Facilities

Our state-of-art computational and imaging tools have been customized to automatically characterize behavioral patterns of organisms using real-time videos, pattern recognition algorithms, and machine learning algorithms. Our lab has the necessary hardware equipment to test the performance of electronic sensors and video cameras, including power supplies, frequency generators, oscilloscopes, current/voltage meters, breadboards, soldering iron, and low-power analysis tools. This lab facility has been valuable for proof-testing, quick validation, and reliability testing for field applications. The lab also conducts projects in remote sensing and data processing. Our computational and imaging tools have provided training opportunities to many undergraduate and graduate students to build new customized software tools using real-time videos, pattern recognition algorithms, and machine/deep learning algorithms.

Our laboratory has the following microscopes with real-time imaging cameras: Leica 205 FA stereo-zoom microscope, Leica MZ16 stereo-zoom microscope with motorized stage and the option of horizontal or vertical imaging, and two benchtop student microscopes. The microscopes are equipped with GFP filters for fluorescence imaging, QImaging digital cameras, and QCapture software for acquiring real-time videos at high speeds (~30 frames per second). The microscopes routinely help us image the sensors, cameras, and microphones for testing and characterization purposes. We also have a 3D printer in our laboratory for printing custom parts. The ECpE department has machining tools for cutting, drilling, resizing, and assembling materials such as wood, plexiglass, and metal. For more sophisticated machining needs, we have access to a machine shop and a glass shop at Gilman Hall within the ISU campus. These tools can be used to customize the form factor of sensors, instruments, and mounting structures for various projects.

We have built computing facilities, resources, and skills to handle the big data from the sensors, video cameras, and microphones. We team up with students, collaborators, contributors, and end-users who inquire about their data quality and information content, primarily in the areas of agriculture, veterinary medicine, bioengineering, and microbiology. Examples of these large datasets have included pre-recorded images, videos, memory space requirements, and numbers related to livestock numbers, feed costs, plant health, farm economics etc.  Our students are proficient in programming languages (such as MatLab, Python, Java, C++, Visual Studio) and have developed software and smartphone apps to automatically process and analyze large data sets. This lab facility has three workstations for processing large data streams. We have two Nvidia GeForce GPUs for testing and running video analytics tools, machine learning models, and multi-level neural networks. We have large storage capacity within the lab and university, which allows us to sync and share files securely over the cloud. Additionally, we have expertise to use software tools to record and produce high-quality videos and podcasts using various open source software tools.