12904695
Computer-aided kidney segmentation on abdominal CT images
In this paper, an effective model-based approach for computer-aided kidney segmentation of abdominal CT images with anatomic structure consideration is presented. This automatic segmentation system is expected to assist physicians in both clinical diagnosis and educational training. The proposed method is a coarse to fine segmentation approach divided into two stages. First, the candidate kidney region is extracted according to the statistical geometric location of kidney within the abdomen. This approach is applicable to images of different sizes by using the relative distance of the kidney region to the spine. The second stage identifies the kidney by a series of image processing operations. The main elements of the proposed system are: 1) the location of the spine is used as the landmark for coordinate references; 2) elliptic candidate kidney region extraction with progressive positioning on the consecutive CT images; 3) novel directional model for a more reliable kidney region seed point identification; and 4) adaptive region growing controlled by the properties of image homogeneity. In addition, in order to provide different views for the physicians, we have implemented a visualization tool that will automatically show the renal contour through the method of second-order neighborhood edge detection. We considered segmentation of kidney regions from CT scans that contain pathologies in clinical practice. The results of a series of tests on 358 images from 30 patients indicate an average correlation coefficient of up to 88% between automatic and manual segmentation
32808871
Trajectory production with the adaptive time-delay neural network
Abstract The adaptive time-delay neural network (ATNN), a paradigm for training a nonlinear neural network with adaptive time delays, has a rich repertoire of capabilities that are characterized in this paper. This network, in which both time delays and weights are adapted, is used to generate circular and figure-eight trajectories, to perform signal production, and to learn repetitive spatial motions. Spatiotemporal signal production has the property that initial segments of signals that contain large amounts of noise can be “cleaned up” to result in trained trajectory motions. Spatiotemporal features are learned as opposed to a point-by-point memorization of the trajectory. Closed-loop, repetitive trajectories that are learned result from training the network to produce attractors consistent with those trajectories. Widely varying starting motions will result in the network being attracted to the repetitive attractor for which it is trained. The network also displays training position invariance, and noise removal when only part of the pattern is trained. Sampling rate effects versus training speed and performance were measured. A comparison between the ATNN and related networks (BP and the TDNN) is reviewed in an example with successful time series prediction of a chaotic series.
2615651
A Probability Model to Improve Word Alignment
Word alignment plays a crucial role in statistical machine translation. Word-aligned corpora have been found to be an excellent source of translation-related knowledge. We present a statistical model for computing the probability of an alignment given a sentence pair. This model allows easy integration of context-specific features. Our experiments show that this model can be an effective tool for improving an existing word alignment.
22202534
Subspace-based active user identification for a collision-free slotted ad hoc network
We propose a novel spreading code scheme, transmitter-receiver-based code, for wireless ad hoc networks. The design facilitates collision resolution using multiuser detection at each node, and is more bandwidth efficient than creating orthogonal channels in time or frequency. A subspace-based receiver structure is introduced, which identifies users of interest, or "active" users, with minimal prior information on the spreading code ensemble. A subspace-based blind multiuser detector can then be implemented to suppress multiaccess interference. The performance of the proposed active user identifier is studied by investigating its false alarm rate P/sub f/ and miss rate P/sub m/. Tradeoffs between P/sub f/ and P/sub m/ are discussed, and a graphical method to determine the threshold value d/sub th/ of the decision statistic used in discriminating between active and inactive channels is introduced.
6409436
Word-for-Word Glossing with Contextually Similar Words
Many corpus-based machine translation systems require parallel corpora. In this paper, we present a word-for-word glossing algorithm that requires only a source language corpus. To gloss a word, we first identify its similar words that occurred in the same context in a large corpus. We then determine the gloss by maximizing the similarity between the set of contextually similar words and the different translations of the word in a bilingual thesaurus.
5803228
Vertex covering by paths on trees with its applications in machine translation
Given a tree and a set of paths in the tree, the problem of finding a minimum number of paths from the given path set to cover all the vertices in the tree is investigated in the paper. To distinguish from the classical path cover problem, such an optimization problem is referred to as vertex covering by paths. The problem and its edge variant, edge covering by paths, find applications in machine translation. Complexity and algorithmic results are presented for the problem and its edge variant.
16556774
Optimizing Moving Queries over Moving Object Data Streams
With the increasing in demand on location-based aware services and RFIDs, efficient processing of continuous queries over moving object streams becomes important. In this paper, we propose an efficient in-memory processing of continuous queries on themoving object streams.We model moving objects using function of time and use it in the prediction of usefulness of objects with respect to the continuous queries. To effectively utilize the limited memory, we derive several replacement policies to discard objects that are of no potential interest to the queries and design efficient algorithms with light data structures. Experimental studies are conducted and the results show that our proposed method is both memory and query efficient.