Search results for: intelligent artificial skin system.

Authors: Mert Bal, Hayri Sever, Oya Kalıpsız

Abstract:

Medical Decision Support Systems (MDSSs) are sophisticated, intelligent systems that can provide inference due to lack of information and uncertainty. In such systems, to model the uncertainty various soft computing methods such as Bayesian networks, rough sets, artificial neural networks, fuzzy logic, inductive logic programming and genetic algorithms and hybrid methods that formed from the combination of the few mentioned methods are used. In this study, symptom-disease relationships are presented by a framework which is modeled with a formal concept analysis and theory, as diseases, objects and attributes of symptoms. After a concept lattice is formed, Bayes theorem can be used to determine the relationships between attributes and objects. A discernibility relation that forms the base of the rough sets can be applied to attribute data sets in order to reduce attributes and decrease the complexity of computation.

Keywords: Formal Concept Analysis, Rough Set Theory, Granular Computing, Medical Decision Support System.

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Authors: Yi-Ping Fang, Hsien-Ting Cheng

Abstract:

Topical photodynamic therapy (PDT) with 5-aminolevulinic acid (ALA) is an alternative therapy for treating superficial cancer, especially for skin or oral cancer. ALA, a precursor of the photosensitizer protoporphyrin IX (PpIX), is present as zwitterions and hydrophilic property which make the low permeability through the cell membrane. Collagen is a traditional carrier; its molecular composed various amino acids which bear positive charge and negative charge. In order to utilize the ion-pairs with ALA and collagen, the study employed various pH values adjusting the net charge. The aim of this study was to compare a series collagen form, including solution, gel and sponge to investigate the topical delivery behavior of ALA. The in vivo confocal laser scanning microscopy (CLSM) study demonstrated that PpIX generation ability was different pattern after apply for 6 h. Gel type could generate high PpIX, and archived more deep of skin depth.

Keywords: 5-Aminolevulinic acid (ALA), Collagen, Ion-pairs, Penetration behavior

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Authors: Peyman Shadman Heidari, Mohammad Khorasani

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The principal purpose of this article is to present a new method based on Adaptive Neural Network Fuzzy Inference System (ANFIS) to generate additional artificial earthquake accelerograms from presented data, which are compatible with specified response spectra. The proposed method uses the learning abilities of ANFIS to develop the knowledge of the inverse mapping from response spectrum to earthquake records. In addition, wavelet packet transform is used to decompose specified earthquake records and then ANFISs are trained to relate the response spectrum of records to their wavelet packet coefficients. Finally, an interpretive example is presented which uses an ensemble of recorded accelerograms to demonstrate the effectiveness of the proposed method.

Keywords: Adaptive Neural Network Fuzzy Inference System, Wavelet Packet Transform, Response Spectrum.

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Authors: V.S.Kale, S.R.Bhide, P.P.Bedekar, G.V.K.Mohan

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The protection of parallel transmission lines has been a challenging task due to mutual coupling between the adjacent circuits of the line. This paper presents a novel scheme for detection and classification of faults on parallel transmission lines. The proposed approach uses combination of wavelet transform and neural network, to solve the problem. While wavelet transform is a powerful mathematical tool which can be employed as a fast and very effective means of analyzing power system transient signals, artificial neural network has a ability to classify non-linear relationship between measured signals by identifying different patterns of the associated signals. The proposed algorithm consists of time-frequency analysis of fault generated transients using wavelet transform, followed by pattern recognition using artificial neural network to identify the type of the fault. MATLAB/Simulink is used to generate fault signals and verify the correctness of the algorithm. The adaptive discrimination scheme is tested by simulating different types of fault and varying fault resistance, fault location and fault inception time, on a given power system model. The simulation results show that the proposed scheme for fault diagnosis is able to classify all the faults on the parallel transmission line rapidly and correctly.

Keywords: Artificial neural network, fault detection and classification, parallel transmission lines, wavelet transform.

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Authors: L. Tao, T. J. Sun, Z. H. Li

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The knowledge base of welding defect recognition is essentially incomplete. This characteristic determines that the recognition results do not reflect the actual situation. It also has a further influence on the classification of welding quality. This paper is concerned with the study of a rough set based method to reduce the influence and improve the classification accuracy. At first, a rough set model of welding quality intelligent classification has been built. Both condition and decision attributes have been specified. Later on, groups of the representative multiple compound defects have been chosen from the defect library and then classified correctly to form the decision table. Finally, the redundant information of the decision table has been reducted and the optimal decision rules have been reached. By this method, we are able to reclassify the misclassified defects to the right quality level. Compared with the ordinary ones, this method has higher accuracy and better robustness.

