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Journal articles on the topic 'Estimation de l'horizon mobile'

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Author: Grafiati

Published: 14 September 2024

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1

THIVOLLE-CAZAT (Alain) and PIGNARD (Gérôme). "Estimation du volume de bois résineux disponible en France à l'horizon 2010." Revue Forestière Française, no. 3-4 (2001): 317. http://dx.doi.org/10.4267/2042/5243.

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2

Kumar, Shailesh, and Anant R. Koppar. "Software Estimation Framework for Mobile Application Projects." International Journal of Productivity Management and Assessment Technologies 7, no. 2 (July 2019): 26–40. http://dx.doi.org/10.4018/ijpmat.2019070102.

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As mobile devices are becoming the primary access channels for information, the authors need to have accurate effort estimation model for mobile application projects. In this paper the authors discuss “Mobile application estimation framework” that was designed based on 14 mobile application projects and was validated against 5 mobile application projects. In this paper the authors discuss the estimation framework for both native/hybrid mobile application projects and mobile web application projects. The proposed “Mobile application estimation framework” provides comprehensive coverage for various factors involved in mobile estimation such as layer-wise components, horizontal components and others. The estimation framework also considers the cost drivers and is used as effort adjustment factor. The proposed mobile application estimation framework achieved the MMRE of 0.207 with pred (0.3) of 80%.

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3

Kim, Cheong-Hwan, Dae-Seung Ban, and Yong-Hwan Lee. "Channel estimation in mobile WiMAX systems." International Conference on Electrical Engineering 6, no. 6 (May 1, 2008): 1–13. http://dx.doi.org/10.21608/iceeng.2008.34233.

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4

Rzeszucinski, Pawel, Daniel Lewandowski, and Cajetan T. Pinto. "Mobile device-based shaft speed estimation." Measurement 96 (January 2017): 52–57. http://dx.doi.org/10.1016/j.measurement.2016.10.005.

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5

McGuire, M., K. N. Plataniotis, and A. N. Venetsanopoulos. "Robust estimation of mobile terminal position." Electronics Letters 36, no. 16 (2000): 1426. http://dx.doi.org/10.1049/el:20000960.

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6

Kaur, Anureet, and Kulwant Kaur. "Effort Estimation in Traditional and Agile Mobile Application Development & Testing." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 3 (December 1, 2018): 1265. http://dx.doi.org/10.11591/ijeecs.v12.i3.pp1265-1272.

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Smartphones<em>/</em>mobile devices are enduring all the aspects of human life. With the significant increase in demand for applications running on smartphones/mobile devices, developers and testers are anticipated to deliver high quality, on time and within budget applications. The estimation of development and testing provides a baseline and act as a tracking gear for stakeholders and developers. There are various approaches for estimation of traditional software development. But mobile applications are considered different from traditional software such as from those running on desktop, laptop or on the web. Many traditional estimation techniques used for these software are adapted to mobile domain. With agile software development (ASD) methodology, the scenario of development and estimation has changed drastically and so as mobile app development and estimation. This paper provides a Systematic Literature Review (SLR) on traditional estimation techniques and agile estimation techniques applied in mobile software/application. Also, effort attributes and accuracy parameters for estimation in mobile apps are presented. However, to date, there are very fewer studies done on the mobile application estimation domain using agile methodology.

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7

Fernandes, Thiago Soares, Álvaro Freitas Moreira, and Érika Cota. "EPE-Mobile-A framework for early performance estimation of mobile applications." Software: Practice and Experience 48, no. 1 (August 24, 2017): 85–104. http://dx.doi.org/10.1002/spe.2518.

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8

Zajic, Alenka G. "Estimation of Mobile Velocities and Direction of Movement in Mobile-to-Mobile Wireless Fading Channels." IEEE Transactions on Vehicular Technology 61, no. 1 (January 2012): 130–39. http://dx.doi.org/10.1109/tvt.2011.2175410.

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9

KIM, SUNGBOK, and SANGHYUP LEE. "ROBUST MOBILE ROBOT VELOCITY ESTIMATION USING A POLYGONAL ARRAY OF OPTICAL MICE." International Journal of Information Acquisition 05, no. 04 (December 2008): 321–30. http://dx.doi.org/10.1142/s0219878908001715.

