic detection of soybean plants suffering from stress induced by the presence of a destructive bug (some caterpillar species) is possible, by means of hyperspectral measurements. Another interesting conclusion is the fact that, the stress induced by caterpillar is a property of the entire plant, and not just of each defoliated leave.
Hyperspectral signature of healthy and stressed plants due to caterpillar defoliation was studied. Dip ratio, maximal derivative in 780–880nm and NDVI (Normalized Difference Vegetation Index) were the signature features chosen. An automatic classification (support vector machine with margin) of healthy and stressed plants due to caterpillar defoliation was implemented with more than 95% of classification success.
By using multispectral images –collected by UAVs- to calculate NDVI it was possible to differentiate the isolated field samples containing stressed plants due to caterpillar defoliation from the samples containing healthy plants.
With the relevant bands of the extracted features integrated into multispectral cameras, a cost-effective solution is possible.
The results of this project were presented and published in SPIE 2015, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII, Ámsterdam