V. Krishna Kumar

University of Maryland, College Park

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Hussein Alnuweiri

Texas A&M University at Qatar

Sid‐Ahmed Boukabara

NASA Earth Science

Robert Atlas

NOAA Atlantic Oceanographic and Meteorological Laboratories

B.F. Cooper

Yahoo (United Kingdom)

Ross N. Hoffman

NOAA Center for Satellite Applications and Research

Kayo Ide

University of Maryland, College Park

Kevin Garrett

Office of Science

Manavendra Misra

University of Southern California

Sean Casey

Trudeau Institute

Zhongtang Cai

Georgia Institute of Technology

Cooperative Institutions

National Oceanic and Atmospheric Administration

NOAA Center for Satellite Applications and Research

University of Maryland, College Park

NOAA National Environmental Satellite Data and Information Service

Cooperative Institute for Climate and Satellites

NOAA National Centers for Environmental Prediction

University of Southern California

Cooperative Institute for Research in Environmental Sciences

University of Wisconsin–Madison

Earth System Science Interdisciplinary Center

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Efficient image template matching on hypercube SIMD arrays.

Proceedings of the International Conference on Parallel Processing (1987)

V. Krishna Kumar Krishnan

SIMD

Source

Cite

Citations (14)

Multi-Objective Optimization of Energy Aware Virtual Machine Placement in Cloud Data Center

Intelligent Automation & Soft Computing (2022)

B. Gomathi B. Saravana Balaji V. Krishna Kumar Mohamed Abouhawwash Sultan Aljahdali

Cloud computing enables cloud providers to outsource their Information Technology (IT) services from data centers in a pay-as-you-go model. However, Cloud infrastructure comprises virtualized physical resources that consume huge amount of energy and emits carbon footprints to environment. Hence, there should be focus on optimal assignment of Virtual Machines (VM) to Physical Machines (PM) to ensure the energy efficiency and service level performance. In this paper, The Pareto based Multi-Objective Particle Swarm Optimization with Composite Mutation (PSOCM) technique has been proposed to improve the energy efficiency and minimize the Service Level Agreement (SLA) violation in Cloud Environment. In this paper, idea of MOPSO is extended with three distinct features such as Largest Processing Time (LPT) rule is applied to improve load balancing across the resources which leads to energy saving in Cloud Environment; Epsilon Fuzzy Dominance technique is used to select solutions near to Pareto front which improves the diversity of Pareto optimal solutions; and Discrete PSO along with Composite Mutation strategy in the proposed algorithm help to provide better convergence than existing approaches. Hence, the proposed algorithm produced better results than other existing algorithm such as GA and heuristics-based approach.

Heuristics

Data center

10.32604/iasc.2022.024052

Cite

Citations (23)

S4: An O2R/R2O Infrastructure for Optimizing Satellite Data Utilization in NOAA Numerical Modeling Systems: A Step Toward Bridging the Gap between Research and Operations

Bulletin of the American Meteorological Society (2016)

Sid‐Ahmed Boukabara Tong Zhu Hendrik L. Tolman Steve Lord Steven J. Goodman

Abstract In 2011, the National Oceanic and Atmospheric Administration (NOAA) began a cooperative initiative with the academic community to help address a vexing issue that has long been known as a disconnection between the operational and research realms for weather forecasting and data assimilation. The issue is the gap, more exotically referred to as the “valley of death,” between efforts within the broader research community and NOAA’s activities, which are heavily driven by operational constraints. With the stated goals of leveraging research community efforts to benefit NOAA’s mission and offering a path to operations for the latest research activities that support the NOAA mission, satellite data assimilation in particular, this initiative aims to enhance the linkage between NOAA’s operational systems and the research efforts. A critical component is the establishment of an efficient operations-to-research (O2R) environment on the Supercomputer for Satellite Simulations and Data Assimilation Studies (S4). This O2R environment is critical for successful research-to-operations (R2O) transitions because it allows rigorous tracking, implementation, and merging of any changes necessary (to operational software codes, scripts, libraries, etc.) to achieve the scientific enhancement. So far, the S4 O2R environment, with close to 4,700 computing cores (60 TFLOPs) and 1,700-TB disk storage capacity, has been a great success and consequently was recently expanded to significantly increase its computing capacity. The objective of this article is to highlight some of the major achievements and benefits of this O2R approach and some lessons learned, with the ultimate goal of inspiring other O2R/R2O initiatives in other areas and for other applications.

10.1175/bams-d-14-00188.1

Cite

Citations (27)

LTTC: A Load Testing Tool for Cloud

Advances in intelligent systems and computing (2017)

M. S. Geetha Devasena V. Krishna Kumar R. Kingsy Grace

Load testing

Software reliability testing

10.1007/978-981-10-2750-5_70

Cite

Citations (1)

<title>Neural network simulation on a reduced-mesh-of-trees organization</title>

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (1990)

Manavendra Misra V. Krishna Kumar

The theory of Artificial Neural Networks (ANN's) shows that ANN's can perform useful image recognition functions. Simulations on uniprocessor sequential machines, however, destroy the parallelism inherent in ANN models and this results in a significant loss of speed. Simulations on parallel machines are therefore essential to fully exploit the advantages of ANN's. We show how to simulate ANN's on an SIMD architecture, the Reduced Mesh of Trees (RMOT). The architecture has p PE's and n² memory arranged in a p x p array of modules (p is a constant less than or equal to n). This massive memory is used to store connection weights. A fully connected, single layer neural network with n neurons can be mapped easily onto the architecture. An update in this case requires O(n²/p) time steps. A sparse network can also be simulated efficiently on the architecture. The proposed architecture can also be used for the efficient simulation of multilayer networks with a Back Propagation learning scheme. The architecture can easily be implemented within the framework of existing hardware technology.

