Search results for: sensor networks.

Authors: A. Ben Yaghlane, M. N. Azaiez, M. Mrad

Abstract:

We investigate the large scale of networks in the context of network survivability under attack. We use appropriate techniques to evaluate and the attacker-based- and the defenderbased- network survivability. The attacker is unaware of the operated links by the defender. Each attacked link has some pre-specified probability to be disconnected. The defender choice is so that to maximize the chance of successfully sending the flow to the destination node. The attacker however will select the cut-set with the highest chance to be disabled in order to partition the network. Moreover, we extend the problem to the case of selecting the best p paths to operate by the defender and the best k cut-sets to target by the attacker, for arbitrary integers p,k>1. We investigate some variations of the problem and suggest polynomial-time solutions.

Keywords: Defense/attack strategies, large scale, networks, partitioning a network.

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Authors: Wen-Tsann Lin, Yung-Tsan Jou, Yih-Chuan Wu, Yuan-Du Hsiao

Abstract:

The Emergency Department of a medical center in Taiwan cooperated to conduct the research. A predictive model of triage system is contracted from the contract procedure, selection of parameters to sample screening. 2,000 pieces of data needed for the patients is chosen randomly by the computer. After three categorizations of data mining (Multi-group Discriminant Analysis, Multinomial Logistic Regression, Back-propagation Neural Networks), it is found that Back-propagation Neural Networks can best distinguish the patients- extent of emergency, and the accuracy rate can reach to as high as 95.1%. The Back-propagation Neural Networks that has the highest accuracy rate is simulated into the triage acuity expert system in this research. Data mining applied to the predictive model of the triage acuity expert system can be updated regularly for both the improvement of the system and for education training, and will not be affected by subjective factors.

Keywords: Back-propagation Neural Networks, Data Mining, Emergency Department, Triage System.

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Authors: Khaja Kamaluddin, Muhammed Yousoof

Abstract:

Bandwidth allocation in wired network is less complex and to allocate bandwidth in wireless networks is complex and challenging, due to the mobility of source end system.This paper proposes a new approach to bandwidth allocation to higher and lower priority mobile nodes.In our proposal bandwidth allocation to new mobile node is based on bandwidth utilization of existing mobile nodes.The first section of the paper focuses on introduction to bandwidth allocation in wireless networks and presents the existing solutions available for allocation of bandwidth. The second section proposes the new solution for the bandwidth allocation to higher and lower priority nodes. Finally this paper ends with the analytical evaluation of the proposed solution.

Keywords: Layered Approach, Bandwidth allocation, CBR, ABR, QOS.

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Authors: Kriuk Boris, Kriuk Fedor

Abstract:

This paper presents a comparative study of fundamental similarity functions for Siamese networks in semantic textual similarity (STS) tasks. We evaluate various similarity functions using the STS Benchmark dataset, analyzing their performance and stability. Additionally, we present a multi-objective approach for optimal threshold selection. Our findings provide insights into the effectiveness of different similarity functions and offer a straightforward method for threshold selection optimization, contributing to the advancement of Siamese network architectures in STS applications.

Keywords: Siamese networks, Semantic textual similarity, Similarity functions, STS Benchmark dataset, Threshold selection.

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Authors: Sarah O. Alhumoud, Mawaheb I. Altuwaijri, Tarfa M. Albuhairi, Wejdan M. Alohaideb

Abstract:

Large-scale data stream analysis has become one of the important business and research priorities lately. Social networks like Twitter and other micro-blogging platforms hold an enormous amount of data that is large in volume, velocity and variety. Extracting valuable information and trends out of these data would aid in a better understanding and decision-making. Multiple analysis techniques are deployed for English content. Moreover, one of the languages that produce a large amount of data over social networks and is least analyzed is the Arabic language. The proposed paper is a survey on the research efforts to analyze the Arabic content in Twitter focusing on the tools and methods used to extract the sentiments for the Arabic content on Twitter.

Keywords: Big Data, Social Networks, Sentiment Analysis.

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Authors: Nadia Hmad, Tony Allen

Abstract:

This paper presents an ESN-based Arabic phoneme recognition system trained with supervised, forced and combined supervised/forced supervised learning algorithms. Mel-Frequency Cepstrum Coefficients (MFCCs) and Linear Predictive Code (LPC) techniques are used and compared as the input feature extraction technique. The system is evaluated using 6 speakers from the King Abdulaziz Arabic Phonetics Database (KAPD) for Saudi Arabia dialectic and 34 speakers from the Center for Spoken Language Understanding (CSLU2002) database of speakers with different dialectics from 12 Arabic countries. Results for the KAPD and CSLU2002 Arabic databases show phoneme recognition performances of 72.31% and 38.20% respectively.

Keywords: Arabic phonemes recognition, echo state networks (ESNs), neural networks (NNs), supervised learning.

