CS3 Monthly Research Exchange

At each CS3 Monthly Research Exchange, three faculty and student researchers in the field of smart urban planning will take the stage to share their latest findings, breakthroughs, and urban projects.

Ariana Galindo, Undergraduate Student at Florida Atlantic University

• Presentation Abstract: This project investigates the development of scalable data processing tools for large-scale spatiotemporal data.

Dimitris A. Pados, Professor and I-SENSE Fellow at Florida Atlantic University

• Presentation Abstract: We examine the problem of dynamically optimizing arbitrary multiple-input multiple-output (MIMO) wireless waveforms in potentially heavily utilized frequency bands with applications to near-field (or far-field) autonomous machine-to-machine communications. We look at the problem from the point of view of spectrum sharing and autonomous interference avoidance. In this context, we seek the transmitter beam weight vector and the pulse code sequence that maximize the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum SINR joint space-time receiver filter. We derive two novel model-based solutions: (a) Disjoint, space first (transmit weight vector) then time (pulse code sequence) waveform optimization and (b) jointly optimal transmit weight vector and pulse code sequence optimization (a mixed integer programming problem.) The proposed formally derived algorithmic solutions are studied in extensive simulations under varying waveform code length, near-field/far-field and spread-spectrum/non-spread-spectrum interference, in light and dense interference scenarios. The findings highlight (a) the effectiveness of the described methods compared to static conventionally designed optimal-receiver MIMO links and (b) the remarkable ability of the joint space-time optimized waveforms to avoid heavy interference.

Navid Salami Pargoo, Ph.D. Student at Rutgers University

• Presentation Abstract: In this talk, join me on a journey into transformative assistive technologies for the visually impaired, crafting a synergy between interactive AI agents and the smart streetscape. Discover our innovative strides in machine learning, deep learning, computer vision, and sensing, forming the heartbeat of this human-centric AI agent. I will show how they harmoniously integrate into streetscapes, enhancing safety, inclusivity, and enriched urban interactions.

The CS3 Monthly Research Exchanges are internal and open only to CS3 affiliated students, faculty, and staff. If you are interested in learning more about the research happening at CS3, please contact our team at streetscapes@columbia.edu.