The generalized sorting problem is to find the first k largest elements among n input elements and to report them in a sorted order. In this paper, we propose a fast generalized sorting algorithm under the single hop wireless networks model with collision detection (WNCD). The algorithm is based on the maximum finding algorithm and the sorting algorithm. The key point of our algorithm is to use successful broadcasts to build broadcasting layers logically and then to distribute the data elements into those logic layers properly. Thus, the number of broadcast conflicts is reduced. We prove that the average time complexity required for our generalized sorting algorithm is Θ(k + log(n - k)). When k = 1, our generalized sorting algorithm does the work of finding maximum, and when k = n, it does the work of sorting. Thus, the analysis of our algorithm builds a connection between the two extremely special cases which are maximum finding and sorting.

A tree 3-spanner T of a graph G is a spanning tree of G such that the distance between any two vertices in T is at most 3 times of their distance in G. Madanlal et al. have presented an O(n + m) time algorithm for finding a tree 3-spanner of a permutation graph. However, the complexity of their algorithm is not optimal, and their algorithm can not be easily parallelized. In this paper, we will propose an improved algorithm to solve the same problem in O(n) time. Moreover, our algorithm can be easily parallelized so that a tree 3-spanner of a permutation graph can be found in O(log n) time with processors on the EREW PRAM computational model.

Given a set of n stations, the initialization problem is to assign each station a unique identification number, from 1 to n. In the single-hop wireless Networks with collision detection, Nakano and Olariu proposed an algorithm to build a partition tree and solve the problem. In this paper, we shall classify the partition tree into four parts. By reviewing the classification, we find that three ideas can improve the algorithm. We show that it needs 2.88n time slots for solving the problem containing n stations. After applying our three ideas, the number of time slots will be improved to 2.46n.

The optimal alignment of two given biosequences is mathematically optimal, but it may not be a biologically optimal one. To investigate more possible alignments with biological meaning, one can relax the scoring functions to get near-optimal alignments. Though the near optimal alignments increase the possibility of finding the correct alignment, they may confuse the biologists because the size of candidates is large. In this paper, we present the filter scheme for the near-optimal alignments. An easy method for tracing the near-optimal alignments and an algorithm for filtering those alignments are proposed. The time complexity of our algorithm is O(dmn) in the worst case, where d is the maximum distance between the near-optimal alignments and the optimal alignment, and m and n are the lengths of the input sequences, respectively.