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Authors: Walaa Khalaf, Calogero Pace, Manlio Gaudioso

Abstract:

We present an Electronic Nose (ENose), which is aimed at identifying the presence of one out of two gases, possibly detecting the presence of a mixture of the two. Estimation of the concentrations of the components is also performed for a volatile organic compound (VOC) constituted by methanol and acetone, for the ranges 40-400 and 22-220 ppm (parts-per-million), respectively. Our system contains 8 sensors, 5 of them being gas sensors (of the class TGS from FIGARO USA, INC., whose sensing element is a tin dioxide (SnO2) semiconductor), the remaining being a temperature sensor (LM35 from National Semiconductor Corporation), a humidity sensor (HIH–3610 from Honeywell), and a pressure sensor (XFAM from Fujikura Ltd.). Our integrated hardware–software system uses some machine learning principles and least square regression principle to identify at first a new gas sample, or a mixture, and then to estimate the concentrations. In particular we adopt a training model using the Support Vector Machine (SVM) approach with linear kernel to teach the system how discriminate among different gases. Then we apply another training model using the least square regression, to predict the concentrations. The experimental results demonstrate that the proposed multiclassification and regression scheme is effective in the identification of the tested VOCs of methanol and acetone with 96.61% correctness. The concentration prediction is obtained with 0.979 and 0.964 correlation coefficient for the predicted versus real concentrations of methanol and acetone, respectively.

Keywords: Electronic nose, Least square regression, Mixture ofgases, Support Vector Machine.

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Authors: Altab Hossain, Ataur Rahman, A. K. M. Mohiuddin, Yulfian Aminanda

Abstract:

Fuzzy logic system (FLS) is used in this study to predict the tractive performance in terms of traction force, and motion resistance for an intelligent air cushion track vehicle while it operates in the swamp peat. The system is effective to control the intelligent air –cushion system with measuring the vehicle traction force (TF), motion resistance (MR), cushion clearance height (CH) and cushion pressure (CP). Ultrasonic displacement sensor, pull-in solenoid electromagnetic switch, pressure control sensor, micro controller, and battery pH sensor are incorporated with the Fuzzy logic system to investigate experimentally the TF, MR, CH, and CP. In this study, a comparison for tractive performance of an intelligent air cushion track vehicle has been performed with the results obtained from the predicted values of FLS and experimental actual values. The mean relative error of actual and predicted values from the FLS model on traction force, and total motion resistance are found as 5.58 %, and 6.78 % respectively. For all parameters, the relative error of predicted values are found to be less than the acceptable limits. The goodness of fit of the prediction values from the FLS model on TF, and MR are found as 0.90, and 0.98 respectively.

Keywords: Cushion pressure, Fuzzy logic, Motion resistance, Traction force.

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Authors: Muhibul Haque Bhuyan, Md. Refat Sarder

Abstract:

If a person can monitor his/her oxygen saturation level intermittently then he/she can identify his/her condition early and thus he/she can seek a doctor’s help. This paper reports the design, simulation, and implementation of a low-cost pulse oxygen saturation measurement device based on a reflective photoplethysmography (PPG) system using an integrated circuit sensor as the fundamental component of this health status checking device. The measurement of the physiological parameter is the blood oxygen saturation level (SpO₂) in the peripheral capillary. This work has been implemented using an Arduino Uno R3 microcontroller along with this sensor integrated circuit (IC). The system is designed in the Proteus environment and then simulated to check its performance. After that, the hardware implementation is performed. We used a clipping type optical sensor to sense the arterial oxygen saturation level of blood signal from the fingertips of an individual and then transformed it into the digital data in the microcontroller through its programming its instruction. The designed system was tested by measuring the SpO₂ level for several people of different ages, from 12 to 57 years of age. Besides, the same people were tested using a standard machine purchased from the market. Test results were found very satisfactory as the average percentage of error was very low, 1.59% only.

Keywords: Digital pulse oxygen saturation level, oximeter, measurement, design, simulation, implementation, proteus, Arduino Uno microcontroller.

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Authors: Roza Dastres, Mohsen Soori

Abstract:

Networks are vulnerable devices due to their basic feature of facilitating remote access and data communication. The information in the networks needs to be kept secured and safe in order to provide an effective communication and sharing device in the web of data. Due to challenges and threats of the data in networks, the network security is one of the most important considerations in information technology infrastructures. As a result, the security measures are considered in the network in order to decrease the probability of accessing the secured data by the hackers. The purpose of network security is to protect the network and its components from unauthorized access and abuse in order to provide a safe and secured communication device for the users. In the present research work a review in recent development of network threats and security measures is presented and future research works are also suggested. Different attacks to the networks and security measured against them are discussed in order to increase security in the web of data. So, new ideas in the network security systems can be presented by analyzing the published papers in order to move forward the research field.

Keywords: Network threats, network security, security measures, firewalls.

