A5073967707- Data Stream Mining Techniques
- Auction Theory and Applications
- Mathematical Analysis and Transform Methods
- Machine Learning and Algorithms
- Complex Network Analysis Techniques
- Advanced Bandit Algorithms Research
- Consumer Market Behavior and Pricing
- Anomaly Detection Techniques and Applications
- Distributed and Parallel Computing Systems
- Mathematical functions and polynomials
- Cognitive Radio Networks and Spectrum Sensing
- Fractional Differential Equations Solutions
- Cooperative Communication and Network Coding
- Probability and Risk Models
- Advanced Multi-Objective Optimization Algorithms
- Web Applications and Data Management
- Underwater Acoustics Research
- Metaheuristic Optimization Algorithms Research
- Educational Technology and Assessment
- Energy Efficient Wireless Sensor Networks
- Wireless Communication Networks Research
- Cloud Computing and Resource Management
- Scheduling and Timetabling Solutions
- Evolutionary Algorithms and Applications
- Game Theory and Voting Systems
Indian Institute of Technology Bombay
2020-2022
National Institute of Technology Kurukshetra
2016
Govind Ballabh Pant University of Agriculture and Technology
2014
Dhirubhai Ambani Institute of Information and Communication Technology
2007
The minimum spanning tree is a classical problem in distributed system environment. We extend this challenge Cognitive Radio Networks (CRN). In CRN, the spectrum mobility and node creates connectivity during neighbour discovery. Thus, finding edges (or relation graph) between SU nodes order to create communication graph for Minimum Spanning Tree (MST) cognitive radio network. present work, we propose solution of creating It message passing based algorithm. MST algorithm find shortest path...
Wireless sensor networks is an emerging field which has tremendous contributions in the area of scheduling. We have selected 8 algorithms on distributed scheduling networks. A summary all given paper with a comparative discussion and classification them. person who new to can start then go into details by referring cited papers. variety exist for are fairly comparable. One find out algorithm suitable his/her application basis requirements.
Time domain matched filtering is a classic method used in radar and sonar applications to maximize signal noise ratio (SNR) gain, estimate time delay, improve range resolution. Fractional Fourier transform, fractional are extensively overcome the drawbacks of shown have improved performance for linear chirp. This paper presents generalized (GFMF) estimating higher order chirp parameters with known delay. It provide SNR gain equivalent filtering. As an application GFMF, novel minimize...
Several applications in online learning involve sequential sampling/polling of an underlying population. A classical task this space is cardinality estimation, where the goal to estimate size a set by sampling elements from (see, for example, [2,4,7]). The key idea here use 'collisions,' i.e., instances same element sampled more than once, set. Another recent application community exploration, agent sample as many distinct possible, given family distributions/domains poll (see [3, 6]).
We consider the problem of correctly identifying \textit{mode} a discrete distribution $\mathcal{P}$ with sufficiently high probability by observing sequence i.i.d. samples drawn from $\mathcal{P}$. This reduces to estimation single parameter when has support set size $K = 2$. After noting that this special case is tackled very well prior-posterior-ratio (PPR) martingale confidence sequences \citep{waudby-ramdas-ppr}, we propose generalisation mode estimation, in which may take \geq 2$...
We consider a population, partitioned into set of communities, and study the problem identifying largest community within population via sequential, random sampling individuals. There are multiple domains, referred to as \emph{boxes}, which also partition population. Each box may consist individuals different each in turn be spread across boxes. The learning agent can, at any time, sample (with replacement) individual from chosen box; when this is done, learns sampled belongs to, whether or...