Touraj Farahmand
Chief Technology Officer (CTO)
I began with Aquatic Informatics at the beginning as a “co-founder” and quickly realized that I'd be able to make an impact in the water industry with my data management and analysis skills solving long standing problems and helping water scientists to protect our most precious natural resource. My background is centered on mathematical algorithms with extensive experience in digital signal processing, computer programming, instrumentation, automation and control for industry and medical applications. When I worked as a manager of the Signal Processing and Software Development at the Electrical and Computer Research Lab, I worked with the development of signal processing algorithms and interfacing software programs for data acquisition, data processing, and monitoring. And before that I was a Senior R&D Engineer at Vector12 where I was responsible for the development of a new design concept on signal processing algorithms for high-speed semiconductor characterization and production tests. Essentially I am the mathematical expert and adept at algorithms and passionate about improving water data management globally.
I attended 62nd OGC Technical Committee meeting in Boulder, Colorado with my colleague Stuart Hamilton in September. As most of you probably know, the Open Geospatial Consortium (OGC) is an international industry consortium of 438 companies, government agencies and universities participating in a consensus process to develop publicly available standards for geospatial data exchange protocol and web services. OGC standards are technical documents that detail interfaces or encodings and software developers use these documents to build these interfaces and encodings into their products and services. Aquatic Informatics is participating in the Hydrology OGC Domain Working Group (DWG or WG). This … Read More
Click here to read Part 1 – background about measurement accuracy and error, definitions and more This series of discussions are to first give you a scientific picture of hydrological measurement errors and then open the interesting discussion of how to automatically detect, validate and correct erroneous sensor data given the observations from Data Acquisition System (DAS) and field visit information. Let’s now have a closer look into the various types and sources of sensor errors. Figure 1 illustrates different types of errors and the concept of uncertainty. The true value of the parameter is shown as a straight line … Read More
Environmental agencies and organizations invest huge amount of money to build the required hardware and software infrastructure for collecting and storing data from field sensors in order to extract valuable information hidden in the time series numbers about the environment. If the sensor measurements could not accurately represent the environmental parameter of interest, the extracted information will be misleading and making decisions based upon false information could even be a risk to the human life. Mission Critical Hydrological Data and Accuracy in Models As an example, it is extremely important for mission critical hydrological data consumers such as flood forecasting … Read More