Keywords: intelligent decision, rough set, welding defects, welding quality level

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Authors: Kuang-Hsiung Tan, Chih-Chan Hu, Chien-Wu Lan, Shih-Sung Lin, Te-Jen Chang

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An active islanding detection method using disturbance signal injection with intelligent controller is proposed in this study. First, a DC\AC power inverter is emulated in the distributed generator (DG) system to implement the tracking control of active power, reactive power outputs and the islanding detection. The proposed active islanding detection method is based on injecting a disturbance signal into the power inverter system through the d-axis current which leads to a frequency deviation at the terminal of the RLC load when the utility power is disconnected. Moreover, in order to improve the transient and steady-state responses of the active power and reactive power outputs of the power inverter, and to further improve the performance of the islanding detection method, two probabilistic fuzzy neural networks (PFNN) are adopted to replace the traditional proportional-integral (PI) controllers for the tracking control and the islanding detection. Furthermore, the network structure and the online learning algorithm of the PFNN are introduced in detail. Finally, the feasibility and effectiveness of the tracking control and the proposed active islanding detection method are verified with experimental results.

Keywords: Distributed generators, probabilistic fuzzy neural network, islanding detection, non-detection zone.

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Authors: Shewit Tsegaye, Fekadu Shewarega

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This paper presents a survey of articles, books and reports, which articulate the recent trends and aspects of Security Constrained Economic Dispatch (SCED). The period under consideration is 2008 through 2018. This is done to provide an up-to-date review of the recent major advancements in SCED, the state-of-the-art since 2008, identify further challenging developments needed in smarter grids, and indicate ways to address these challenges. This study consists of three areas of interest, which are very important and relevant for articulating the recent trends of SCED. These areas are: (i) SCED of power system with integrated renewable energy sources (IRES), (ii) SCED with post contingency corrective actions and (iii) Artificial intelligence based SCED.

Keywords: Security constrained economic dispatch, SCED of power system with IRES, SCED with post contingency corrective actions, artificial intelligence based SCED, IRES.

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Authors: Mohammad Saleh Vahdatpour, Yanqing Zhang

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Understanding the neural mechanisms underlying motion detection in the human visual system has long been a fascinating challenge in neuroscience and artificial intelligence. This paper presents a spiking neural network model inspired by the processing of motion information in the primate visual system, particularly focusing on the Middle Temporal (MT) area. In our study, we propose a multi-layer spiking neural network model to perform motion detection tasks, leveraging the idea that synaptic delays in neuronal communication are pivotal in motion perception. Synaptic delay, determined by factors like axon length and myelin insulation, affects the temporal order of input spikes, thereby encoding motion direction and speed. Overall, our spiking neural network model demonstrates the feasibility of capturing motion detection principles observed in the primate visual system. The combination of synaptic delays, learning mechanisms, and shared weights and delays in SMD provides a promising framework for motion perception in artificial systems, with potential applications in computer vision and robotics.

Keywords: Neural networks, motion detection, signature detection, convolutional neural network.

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Authors: Takahiro Takayama, Takao Kawamura, Toshihiko Sasama, Kazunori Sugahara

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Recently, web services to access from many type devices are often used. We have developed the shortest path planning system called "Bus-Net" in Tottori prefecture as a web application to sustain the public transport. And it used the same user interface for both devices. To support both devices, the interface cannot use JavaScript and so on. Thus, we developed the method that use individual user interface for each device type to improve its convenience. To be concrete, we defined formats of condition input to the path planning system and result output from it and separate the system into the request processing part and user interface parts that depend on device types. By this method, we have also developed special device for Bus-Net named "Intelligent-Bus-Stop".

