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Mobile position estimation using artificial neural network in CDMA cellular systems
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Using the Neural network as a type of associative memory will be introduced in this paper through the problem of mobile position estimation where mobile estimate its location depending on the signal strength reach to it from several around base stations where the neural network can be implemented inside the mobile. Traditional methods of time of arrival (TOA) and received signal strength (RSS) are used and compared with two analytical methods, optimal positioning method and average positioning method. The data that are used for training are ideal since they can be obtained based on geometry of CDMA cell topology. The test of the two methods TOA and RSS take many cases through a nonlinear path that MS can move through that region. The results show that the neural network has good performance compared with two other analytical methods which are average positioning method and optimal positioning method.

Publication Date
Mon Jan 01 2024
Journal Name
Explainable Artificial Intelligence In The Digital Sustainability Administration
Artificial Intelligence and Trends Using in Sustainability Audit: A Bibliometric Analysis
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Publication Date
Fri Jul 21 2023
Journal Name
Journal Of Engineering
Performance Evaluation and Comparison Between LDPC and Turbo Coded MC-CDMA
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This work presents a comparison between the Convolutional Encoding CE, Parallel Turbo code and Low density Parity Check (LDPC) coding schemes with a MultiUser Single Output MUSO Multi-Carrier Code Division Multiple Access (MC-CDMA) system over multipath fading channels. The decoding technique used in the simulation was iterative decoding since it gives maximum efficiency at higher iterations. Modulation schemes used is Quadrature Amplitude Modulation QAM. An 8 pilot carrier were
used to compensate channel effect with Least Square Estimation method. The channel model used is Long Term Evolution (LTE) channel with Technical Specification TS 25.101v2.10 and 5 MHz bandwidth bandwidth including the channels of indoor to outdoor/ pedestrian

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Publication Date
Sun May 28 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Science
On-Line Navigational Problem of a Mobile Robot Using Genetic Algorithm
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Publication Date
Tue Dec 21 2021
Journal Name
Contemporary Clinical Dentistry
Assessment of maxillary incisors' angulation and position in different types of malocclusions using cone-beam computed tomography
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Abstract Background: This study is aimed to assess the maxillary incisors’ root position, angulation, and buccal alveolar bone thickness in both genders and different classes of malocclusion using cone‑beam computed tomography (CBCT). Materials and Methods: Two hundred and six CBCT images were gathered and analyzed by three‑dimensional On‑Demand software to measure the variables of 803 maxillary central and lateral incisors. Genders and class difference was determined by unpaired t‑test, one‑way ANOVA, and Chi‑square tests. Results: Buccal root position of the maxillary incisors accounted for in the majority of the cases followed by the middle and palatal positions. The thickness of alveolar bone appears to have nearly the sam

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Publication Date
Sat Apr 01 2023
Journal Name
International Journal Of Electrical And Computer Engineering (ijece)
Intrusion detection method for internet of things based on the spiking neural network and decision tree method
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The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices

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Publication Date
Sat Apr 01 2023
Journal Name
International Journal Of Electrical And Computer Engineering
Intrusion detection method for internet of things based on the spiking neural network and decision tree method
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The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices

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Publication Date
Thu Jun 15 2017
Journal Name
Journal Of Baghdad College Of Dentistry
Assessment of mandibular third molar position by using computed tomography and reconstructed lateral radiograph
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Background: Consideration of mandibular third molar is important from orthodontic perspective due to several factors such as, lower anterior arch crowding, relapse in lower anterior region, interference with uprighting of mandibular first and second molars during anchorage preparation and molar distalization. The aims of this study were to assess of gender differences in the mandibular third molar position and compare and evaluate whether there is any differences in the results provided by CT scan and lateral reconstructed radiograph. Materials and Methods: The sample of present study consisted of 39 patients (18 males and 21 females) with age range 11-15 years. CT images for patients who were attending at Al Suwayra General Hospital/the C

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Publication Date
Tue Sep 19 2017
Journal Name
Al-academy
Aesthetic performance in the mobile sculptures
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Aesthetic performance in the mobile sculptures
Ali Abd Almohsen Ali
Abstract
Chapters of this research are discussed one of the turning points in the In the history of sculpture, which sparked a debate in artistic circles, namely the introduction of the actual movement in sculpture . The researcher highlighted on one of the corners of the controversy on this subject for discussion and looking in and Found results contribute to answer some questions . the Beginning been allocated to the statement of this Research Methodology , research problem, and its importance, and its goal, and borders , the main problem is Lack of clarity of the concept of real motion (physical) in the sculpture , And how the audience understand the aspects

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Publication Date
Fri Apr 12 2019
Journal Name
Journal Of Economics And Administrative Sciences
Accounting Mining Data Using Neural Networks (Case study)
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Business organizations have faced many challenges in recent times, most important of which is information technology, because it is widely spread and easy to use. Its use has led to an increase in the amount of data that business organizations deal with an unprecedented manner. The amount of data available through the internet is a problem that many parties seek to find solutions for. Why is it available there in this huge amount randomly? Many expectations have revealed that in 2017, there will be devices connected to the internet estimated at three times the population of the Earth, and in 2015 more than one and a half billion gigabytes of data was transferred every minute globally. Thus, the so-called data mining emerged as a

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Publication Date
Tue Jun 30 2009
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Application of Neural Network in the Identification of the Cumulative Production from AB unit in Main pays Reservoir of South Rumaila Oil Field.
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A common field development task is the object of the present research by specifying the best location of new horizontal re-entry wells within AB unit of South Rumaila Oil Field. One of the key parameters in the success of a new well is the well location in the reservoir, especially when there are several wells are planned to be drilled from the existing wells. This paper demonstrates an application of neural network with reservoir simulation technique as decision tool. A fully trained predictive artificial feed forward neural network (FFNNW) with efficient selection of horizontal re-entry wells location in AB unit has been carried out with maintaining a reasonable accuracy. Sets of available input data were collected from the exploited g

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