Extending IP to Low-Power, Wireless Personal Area Networks

Extending IP to low-power, wireless personal area networks (LoWPANs) was once considered impractical because these networks are highly constrained and must operate unattended for multiyear lifetimes on modest batteries. Many vendors embraced proprietary protocols, assuming that IP was too resource-intensive to be scaled down to operate on the microcontrollers and low-power wireless links used in LoWPAN settings. However, 6LoWPAN radically alters the calculation by introducing an adaptation layer that enables efficient IPv6 communication over IEEE 802.15.4 LoWPAN links.     

A neural network job-shop scheduler

This paper focuses on the development of a neural network (NN) scheduler for scheduling job-shops. In this hybrid intelligent system, genetic algorithms (GA) are used to generate optimal schedules to a known benchmark problem. In each optimal solution, every individually scheduled operation of a job is treated as a decision which contains knowledge. Each decision is modeled as a function of a set of job characteristics (e.g., processing time), which are divided into classes using domain knowledge from common dispatching rules (e.g., shortest processing time). A NN is used to capture the predictive knowledge regarding the assignment of operation’s position in a sequence. The trained NN could successfully replicate the performance of the GA on the benchmark problem. The developed NN scheduler was then tested against the GA, Attribute-Oriented Induction data mining methodology and common dispatching rules on a test set of randomly generated problems. The better performance of the NN scheduler on the test problem set compared to other methods proves the feasibility of NN-based scheduling. The scalability of the NN scheduler on larger problem sizes was also found to be satisfactory in replicating the performance of the GA.     

The use of data mining and neural networks for forecasting stock market returns

It has been widely accepted by many studies that non-linearity exists in the financial markets and that neural networks can be effectively used to uncover this relationship. Unfortunately, many of these studies fail to consider alternative forecasting techniques, the relevance of input variables, or the performance of the models when using different trading strategies. This paper introduces an information gain technique used in machine learning for data mining to evaluate the predictive relationships of numerous financial and economic variables. Neural network models for level estimation and classification are then examined for their ability to provide an effective forecast of future values. A cross-validation technique is also employed to improve the generalization ability of several models. The results show that the trading strategies guided by the classification models generate higher risk-adjusted profits than the buy-and-hold strategy, as well as those guided by the level-estimation based forecasts of the neural network and linear regression models.     

A bibliography of applications of operational research in West Africa

There is considerable interest in the potential for using operational research (O.R.) in developing countries. One sign of this is the formation of new societies for O.R. scientists in countries and regions where no such society had existed. Since 2003, such societies have been formed in several parts of Africa. This paper focuses on West Africa, and presents a bibliography of papers relating to applications of O.R. in the nations of this part of the continent. The paper describes the way in which the bibliography was collated and discusses the overall picture that the list of papers presents of the state of O.R. in the 18 countries that are considered.     

Online Palmprint Identification System for Civil Applications

In this paper, a novel biometric identification system is presented to identity a person's identity by his/her palmprint. In contrast to existing palmprint systems for criminal applications, the proposed system targets at the civil applications, which require identifying a person in a large database with high accuracy in real-time. The system is constituted by four major components: User Interface Module, Acquisition Module, Recognition Module and External Module. More than 7,000 palmprint images have been collected to test the performance of the system. The system can identify 400 palms with a low false acceptance rate, 0.02%, and a high genuine acceptance rate, 98.83%. For verification, the system can operate at a false acceptance rate, 0.017% and a false rejection rate, 0.86%. The execution time for the whole process including image collection, preprocessing, feature extraction and matching is less than 1 second.     

A comparative evaluation of length estimators of digital curves

This paper compares previously published length estimators in image analysis having digitized curves as input. The evaluation uses multigrid convergence (theoretical results and measured speed of convergence) and further measures as criteria. This paper also suggests a new gradient-based method for length estimation, and combines a previously proposed length estimator for straight segments with a polygonalization method.     

An experimental study on the role of 3D sound in augmented reality environment

Investigation of augmented reality (AR) environments has become a popular research topic for engineers, computer and cognitive scientists. Although application oriented studies focused on audio AR environments have been published, little work has been done to vigorously study and evaluate the important research questions of the effectiveness of three-dimensional (3D) sound in the AR context, and to what extent the addition of 3D sound would contribute to the AR experience.

Thus, we have developed two AR environments and performed vigorous experiments with human subjects to study the effects of 3D sound in the AR context. The study concerns two scenarios. In the first scenario, one participant must use vision only and vision with 3D sound to judge the relative depth of augmented virtual objects. In the second scenario, two participants must cooperate to perform a joint task in a game-based AR environment.

Hence, the goals of this study are (1) to access the impact of 3D sound on depth perception in a single-camera AR environment, (2) to study the impact of 3D sound on task performance and the feeling of ‘human presence and collaboration’, (3) to better understand the role of 3D sound in human–computer and human–human interactions, (4) to investigate if gender can affect the impact of 3D sound in AR environments. The outcomes of this research can have a useful impact on the development of audio AR systems, which provide more immersive, realistic and entertaining experiences by introducing 3D sound. Our results suggest that 3D sound in AR environment significantly improves the accuracy of depth judgment and improves task performance. Our results also suggest that 3D sound contributes significantly to the feeling of human presence and collaboration and helps the subjects to ‘identify spatial objects’.     

Unsupervised image segmentation using a simple MRF model with a new implementation scheme

A simple Markov random field model with a new implementation scheme is proposed for unsupervised image segmentation based on image features. The traditional two-component MRF model for segmentation requires training data to estimate necessary model parameters and is thus unsuitable for unsupervised segmentation. The new implementation scheme solves this problem by introducing a function-based weighting parameter between the two components. Using this method, the simple MRF model is able to automatically estimate model parameters and produce accurate unsupervised segmentation results. Experiments demonstrate that the proposed algorithm is able to segment various types of images (gray scale, color, texture) and achieves an improvement over the traditional method.     

Neuronal Signal Transduction Aided by Noise at Threshold and at Saturation

A classical model of neuronal signal transmission describing the presence of both a threshold and a saturation in the neuron response is considered. This model is used to analyze the transduction by the neuron of various types of information-carrying input signals in the presence of noise. Improvement by noise of the performance via stochastic resonance is established for transmission in both the threshold and the saturation regimes. Stochastic resonance at saturation is a novel form, expressing that the distortion experienced by large input signals transmitted at saturation, can be reduced by addition of noise.