Keywords: Bus, Path planning, Public transport, User interface

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Authors: Somlak Wannarumon, Apichai Ritvirool, Thana Boonrit

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The paper proposes a new concept in developing collaborative design system. The concept framework involves applying simulation of supply chain management to collaborative design called – 'SCM–Based Design Tool'. The system is developed particularly to support design activities and to integrate all facilities together. The system is aimed to increase design productivity and creativity. Therefore, designers and customers can collaborate by the system since conceptual design. JAG: Jewelry Art Generator based on artificial intelligence techniques is integrated into the system. Moreover, the proposed system can support users as decision tool and data propagation. The system covers since raw material supply until product delivery. Data management and sharing information are visually supported to designers and customers via user interface. The system is developed on Web–assisted product development environment. The prototype system is presented for Thai jewelry industry as a system prototype demonstration, but applicable for other industry.

Keywords: Collaborative design, evolutionary art, jewelry design, supply chain management.

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Authors: Somlak Wannarumon, Apichai Ritvirool, Thana Boonrit

Abstract:

The paper proposes a new concept in developing collaborative design system. The concept framework involves applying simulation of supply chain management to collaborative design called – 'SCM–Based Design Tool'. The system is developed particularly to support design activities and to integrate all facilities together. The system is aimed to increase design productivity and creativity. Therefore, designers and customers can collaborate by the system since conceptual design. JAG: Jewelry Art Generator based on artificial intelligence techniques is integrated into the system. Moreover, the proposed system can support users as decision tool and data propagation. The system covers since raw material supply until product delivery. Data management and sharing information are visually supported to designers and customers via user interface. The system is developed on Web–assisted product development environment. The prototype system is presented for Thai jewelry industry as a system prototype demonstration, but applicable for other industry.

Keywords: Collaborative design, evolutionary art, jewelry design, supply chain management.

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Authors: Achuthsankar S. Nair, J. S. Jayasudha

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The ever increasing use of World Wide Web in the existing network, results in poor performance. Several techniques have been developed for reducing web traffic by compressing the size of the file, saving the web pages at the client side, changing the burst nature of traffic into constant rate etc. No single method was adequate enough to access the document instantly through the Internet. In this paper, adaptive hybrid algorithms are developed for reducing web traffic. Intelligent agents are used for monitoring the web traffic. Depending upon the bandwidth usage, user-s preferences, server and browser capabilities, intelligent agents use the best techniques to achieve maximum traffic reduction. Web caching, compression, filtering, optimization of HTML tags, and traffic dispersion are incorporated into this adaptive selection. Using this new hybrid technique, latency is reduced to 20 – 60 % and cache hit ratio is increased 40 – 82 %.

Keywords: Bandwidth, Congestion, Intelligent Agents, Prefetching, Web Caching.

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Authors: Sachin Man Bajimaya, SangChul Park, Gi-Nam Wang

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The objective of this paper is to estimate realistic principal extrusion process parameters by means of artificial neural network. Conventionally, finite element analysis is used to derive process parameters. However, the finite element analysis of the extrusion model does not consider the manufacturing process constraints in its modeling. Therefore, the process parameters obtained through such an analysis remains highly theoretical. Alternatively, process development in industrial extrusion is to a great extent based on trial and error and often involves full-size experiments, which are both expensive and time-consuming. The artificial neural network-based estimation of the extrusion process parameters prior to plant execution helps to make the actual extrusion operation more efficient because more realistic parameters may be obtained. And so, it bridges the gap between simulation and real manufacturing execution system. In this work, a suitable neural network is designed which is trained using an appropriate learning algorithm. The network so trained is used to predict the manufacturing process parameters.

Keywords: Artificial Neural Network (ANN), Indirect Extrusion, Finite Element Analysis, MES.

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Authors: Noor A. Draman, Campbell Wilson, Sea Ling

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Content-based music retrieval generally involves analyzing, searching and retrieving music based on low or high level features of a song which normally used to represent artists, songs or music genre. Identifying them would normally involve feature extraction and classification tasks. Theoretically the greater features analyzed, the better the classification accuracy can be achieved but with longer execution time. Technique to select significant features is important as it will reduce dimensions of feature used in classification and contributes to the accuracy. Artificial Immune System (AIS) approach will be investigated and applied in the classification task. Bio-inspired audio content-based retrieval framework (B-ACRF) is proposed at the end of this paper where it embraces issues that need further consideration in music retrieval performances.

