Showing 1–12 of 380 results
3D Action Recognition from Novel Viewpoints
We propose a human pرایگان!
We propose a human pose representation model that transfers human poses acquired from different unknown views to a view-invariant high-level space. The model is a deep convolutional neural network and requires a large corpus of multiview training data which is very expensive to acquire. Therefore, we propose a method to generate this data by fitting synthetic 3D human models to real motion capture data and rendering the human poses from numerous viewpoints. While learning the CNN model, we do not use action labels but only the pose labels after clustering all training poses into k clusters. The proposed model is able to generalize to real depth images of unseen poses without theneed for re-training or fine-tuning. Real depth videos are passed through the model frame-wise to extract viewinvariant features. For spatio-temporal representation, we propose group sparse Fourier Temporal Pyramid which robustly encodes the action specific most discriminative output features of the proposed human pose model. Experiments on two multiview and three single-view benchmark datasets show that the proposed method dramatically outperforms existing state-of-the-art in action recognition
A Bluetooth Routing Protocol Using Evolving Fuzzy Neural Networks
In this paper, a rouرایگان!
In this paper, a routing protocol which utilizes the characteristics of Bluetooth technology is proposed for Bluetooth-based mobile ad hoc networks. The routing tables are maintained in the master devices and the routing zone radius for each table is adjusted dynamically by using evolving fuzzy neural networks. Observing there exists some useless routing packets which are helpless to build the routing path and increase the network loads in the existing ad hoc routing protocols, we selectively use multiple unicasts or one broadcast when the destination device is out of the routing zone radius coverage of the routing table. The simulation results show that the dynamic adjustment of the routing table size in each master device results in much less reply time of routing request, fewer request packets and useless packets compared with two representative protocols, Zone Routing Protocol and Dynamic Source Routing
A Case-based Data Warehousing Courseware
Data warehousing isرایگان!
Data warehousing is one of the important approaches for data integration and data preprocessing. The objective of this project is to develop a web-based interactive courseware to help beginner data warehouse designers to reinforce the key concepts of data warehousing using a case study approach. The case study is to build a data warehouse for a university student enrollment prediction data mining system. This data warehouse is able to generate summary reports as input data files for a data mining system to predict future student enrollment. The data sources include: (1) the enrollment data from California State University, Sacramento and (2) the related public data of California. The ourseware is designed to build the data warehouse systematically using a set of 4 demonstrations covering the following data warehousing topics: fundamentals, design principle, building an enterprise data warehouse using incremental approach, and aggregation.
A cloud based and Android supported scalable home automation system
In this paper, an Anرایگان!
In this paper, an Android based home automation system that allows multiple users to control the appliances by an Android application or through a web site is presented. The system has three hardware components: a local device to transfer signals to home appliances, a web server to store customer records and support services to the other components, and a mobile smart device running Android application. Distributed cloud platforms and Google services are used to support messaging between the components .The prototype implementation of the proposed system is evaluated based on the criteria considered after the requirement analysis for an adequate home automation system. The paper presents the outcomes of a survey carried out regarding the properties of home automation systems, and also the evaluation results of the experimental tests conducted with volunteers on running prototype.
A cloud computing framework on demand side management game in smart energy hubs
The presence of enerرایگان!
The presence of energy hubs in the future vision of energy networks creates an opportunity for electrical engineers to move toward more efficient energy systems. At the same time, it is envisioned that smart grid can cover the natural gas network in the near future. This paper modifies the classic Energy Hub model to present an upgraded model in the smart environment entitling ‘‘Smart Energy Hub’’. Supporting real time, two-way communication between utility companies and smart energy hubs, and allowing intelligent infrastructures at both ends to manage power consumption necessitates large-scale real-time computing capabilities to handle the communication and the storage of huge transferable data. To manage communications to large numbers of endpoints in a secure, scalable and highly-available environment, in this paper we provide a cloud computing framework for a group of smart energy hubs. Then, we use game theory to model the demand side management among the smart energy hubs. Simulation results confirm that at the Nash equilibrium, peak to average ratio of the total electricity demand reduces significantly and at the same time the hubs will pay less considerably for their energy bill.
A Cluster Based Multi-Radio Multi-Channel Assignment Approach in Wireless Mesh Networks
Wireless mesh networرایگان!
Wireless mesh networks (WMNs) are receiving increasing attention as an effective means to provide broadband internet. Throughput is a major QoS in WMNs keeping in view of their perceived application areas and others being connectivity and reliability. WMNs use multiple radios and orthogonal communication channels to reduce interference and increase throughput and at the same time providing path redundancy, reliability and connectivity. In our work we propose a cluster based channel assignment scheme for WMNs and assume that the wireless radio interfaces are equipped with IEEE 802.11 network interface cards (NICs). We have extensively simulated our work using ns-2 network simulator and compared it against well-known channel assignment techniques and our result exhibits a significant increase in throughput.
A collaborative methodology for tacit knowledge management: Application to scientific research
Tacit knowledge, whiرایگان!
Tacit knowledge, which refers to the know-how, is critical to understand and reuse since it is located in the human heads. It represents the foremost element for human and team evaluation. Seeking for tacit knowledge is achieved only by communicating with the concerned persons, which makes losing it axiomatic if people leave their work without documenting their know-how. Thus, providing a collaborative environment based on a common conceptualization of the domain to formalize the experts’ knowledge and to share their outcomes is required. However, some barriers pertaining to cultural and social factors such as personality traits impede capturing the conceptual model. To cope with these issues, we have proposed a generic two-step methodology that copes with human barriers when capturing the domain experts’ tacit knowledge, their skills, and seeds terms in order to converge to a common knowledge representation. Considering the scientific research management as a use case, we followed the proposed methodology to formalize our scientific research knowledge in the context of network and communication research field. Based on the generated ontology, we have developed a semantic web platform that allows collaboratively annotating experts’ knowledge in a computer interpretable format that can be shared and reused by human and machines. Our evaluation is based on end users’ quality of experience and feedbacks.
A comparison of neural network models, fuzzy logic, and multiple linear regression for prediction of hatchability
Application of approرایگان!
Application of appropriate models to approximate the performance function warrants more precise prediction and helps to make the best decisions in the poultry industry. This study reevaluated the factors affecting hatchability in laying hens from 29 to 56 wk of age. Twenty-eight data lines representing 4 inputs consisting of egg weight, eggshell thickness, egg sphericity, and yolk/albumin ratio and 1 output, hatchability, were obtained from the literature and used to train an artificial neural network (ANN). The prediction ability of ANN was compared with that of fuzzy logic to evaluate the fitness of these 2 methods. The models were compared using R2, mean absolute deviation (MAD), mean squared error (MSE), mean absolute percentage error (MAPE), and bias. The developed model was used to assess the relative importance of each variable on the hatchability by calculating the variable sensitivity ratio. The statistical evaluations showed that the ANN-based model predicted hatchability more accurately than fuzzy logic. The ANNbased model had a higher determination of coefficient (R2 = 0.99) and lower residual distribution (MAD = 0.005; MSE = 0.00004; MAPE = 0.732; bias = 0.0012) than fuzzy logic (R2 = 0.87; MAD = 0.014; MSE = 0.0004; MAPE = 2.095; bias = 0.0046). The sensitivity analysis revealed that the most important variable in the ANN-based model of hatchability was egg weight (variable sensitivity ratio, VSR = 283.11), followed by yolk/albumin ratio (VSR = 113.16), eggshell thickness (VSR = 16.23), and egg sphericity (VSR = 3.63). The results of this research showed that the universal approximation capability of ANN made it a powerful tool to approximate complex functions such as hatchability in the incubation process
A Configurational Comparative Method to Identify Multiple Pathways to Improve Patient-Centered Medical Home Models
This brief focuses oرایگان!
This brief focuses on using fuzzy set Qualitative Comparative Analysis (fsQCA) to evaluate patientcentered medical home (PCMH) models. It is part of a series commissioned by the Agency for Healthcare Research and Quality (AHRQ) and developed by Mathematica Policy Research under contract, with input from other nationally recognized thought leaders in research methods and PCMH models. The series is designed to expand the toolbox of methods used to evaluate and refine PCMH models. The PCMH is a primary care approach that aims to improve quality, cost, and patient and provider experience. PCMH models emphasize patient-centered, comprehensive, coordinated, accessible care, and a systematic focus on quality and safety
A data mining approach for grouping and analyzing trajectories of care using claim data
Background: With th...رایگان!
Background: With the increasing burden of chronic diseases, analyzing and understanding trajectories of care is essential for efficient planning and fair allocation of resources. We propose an approach based on mining claim data.to support the exploration of trajectories of car
Methods: A clustering of trajectories of care for breast cancer was performed with Formal Concept Analysis. We exported Data from the French national casemix system, covering all inpatient admissions in the country. Patients admitted for breast cancer surgery in 2009 were selected and their trajectory of care was recomposed with all hospitalizations occuring within one year after surgery. The main diagnoses of hospitalizations were used to produce morbidity profiles. Cumulative hospital costs were computed for each profile
A DFO technique to calibrate queueing models
A crucial step in thرایگان!
A crucial step in the modeling of a system is to determine the values of the parameters to use in the model. In this paper we assume that we have a set of measurements collected from an operational system, and that an appropriate model of the system (e.g., based on queueing theory) has been developed Not infrequently proper values for certain parameters of this model may be difficult to estimate from available data (because the corresponding parameters have unclear physical meaning or because they cannot be directly obtained from available measurements, etc.). Hence, we need a technique to determine the missing parameter values, i.e., to calibrate the model. As an alternative to unscalable “brute force” technique, we propose to view model calibration as a nonlinear optimization problem with constraints. The resulting method is conceptually simple and easy to implement. Our contribution is twofold. First, we propose improved definitions of the “objective function” to quantify the “distance” between performance indices produced by the model and the values obtained from measurements. Second, we develop a customized derivative-free optimization (DFO) technique hose original feature is the ability to allow temporary constraint violations. This technique allows us to solve this optimization problem accurately, thereby providing the “right” parameter values. We illustrate
our method using two simple real-life case studies.
A Fair Solution to DNS Amplification Attacks
Recent serious securرایگان!
Recent serious security incidents reported several attackers employing IP spoofing to massively exploit recursive name servers to amplify DDoS attacks against numerous networks. DNS amplification attack scenarios utilize DNS servers mainly for performing bandwidth consumption DoS attacks. This kind of attack takes advantage of the fact that DNS response messages may be substantially larger than DNS query messages. In this paper we present a novel, simple and practical scheme that enable administrators to distinguish between genuine and falsified DNS replies. The proposed scheme, acts proactively by monitoring in real time DNS traffic and alerting security supervisors when necessary. It also acts reactively in co- operation with the firewalls by automatically updating rules to ban bogus packets. Our analysis and the corresponding experimental results show that the proposed scheme offers an effective solution, when the specific attack unfolds.