مروان الاخرس

ASUجامعة العلوم التطبيقية الخاصة - الصفحة الرئيسيةكلية تكنولوجيا المعلوماتمروان الاخرسالأبحاث

الأبحاث

Fuzzy logic in localisation: A survey

Authors : M. Alakhras, M. Oussalah, and M. Hussein

Abstract :Positioning and location estimation of targets either for mobile or stationary nodes is very important, especially after the astonishing developments of the wired and/or wireless communications, as well as the popularity of handheld devices. To assist in accessing services or managing resources, numerous users, application developers, and service providers require accurate position information. Owing to the boom of multisensory systems, wireless technology, and autonomous systems, many positioning systems have been proposed. These positioning systems differ based on the adopted technology, type of sensory information, communication protocol, algorithm, and accuracy level. These different approaches impose a non-negligible amount of uncertainty and challenges; in such case, fuzzy-logic-based localization systems offer an attractive and feasible approach to overcome some of the reported challenges and handle uncertainties, depending on its proven mathematical framework for handling vagueness and uncertainty. Intuitively, fuzzy sets and fuzzy inference systems are initially intended to incorporate granularity and flexibility of human knowledge, which characterize complex system behaviors without explicitly requiring precise mathematical models or when a complete and exhaustive list of comprehending factors are ill known. This study demonstrates the benefits of fuzzy sets, fuzzy logic, and fuzzy inference systems in wireless positioning problems. Notably, as a way to benefit both the academic community and practitioners of system positioning, we review and investigate various fuzzy-related techniques and methodologies. To the best of our knowledge, this is the first comprehensive review that focuses solely on fuzzy-based positioning systems.

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Location fixing and fingerprint matching fingerprint map construction for indoor localisation

Authors : M. Alakhras, M. I. Hussein, M. Oussalah

Abstract :Building the fingerprint map for indoor localization problems is a labour-intensive and time-consuming process. However, due to its direct influence on the location estimation accuracy, finding a proper mechanism to construct the fingerprint map is essential to enhance the position estimation accuracy. Therefore, in this work, we present a fingerprint map construction technique based on location fix determination and fingerprint matching motivated by the availability of advanced sensing capabilities in smartphones to reduce the time and labour cost required for the site survey. The proposed Location Fixing and Finger Matching (LFFM) method use a landmark graph-based localization approach to automatically estimate the location fixes for the Reference Points and matching the collected fingerprints, without requiring active user participation. Experimental results show that the proposed LFFM is faster than the manual fingerprint map construction method and remarkably improves the positioning accuracy.

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Fuzzy sets and game theory in green supply chain: An optimisation model

Authors :M. Alakhras, M. I. Hussein, and M. Oussalah

Abstract :An optimization model using fuzzy sets and game theory for three players is put forward in this wok. The decision model is influenced by customer demands in a green supply chain. The proposed model includes an empirical solution to enhance the confidence level of players to choose plausible green strategy. Initially, the strategies are formulated using the game theory as manufacturer, costumer and government, to be able to optimize the pay-off uncertainty conditions of demands, by combining computational fuzzy set with ability of sensitive analysis of related fuzzy parameters to enhance the calculations and problem solving, with presenting Nash equilibrium the problem solving part

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Fuzzy system and game theory for green supply chain

Authors : M. Alakhras

Abstract :The main issue in the for the green supply chain strategies is the ability to find exact and practical solutions (pay off function), which can motivate the main supply chain members to choose an approach under uncertainty conditions, where, any practical solution should include different mathematical techniques and use engineering sciences to make an effectiveness relation between scientific researches and trading industries. In this paper, an optimization model using fuzzy game theory for three players is developed, which is affected by customer demands in a green supply chain. The proposed model includes a practical solution to increase the confidence level of players to choose plausible green strategy. Initially, the strategies are formulated using the game theory for three players, particularly, manufacturer, costumer and government, to be able to optimize the pay-off uncertainty conditions of demands, this can be achieved by combining computational fuzzy set with ability of sensitive analysis of related fuzzy parameters to enhance the calculations and problem solving, with focus on presenting Nash equilibrium the problem-solving part

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Resource allocation model based on interval for wireless network with guaranteed QoS

Authors :M. Alakhras, M. Oussalah, and M. Hussein

Abstract :The resource allocation in WLAN network is of paramount importance especially when services are considered to achieve a guaranteed QoS. In this work a two steps algorithm is proposed to model adaptable resource allocation algorithm. Where subcarriers are allocated considering the channel quality and priority of multicast services, then, the power reallocation algorithm is carried out based on small intervals to enhance the system capacity while guaranteeing the whole multicast QoS

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ANFIS: General description for modelling dynamic objects

Authors : M. Alakhras, M. Oussalah, and M. Hussein

Abstract :This work advocates the use of neuro-fuzzy based approach for the task of modeling nonlinear dynamic objects using Adaptive (Hybrid) learning mechanism in unknown environment. It aims to give to new researchers description on ANFIS capability to model nonlinear plants; i.e., combining (NN) and (FL) in what is referred in literature as "neuro-fuzzy". This combination seems natural because the two approaches generally attack the design of "intelligent" systems from different angles. NNs provide algorithms for learning, classification, and optimization, whereas FL deals with issues such as reasoning on a higher (semantic or linguistic) level. Consequently, the two technologies complement each other. By integrating neural networks with fuzzy logic, it is possible to bring the low-level of computational power and learning of NNs into FL systems