Keywords: Bio-inspired audio content-based retrieval framework, features selection technique, low/high level features, artificial immune system

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Authors: Anders Thorsèn, Behrooz Sangchoolie, Peter Folkesson, Ted Strandberg

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As more parts of the power grid become connected to the internet, the risk of cyberattacks increases. To identify the cybersecurity threats and subsequently reduce vulnerabilities, the common practice is to carry out a cybersecurity risk assessment. For safety classified systems and products, there is also a need for safety risk assessments in addition to the cybersecurity risk assessment to identify and reduce safety risks. These two risk assessments are usually done separately, but since cybersecurity and functional safety are often related, a more comprehensive method covering both aspects is needed. Some work addressing this has been done for specific domains like the automotive domain, but more general methods suitable for, e.g., Intelligent Distributed Grids, are still missing. One such method from the automotive domain is the Security-Aware Hazard Analysis and Risk Assessment (SAHARA) method that combines safety and cybersecurity risk assessments. This paper presents an approach where the SAHARA method has been modified to be more suitable for larger distributed systems. The adapted SAHARA method has a more general risk assessment approach than the original SAHARA. The proposed method has been successfully applied on two use cases of an intelligent distributed grid.

Keywords: Intelligent distribution grids, threat analysis, risk assessment, safety, cybersecurity.

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Authors: Sheikh I. Azid, Bibhya Sharma

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A low cost Short Message System (SMS) based Home security system equipped with motion, smoke, temperature, humidity and light sensors has been studied and tested. The sensors are controlled by a microprocessor PIC 18F4520 through the SMS having password protection code for the secure operation. The user is able to switch light and the appliances and get instant feedback. Also in cases of emergencies such as fire or robbery the system will send alert message to occupant and relevant civil authorities. The operation of the home security has been tested on Vodafone- Fiji network and Digicel Fiji Network for emergency and feedback responses for 25 samples. The experiment showed that it takes about 8-10s for the security system to respond in case of emergency. It takes about 18-22s for the occupant to switch and monitor lights and appliances and then get feedback depending upon the network traffic.

Keywords: Smart Home, SMS, Sensors, Microprocessor.

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Authors: Dipankaj G Medhi, Juri Dakua

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Recommender Systems act as personalized decision guides, aiding users in decisions on matters related to personal taste. Most previous research on Recommender Systems has focused on the statistical accuracy of the algorithms driving the systems, with no emphasis on the trustworthiness of the user. RS depends on information provided by different users to gather its knowledge. We believe, if a large group of users provide wrong information it will not be possible for the RS to arrive in an accurate conclusion. The system described in this paper introduce the concept of Testing the knowledge of user to filter out these “bad users". This paper emphasizes on the mechanism used to provide robust and effective recommendation.

Keywords: Collaborative Filtering, Content Based Filtering, Intelligent Agent, Level of Interest, Recommendation System.

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Authors: C. Oprean, C. V. Kifor, S. C. Negulescu, C. Candea, L. Oprean, C. Oprean, S. Kifor

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This paper presents an innovative approach within the area of Group Decision Support System (GDSS) by using tools based on intelligent agents. It introduces iGDSS, a software platform for decision support and collaboration and an application of this platform - eCollaborative Decisions - for academic environment, all these developed within a framework of a research project.

Keywords: Group Decision Support System, Managerial Academic Decisions, Computer Interaction.

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Authors: M. Massour El Aoud, M. Franceschi, M. Maher

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The approach proposed here is oriented in the direction of fuzzy system for the analysis and the synthesis of intelligent climate controllers, the simulation of the internal climate of the greenhouse is achieved by a linear model whose coefficients are obtained by identification. The use of fuzzy logic controllers for the regulation of climate variables represents a powerful way to minimize the energy cost. Strategies of reduction and optimization are adopted to facilitate the tuning and to reduce the complexity of the controller.

Keywords: Greenhouse, fuzzy logic, optimization, gradient descent.

