@conference {19295,
	title = {Buffer management for multi-application image processing on multi-core platforms: Analysis and case study},
	booktitle = {2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)},
	year = {2010},
	month = {2010},
	pages = {1662 - 1665},
	abstract = {Due to the limited amounts of on-chip memory, large volumes of data, and performance and power consumption overhead associated with interprocessor communication, efficient management of buffer memory is critical to multi-core image processing. To address this problem, this paper develops new modeling and analysis techniques based on dataflow representations, and demonstrates these techniques on a multi-core implementation case study involving multiple, concurrently-executing image processing applications. Our techniques are based on careful representation and exploitation of frame- or block-based operations, which involve repeated invocations of the same computations across regularly- arranged subsets of data. Using these new approaches to manage block-based image data, this paper demonstrates methods to analyze synchronization overhead and FIFO buffer sizes when mapping image processing applications onto heterogeneous, multi core architectures.},
	keywords = {block-based image data, buffer memory management, buffer storage, concurrently-executing image processing application, data flow computing, data flow representation, data subset, Dataflow, Digital signal processing, Energy consumption, Energy management, Engineering management, FIFO buffer sizes, Hardware, Image analysis, IMAGE PROCESSING, image processing application, image representation, interprocessor communication, memory architecture, Memory management, multiapplication image processing, multicore image processing, multiprocessing, on-chip memory, power consumption, power consumption overhead, Runtime, scheduling, set theory, shared memory, storage management chips, synchronization overhead},
	author = {Ko,Dong-Ik and Won, N. and Bhattacharyya, Shuvra S.}
}
@conference {11900,
	title = {A learning automata based power management for ad-hoc networks},
	booktitle = {2005 IEEE International Conference on Systems, Man and Cybernetics},
	volume = {4},
	year = {2005},
	month = {2005/10//},
	pages = {3569- 3573 Vol. 4 - 3569- 3573 Vol. 4},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Power management is a very important aspect of ad-hoc networks. It directly impacts the network throughput among other network metrics. On the other hand, transmission power management may result in disconnected networks and increased level of collisions. In this paper, we introduce a transmission power control based on stochastic learning automata (SLA) to modify the transmission power. Based on the level of successful transmissions and the level of packet retransmissions, the SLA will modify the transmission power level either by increasing it or decreasing it. The probabilistic nature of SLA makes it a useful choice for ad-hoc networks. Using the network simulator NS, we show that using SLA for transmission power will result in an increased system bandwidth and a decrease in the collision levels.},
	keywords = {ad hoc networks, ad-hoc networks, Computer network management, Computer networks, Energy management, Engineering management, learning automata, network metrics, network simulator, packet retransmissions, power control, power system management, power transmission control, stochastic learning automata, stochastic learning automta, Stochastic processes, system bandwidth, Technology management, Throughput, transmission power control, transmission power level, transmission power management},
	isbn = {0-7803-9298-1},
	doi = {10.1109/ICSMC.2005.1571701},
	author = {El-Osery,A. I and Baird,D. and Abd-Almageed, Wael}
}
