Algorithms2014, 7(4), 650-662; doi:10.3390/a7040650 (registering DOI) - published 26 November 2014 Show/Hide Abstract
Abstract: In order to improve the accuracy of all kinds of information in the cash business and enhance the linkage between cash inventory forecasting and cash management information in the commercial bank, the first moving average prediction method, the second moving average prediction method, the first exponential smoothing prediction and the second exponential smoothing prediction methods are adopted to realize the time series prediction of bank cash flow, respectively. The prediction accuracy of the cash flow time series is improved by optimizing the algorithm parameters. The simulation experiments are carried out on the reality commercial bank’s cash flow data and the predictive performance comparison results show the effectiveness of the proposed methods.
Algorithms2014, 7(4), 635-649; doi:10.3390/a7040635 - published 24 November 2014 Show/Hide Abstract
Abstract: Chest computed tomography (CT) is the most commonly used technique for the inspection of lung lesions. However, the lobe fissures in lung CT is still difficult to observe owing to its imaging structure. Therefore, in this paper, we aimed to develop an efficient tracking framework to extract the lobe fissures by the proposed modified ant colony optimization (ACO) algorithm. We used the method of increasing the consistency of pheromone on lobe fissure to improve the accuracy of path tracking. In order to validate the proposed system, we had tested our method in a database from 15 lung patients. In the experiment, the quantitative assessment shows that the proposed ACO method achieved the average F-measures of 80.9% and 82.84% in left and right lungs, respectively. The experiments indicate our method results more satisfied performance, and can help investigators detect lung lesion for further examination.
Algorithms2014, 7(4), 621-634; doi:10.3390/a7040621 - published 13 November 2014 Show/Hide Abstract
Abstract: This paper documents the research towards the development of a system based on Artificial Neural Networks to predict muscle force patterns of an athlete during cycling. Two independent inverse problems must be solved for the force estimation: evaluation of the kinematic model and evaluation of the forces distribution along the limb. By solving repeatedly the two inverse problems for different subjects and conditions, a training pattern for an Artificial Neural Network was created. Then, the trained network was validated against an independent validation set, and compared to evaluate agreement between the two alternative approaches using Bland-Altman method. The obtained neural network for the different test patterns yields a normalized error well below 1% and the Bland-Altman plot shows a considerable correlation between the two methods. The new approach proposed herein allows a direct and fast computation for the inverse dynamics of a cyclist, opening the possibility of integrating such algorithm in a real time environment such as an embedded application.
Algorithms2014, 7(4), 608-620; doi:10.3390/a7040608 - published 3 November 2014 Show/Hide Abstract
Abstract: We design practical implementations of data structures for compressing bit-vectors to support efficient rank-queries (counting the number of ones up to a given point). Unlike previous approaches, which either store the bit vectors plainly, or focus on compressing bit-vectors with low densities of ones or zeros, we aim at low entropies of higher order, for example 101010...10. Our implementations achieve very good compression ratios, while showing only a modest increase in query time.
Algorithms2014, 7(4), 597-607; doi:10.3390/a7040597 - published 28 October 2014 Show/Hide Abstract
Abstract: The Eight-Scale parameter adjustment is a natural extension of Adaptive Inverse Hyperbolic Tangent (AIHT) algorithm. It has long been known that the Human Vision System (HVS) heavily depends on detail and edge in the understanding and perception of scenes. The main goal of this study is to produce a contrast enhancement technique to recover an image from blurring and darkness, and at the same time to improve visual quality. Eight-scale coefficient adjustments can provide a further local refinement in detail under the AIHT algorithm. The proposed Eight-Scale Adaptive Inverse Hyperbolic Tangent (8SAIHT) method uses the sub-band to calculate the local mean and local variance before the AIHT algorithm is applied. This study also shows that this approach is convenient and effective in the enhancement processes for various types of images. The 8SAIHT is also capable of adaptively enhancing the local contrast of the original image while simultaneously extruding more on object details.
Algorithms2014, 7(4), 582-596; doi:10.3390/a7040582 - published 23 October 2014 Show/Hide Abstract
Abstract: Recently, developing efficient processing techniques in spatio-temporal databases has been a much discussed topic. Many applications, such as mobile information systems, traffic control system, and geographical information systems, can benefit from efficient processing of spatio-temporal queries. In this paper, we focus on processing an important type of spatio-temporal queries, the K-nearest neighbor (KNN) queries. Different from the previous research, the locations of objects are located by the sensors which are deployed in a grid-based manner. As the positioning technique used is not the GPS technique, but the sensor network technique, this results in a greater uncertainty regarding object location. With the uncertain location information of objects, we try to develop an efficient algorithm to process the KNN queries. Moreover, we design a probability model to quantify the possibility of each object being the query result. Finally, extensive experiments are conducted to demonstrate the efficiency of the proposed algorithms.