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Authors: Alexandros Lioulemes, Michail Theofanidis, Varun Kanal, Konstantinos Tsiakas, Maher Abujelala, Chris Collander, William B. Townsend, Angie Boisselle, Fillia Makedon

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This paper presents a home-based robot-rehabilitation instrument, called ”MAGNI Dynamics”, that utilized a vision-based kinematic/dynamic module and an adaptive haptic feedback controller. The system is expected to provide personalized rehabilitation by adjusting its resistive and supportive behavior according to a fuzzy intelligence controller that acts as an inference system, which correlates the user’s performance to different stiffness factors. The vision module uses the Kinect’s skeletal tracking to monitor the user’s effort in an unobtrusive and safe way, by estimating the torque that affects the user’s arm. The system’s torque estimations are justified by capturing electromyographic data from primitive hand motions (Shoulder Abduction and Shoulder Forward Flexion). Moreover, we present and analyze how the Barrett WAM generates a force-field with a haptic controller to support or challenge the users. Experiments show that by shifting the proportional value, that corresponds to different stiffness factors of the haptic path, can potentially help the user to improve his/her motor skills. Finally, potential areas for future research are discussed, that address how a rehabilitation robotic framework may include multisensing data, to improve the user’s recovery process.

Keywords: Human-robot interaction, kinect, kinematics, dynamics, haptic control, rehabilitation robotics, artificial intelligence.

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Authors: Mohammed Benbrahim, Adil Daoudi, Khalid Benjelloun, Aomar Ibenbrahim

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The automatic discrimination of seismic signals is an important practical goal for earth-science observatories due to the large amount of information that they receive continuously. An essential discrimination task is to allocate the incoming signal to a group associated with the kind of physical phenomena producing it. In this paper, two classes of seismic signals recorded routinely in geophysical laboratory of the National Center for Scientific and Technical Research in Morocco are considered. They correspond to signals associated to local earthquakes and chemical explosions. The approach adopted for the development of an automatic discrimination system is a modular system composed by three blocs: 1) Representation, 2) Dimensionality reduction and 3) Classification. The originality of our work consists in the use of a new wavelet called "modified Mexican hat wavelet" in the representation stage. For the dimensionality reduction, we propose a new algorithm based on the random projection and the principal component analysis.

Keywords: Seismic signals, Wavelets, Dimensionality reduction, Artificial neural networks, Classification.

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Authors: Mustafa Al Sukar, Azzam Sleit, Abdullatif Abu-Dalhoum, Bassam Al-Kasasbeh

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Use and abuse of drugs by teens is very common and can have dangerous consequences. The drugs contribute to physical and sexual aggression such as assault or rape. Some teenagers regularly use drugs to compensate for depression, anxiety or a lack of positive social skills. Teen resort to smoking should not be minimized because it can be "gateway drugs" for other drugs (marijuana, cocaine, hallucinogens, inhalants, and heroin). The combination of teenagers' curiosity, risk taking behavior, and social pressure make it very difficult to say no. This leads most teenagers to the questions: "Will it hurt to try once?" Nowadays, technological advances are changing our lives very rapidly and adding a lot of technologies that help us to track the risk of drug abuse such as smart phones, Wireless Sensor Networks (WSNs), Internet of Things (IoT), etc. This technique may help us to early discovery of drug abuse in order to prevent an aggravation of the influence of drugs on the abuser. In this paper, we have developed a Decision Support System (DSS) for detecting the drug abuse using Artificial Neural Network (ANN); we used a Multilayer Perceptron (MLP) feed-forward neural network in developing the system. The input layer includes 50 variables while the output layer contains one neuron which indicates whether the person is a drug addict. An iterative process is used to determine the number of hidden layers and the number of neurons in each one. We used multiple experiment models that have been completed with Log-Sigmoid transfer function. Particularly, 10-fold cross validation schemes are used to access the generalization of the proposed system. The experiment results have obtained 98.42% classification accuracy for correct diagnosis in our system. The data had been taken from 184 cases in Jordan according to a set of questions compiled from Specialists, and data have been obtained through the families of drug abusers.

Keywords: Artificial Neural Network, Decision Support System, drug abuse, drug addiction, Multilayer Perceptron.

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Authors: Saeed Mohammed Baneamoon, Rosalina Abdul Salam

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Robotic system is an important area in artificial intelligence that aims at developing the performance techniques of the robot and making it more efficient and more effective in choosing its correct behavior. In this paper the distributed learning classifier system is used for designing a simulated control system for robot to perform complex behaviors. A set of enhanced approaches that support default hierarchies formation is suggested and compared with each other in order to make the simulated robot more effective in mapping the input to the correct output behavior.

Keywords: Learning Classifier System, Default Hierarchies, Robot Behaviors.

