Demo Data from SIGIR 2026 / reviewers in the wild / expert
Neil L. Harper
dblp:92/194
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1997
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author
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% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping
landmark detection |
0.0 | 1 | 1997 | Recognition of plants with CTFM ultrasonic range data using a neural network · ICRA 1997 |
Robotics › Robot navigation and mapping › acoustic sensing
ultrasonic sensing |
0.0 | 1 | 1997 | Recognition of plants with CTFM ultrasonic range data using a neural network · ICRA 1997 |
Methods — techniques the papers use, named apart from their topics
neural network · 0.0CTFM ultrasonic ranging · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1997 | Recognition of plants with CTFM ultrasonic range data using a neural networkabstractThe success of any mobile robot is dependant on its ability to perceive the environment in which it is working. This paper describes some work to verify the suitability of a new sensor as a landmark detector for a mobile robot. The landmarks that we are investigating are plants. An ultrasonic sensor continually transmits a frequency modulated signal. Echoes detected by a second transducer are demodulated with the transmitted signal to produce audio tones which are proportional to range. The spectrum of these tones is determined by the geometry of the object. An artificial neural network (ANN) is used to recognise plants. This paper discusses a series of experiments that prove that the machine perception system is independent of the range, size, and the orientation of the plant. This system has potential applications in industrial and office robots. Neil L. Harper, Phillip J. McKerrow |
ICRA | 1 |