This paper describes a fast and effective algorithm for refining the parameter estimates of multicomponent third-order polynomial phase signals (PPSs). The efficiency of the proposed algorithm is accompanied by lower signal-to-noise ratio (SNR) threshold, and computational complexity. A two-step procedure is used to estimate the parameters of multicomponent third-order PPSs. In the first step, an initial estimate for the phase parameters can be obtained by using fast Fourier transformation (FFT), k-means algorithm and three time positions. In the second step, these initial estimates are refined by a simple moving average filter and singular value decomposition (SVD). The SNR threshold of the proposed algorithm is lower than those of the non-linear least square (NLS) method and the estimation refinement method even though it uses a simple moving average filter. In addition, the proposed method is characterized by significantly lower complexity than computationally intensive NLS methods. Simulations confirm the effectiveness of the proposed method.

Clustering methods are divided into hierarchical clustering, partitioning clustering, and more. K-Means is a method of partitioning clustering. We improve the performance of a K-Means, selecting the initial centers of a cluster through a calculation rather than using random selecting. This method maximizes the distance among the initial centers of clusters. Subsequently, the centers are distributed evenly and the results are more accurate than for initial cluster centers selected at random. This is time-consuming, but it can reduce the total clustering time by minimizing allocation and recalculation. Compared with the standard algorithm, F-Measure is more accurate by 5.1%.

Automatic text summarization has the goal of reducing the size of a document while preserving its content. Generally, producing a summary as extracts is achieved by including only sentences which are the most topic-related. DOCUSUM is our summarization system based on a new topic keyword identification method. The process of DOCUSUM is as follows. First, DOCUSUM converts the content words of a document into elements of a context vector space. It then constructs lexical clusters from the context vector space and identifies core clusters. Next, it selects topic keywords from the core clusters. Finally, it generates a summary of the document using the topic keywords. In the experiments on various compression ratios (the compression of 30%, the compression of 10%, and the extraction of the fixed number of sentences: 4 or 8 sentences), DOCUSUM showed better performance than other methods.

The rosette-scanning infrared seeker (RSIS) is a tracker that a single infrared detector scans the total field of view (TFOV) in a rosette pattern, and then produces 2D image about a target. Since the detected image has various shapes in accordance with the target position in the TFOV, it is difficult to determine a precise target position from the obtained image. In order to track this type of target, therefore, we propose an efficient tracking method using the K-means algorithm (KMA). The KMA, which classifies image clusters and calculates their centers, is used to cope with an countermeasure (CM) such as an IR flare. To evaluate the performance of the RSIS using the KMA dynamically, we simulate the RSIS in the various conditions, and discuss the tracking results.