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Authors: Saudagar Abdul Khader Jilani, Syed Abdul Sattar

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Image compression plays a vital role in today-s communication. The limitation in allocated bandwidth leads to slower communication. To exchange the rate of transmission in the limited bandwidth the Image data must be compressed before transmission. Basically there are two types of compressions, 1) LOSSY compression and 2) LOSSLESS compression. Lossy compression though gives more compression compared to lossless compression; the accuracy in retrievation is less in case of lossy compression as compared to lossless compression. JPEG, JPEG2000 image compression system follows huffman coding for image compression. JPEG 2000 coding system use wavelet transform, which decompose the image into different levels, where the coefficient in each sub band are uncorrelated from coefficient of other sub bands. Embedded Zero tree wavelet (EZW) coding exploits the multi-resolution properties of the wavelet transform to give a computationally simple algorithm with better performance compared to existing wavelet transforms. For further improvement of compression applications other coding methods were recently been suggested. An ANN base approach is one such method. Artificial Neural Network has been applied to many problems in image processing and has demonstrated their superiority over classical methods when dealing with noisy or incomplete data for image compression applications. The performance analysis of different images is proposed with an analysis of EZW coding system with Error Backpropagation algorithm. The implementation and analysis shows approximately 30% more accuracy in retrieved image compare to the existing EZW coding system.

Keywords: Accuracy, Compression, EZW, JPEG2000, Performance.

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Authors: Naoto Suzuki

Abstract:

Japanese are affected by the most common causes of eyesight loss such as glaucoma, diabetic retinopathy, pigmentary retinal degeneration, and age-related macular degeneration. We developed an ophthalmological examination apparatus with a fundus camera, precisely fundus-related perimetry (microperimetry), and electroretinogram (ERG) functions to diagnose a variety of diseases that cause eyesight loss. The experimental apparatus was constructed with the same optical system as a fundus camera. The microperimetry optical system was calculated and added to the experimental apparatus using the German company Optenso's optical engineering software (OpTaliX-LT 10.8). We also added an Edmund infrared camera (EO-0413), a lens with a 25 mm focal length, a 45° cold mirror, a 12 V/50 W halogen lamp, and an 8-inch monitor. We made the artificial eye of a plane-convex lens, a black spacer, and a hemispherical cup. The hemispherical cup had a small section of the paper at the bottom. The artificial eye was photographed five times using the experimental apparatus. The software was created to display the examination target on the monitor and save examination data using C++Builder 10.2. The retinal fundus was displayed on the monitor at a length and width of 1 mm and a resolution of 70.4 ± 4.1 and 74.7 ± 6.8 pixels, respectively. The microperimetry and ERG functions were successfully added to the experimental ophthalmological apparatus. A moving machine was developed to measure the artificial eye's movement. The artificial eye's rear part was painted black and white in the central area. It was rotated 10 degrees from one side to the other. The movement was captured five times as motion videos. Three static images were extracted from one of the motion videos captured. The images display the artificial eye facing the center, right, and left directions. The three images were processed using Scilab 6.1.0 and Image Processing and Computer Vision Toolbox 4.1.2, including trimming, binarization, making a window, deleting peripheral area, and morphological operations. To calculate the artificial eye's fundus center, we added a gravity method to the program to calculate the gravity position of connected components. From the three images, the image processing could calculate the center position.

Keywords: Ophthalmological examination apparatus, microperimetry, electroretinogram, eye movement.

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Authors: Richard Joseph, Nerey Mvungi

Abstract:

An electric power system includes a generating, a transmission, a distribution, and consumers subsystems. An electrical power network in Tanzania keeps growing larger by the day and become more complex so that, most utilities have long wished for real-time monitoring and remote control of electrical power system elements such as substations, intelligent devices, power lines, capacitor banks, feeder switches, fault analyzers and other physical facilities. In this paper, the concept of automation of management of power systems from generation level to end user levels was determined by using Power System Simulator for Engineering (PSS/E) version 30.3.2.

Keywords: Automation, Distribution subsystem, Generating subsystem, PSS/E, TANESCO, Transmission subsystem.