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This paper presents the robust velocity estimation of a mobile robot using a polygonal array of optical mice that are installed at the bottom of the mobile robot. First, the velocity kinematics from a mobile robot to an array of optical mice is derived, from which the least squares estimation of a mobile robot velocity is obtained. Second, the least squares mobile robot velocity estimation is shown to be robust against measurement noises and partial malfunctions of optical mice. Third, in the presence of installation error, a practical method for optical mouse position calibration is devised. Finally, some experimental results are given to demonstrate the validity and performance of the proposed mobile robot velocity estimation.

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10

Röhrig, Christof, and Frank Künemund. "WLAN based Pose Estimation for Mobile Robots." IFAC Proceedings Volumes 41, no. 2 (2008): 10433–38. http://dx.doi.org/10.3182/20080706-5-kr-1001.01768.

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11

CHEN, Chien-Sheng, Szu-Lin SU, and Yih-Fang HUANG. "Mobile Location Estimation in Wireless Communication Systems." IEICE Transactions on Communications E94-B, no. 3 (2011): 690–93. http://dx.doi.org/10.1587/transcom.e94.b.690.

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12

Cheng, Long, Cheng-Dong Wu, Yun-Zhou Zhang, and Hao Chu. "Mobile location estimation scheme in NLOS environment." IEICE Electronics Express 8, no. 21 (2011): 1829–35. http://dx.doi.org/10.1587/elex.8.1829.

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13

Abd Elfatah, Mohamed G., Hany Nasry Zaky, and Ahmed Shams. "Mobile Robot Position Estimation using Milstein Algorithm." Journal of Physics: Conference Series 1970, no. 1 (July 1, 2021): 012005. http://dx.doi.org/10.1088/1742-6596/1970/1/012005.

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14

Abdul Razak, Rihab, Srikant Sukumar, and Hoam Chung. "Scalar field estimation with mobile sensor networks." International Journal of Robust and Nonlinear Control 31, no. 9 (March 5, 2021): 4287–305. http://dx.doi.org/10.1002/rnc.5469.

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15

Panichpapiboon, Sooksan, and Puttipong Leakkaw. "Traffic Density Estimation: A Mobile Sensing Approach." IEEE Communications Magazine 55, no. 12 (December 2017): 126–31. http://dx.doi.org/10.1109/mcom.2017.1700693.

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16

Zhou, Yuchao, Suparna De, Wei Wang, Ruili Wang, and Klaus Moessner. "Missing Data Estimation in Mobile Sensing Environments." IEEE Access 6 (2018): 69869–82. http://dx.doi.org/10.1109/access.2018.2877847.

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17

Krasny, L., H. Arslan, D. Koilpillai, and S. Chennakeshu. "Doppler spread estimation in mobile radio systems." IEEE Communications Letters 5, no. 5 (May 2001): 197–99. http://dx.doi.org/10.1109/4234.922758.

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18

Hata, M., and M. Sakamoto. "Capacity estimation of cellular mobile radio systems." Electronics Letters 22, no. 9 (1986): 449. http://dx.doi.org/10.1049/el:19860305.

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19

Aldoshkin, D. N., T. N. Yamskikh, and R. Yu Tsarev. "Mobile robot motion estimation using Hough transform." Journal of Physics: Conference Series 1015 (May 2018): 032161. http://dx.doi.org/10.1088/1742-6596/1015/3/032161.

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20

KO, Y. C. "Doppler Spread Estimation in Mobile Communication Systems." IEICE Transactions on Communications E88-B, no. 2 (February 1, 2005): 724–28. http://dx.doi.org/10.1093/ietcom/e88-b.2.724.

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21

Ries, Michal, and Bruno Gardlo. "Audiovisual quality estimation for mobile video services." IEEE Journal on Selected Areas in Communications 28, no. 3 (April 2010): 501–9. http://dx.doi.org/10.1109/jsac.2010.100420.

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22

Wang, Jian, and Feng-Xiang Jin. "PRECISION ESTIMATION OF MOBILE LASER SCANNING SYSTEM." Survey Review 42, no. 317 (July 2010): 270–78. http://dx.doi.org/10.1179/003962610x12747001420302.

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23

Clark, A. P., and S. G. Jayasinghe. "Channel estimation for land mobile radio systems." IEE Proceedings F Communications, Radar and Signal Processing 134, no. 4 (1987): 383. http://dx.doi.org/10.1049/ip-f-1.1987.0066.

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24

Tsarouhas, Panagiotis H., and George K. Fourlas. "Mission reliability estimation of mobile robot system." International Journal of System Assurance Engineering and Management 7, no. 2 (January 11, 2016): 220–28. http://dx.doi.org/10.1007/s13198-015-0408-9.

