Demo Data from EDBT 2026 / reviewers in the wild / expert
Alexander C. Abad
dblp:223/0867
· DBLP profile ↗
2ranked-venue papers
1as first author
1since 2021 · last 2024
0000-0002-3966-4539ORCID · corroborated
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-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
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 manipulation · 67% Image recognition and object detection · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation › tactile sensing › vision-based tactile sensing
gelsight sensor |
0.4 | 1 | 2020 | Low-cost GelSight with UV Markings: Feature Extraction of Objects Using AlexNet and Optical Flow without 3D Image Reconstruction · ICRA 2020 |
Computer vision › Image recognition and object detection
object recognition |
0.4 | 1 | 2020 | Low-cost GelSight with UV Markings: Feature Extraction of Objects Using AlexNet and Optical Flow without 3D Image Reconstruction · ICRA 2020 |
Robotics › Robot manipulation
tactile sensing |
0.4 | 1 | 2020 | Low-cost GelSight with UV Markings: Feature Extraction of Objects Using AlexNet and Optical Flow without 3D Image Reconstruction · ICRA 2020 |
Methods — techniques the papers use, named apart from their topics
optical flow · 0.4convolutional neural network · 0.4alexnet · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Design and Development of IoT-Enabled Semi-Autonomous Medicine Dispensing Mobile Robot with Obstacle-Detection CapabilitiesabstractTo protect healthcare workers from contagious diseases, automated medicine dispensers offer a safe way to deliver medication to isolated patients, minimizing physical contact. This study introduces a self-navigating robot that dispenses medication (Biogesic, Advil, Fern-C) on schedule. It uses line-following to navigate and allows users to set schedules through a user-friendly app or an on-board LCD screen. The designed robot has effectively demonstrated its navigation capabilities using line-following, recognizing landmarks via RFID tags, and accurately detecting obstacles with timely notification. Maria Victoria T. Ching, Margaret J. Dionisio, Stephen Gabriel F. Garbo, Jann Carlo N. Red, Vince M. Salloman, Dino Dominic F. Ligutan, Alexander C. Abad |
TENCON | 7 |
| 2020 | Low-cost GelSight with UV Markings: Feature Extraction of Objects Using AlexNet and Optical Flow without 3D Image ReconstructionabstractGelSight sensor has been used to study microgeometry of objects since 2009 in tactile sensing applications. Elastomer, reflective coating, lighting, and camera were the main challenges of making a GelSight sensor within a short period. The recent addition of permanent markers to the GelSight was a new era in shear/slip studies. In our previous studies, we introduced Ultraviolet (UV) ink and UV LEDs as a new form of marker and lighting respectively. UV ink markers are invisible using ordinary LED but can be made visible using UV LED. Currently, recognition of objects or surface textures using GelSight sensor is done using fusion of camera-only images and GelSight captured images with permanent markings. Those images are fed to Convolutional Neural Networks (CNN) to classify objects. However, our novel approach in using low-cost GelSight sensor with UV markings, the 3D height map to 2D image conversion, and the additional non-Gelsight captured images for training the CNN can be eliminated. AlexNet and optical flow algorithm have been used for feature recognition of five coins without UV markings and shear/slip of the coin in GelSight with UV markings respectively. Our results on confusion matrix show that, on average coin recognition can reach 93.4% without UV markings using AlexNet. Therefore, our novel method of using GelSight with UV markings would be useful to recognize full/partial object, shear/slip, and force applied to the objects without any 3D image reconstruction. Alexander C. Abad, Anuradha Ranasinghe 0001 |
ICRA | 1 |