Demo Data from SIGIR 2026 / reviewers in the wild / expert
Yash Manian
dblp:226/6166
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Robot navigation and mapping · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Wearable and physiological sensing · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping › mobile robot perception
aerial robot perception |
0.3 | 1 | 2018 | A Lightweight, Compliant, Contact-Resistance-Based Airflow Sensor for Quadcopter Ground Effect Sensing · ICRA 2018 |
Methods — techniques the papers use, named apart from their topics
wind tunnel characterization · 0.7elastomeric sensor design · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | A Lightweight, Compliant, Contact-Resistance-Based Airflow Sensor for Quadcopter Ground Effect SensingabstractSensors to measure quadcopter ground effect are often relatively large, heavy, and require significant power, which restricts their applicability when it comes to small quadcopters and other aircraft that require lightweight and non-obstructive solutions. This paper presents the design of an elastomeric contact-resistance-based airflow sensor to measure ground effect with a mass of approximately 0.04 g and a power draw of 42 μW when in operation. It uses a rigid flap attached to the top of a flexible conductive pillar (CNT/PDMS), which deflects with varying winds speeds. A simple model is presented to describe expected trends between air flow speeds and sensor deflection and is compared with a wind tunnel characterization of the sensor for varying airflows. The sensor is characterized in a wind tunnel to identify a minimum airflow necessary for sensor functionality. Finally, sensors are attached to a Crazyflie 2.0 (Bitcraze) quadcopter and tested for performance in detecting ground effect, where there is a clear trend between sensor output and the intensity of the turbulent flow, related to proximity to ground and thrust level. Samuel Dutra Gollob, Yash Manian, Ryan St. Pierre, Abraham Simpson Chen, Sarah Bergbreiter |
ICRA | 2 |