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25

Márquez-Neila, Pablo, Javier López-Alberca, José M. Buenaposada, and Luis Baumela. "Speeding-up homography estimation in mobile devices." Journal of Real-Time Image Processing 11, no. 1 (January 9, 2013): 141–54. http://dx.doi.org/10.1007/s11554-012-0314-1.

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26

El Husseini, Ali Houssa, Laurent Ros, and Eric Pierre Simon. "Kalman Filter-Based Channel Estimation for Mobile-to-Mobile and Relay Networks." IEEE Signal Processing Letters 26, no. 5 (May 2019): 680–84. http://dx.doi.org/10.1109/lsp.2019.2904439.

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27

Bhawana Verma, Satish Kumar Alaria. "Design & Analysis of Cost Estimation for New Mobile-COCOMO Tool for Mobile Application." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 1 (January 31, 2019): 27–34. http://dx.doi.org/10.17762/ijritcc.v7i1.5222.

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Software cost estimation is a resource forecasting method, which is required by the software development process. However, estimating the workload, schedule and cost of a software project is a complex task because it involves predicting the future using historical project data and extrapolating to see future values. For cost estimates for software projects, several methods are used. Among the various software cost estimation methods available, the most commonly used technology is the COCOMO method. Similarly, to calculate software costs, there are several cost estimating tools available for software developers to use. But these released cost estimation tools can only provide parameters (i.e. cost, development time, average personnel) for large software with multiple lines of code. However, if a software developer wants to estimate the cost of a small project that is usually a mobile application, the available tools will not give the right results. Therefore, to calculate the cost of the mobile application, the available cost estimation method COCOMO II is improved to a new model called New Mobile COCOMO Tool. The New Mobile COCOMO tool developed specifically for mobile applications is a boon for software developers working in small software applications because it only includes important multipliers that play a vital role in estimating the cost of developing mobile applications. Therefore, the objective of this paper is to propose a cost estimation model with a special case of COCOMO II, especially for mobile applications, which calculates the person-month, the programmed time and the average personnel involved in the development of any mobile app.

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28

Mushtaq, Ziema, and Abdul Wahid. "Mobile Complex Factors: An Approach for the Prediction of Mobile Size Parameters." Recent Advances in Computer Science and Communications 13, no. 4 (October 19, 2020): 595–603. http://dx.doi.org/10.2174/2213275912666190218152109.

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Background: Mobile application and Effort estimation have direct relationship where on the basis of size, mobile application development efforts can be determined. Inaccuracy or inappropriateness in this approach can cause underestimation or overestimation. The main phase of Mobile application development is to standardize the approach to predict the size of an application. Objectives: The primary objective of this study is to quantify the functionality provided by the software to the end users it is necessary to know the size of an application. This paper focuses on the background of Mobile application size measures, Mobile complexity factors and the future work of the size measure. Methods: This is a survey based study where the primary endpoint was to see the resemblance of selected parameters with modern day mobile application development, a list of questions commonly known as questionnaire was prepared and was sent to more than 140 people including practitioners, researchers and industry people. Results: Out of 40 Parameters 9 parameters were selected to be includes as Mobile complex factors in order to calculate the functional size of a mobile application. Hence new concept for mobile size measures is introduced. Conclusion: Mobile complexity factors were proposed to form a standard to be used as an input in proposed size metrics for estimation of Mobile application development. To validate the effectiveness of this research work, there is something that is to be achieved in future: a) Propose a New Sizing metrics to calculate size of a Mobile application. b) Proposing a model for estimation of Cost in Mobile application development so that the there will be more accuracy in the resultant value and the process of estimation will be more streamlined.

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29

Do, Choi Hyun, Kang Hyunsuk, Hyun Kyung Hak, Kim Soohyun, and Kwak Yoon Keun. "Force Distribution Estimation of Wheeled Mobile Robot: Application to Friction Coefficients Estimation." IFAC Proceedings Volumes 41, no. 2 (2008): 10451–55. http://dx.doi.org/10.3182/20080706-5-kr-1001.01771.

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30

Mamchych, Oleksandr, and Maksym Volk. "ESTIMATION OF POWER CONSUMPTION OF MOBILE DEVICES IN CLOUD COMPUTING." Innovative Technologies and Scientific Solutions for Industries, no. 1 (23) (April 20, 2023): 72–82. http://dx.doi.org/10.30837/itssi.2023.23.072.

