Demo Data from VLDB 2026 / reviewers in the wild / expert
A. Ravichandran
dblp:50/5642
· DBLP profile ↗
6ranked-venue papers
4as first author
0since 2021 · last 1995
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 2 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-authorArtificial intelligence and machine learning · 1 · 1 first-authorSystems, 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.
| Databases, data mining, and information retrieval
1 paper |
Data models and query languages · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Hardware reliability and fault tolerance · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Hardware reliability and fault tolerance
fault detection and correction |
0.0 | 1 | 1990 | Synthesizing Robust Data STructures - An Introduction · IEEE Trans. Computers 1990 |
Methods — techniques the papers use, named apart from their topics
first-order logic · 0.0axiom-based fault detection · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1995 | Studies on object recognition from degraded images using neural networks
A. Ravichandran, Bayya Yegnanarayana |
Neural Networks | 1 |
| 1992 | A general approach to the identification of compensated faults in robust data structures
A. Ravichandran, Krishna Kant 0001 |
Inf. Sci. | 1 |
| 1991 | A two-stage neural network for translation, rotation and size-invariant visual pattern recognitionabstractA two-stage neural network is described for transformation-invariant visual pattern recognition. In the first stage, features are extracted after normalizing the image. It is shown how parameters of spatial transformation can be estimated even in the presence of noise by using knowledge about rigid objects. Circular arcs in the normalized image are used as generalized features to describe the input pattern. Each image pixel contributes to the features which it can constitute. Contributions from noisy pixels are distributed over the feature space, whereas meaningful parts contribute to clusters that correspond to features of the image. In the second stage, the image is classified on the basis of these features by a multilayer perceptron network trained using a backpropagation algorithm.> A. Ravichandran, Bayya Yegnanarayana |
ICASSP | 1 |
| 1990 | An algorithm for thinning noisy imagesabstractA two-step algorithm is presented for thinning noisy images. The first step consists of removing noisy spurs from an image using a combination of morphological operations. In the second step, thinning is performed by using the standard morphological skeleton transform (MST). This approach is useful in mode-based vision applications where the features of the image are required for object recognition. The use of this algorithm in sensor array imaging situations is demonstrated.> Bayya Yegnanarayana, R. Ramaseshan, A. Ravichandran |
ICASSP | 3 |
| 1990 | Synthesizing Robust Data STructures - An IntroductionabstractA formal approach is presented for the analysis and synthesis of robust data structures. The entire data structure is viewed as a collection of data elements related via some attributes. The relationships are specified by a set of axioms in first-order logic. Faults in attributes invalidate some of the axioms. The invalidated axioms are used to detect and correct the faulty attributes. The authors show how detection and correction can be localized to small portions of the data structure, thereby allowing concurrent repair in several disjoint portions. This property makes local correction attractive for B-trees and other structures used in databases. They then show how the ideas developed for attaining structural integrity can be applied to achieve data integrity as well.> Krishna Kant 0001, A. Ravichandran |
IEEE Trans. Computers | 2 |
| 1986 | Correctness proof for the majority consensus algorithm
A. Ravichandran, R. K. Shyamasundar |
Inf. Sci. | 1 |