A new method for expiration code detection and recognition using gabor features based collaborative representation

Text in images and video contains important information for visual content understanding, indexing, and recognizing. Extraction of this information involves preprocessing, localization and extraction of the text from a given image. In this paper, we propose a novel expiration code detection and recognition algorithm by using Gabor features and collaborative representation based classification. The proposed system consists of four steps: expiration code location, character isolation, Gabor features extraction and characters recognition. For expiration code detection, the Gabor energy (GE) and the maximum energy difference (MED) are extracted. The performance of the recognition algorithm is tested over three Gabor features: GE, magnitude response (MR) and imaginary response (IR). The Gabor features are classified based on collaborative representation based classifier (GCRC). To encompass all frequencies and orientations, downsampling and principal component analysis (PCA) are applied in order to reduce the features space dimensionality. The effectiveness of the proposed localization algorithm is highlighted and compared with other existing methods. Extensive testing shows that the suggested detection scheme outperforms existing methods in terms of detection rate for large image database. Also, GCRC show very competitive results compared with Gabor feature sparse representation based classification (GSRC). Also, the proposed system outperforms the nearest neighbor (NN) classifier and the collaborative representation based classification (CRC).     

改进型Helsinki协议的串空间模型分析

Helsinki协议是ISO/IEC DIS 11770-3 中提出的重要认证协议,由于协议受到来自内部的攻击,Mithcell-Yeun对其进行了改进.但改进后协议的安全性仍未得到确认,为了验证改进协议是否满足其安全目标,利用串空间模型对协议进行了建模和分析.通过分析极小元所在串与其它串的关系说明协议的一致性,通过对理想的分析说明协议的保密性.结果表明改进型协议满足其安全要求,原协议存在安全缺陷的原因是最小元可能存在M1串上,这为 Mithcell-Yeun的改进提供了理论的证明与依据.     

Using shorelines for autonomous air vehicle guidance

We suggest the use of extended landmarks, such as shorelines, creeks, tree lines, and railroads, as well as roads for autonomous navigation of an unmanned air vehicle (UAV). In particular, we recommend the use of shorelines, because of their common availability, their ease of detection, and their significance in terms of events happening along them. Monitoring coastlines and waterways from low flying UAVs has many applications for military and civilian use. We report the development of a vision system that has enabled a prototype UAV to follow shorelines autonomously (without requiring maps or GPS). Using a near-infrared sensor the vision system distinguishes water from land (irrespective of water’s color) and issues commands to the autopilot to follow the coastline or the riverbank. One insight of this problem is that the control algorithm could be integrated deeply with the vision system. This has the benefit of delaying smoothing/regularization so that it could occur in the context of the control coordinate system rather than the image or ground coordinate system. The algorithm itself is simple, but it possibly points the way to future algorithms which could more closely couple image processing and control. Furthermore, the experience gained in this work may be of value in the development of vision systems for following other types of paths.     

Multiple objects tracking in the presence of long-term occlusions

We present a robust object tracking algorithm that handles spatially extended and temporally long object occlusions. The proposed approach is based on the concept of “object permanence” which suggests that a totally occluded object will re-emerge near its occluder. The proposed method does not require prior training to account for differences in the shape, size, color or motion of the objects to be tracked. Instead, the method automatically and dynamically builds appropriate object representations that enable robust and effective tracking and occlusion reasoning. The proposed approach has been evaluated on several image sequences showing either complex object manipulation tasks or human activity in the context of surveillance applications. Experimental results demonstrate that the developed tracker is capable of handling several challenging situations, where the labels of objects are correctly identified and maintained over time, despite the complex interactions among the tracked objects that lead to several layers of occlusions.     

River run: an interactive GIS and dynamic graphing website for decision support and exploratory data analysis of water quality parameters of the lower Cape Fear river

To visualize the water quality of the Lower Cape Fear River basin a Decision Support System (DSS) web site was developed which incorporates dynamic graphing and Geographic Information System (GIS) software. Together, these two software packages enable users to interact with water quality variables in order to investigate the spatial and temporal complexity of the data. The result of this research is that graphing and GIS technology are capable of being implemented over the Internet and that complex water quality data can be successfully disseminated using this medium. However, there remains a need to seamlessly link the graphing and GIS software which will make the visualization tools much more successful at communicating the complex spatial and temporal data.     