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Multivariable fuzzy inference system for fingerprinting indoor localisation

Authors :M. Oussalah, M. Alakhras, and M. I. Hussein

Abstract :The emergence of wireless sensor network has raised the need for cheap wireless indoor localization technique. This paper considers the problem of fingerprinting indoor localization based on signal strength measurements RSS. A new approach based on Fuzzy logic has been put forward. The proposal makes use of k-nearest neighbor classification in signal space. The localization of target node is then determined as a weighted combination of nearest fingerprints. The weights are determined using Takagi–Sugeno fuzzy controller with two inputs. A new enhancement to the kNN is proposed to enhance the accuracy of location estimation; this enhancement allows the kNN to outlier some miss elected neighbors based on triangular area measurements. The performance of the developed estimation algorithm has been evaluated using both Monte Carlo simulations and real testbed scenarios while compared to other alternative approaches

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Fuzzy inference with parameter identification for indoor WLAN positioning

Authors : M. Alakhras, M. Oussalah, and M. I. Hussein

Abstract :This paper considers the fuzzy inference as position estimator for WLAN indoor environments, based on received signal strength measurements RSS. The proposal algorithm includes a fuzzy inference which uses the k-nearest neighbor classification in signal space, where the position of target node is calculated as a weighted combination of nearest fingerprints, where the weights are estimated using enhanced Takagi–Sugeno fuzzy controller with multivariable inputs and parameter identification with constrained optimization. The new developed technique is proposed to enhance the accuracy of position estimation in WLAN indoor environments

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Multivariable fuzzy inference with KNN for indoor WLAN localisation based on RSS fingerprint

Authors :M. Alakhras, M. Hussein, and M. Oussalah

Abstract :The power of fuzzy systems was approved in overcoming uncertainties and identifying ambiguities. Localization in wireless environments is found to have many uncertainties. In this paper a multivariable fuzzy inference system is combined with multi-nearest neighbor algorithm to estimate location of an object in an indoor wireless local area network. The proposed combination utilizes from the robustness of fuzzy systems to overcome some of the uncertainties associated with radio wave propagation and small scale signal variation. This work is focusing on the available localization techniques and how fuzzy systems are merged with other techniques to enhance the location estimation then it utilizes from the estimated position parameters and its correlation with the received signal strength, where the augmentation of one more input variable has grabbed non negligible attention in overcoming some of the uncertainties in location estimation especially the ones associated with the small scale variations due to changes in the physical environment

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Bluetooth -model analysis and simulation using NS2

Authors :E. Souron, M. Oussalah, and M. Alakhras

Abstract :Bluetooth-based devices become very popular nowadays with boom in mobile phone market. This paper investigates ad-hoc communication network architecture where a set of Bluetooth devices are connected to each others, and allowing possible location based services. An implementation using master-slave configuration and OpenLAPI open source has been achieved. Besides, in order to test the network parameters, a simulation using NS-2 framework has been successfully implemented

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Neural network-based fuzzy inference system for modelling of dynamic objects

Authors :Marwan N. Y. Alakhras, and H. Habboub

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Time Series Prediction Using Fuzzy Wavelet Neural Network Model

Authors :R. H. Abiyev, and M. Alakhras

Abstract :The fuzzy wavelet neural network (FWNN) for time series prediction is presented in this paper. Using wavelets the fuzzy rules are constructed. The gradient algorithm is applied for learning parameters of fuzzy system. The application of FWNN for modelling and prediction of complex time series and prediction of electricity consumption is considered. Results of simulation of FWNN based prediction system is compared with the simulation results of other methodologies used for prediction. Simulation results demonstrate that FWNN based system can effectively learn complex nonlinear processes and has better performance than other models

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Neural network-based fuzzy inference system for exchange rate prediction

Authors : Marwan N. Y. Alakhras

Abstract :In this work the power of learning and parallelism of neural network is combined with the reasoning mechanism of fuzzy logic to build up a more powerful system referred in the literature as neuro-fuzzy system. So here we will talk about the combination models, learning mechanism, hybrid systems architecture, then the discussion is expanded to cover ANFIS, its learning algorithm and its structure for the different types of fuzzy, i.e. Mamadani and Takagi-Sugeno types of fuzzy. The system is used to model an time series algorithm specifically prediction of currency exchange rate of Turkish Lira (TL) versus United States Dollar (USD)

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ANN system for monitoring nonlinear Foetal parameters

Authors :Khashman A. Marwan

Abstract :Research into medical applications has been and is always taking advantage of the high-speed technologies that are emerging year after year. Image processing is one such tool that is popularly utilised in such applications as monitoring foetal measurements. This paper reports on a new approach to detecting foetal head and abdominal circumferences in low to medium contrast images. The novel idea is based on combining neural network arbitration and scale space analysis to automatically select one optimum scale for the entire image at which scale space edge detection can be applied. This new approach to edge detection is formalised in the Automatic Edge Detection Scheme(AEDS). The AEDS is implemented on a real-life application namely, the detection of foetal head and abdominal circumferences. Results are presented to show the AEDS overcoming the forementioned common problems with image processing techniques