S
Shahram Latifi
Researcher at University of Nevada, Las Vegas
Publications - 181
Citations - 3025
Shahram Latifi is an academic researcher from University of Nevada, Las Vegas. The author has contributed to research in topics: Star (graph theory) & Hypercube. The author has an hindex of 24, co-authored 179 publications receiving 2785 citations. Previous affiliations of Shahram Latifi include University of Nevada, Reno.
Papers
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Proceedings ArticleDOI
Optimal simulation of linear array and ring architectures on multiply-twisted hypercube
Shahram Latifi,Si-Qing Zheng +1 more
TL;DR: The authors define a new concept of reflected link label sequence and use it to define a generalized Gray code (GCC), and it is shown that by using the GCC at least n-factorial distinct Hamiltonian paths and at leastn- Factorial/2+(n-2)-factorial separate Hamiltonian cycles of Q/sub n//sup MT/ can be identified.
Journal ArticleDOI
Reliable data transmission in mobile ad hoc sensor networks
Vasu Jolly,Shahram Latifi +1 more
TL;DR: A new routing scheme for mobile ad hoc sensor networks, which effectively transports the information from source to sink by curbing the energy requirements, both at node and system level is introduced.
Journal ArticleDOI
A robustness measure for hypercube networks
Shahram Latifi,Suresh Rai +1 more
TL;DR: This paper addresses a constrained two-terminal reliability measure referred to as distance reliability (DR) as it considers the probability that a message can be delivered in optimal time from a given node s to a node t.
Journal ArticleDOI
Application of Machine Learning to Biometric Systems- A Survey
Lina Chato,Shahram Latifi +1 more
TL;DR: This study shows that machine learning has a high potential to improve the performance of biometrics systems due to ML's ability to mine, search and analyze big sets of data, performing matching tasks more quickly and reliably than the conventional methods.
Proceedings ArticleDOI
A genetic algorithm to optimize the adaptive Support Vector Regression model for forecasting the reliability of diesel engine systems
TL;DR: This paper presents the use of the Support Vector Regression (SVR) technique to forecast the reliability of a system, and shows that Order 5 of the polynomial kernel outperformed both Gaussian and linear kernel functions in predicting the future reliability values with minimal NRMSE.