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Computers, Volume 4, Issue 4 (December 2015) – 2 articles , Pages 283-321

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809 KiB

Open AccessArticle

Linear and Quadratic Interpolators Using Truncated-Matrix Multipliers and Squarers

by E. George Walters III

Computers 2015, 4(4), 293-321; https://doi.org/10.3390/computers4040293 - 16 Nov 2015

Cited by 8 | Viewed by 6295

Abstract

This paper presents a technique for designing linear and quadratic interpolators for function approximation using truncated multipliers and squarers. Initial coefficient values are found using a Chebyshev-series approximation and then adjusted through exhaustive simulation to minimize the maximum absolute error of the interpolator output. This technique is suitable for any function and any precision up to 24 bits (IEEE single precision). Designs for linear and quadratic interpolators that implement the 1/x, 1/ √ x, log2(1+2^x), log2(x) and 2^x functions are presented and analyzed as examples. Results show that a proposed 24-bit interpolator computing 1/x with a design specification of ±1 unit in the last place of the product (ulp) error uses 16.4% less area and 15.3% less power than a comparable standard interpolator with the same error specification. Sixteen-bit linear interpolators for other functions are shown to use up to 17.3% less area and 12.1% less power, and 16-bit quadratic interpolators are shown to use up to 25.8% less area and 24.7% less power. Full article

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226 KiB

Open AccessArticle

Hop-by-Hop Worm Propagation with Carryover Epidemic Model in Mobile Sensor Networks

by Jun-Won Ho

Computers 2015, 4(4), 283-292; https://doi.org/10.3390/computers4040283 - 20 Oct 2015

Cited by 2 | Viewed by 6292

Abstract

In the internet, a worm is usually propagated in a random multi-hop contact manner. However, the attacker will not likely select this random multi-hop propagation approach in a mobile sensor network. This is because multi-hop worm route paths to random vulnerable targets can be often breached due to node mobility, leading to failure of fast worm spread under this strategy. Therefore, an appropriate propagation strategy is needed for mobile sensor worms. To meet this need, we discuss a hop-by-hop worm propagation model in mobile sensor networks. In a hop-by-hop worm propagation model, benign nodes are infected by worm in neighbor-to-neighbor spread manner. Since worm infection occurs in hop-by-hop contact, it is not substantially affected by a route breach incurred by node mobility. We also propose the carryover epidemic model to deal with the worm infection quota deficiency that might occur when employing an epidemic model in a mobile sensor network. We analyze worm infection capability under the carryover epidemic model. Moreover, we simulate hop-by-hop worm propagation with carryover epidemic model by using an ns-2 simulator. The simulation results demonstrate that infection quota carryovers are seldom observed where a node’s maximum speed is no less than 20 m/s. Full article

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Computers, Volume 4, Issue 4 (December 2015) – 2 articles , Pages 283-321

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