Optical navigation (OPNAV) is the use of the on-board imaging data to provide a direct measurement of the image coordinates of the target as navigation information. Among the optical observables in deep-space, the edge of the celestial body is an important feature that can be utilized for locating the planet centroid. However, traditional edge detection algorithms like Canny algorithm cannot be applied directly for OPNAV due to the noise edges caused by surface markings. Moreover, due to the constrained computation and energy capacity on-board, light-weight image-processing algorithms with less computational complexity are desirable for real-time processing. Thus, to fast and accurately extract the edge of the celestial body from high-resolution satellite imageries, this paper presents an algorithm-hardware co-design of real-time edge detection for OPNAV. First, a light-weight edge detection algorithm is proposed to efficiently detect the edge of the celestial body while suppressing the noise edges caused by surface markings. Then, we further present an FPGA implementation of the proposed algorithm with an optimized real-time performance and resource efficiency. Experimental results show that, compared with the traditional edge detection algorithms, our proposed one enables more accurate celestial body edge detection, while simplifying the hardware implementation.

This manuscript discusses a new indoor positioning method and proposes a multi-distance function trilateration over k-NN fingerprinting method using radio signals. Generally, the strength of radio signals, referred to received signal strength indicator or RSSI, decreases as they travel in space. Our method employs a list of fingerprints comprised of RSSIs to absorb interference between radio signals, which happens around the transmitters and it also employs multiple distance functions for conversion from distance between fingerprints to the physical distance in order to absorb the interference that happens around the receiver then it performs trilateration between the top three closest fingerprints to locate the receiver's current position. An experiment in positioning performance is conducted in our laboratory and the result shows that our method is viable for a position-level indoor positioning method and it could improve positioning performance by 12.7% of positioning error to 0.406 in meter in comparison with traditional methods.

Text Categorization (TC) is a task of classifying a set of documents into one or more predefined categories. Centroid-based method, a very popular TC method, aims to make classifiers simple and efficient by constructing one prototype vector for each class. It classifies a document into the class that owns the prototype vector nearest to the document. Many studies have been done on constructing prototype vectors. However, the basic philosophies of these methods are quite different from each other. It makes the comparison and selection of centroid-based TC methods very difficult. It also makes the further development of centroid-based TC methods more challenging. In this paper, based on the observation of its general procedure, the centroid-based text classification is treated as a kind of ranking task, and a unified framework for centroid-based TC methods is proposed. The goal of this unified framework is to classify a text via ranking all possible classes by document-class similarities. Prototype vectors are constructed based on various loss functions for ranking classes. Under this framework, three popular centroid-based methods: Rocchio, Hypothesis Margin Centroid and DragPushing are unified and their details are discussed. A novel centroid-based TC method called SLRCM that uses a smoothing ranking loss function is further proposed. Experiments conducted on several standard databases show that the proposed SLRCM method outperforms the compared centroid-based methods and reaches the same performance as the state-of-the-art TC methods.

Music-similarity computation is an essential building block for the browsing, retrieval, and indexing of digital music archives. This paper proposes a music similarity function based on the centroid model, which divides the feature space into non-overlapping clusters for the efficient computation of the timber distance of two songs. We place particular emphasis on the centroid deviation as a feature for music-similarity computation. Experiments show that the centroid-model representation of the auditory features is promising for music-similarity computation.

We propose a method for halftoning grayscale images by drawing weighted centroidal Voronoi tessellations (WCVTs) with black lines on white image planes. Based on the fact that CVT approaches a uniform hexagonal lattice asymptotically, we derive a relationship of darkness between input grayscale images and the corresponding halftone images. Then the derived relationship is used for adjusting the contrast of the halftone images. Experimental results show that the generated halftone images can reproduce the original tone in the input images faithfully.

Speaker change detection involves the identification of the time indices of an audio stream, where the identity of the speaker changes. This paper proposes novel measures for speaker change detection over the centroid model, which divides the feature space into non-overlapping clusters for effective speaker-change comparison. The centroid model is a computationally-efficient variant of the widely-used mixture-distribution based background models for speaker recognition. Experiments on both synthetic and real-world data were performed; the results show that the proposed approach yields promising results compared with the conventional statistical measures.

