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Compressed Sensing (CS) is a recently emerged framework for simultaneous sampling and compression of signals that are sparse or compressible in some representation. Besides signal reconstruction, the CS framework is often adopted for compressive parameter estimation. Performance metrics commonly used in CS are well suited for performance evaluation in terms of recovery rates but provide little insight...
Cognitive radio (CR) is considered as a key enabling technology for dynamic spectrum access to improve spectrum efficiency. Although the CR concept was invented with the core idea of realizing "cognition", the research on measuring CR cognition capabilities and intelligence is largely open. Deriving the intelligence capabilities of CR not only can lead to the development of new CR technologies,...
Abstract-We consider a partially interdependent network and develop mathematical equations relating the fractional size of the connected component of the network, surviving the cascading failure, to the intra-layer degree distribution of the nodes. We show that these system equations can be mathematically analyzed and closed form expressions for the metrics of robustness can be obtained for the Erdos-Renyi...
Authorship identification is a problem of data mining and classification. There are numerous methods and algorithms have been published to understand its nature. Although, researchers still investigate best and simple solutions due to its heterogeneous and multilingual characteristics. This study introduced new authorship identification process based on artificial neural network (ANN) model using...
In this paper, we report our primary findings in detecting spam profiles on Facebook social media. As we already know, spam profiles on social media especially Facebook is often used as a place to spread spam. Spammers generally use wall post feature on Facebook to spread spam. In addition spammers are also targeting page on Facebook which is a community of many people. In this community, spammers...
An e-commerce website provides a platform for merchants to sell products to customers. While most existing research focuses on providing customers with personalized product suggestions by recommender systems, in this paper, we consider the role of merchants and introduce a parallel problem, i.e., how to select the most valuable customers for a merchant? Accurately answering this question can not only...
Classifying educational resources such as videos and articles can be challenging in low-resource languages due to lack of appropriate tools and sufficient labeled data. To overcome this problem, a crosslingual classification method that utilizes resources created in one high-resource language, such as English, to perform classification in many low-resource languages, is proposed. Data scarcity issue...
Aiming at the information overload caused by rich resources of Broadcast Television programs, this paper puts forward Broadcast Television Programs Recommendation Technology based on user clustering. According to user rating data and programs broadcasting data, we cluster users by the improved K-MEANS algorithm, divide the users with similar viewing preference into the same community groups, and generate...
Cycle time prediction represents a challenging problem in complex manufacturing scenarios. This paper demonstrates an approach that uses genetic programming (GP) and effective process time (EPT) to predict cycle time using a discrete event simulation model of a production line, an approach that could be used in complex manufacturing systems, such as a semiconductor fab. These predictive models could...
We study the graduate admission process in American universities from students' perspective. Our goal is to build a decision support model that provides candidates with pertinent information as well as the ability to assess their choices during the application process. This model is driven by extensive machine learning based analysis of large amounts of historic data available on the web. Our analysis...
Identifying brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data is an important advanced subject in neuroinformatics in recent years, where the learning method based on bayesian networks (BN) has become a new hot topic in the field. This paper proposes a new method to learn the brain effective connectivity network structure by combining ant colony optimization...
Heating, Ventilation, and Air Conditioning (HVAC) systems account for a large share of the energy consumed in commercial buildings. Simple strategies such as adjusting HVAC set point temperatures can lead to significant energy savings at no additional financial costs. Despite their promising results, it is currently unclear if such operation strategies can have unintended consequences on other building...
Hiring workers under seasonal recruiting contracts causes significant variation of workers skills in the vineyards. This leads to inconsistent workers performance, reduction in harvesting efficiency, and increasing in grape losses rates. The objective of this research is to investigate how the variation in workers experience could impact vineyard harvesting productivity and operational cost. The complexity...
The main aim of this research is investigate the relationship between reverse logistics barriers and reverse logistics performance measurement of electronics industry in Thailand. A conceptual model is developed by proposing eight hypotheses. The model is examined using structural equation modeling (SEM). Data are collected via a distributed questionnaire to electronic companies of Thailand. The interesting...
Nowadays it is paramount to design recycling supply chains efficiently because of both economic and environmental concerns. In this article, a dynamic fuzzy mathematical model for a recycling supply chain network is presented with the dual aims of total costs and the environmental impacts minimization. The Artificial Bee Colony algorithm is utilized to discover Pareto optimal solutions. To show the...
The massive amounts of data processed by information systems raise the importance of detailed database performance analysis. Column-oriented data stores are becoming increasingly popular in big data appliances. This paper identifies database performance factors on the basis of empirical studies on a custom implementation. To summarize the research, a simple performance mathematical model has been...
Since our last paper, cyber attacks have shown no evidence of declining in frequency or sophistication. We claim that applying isolation zones is an effective way to defend cyber systems; our team proposes a simulation and mathematical model that provide numerical data that supports this claim. This paper extends our earlier cyber zone defense (CZD) framework in two critical ways. First, we relax...
In this work, the piezoresistive accelerometer step and impulse responses are enhanced by using appropriate parameters (damping rate and frequency range). The proposed parameters provide stable and accurate accelerometer. Therefore, tests were conducted by computer simulation and the obtained results have shown the difference between the accelerometer with the proposed parameters and the accelerometer...
Batik is one of Indonesian cultural products in the form of traditional fabrics which has many motifs. One of famous batik is madurese batik. Here, Madura is one of Indonesia islands which has a distinctive motif in terms of colors and motif characteristic that distinguishes it from other regions in Indonesia, but the Madurese communities themselves have not understood the meaning of Madurese batik...
Spectrum sensing is important step for better utilization of unused frequency band in cognitive radio technology. In spectrum sensing, threshold determination plays the key role. In this paper, we analyze the performance metrics of cognitive radio network under conventional threshold and adaptive threshold. Conventional theory considers only one metric at a time i.e. either probability of false alarm...
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