For the easy design of very small normal-mode helical antennas (NMHAs), an equation that helps determine the self-resonant structures of these antennas is developed. For this purpose, the expression for the capacitance of an NMHA is established. The accuracy of this design equation is confirmed by comparing the results obtained using the equation with the simulation results.

A novel grouping approach to segment text lines from handwritten documents is presented. In this text line segmentation algorithm, for each text line, a text string that connects the center points of the characters in this text line is built. The text lines are then segmented using the resulting text strings. Since the characters of the same text line are situated close together and aligned on a smooth curve, 2D tensor voting is used to reduce the conflicts when building these text strings. First, the text lines are represented by separate connected components. The center points of these connected components are then encoded by second order tensors. Finally, a voting process is applied to extract the curve saliency values and normal vectors, which are used to remove outliers and build the text strings. The experimental results obtained from the test dataset of the ICDAR 2009 Handwriting Segmentation Contest show that the proposed method generates high detection rate and recognition accuracy.

Lin-Kernighan (LK) is the most powerful local search for the Traveling Salesman Problem (TSP). The choice of data structure for tour representation plays a vital role in LK's performance. Binary trees are asymptotically the best tour representation but they perform empirically best only for TSPs with one million or more cities due to a large overhead. Arrays and two-level trees are used for smaller TSPs. This paper proposes a new three-level tree data structure for tour representation. Although this structure is asymptotically not better than the binary tree structure, it performs empirically better than the conventional structures for TSPs having from a thousand to three million cities.

We study the use of network coding to speed up content distribution in peer-to-peer (P2P) networks. Our goal is to get the underlying reason for network coding's improved performance in P2P content distribution and to optimize resource consumption of network coding. We observe analytically and experimentally that in pure P2P networks, a considerable amount of data is sent multiple times from one peer to another when there are multiple paths connecting those two particular peers. Network coding, on the other hand, when applied at upstream peers, eliminates information duplication on paths to downstream peers, which results in more efficient content distribution. Based on that insight, we propose a network coder placement algorithm which achieves comparable distribution time as network coding, yet substantially reduces the number of encoders compared to a pure network coding solution in which all peers have to encode. Our placement method puts encoders at critical network positions to eliminate information duplication the most, thus, effectively shortens distribution time with just a portion of encoders.