Information2015, 6(1), 23-48; doi:10.3390/info6010023 - published 27 January 2015 Show/Hide Abstract
Abstract: This paper builds an integrated framework of measures of information based on the Model for Information (MfI) developed by the author. Since truth is expressed using information, an analysis of truth depends on the nature of information and its limitations. These limitations include those implied by the geometry of information and those implied by the relativity of information. This paper proposes an approach to truth and truthlikeness that takes these limitations into account by incorporating measures of the quality of information. Another measure of information is the amount of information. This has played a role in two important theoretical difficulties—the Bar-Hillel Carnap paradox and the “scandal of deduction”. This paper further provides an analysis of the amount of information, based on MfI, and shows how the MfI approach can resolve these difficulties.
Information2015, 6(1), 14-22; doi:10.3390/info6010014 - published 22 January 2015 Show/Hide Abstract
Abstract: Adsorption is considered to be one of the most effective technologies widely used in global environmental protection areas. Modeling of experimental adsorption isotherm data is an essential way for predicting the mechanisms of adsorption, which will lead to an improvement in the area of adsorption science. In this paper, we employed three isotherm models,namely: Langmuir, Freundlich, and Dubinin-Radushkevich to correlate four sets of experimental adsorption isotherm data, which were obtained by batch tests in lab. The linearized and non-linearized isotherm models were compared and discussed. In order to determine the best fit isotherm model, the correlation coefficient (r2) and standard errors (S.E.) for each parameter were used to evaluate the data. The modeling results showed that non-linear Langmuir model could fit the data better than others, with relatively higher r2 values and smaller S.E. The linear Langmuir model had the highest value of r2, however, the maximum adsorption capacities estimated from linear Langmuir model were deviated from the experimental data.
Information2015, 6(1), 3-13; doi:10.3390/info6010003 - published 20 January 2015 Show/Hide Abstract
Abstract: Multi-user detection is an effective method to reduce multiple access interference in code division multiple access (CDMA) systems. This paper discusses a signal subspace based blind adaptive multiuser detector and a Kalman filtering blind adaptive multiuser detector. Combining them together, a new Kalman filtering blind adaptive multiuser detector based on a tracking algorithm of the signal subspace is proposed. Analysis and simulation show that the proposed blind multiuser detector achieves better suppression of multiple access interference and has a higher convergence rate.
Information2014, 5(4), 652-660; doi:10.3390/info5040652 - published 3 December 2014 Show/Hide Abstract
Abstract: Conventional fluorescent light sources, as well as incandescent light sources are gradually being replaced by Light Emitting Diodes (LEDs) for reducing power consumption in the image display area for multimedia application. An LED light source requires a controller with a low-power operation. In this paper, a low-power technique using adiabatic operation is applied for the implementation of LED controller with a stable constant-current, a low-power and low-heat function. From the simulation result, the power consumption of the proposed LED controller using adiabatic operation was reduced to about 87% in comparison with conventional operation with a constant VDD. The proposed circuit is expected to be an alternative LED controller which is sensitive to external conditions such as heat.
Information2014, 5(4), 634-651; doi:10.3390/info5040634 - published 1 December 2014 Show/Hide Abstract
Abstract: To surface the Deep Web, one crucial task is to predict whether a given web page has a search interface (searchable HyperText Markup Language (HTML) form) or not. Previous studies have focused on supervised classification with labeled examples. However, labeled data are scarce, hard to get and requires tediousmanual work, while unlabeled HTML forms are abundant and easy to obtain. In this research, we consider the plausibility of using both labeled and unlabeled data to train better models to identify search interfaces more effectively. We present a semi-supervised co-training ensemble learning approach using both neural networks and decision trees to deal with the search interface identification problem. We show that the proposed model outperforms previous methods using only labeled data. We also show that adding unlabeled data improves the effectiveness of the proposed model.