What makes mobile computer supported cooperative work mobile? Towards a better understanding of cooperative mobile interactions

Despite the high availability of mobile phones and personal digital assistants with online capabilities, mobile computer supported cooperative work is still in its infancy. So far, only little is known about the distinct attributes of mobile cooperative work in comparison to its stationary counterpart. Across which dimensions does cooperation via mobile devices differ from traditional hard-wired settings and what implications have to be drawn for future research? To bring more light to this question, we conducted an experimental business-case at Frankfurt University with 16 graduate students and analysed their collaborative behaviour across mobile and non-mobile channels of communication over a 5 week period. We find that mobile messages differed from stationary messages in terms of size and that the use of mobile emails prevailed over stationary emails under conditions of stress. Finally, we found that the social structure of mobile communication corresponded with the structure of stationary communication. This indicates that mobile communication technologies support existing communication relations rather than creating new relations. From the perspective of system designers, these results may serve as practical insights into the user behaviour of mobile technologies and might support the future development of mobile computer supported cooperative work environments.     

Cattle behavior recognition based on feature fusion under a dual attention mechanism

In recent years, artificial intelligence has been widely used in such fields as agricultural informatization, precision agriculture and precision animal husbandry. Due to limited research on deep learning in real-time agricultural and pastoral situations, deep learning and computer vision have become very important topics in the agricultural field. Recent studies have shown that the fusion of features under different attention mechanisms will help advance the utilization of such features, and will thus influence the accuracy and generalization ability of the models used. In this paper, we propose a lightweight network structure based on feature fusion under a dual attention mechanism with the same activation and joint loss functions. More specifically, we propose an innovative method to improve the network structure of two different attention mechanisms, and achieve feature fusion by combining the two. At the same time, we keep the activation functions consistent with those of the original network structure, and we develop a joint loss function to expand the use of various features. We also take the novel approach of applying the trajectory behavior analysis method to walking and standing. Experiments using both a publicly available data set and a data set obtained from a farm show that our algorithm achieves state-of-the-art performance in terms of accuracy and generalization ability, as compared to other methods.     

Efficient quantum inspired meta-heuristics for multi-level true colour image thresholding

Thresholding is a commonly used simple and effective technique for image segmentation. The computational time in multi-level thresholding significantly increases with the level of computation because of exhaustive searching, adding to exponential growth of computational complexity. Hence, in this paper, the features of quantum computing are exploited to introduce four different quantum inspired meta-heuristic techniques to accelerate the execution of multi-level thresholding. The proposed techniques are Quantum Inspired Genetic Algorithm, Quantum Inspired Simulated Annealing, Quantum Inspired Differential Evolution and Quantum Inspired Particle Swarm Optimization. The effectiveness of the proposed techniques is exhibited in comparison with the backtracking search optimization algorithm, the composite DE method, the classical genetic algorithm, the classical simulated annealing, the classical differential evolution and the classical particle swarm optimization for ten real life true colour images. The experimental results are presented in terms of optimal threshold values for each primary colour component, the fitness value and the computational time (in seconds) at different levels. Thereafter, the quality of thresholding is judged in terms of the peak signal-to-noise ratio for each technique. Moreover, statistical test, referred to as Friedman test, and also median based estimation among all techniques, are conducted separately to judge the preeminence of a technique among them. Finally, the performance of each technique is visually judged from convergence plots for all test images, which affirms that the proposed quantum inspired particle swarm optimization technique outperforms other techniques.     

Optimal vibration control for nonlinear systems of tracked vehicle half-car suspensions

International Journal of Control, Automation and Systems - Technique of optimal vibration control and simulation for vehicle active suspension systems is developed. Considered the nonlinear damping...     

Many-objective evolutionary optimization based on reference points

Many-objective optimization problems are common in real-world applications, few evolutionary optimization methods, however, are suitable for solving them up to date due to their difficulties. A reference points-based evolutionary algorithm (RPEA) was proposed in this paper to solve many-objective optimization problems. The aim of this study is to exploit the potential of the reference points-based approach to strengthen the selection pressure towards the Pareto front while maintaining an extensive and uniform distribution among solutions. In RPEA, a series of reference points with good performances in convergence and distribution are continuously generated according to the current population to guide the evolution. Furthermore, superior individuals are selected based on the evaluation of each individual by calculating the distances between the reference points and the individual in the objective space. The proposed algorithm was applied to seven benchmark optimization problems and compared with ɛ-MOEA, HypE, MOEA/D and NSGA-III. The results empirically show that the proposed algorithm has a good adaptability to problems with irregular or degenerate Pareto fronts, whereas the other reference points-based algorithms do not. Moreover, it outperforms the other four in 8 out of 21 test instances, demonstrating that it has an advantage in obtaining a Pareto optimal set with good performances.