• Proceedings of the IEEK Conference
• /
• 2005.11a
• /
• pp.837-840
• /
• 2005

This paper presents 3D graphics lighting processor based on vector processing using pipeline chaining. The lighting process of 3D graphics rendering contains many arithmetic operations and its complexity is very high. For high throughput, proposed processor uses pipelined functional units. To implement fully pipelined architecture, we have to use many functional units. Hence, the number of functional units is restricted. However, with the restricted number of pipelined functional units, the utilization of the units is reduced and a resource reservation problem is caused. To resolve these problems, the proposed architecture uses vector processing using pipeline chaining. Due to its pipeline chaining based architecture, it can perform 4.09M vertices per 1 second with 100MHz frequency. The proposed 3D graphics lighting processor is compatible with OpenGL ES API and the design is implemented and verified on FPGA.

• Bulletin of the Korean Mathematical Society
• /
• v.47 no.1
• /
• pp.39-51
• /
• 2010

In this paper we consider the uniform central limit theorem for a martingale-difference array of a function-indexed stochastic process under the uniformly integrable entropy condition. We prove a maximal inequality for martingale-difference arrays of process indexed by a class of measurable functions by a method as Ziegler [19] did for triangular arrays of row wise independent process. The main tools are the Freedman inequality for the martingale-difference and a sub-Gaussian inequality based on the restricted chaining. The results of present paper generalizes those of Ziegler [19] and other results of independent problems. The results also generalizes those of Bae and Choi [3] to martingale-difference array of a function-indexed stochastic process. Finally, an application to classes of functions changing with n is given.

• Communications of the Korean Mathematical Society
• /
• v.25 no.2
• /
• pp.291-301
• /
• 2010

This paper consists of two main parts. Firstly, we introduce an evolving binomial process from a binomial stock model and consider various types of limiting behavior of the logarithm of the evolving binomial process. Among others we find that the logarithm of the binomial process converges weakly to a Gaussian process. Secondly, we provide new approaches for proving the limit theorems for an integral process motivated by the evolving binomial process. We provide a new proof for the uniform strong LLN for the integral process. We also provide a simple proof of the functional CLT by using a restriction of Bernstein inequality and a restricted chaining argument. We apply the functional CLT to derive the LIL for the IID random variables from that for Gaussian.

• Journal of KIISE
• /
• v.43 no.12
• /
• pp.1428-1436
• /
• 2016

A multipath routing in wireless sensor networks (WSNs) provides advantage such as reliability improvement and load balancing by transmitting data through divided paths. For these reasons, existing multipath routing protocols divide path appropriately or create independent paths efficiently. However, when the sink node moves to avoid hotspot problem or satisfy the requirement of the applications, the existing protocols have to reconstruct multipath or exploit foot-print chaining mechanism. As a result, the existing protocols will shorten the lifetime of a network due to excessive energy consumption, and lose the advantage of multipath routing due to the merging of paths. To solve this problem, we propose a multipath creation and maintenance scheme to support the mobile sink node. The proposed protocol can be used to construct local grid structure with restricted area and exploit grid structure for constructing the multipath. The grid structure can also be extended depending on the movement of the sink node. In addition, the multipath can be partially reconstructed to prevent merging paths. Simulation results show that the proposed protocol is superior to the existing protocols in terms of energy efficiency and packet delivery ratio.