Phase synchronization is a crucial problem in Bistatic Synthetic Aperture Radar (BiSAR). As phase synchronization error and Doppler phase have nearly the same form, Doppler Centroid (DC) cannot be estimated with traditional method in BiSAR. A DC estimation method is proposed through phase-interferometry of Dual-channel direct signal. Through phase interferometry, phase synchronization error can be counteracted while Doppler phase is reserved and DC can be estimated from the reserved phase.

Device mismatch plays an important role in the design of accurate analog circuits. The common centroid structure is commonly employed to reduce device mismatches caused by symmetrical layouts and processing gradients. Among the candidate placements generated by the common centroid approach, however, whichever achieves better matching is generally difficult to be determined without performing the time-consuming yield evaluation process. In addition, this rule-based methodology makes it difficult to achieve acceptable matching between multiple capacitors and to handle an irregular layout area. Based on a spatial correlation model, this study proposed a design methodology for yield enhancement of analog circuits using switched-capacitor techniques. An efficient and effective placement generator is developed to derive a placement for a circuit to achieve the highest or near highest correlation coefficient and thus accomplishing a better yield performance. A simple yield analysis is also developed to evaluate the achieved yield performance of a derived placement. Results show that the proposed methodology derives a placement which achieves better yield performance than those generated by the common centroid approach.

This paper describes a dynamic and adaptive scheme for three-dimensional mesh morphing. Using several control maps, the connectivity of intermediate meshes is dynamically changing and the mesh vertices are adaptively modified. The 2D control maps in parametric space that include curvature map, area deformation map and distance map, are used to schedule the inserting and deleting vertices in each frame. Then, the positions of vertices are adaptively moved to better positions using weighted centroidal voronoi diagram (WCVD) and a Delaunay triangulation is finally used to determine the connectivity of mesh. In contrast to most previous work, the intermediate mesh connectivity gradually changes and is much less complicated. We demonstrate several examples of aesthetically pleasing morphs created by the proposed method.

A novel power spectral density accumulation (PSDA) method for estimating the bandwidth of the clutter spectra is proposed, based on a priori knowledge of the shape of the clutter spectra. The comparison of the complexity and the performance between the PSDA method and the general ones is presented. It is shown that the PSDA method is effective for the short-time clutter data in the practical application.

Locating corner points from an edge detected image is very important in view of simplifying the post processing part of a system that utilizes a corner information. In this paper, we propose a robust geometrical approach for corner detection. Unlike classical corner detection methods, which idealize corners as junction points of two line segments, our approach considers the possibility of multiple line segments intersecting at a point. Moreover, junctions caused by two or more curved segments of different curvature are thought of as a corner point. The algorithm has been tested and proved competence with different types of images demonstrating its ability to detect and localize the corners in the image, though we found it to be best suited for images with relatively few curved segments. With the help of non-maximum response suppression technique our approach yields comparatively better result than any other method.

A real-time adaptive segmentation method based on new distance features is proposed for the binary centroid tracker. These novel features are distances between the predicted center pixel of a target object by a tracking filter and each pixel in extraction of a moving target. The proposed method restricts clutters with target-like intensity from entering a tracking window and has low computational complexity for real-time applications compared with other complex feature-based methods. Comparative experiments show that the proposed method is superior to other segmentation methods based on the intensity feature only in target detection and tracking.

The problem of finding the location of the center and the problem of finding the median in a graph are important and basic among many network location problems. In connection with these two problems, the following two theorems are well-known. One is proved by Jordan and Sylvester, and it shows that the center of every tree consists of either one vertex or two adjacent vertices. The other is proved by Jordan and it shows that the centroid (median) of every tree consists of either one vertex or two adjacent vertices. These theorems have been generalized by many researchers so far. Harary and Norman proved that the center of every connected graph G lies in a single block of G. Truszczynski proved that the median of every connected graph G lies in a single block of G. Slater defined k-centrum, which can express both center and median, and proved that the k-centrum of every tree consists of either one vertex or two adjacent vertices. This paper discusses generalization of these theorems. We define the -blocks of a graph G as a generalization of the blocks of G, where is a subset of the vertex set of G; and define the -centroid of G as a generalization of the centroid of G. First, we prove that the -centroid of G is included in an -block of G. This is a generalization of the above theorems concerning centroid, by Jordan and Truszczynski. Secondly, we define the -centrum of G as a generalization of the k-centrum of G and prove some theorems concerning the location of -centrum. Using one of theorems proved here, we can easily obtain the theorem showing that the k-centrum of every connected graph G lies in a single block of G. This theorem is a generalization of the above theorem by Slater.