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Modern computing tasks require an increase in computing power. This necessitates the creation and production of new equipment for cloud computing. At the same time, the number of personal mobile devices is already measured in billions, and even their partial use could reduce production requirements. In addition, mobile hardware is more energy efficient, which contributes to significant energy savings. The article investigates the issue of qualitative and quantitative assessment of the efficiency of using mobile devices for computing compared to traditional stationary solutions. The purpose of the work is to substantiate the following hypothesis: computing in the cloud based on mobile devices significantly reduces energy consumption than computing on stationary equipment. For this purpose, we show that computing on a mobile GPU is more energy efficient than computing on a stationary processor. Public sources and benchmarks were analyzed to determine the qualitative advantage. On the basis of the studied data, efficiency indicators for various mobile and desktop GPUs are calculated. It is argued that in most cases, mobile solutions consume significantly less energy compared to desktop solutions. To calculate the quantitative advantage, an experiment was conducted on the basis of two platforms: mobile and desktop. The same computational task was implemented using Apple Metal and NVidia CUDA. Based on this task, the energy efficiency indicators of the mobile and stationary graphic professor were calculated. According to the results of the study, a significant advantage of the mobile GPU in terms of energy efficiency has been determined. This result is relevant because the platforms were released in the same year with a difference of several months, so they can be considered peers of each other. The approaches presented here do not take into account the consumption of all other parts of the system, except for the GPUs. This means that the consumption of the motherboard, power supply, etc. can tilt the balance in favor of the mobile processor even more. But for distributed computing, the network connection is very important, and it can consume a significant amount of power on a mobile device. Further research will focus on a more comprehensive accounting of the energy consumption of various computer subsystems.

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31

Семенова, Олена, Андрій Семенов, Андрій Луцишин, and Вадим Дира. "Artificial Intelligence Techniques for Mobile Station Location Estimation." Security of Infocommunication Systems and Internet of Things, no. 1 (June 30, 2023): 01006. http://dx.doi.org/10.31861/sisiot2023.1.01006.

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Modern wireless communication systems require positioning functions, which provide are automatic location estimation of stations within a network. However, when new networks are implemented, much higher accuracy is required when determining geographical coordinates of a mobile station to develop of services related to the station location. To solve the problem of mobile station positioning, its geographical coordinates are calculated, coordinates of the closest base stations being known. The paper proposes to use a genetic neuro-fuzzy controller for improving the effectiveness of positioning a mobile station. Positioning methods providing usage of artificial intelligence methods are based on measurements of levels for signals from the closets access points or base stations, their coordinates are known. The proposed localization method is based on values of received signal strength indicator – RSSI. At the same time, the RSSI method has a disadvantage – low accuracy, which is proposed to be increased by applying methods of artificial intelligence – fuzzy logic, neural networks, genetic algorithms. Therefore, the objective of this paper is to elaborate an optimized method for determining location of a mobile station. In compliance with the suggested method, RSSI values and ToA values enter the genetic neuro-fuzzy controller, after corresponding processing, the distance from the mobile station to the base station appears at its output.

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32

Chouraqui, S., and M. Benyettou. "State Estimation for Mobile Robot Using Neural Networks." Journal of Applied Sciences 9, no. 22 (November 1, 2009): 3957–65. http://dx.doi.org/10.3923/jas.2009.3957.3965.

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33

Semenova, O. O., and А. O. Semenov. "Using Neural Networks for Mobile Station Location Estimation." Visnyk of Vinnytsia Politechnical Institute 145, no. 4 (2019): 66–70. http://dx.doi.org/10.31649/1997-9266-2019-145-4-66-70.

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34

Rui Gao, Wenjun Wang, Shanshan Wang, and Yao Lu. "Privacy preserving traffic speed estimation via mobile probe." International Journal of Digital Content Technology and its Applications 6, no. 1 (January 31, 2012): 446–53. http://dx.doi.org/10.4156/jdcta.vol6.issue1.54.

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35

Kulakov, Y., V. Vorotnikov, and O. Boychenko. "Multicriterion Estimation of Efficiency of Mobile Network Clustering." Advanced Science Journal 2015, no. 1 (February 2, 2015): 61–67. http://dx.doi.org/10.15550/asj.2015.01.061.

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36

Chen, Qian, Bin Feng Yan, Jun Liao, and Gang An. "Analysis of Mobile Phone Camera Performance Estimation Method." Applied Mechanics and Materials 347-350 (August 2013): 1824–27. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.1824.