Uniprocessor system

SIMD

Constant (computer programming)

Cellular architecture

Memory architecture

10.1117/12.19586

Cite

Citations (5)

Modular VLSI architectures for real-time vector quantization

2022 IEEE International Symposium on Circuits and Systems (ISCAS) (1991)

H. Park V. Krishna Kumar

The authors propose modular linearly connected VLSI architectures for VQ (vector quantization) which can support real-time image processing applications. The design considered requires fixed memory bandwidth with the host and allows codebook changes. The throughput is independent of the codebook size. Each processing element consists of an adder and a shift register instead of a multiplier. These designs can be extended to the case when a fixed number of processors are available. A number of VQ schemes such as single-stage and multistage VQ and classified VQ can be implemented using this approach.< >

Linde–Buzo–Gray algorithm

10.1109/iscas.1991.176305

Cite

Citations (5)

Role of Machine Learning Algorithms on Alzheimer Disease Prediction

Lecture notes in networks and systems (2022)

V. Krishna Kumar M. S. Geetha Devasena Gopu Govindasamy N. Sivakumaran

Sørensen–Dice coefficient

Value (mathematics)

10.1007/978-981-19-2500-9_57

Cite

Citations (1)

Spatio-Temporal Data Mining for Scalable Ocean Eddy Monitoring

AGU Fall Meeting Abstracts (2012)

Karsten Steinhaeuser James H. Faghmous Shyam Boriah Stefan Liess F. Vikebo

Source

Cite

Citations (0)

Distributed Computing in Sensor Systems: First IEEE International Conference, DCOSS 2005, Marina del Rey, CA, USA, June 30-July 1, 2005, Proceedings

V. Krishna Kumar S. S. Iyengar Paul Spirakis Matt Welsh

Invited Talks.- Algorithmic Problems in Ad Hoc Networks.- Five Challenges in Wide-Area Sensor Systems.- Challenges in Programming Sensor Networks.- Regular Papers.- Distributed Proximity Maintenance in Ad Hoc Mobile Networks.- Adaptive Triangular Deployment Algorithm for Unattended Mobile Sensor Networks.- An Adaptive Blind Algorithm for Energy Balanced Data Propagation in Wireless Sensors Networks.- Sensor Localization in an Obstructed Environment.- Stably Computable Properties of Network Graphs.- Routing Explicit Side Information for Data Compression in Wireless Sensor Networks.- Database-Centric Programming for Wide-Area Sensor Systems.- Using Clustering Information for Sensor Network Localization.- Macro-programming Wireless Sensor Networks Using Kairos.- Sensor Network Calculus - A Framework for Worst Case Analysis.- Design and Comparison of Lightweight Group Management Strategies in EnviroSuite.- Design of Adaptive Overlays for Multi-scale Communication in Sensor Networks.- Fault-Tolerant Self-organization in Sensor Networks.- TARA: Thermal-Aware Routing Algorithm for Implanted Sensor Networks.- Multiresolutional Filtering of a Class of Dynamic Multiscale System Subject to Colored State Equation Noise.- Design and Analysis of Wave Sensing Scheduling Protocols for Object-Tracking Applications.- Multiple Controlled Mobile Elements (Data Mules) for Data Collection in Sensor Networks.- Analysis of Gradient-Based Routing Protocols in Sensor Networks.- Analysis of Target Detection Performance for Wireless Sensor Networks.- Collaborative Sensing Using Sensors of Uncoordinated Mobility.- Multi-query Optimization for Sensor Networks.- Distributed Energy-Efficient Hierarchical Clustering for Wireless Sensor Networks.- A Distributed Greedy Algorithm for Connected Sensor Cover in Dense Sensor Networks.- Infrastructure-Establishment from Scratch in Wireless Sensor Networks.- Short Papers.- A Local Facility Location Algorithm for Sensor Networks.- jWebDust : A Java-Based Generic Application Environment for Wireless Sensor Networks.- Invited Posters.- Networked Active Sensing of Structures.- Wireless Technologies for Condition-Based Maintenance (CBM) in Petroleum Plants.- SensorNet Operational Prototypes: Building Wide-Area Interoperable Sensor Networks - Extended Abstract.- Project ExScal.- NetRad: Distributed, Collaborative and Adaptive Sensing of the Atmosphere Calibration and Initial Benchmarks.- Service-Oriented Computing in Sensor Networks.- Wireless Sensors: Oyster Habitat Monitoring in the Bras d'Or Lakes.- Heavy Industry Applications of Sensornets.- Coordinated Static and Mobile Sensing for Environmental Monitoring.- Contributed Posters.- Ayushman: A Wireless Sensor Network Based Health Monitoring Infrastructure and Testbed.- Studying Upper Bounds on Sensor Network Lifetime by Genetic Clustering.- Sensor Network Coverage Restoration.- A Biologically-Inspired Data-Centric Communication Protocol for Sensor Networks.- RAGOBOT: A New Platform for Wireless Mobile Sensor Networks.- Energy Conservation Stratedgy for Sensor Networks.- Meteorological Phenomena Measurement System Based on Embedded System and Wireless Network.- An Architecture Model for Supporting Power Saving Services for Mobile Ad-Hoc Wireless Networks.- Distributed Recovery Units for Demodulation in Wireless Sensor Networks.- System Integration Using Embedded Web Server and Wireless Communication.

Visual sensor network

Sensor web

Geographic routing

Source

Cite

Citations (2)

A review of AI-powered production scheduling in smart factories: Impacts on lead times and resource utilization

AIP conference proceedings (2025)

P. Suganthi R. Suyam Praba Thilaga Sethuraman V. Krishna Kumar J. Deepa

Lead (geology)

10.1063/5.0246414

Cite

Citations (0)