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Authors: Robert O. Abolade, Olumide O. Ajayi, Zacheaus K. Adeyemo, Solomon A. Adeniran

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Link adaptation is an important strategy for achieving robust wireless multimedia communications based on quality of service (QoS) demand. Scheme switching in multiple-input multiple-output (MIMO) systems is an aspect of link adaptation, and it involves selecting among different MIMO transmission schemes or modes so as to adapt to the varying radio channel conditions for the purpose of achieving QoS delivery. However, finding the most appropriate switching method in MIMO links is still a challenge as existing methods are either computationally complex or not always accurate. This paper presents an intelligent switching method for the MIMO system consisting of two schemes - transmit diversity (TD) and spatial multiplexing (SM) - using fuzzy logic technique. In this method, two channel quality indicators (CQI) namely average received signal-to-noise ratio (RSNR) and received signal strength indicator (RSSI) are measured and are passed as inputs to the fuzzy logic system which then gives a decision – an inference. The switching decision of the fuzzy logic system is fed back to the transmitter to switch between the TD and SM schemes. Simulation results show that the proposed fuzzy logic – based switching technique outperforms conventional static switching technique in terms of bit error rate and spectral efficiency.

Keywords: Channel quality indicator, fuzzy logic, link adaptation, MIMO, spatial multiplexing, transmit diversity.

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Authors: Mohammed Benbrahim, Khalid Benjelloun, Aomar Ibenbrahim, Adil Daoudi

Abstract:

The automatic classification of non stationary signals is an important practical goal in several domains. An essential classification task is to allocate the incoming signal to a group associated with the kind of physical phenomena producing it. In this paper, we present a modular system composed by three blocs: 1) Representation, 2) Dimensionality reduction and 3) Classification. The originality of our work consists in the use of a new wavelet called "Ben wavelet" in the representation stage. For the dimensionality reduction, we propose a new algorithm based on the random projection and the principal component analysis.

Keywords: Seismic signals, Ben Wavelet, Dimensionality reduction, Artificial neural networks, Classification.

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Authors: Talal Alsulaiman, Khaldoun Khashanah

Abstract:

In this paper, we provided a literature survey on the artificial stock problem (ASM). The paper began by exploring the complexity of the stock market and the needs for ASM. ASM aims to investigate the link between individual behaviors (micro level) and financial market dynamics (macro level). The variety of patterns at the macro level is a function of the AFM complexity. The financial market system is a complex system where the relationship between the micro and macro level cannot be captured analytically. Computational approaches, such as simulation, are expected to comprehend this connection. Agent-based simulation is a simulation technique commonly used to build AFMs. The paper proceeds by discussing the components of the ASM. We consider the roles of behavioral finance (BF) alongside the traditionally risk-averse assumption in the construction of agent’s attributes. Also, the influence of social networks in the developing of agents interactions is addressed. Network topologies such as a small world, distance-based, and scale-free networks may be utilized to outline economic collaborations. In addition, the primary methods for developing agents learning and adaptive abilities have been summarized. These incorporated approach such as Genetic Algorithm, Genetic Programming, Artificial neural network and Reinforcement Learning. In addition, the most common statistical properties (the stylized facts) of stock that are used for calibration and validation of ASM are discussed. Besides, we have reviewed the major related previous studies and categorize the utilized approaches as a part of these studies. Finally, research directions and potential research questions are argued. The research directions of ASM may focus on the macro level by analyzing the market dynamic or on the micro level by investigating the wealth distributions of the agents.

Keywords: Artificial stock markets, agent based simulation, bounded rationality, behavioral finance, artificial neural network, interaction, scale-free networks.

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Authors: S. Areerachakul

Abstract:

Nowadays, several techniques such as; Fuzzy Inference System (FIS) and Neural Network (NN) are employed for developing of the predictive models to estimate parameters of water quality. The main objective of this study is to compare between the predictive ability of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model and Artificial Neural Network (ANN) model to estimate the Biochemical Oxygen Demand (BOD) on data from 11 sampling sites of Saen Saep canal in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage, Bangkok Metropolitan Administration, during 2004-2011. The five parameters of water quality namely Dissolved Oxygen (DO), Chemical Oxygen Demand (COD), Ammonia Nitrogen (NH3N), Nitrate Nitrogen (NO3N), and Total Coliform bacteria (T-coliform) are used as the input of the models. These water quality indices affect the biochemical oxygen demand. The experimental results indicate that the ANN model provides a higher correlation coefficient (R=0.73) and a lower root mean square error (RMSE=4.53) than the corresponding ANFIS model.

Keywords: adaptive neuro-fuzzy inference system, artificial neural network, biochemical oxygen demand, surface water.

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