The “Laboratory for Quantum Cognitive Imaging and Neuromorphic Engineering (CINE), Bioinspired Vision Research”, founded by Professor Giakos, is a state-of-the art, multifunctional laboratory; located in the heart of NYC, it is fully dedicated to the education, training and research advancement of our students and the NYC community. By integrating physics, engineering, and bioinspired distributed architectures, it is aimed to enhance cognitive vision using spiking networks and machine learning. During the last fifteen years several discoveries came out of this lab with emphasis on the detection, characterization, and discrimination of unresolved resident space objects (RSO). With the support of the Airforce Research Laboratories (AFRL), through research contracts and awards, it has been possible to design a fully automated multifunctional polarimetric platform, consisting of a suite of multispectral polarimetric sensors and innovative control and image processing algorithms, including artificial intelligence and machine learning; for surveillance, imaging, material characterization and discrimination of space objects.

The presentation of professor Giakos is articulated into two parts:

Firstly, the research team of Prof. Giakos pioneered the design of single-pixel linear mode avalanche photodiodes architectures, operating under polarimetric principles, for space objects detection and identification. These linear avalanche photodetectors (AP) operate at a bias slightly below breakdown, providing linear amplification with negligible after pulsing. In addition, they exhibit high dynamic range, high speed, and high responsivity at the infrared (IR). Combining these sensors with polarimetric single-pixel detection allows one to focus on a very few pixels of the object, obtaining information, with a high scatter rejection, decoupled from any interfering signals (noise) that may arise from the adjacent pixels of the target. Therefore, it can be a very effective method to detect polarimetric signatures from cluttered or unresolved targets, with high sensitivity and high background rejection. Single-pixel detection can keep spatial-frequency variation within a single pixel; while, polarization states of light offer unique advantages for a wide range of detection and classification problems, due to intrinsic potential of high-contrast and high dynamic range. (2007-present).

Secondly, Giakos and coworkers introduced novel and efficient bioinspired vision architectures, operating on polarimetric neuromorphic detection principles, in conjunction with efficient deep learning architectures, namely, the polarimetric Dynamic Vision Sensor p(DVS); integrating human cognition capabilities, such as computation and memory emulating neurons and synapses, together with polarization of light principles. The p(DVS) would ultimately revolutionize and give rise to the next-generation highly efficient augmented intelligence vision systems with potential applications in space research. The experimental results clearly indicate that both high computational efficiency and classification accuracy can be achieved on detecting the shape, texture, and motion patterns of resident space objects (RSO) and space debris; while operating at low bandwidth, low memory, and low-storage (2016-present).