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As the forefront of mobile communications, mobile phone industry broad market prospect attracts more and more enterprises to enter this field. With the rapid improvement of the industry, mobiles develop from original feature phone to smart phones, which can take pictures, catch video, access Internet and so on. Meanwhile, performance of camera also becomes the focus of peoples attention. This paper analyzes the performance of the camera objective and subjective evaluation methods, and raises the appropriate test standards. Finally, it suggests a comprehensive evaluation of subjective and objective scoring method, hope can be promoted.

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37

Nasir, Qassim. "Predictive FTF Adaptive Algorithm for Mobile Channels Estimation." International Journal of Communications, Network and System Sciences 05, no. 09 (2012): 569–78. http://dx.doi.org/10.4236/ijcns.2012.59067.

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38

Oda, Naoki, and Hiroyuki Shimizu. "Vision-based External Force Estimation for Mobile Robots." IFAC Proceedings Volumes 41, no. 2 (2008): 14732–37. http://dx.doi.org/10.3182/20080706-5-kr-1001.02494.

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39

Jun, WANG, XU Xiaofeng, DONG Mingli, SUN Peng, and CHEN Min. "Relative pose estimation method of monocular mobile robot." Journal of Applied Optics 40, no. 4 (2019): 535–41. http://dx.doi.org/10.5768/jao201940.0401002.

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40

Lee, Jewang, Jungwoo Lee, and Chang Hee Han. "Mobile Device NDF(No Defect Found) Cost Estimation." Journal of Society of Korea Industrial and Systems Engineering 44, no. 2 (June 30, 2021): 102–14. http://dx.doi.org/10.11627/jkise.2021.44.2.102.

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41

Lee, Hyun-Jin, and Jae-Hyun Kim. "An Estimation-Based Scanning Method of Mobile Relay." Journal of Korean Institute of Communications and Information Sciences 37A, no. 10 (October 30, 2012): 850–57. http://dx.doi.org/10.7840/kics.2012.37a.10.850.

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42

Chen, Guo-bin. "A fast motion estimation algorithm for mobile communications." Journal of Zhejiang University-SCIENCE A 7, S1 (January 2006): 13–18. http://dx.doi.org/10.1631/jzus.2006.as0013.

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43

Mukherjee, Sankar, and G. P. Biswas. "Location estimation based routing for mobile adhoc network." Journal of Intelligent & Fuzzy Systems 35, no. 2 (August 26, 2018): 1209–24. http://dx.doi.org/10.3233/jifs-169666.

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44

Cechowicz, Radosław. "Indoor mobile robot attitude estimation with MEMS gyroscope." ITM Web of Conferences 15 (2017): 05010. http://dx.doi.org/10.1051/itmconf/20171505010.

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45

Borges, G. A., and M. J. Aldon. "Optimal mobile robot pose estimation using geometrical maps." IEEE Transactions on Robotics and Automation 18, no. 1 (2002): 87–94. http://dx.doi.org/10.1109/70.988978.

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46

Yang, Q., and K. S. Kwak. "Superimposed-pilot-aided channel estimation for mobile OFDM." Electronics Letters 42, no. 12 (2006): 722. http://dx.doi.org/10.1049/el:20060758.

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47

Popović, B. P. "Class of binary sequences for mobile channel estimation." Electronics Letters 31, no. 12 (June 8, 1995): 944–45. http://dx.doi.org/10.1049/el:19950678.

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48

Irio, Luis, Daniela Oliveira, and Rodolfo Oliveira. "Interference estimation in wireless mobile random waypoint networks." Telfor Journal 8, no. 2 (2016): 93–97. http://dx.doi.org/10.5937/telfor1602093i.

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49

Xie, Duosi, Shouxu Zhang, and Jianquan Xie. "Distributed dynamic state estimation with flocking mobile agents." Physica A: Statistical Mechanics and its Applications 509 (November 2018): 1195–206. http://dx.doi.org/10.1016/j.physa.2018.05.146.

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50

Zhang, Xinglin, Zheng Yang, Chenshu Wu, Wei Sun, Yunhao Liu, and Kai Liu. "Robust Trajectory Estimation for Crowdsourcing-Based Mobile Applications." IEEE Transactions on Parallel and Distributed Systems 25, no. 7 (July 2014): 1876–85. http://dx.doi.org/10.1109/tpds.2013.250.

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