Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
Articles by B. Shirazi
Total Records ( 2 ) for B. Shirazi
  A Kiely , M Xu , W. Z Song , R Huang and B. Shirazi

We present a lightweight lossless compression algorithm for realtime sensor networks. Our proposed adaptive linear filtering compression (ALFC) algorithm performs predictive compression using adaptive linear filtering to predict sample values followed by entropy coding of prediction residuals, encoding a variable number of samples into fixed-length packets. Adaptive prediction eliminates the need to determine prediction coefficients a priori and, more importantly, allows compression to dynamically adjust to a changing source. The algorithm requires only integer arithmetic operations and thus is compatible with sensor platforms that do not support floating-point operations. Significant robustness to packets losses is provided by including small but sufficient overhead data to allow each packet to be independently decoded. Real-world evaluations on seismic data from a wireless sensor network testbed show that ALFC provides more effective compression and uses less resources than an alternative recent work of lossless compression, S-LZW. Experiments in a multi-hop sensor network also show that ALFC can significantly improve raw data throughput and energy efficiency. We also implement the algorithm in our real sensor network, and show that our linear prediction based compression algorithm significantly improves data reliability and network efficiency.

  B. Shirazi , H. Fazlollahtabar and D. Shafiei
  In this study, a mathematical model is proposed for facilities relocation problem in supply chain. Facilities` location in supply chain is one of the fundamental and strategic decisions of supply chain management. As location models in supply chain take in to account more factors simultaneously, they are commonly more complicated than traditional location models, but their structures are almost resemble to the traditional models. This model can help for deciding about supply chain location changes, constructing new facilities and appropriate time for those jobs. Also Genetic algorithm is used for optimization of facilities relocation problem in supply chain.
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility