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Investigation of the IOT Network of Packet Loss’s Long-Range Dependence and QOE
Issue:
Volume 2, Issue 1, March 2017
Pages:
1-9
Received:
4 January 2017
Accepted:
21 January 2017
Published:
20 February 2017
DOI:
10.11648/j.mlr.20170201.11
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Abstract: The Internet of things, including Internet technology, including wired and wireless networks. Internet of Things and the Internet is the relationship between the parent and the child. In this paper, we aim to study the Investigation on the network packet loss’s long-range dependence and QOE and gain a good result and conclusion. In order to better establish no-reference video quality assessment model considering the network packet loss and further gain a better QoE evaluation, so we build NS2 + MyEvalvid simulation platform to study the scale characteristic of the network packet loss, scale characteristic of packet loss through the influence of packet loss rate to influence QoE. The experimental results show that, packet loss processes have long-range dependence, the number of superimposed source N, shape parameter, Hurst parameter, the output link speed have impacts on long-range dependence. We came to the conclusion that when superimposed source N is more, the shape parameter is smaller, Hurst parameter is bigger, the output link speed is smaller, packet loss’s long range dependence is larger, packet loss rate is high.
Abstract: The Internet of things, including Internet technology, including wired and wireless networks. Internet of Things and the Internet is the relationship between the parent and the child. In this paper, we aim to study the Investigation on the network packet loss’s long-range dependence and QOE and gain a good result and conclusion. In order to better ...
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An Overview of Big Data Applications in Water Resources Engineering
Issue:
Volume 2, Issue 1, March 2017
Pages:
10-18
Received:
23 January 2017
Accepted:
7 February 2017
Published:
1 March 2017
DOI:
10.11648/j.mlr.20170201.12
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Abstract: One of the emerging challenges in the 21th century era is collecting and handling ‘Big Data’. The definition of big data changes from one area to other over time. Big data as its name implies is unstructured data that is very big, fast, hard and comes in many forms. Though the applications of big data was confined to information technology before 21st technology, now it is of emerging area in almost all engineering specializations. But for water managers/engineers, big data is showing big promise in many water related applications such as planning optimum water systems, detecting ecosystem changes through big remote sensing and geographical information system, forecasting/predicting/detecting natural and manmade calamities, scheduling irrigations, mitigating environmental pollution, studying climate change impacts etc. This study reviewed the basic information about big data, applications of big data in water resources engineering related studies, advantages and disadvantages of big data. Further, this study presented some of review of literature which has been done on big data applications in water resources engineering.
Abstract: One of the emerging challenges in the 21th century era is collecting and handling ‘Big Data’. The definition of big data changes from one area to other over time. Big data as its name implies is unstructured data that is very big, fast, hard and comes in many forms. Though the applications of big data was confined to information technology before 2...
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Recursive Algorithms of Closed Loop Identification with a Tailor Made Parameterization
Issue:
Volume 2, Issue 1, March 2017
Pages:
19-25
Received:
10 January 2017
Accepted:
14 February 2017
Published:
2 March 2017
DOI:
10.11648/j.mlr.20170201.13
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Abstract: In this paper, we propose two recursive algorithms for closed loop identification under the framework of a tailor made parameterization. The closed loop transfer function is parameterized using the parameters of the open loop plant model, and utilizing knowledge of the feedback controller. When the plant model and feedback controller are all polynomial forms, a recursive least squares method with forgetting schemes is proposed to verify that this recursive method can be regarded as regularization least squares problem. Furthermore we also extend the tailor made parameterization method to nonlinear system and nonlinear controller, then an iterative least squares algorithm is applied to solve one nonlinear optimization problem.
Abstract: In this paper, we propose two recursive algorithms for closed loop identification under the framework of a tailor made parameterization. The closed loop transfer function is parameterized using the parameters of the open loop plant model, and utilizing knowledge of the feedback controller. When the plant model and feedback controller are all polyno...
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A Decision Tree Algorithm Based System for Predicting Crime in the University
Adewale Opeoluwa Ogunde,
Gabriel Opeyemi Ogunleye,
Oluwaleke Oreoluwa
Issue:
Volume 2, Issue 1, March 2017
Pages:
26-34
Received:
27 January 2017
Accepted:
13 February 2017
Published:
2 March 2017
DOI:
10.11648/j.mlr.20170201.14
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Abstract: CRIME is one of the major problems encountered in any society and universities together with other higher institutions of learning are not exceptions. Thus, there is an urgent need for security agents and agencies to battle and eradicate crime. The Directorate of Students and Services Development (DSSD) are responsible for investigating and detecting criminals of any crime committed within the Redeemer’s University. DSSD faces major challenges when it comes to detecting the real perpetrators of several crimes. An improvement in their strategy can produce positive results and high success rates, which is the basic objective of this project. Several methods have been applied to solve similar problems in the literature but none was tailored to solving the problem in Redeemer’s University and other universities. This work therefore applied classification rule mining method to develop a system for detecting crimes in universities. Past data for both crimes and criminals were collected from DSSD. In order to develop and test the proposed model, the data was pre-processed to get clean and accurate data. The Iterative Dichotomiser 3 (ID3) decision tree algorithm obtained from WEKA mining software was used to analyze and train the data. The model obtained was then used to develop a system that showed the hidden relationships between the crime-related data, in form of decision trees. This result was then used as a knowledge base for the development of the crime prediction system. The developed system could effectively predict a list of possible suspects by simply analyzing data retrieved from the crime scene with already existing data in the database. This system has all the potentials of helping the students’ affairs department and security apparatus of any university and other institutions to quickly detect either the real or possible perpetrators of crimes in the system.
Abstract: CRIME is one of the major problems encountered in any society and universities together with other higher institutions of learning are not exceptions. Thus, there is an urgent need for security agents and agencies to battle and eradicate crime. The Directorate of Students and Services Development (DSSD) are responsible for investigating and detecti...
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Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis
Issue:
Volume 2, Issue 1, March 2017
Pages:
35-50
Received:
12 January 2017
Accepted:
16 February 2017
Published:
3 March 2017
DOI:
10.11648/j.mlr.20170201.15
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Abstract: Face recognition has long been a goal of computer vision, but only in recent years reliable automated face recognition has become a realistic target of biometrics research. In this paper the contribution of classifier analysis to the Face Biometrics Verification performance is examined. It refers to the paradigm that in classification tasks, the use of multiple observations and their judicious fusion at the data, hence the decision fusions at different levels improve the correct decision performance. The fusion tasks reported in this work were carried through fusion of two well-known face recognizers, ICA I and ICA II. It incorporates the decision at matching score level, a novel fusion strategy is employed; the Likelihood Ratio Fusion within scores. This strategy increases the accuracy of the face recognition system and at the same time reduces the limitations of individual recognizer. The performance of the analysis studies were tested based on three different face databases ORL 94, Indian face database and eNTERFACE2005 Dynamic Face Database and the simulation results are showed a significant performance achievements.
Abstract: Face recognition has long been a goal of computer vision, but only in recent years reliable automated face recognition has become a realistic target of biometrics research. In this paper the contribution of classifier analysis to the Face Biometrics Verification performance is examined. It refers to the paradigm that in classification tasks, the us...
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