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Authors: O. S. Ebrahim, K. O. Shawky, M. A. Badr, P. K. Jain

Abstract:

This paper describes an efficient; low-cost; high-availability; induction motor (IM) drive system with intelligent supervisory control for water distribution networks including storage tank. To increase the operational efficiency and reduce cost, the IM drive system includes main pumping unit and an auxiliary voltage source inverter (VSI) fed unit. The main unit comprises smart star/delta starter, regenerative fluid clutch, switched VAR compensator, and hysteresis liquid-level controller. Three-state energy saving mode (ESM) is defined at no-load and a logic algorithm is developed for best energetic cost reduction. To reduce voltage sag, the supervisory controller operates the switched VAR compensator upon motor starting. To provide smart star/delta starter at low cost, a method based on current sensing is developed for interlocking, malfunction detection, and life–cycles counting and used to synthesize an improved fuzzy logic (FL) based availability assessment scheme. Furthermore, a recurrent neural network (RNN) full state estimator is proposed to provide sensor fault-tolerant algorithm for the feedback control. The auxiliary unit is working at low flow rates and improves the system efficiency and flexibility for distributed generation during islanding mode. Compared with doubly-fed IM, the proposed one ensures 30% working throughput under main motor/pump fault conditions, higher efficiency, and marginal cost difference. This is critically important in case of water networks. Theoretical analysis, computer simulations, cost study, as well as efficiency evaluation, using timely cascaded energy-conservative systems, are performed on IM experimental setup to demonstrate the validity and effectiveness of the proposed drive and control.

Keywords: Artificial Neural Network, ANN, Availability Assessment, Cloud Computing, Energy Saving, Induction Machine, IM, Supervisory Control, Fuzzy Logic, FL, Pumped Storage.

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Authors: A. Pajaziti, H. Cana

Abstract:

In this paper, the structural genetic algorithm is used to optimize the neural network to control the joint movements of robotic arm. The robotic arm has also been modeled in 3D and simulated in real-time in MATLAB. It is found that Neural Networks provide a simple and effective way to control the robot tasks. Computer simulation examples are given to illustrate the significance of this method. By combining Genetic Algorithm optimization method and Neural Networks for the given robotic arm with 5 D.O.F. the obtained the results shown that the base joint movements overshooting time without controller was about 0.5 seconds, while with Neural Network controller (optimized with Genetic Algorithm) was about 0.2 seconds, and the population size of 150 gave best results.

Keywords: Robotic Arm, Neural Network, Genetic Algorithm, Optimization.

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Authors: Khalid Ali, Manar Jammal

Abstract:

In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity.

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Authors: Amany M. Alshawi, Khaled Alduhaiman

Abstract:

This paper proposes a technique to protect against email bombing. The technique employs a statistical approach, Naïve Bayes (NB), and Neural Networks to show that it is possible to differentiate between good and bad traffic to protect against email bombing attacks. Neural networks and Naïve Bayes can be trained by utilizing many email messages that include both input and output data for legitimate and non-legitimate emails. The input to the model includes the contents of the body of the messages, the subject, and the headers. This information will be used to determine if the email is normal or an attack email. Preliminary tests suggest that Naïve Bayes can be trained to produce an accurate response to confirm which email represents an attack.

Keywords: Email bombing, Legitimate email, Naïve Bayes, Neural networks, Non-legitimate email.

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Authors: Mohamed Aymen Slim, Afef Abdelkrim, Mohamed Benrejeb

Abstract:

The handwriting is a physical demonstration of a complex cognitive process learnt by man since his childhood. People with disabilities or suffering from various neurological diseases are facing so many difficulties resulting from problems located at the muscle stimuli (EMG) or signals from the brain (EEG) and which arise at the stage of writing. The handwriting velocity of the same writer or different writers varies according to different criteria: age, attitude, mood, writing surface, etc. Therefore, it is interesting to reconstruct an experimental basis records taking, as primary reference, the writing speed for different writers which would allow studying the global system during handwriting process. This paper deals with a new approach of the handwriting system modeling based on the velocity criterion through the concepts of artificial neural networks, precisely the Radial Basis Functions (RBF) neural networks. The obtained simulation results show a satisfactory agreement between responses of the developed neural model and the experimental data for various letters and forms then the efficiency of the proposed approaches.

Keywords: ElectroMyoGraphic (EMG) signals, Experimental approach, Handwriting process, Radial Basis Functions (RBF) neural networks, Velocity Modeling.

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Authors: Hyun-Koo Kim, Sangmoon Lee, Ho-Youl Jung, Ju H. Park

Abstract:

We present a system that finds road boundaries and constructs the virtual lane based on fusion data from a laser and a monocular sensor, and detects forward vehicle position even in no lane markers or bad environmental conditions. When the road environment is dark or a lot of vehicles are parked on the both sides of the road, it is difficult to detect lane and road boundary. For this reason we use fusion of laser and vision sensor to extract road boundary to acquire three dimensional data. We use parabolic road model to calculate road boundaries which is based on vehicle and sensors state parameters and construct virtual lane. And then we distinguish vehicle position in each lane.

Keywords: Vehicle Detection, Adaboost, Haar-like Feature, Road Boundary Detection

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Authors: Xiaomei Wang, Shouming Zhong

Abstract:

In this paper, a class of generalized bi-directional associative memory (BAM) neural networks with mixed delays is investigated. On the basis of Lyapunov stability theory and contraction mapping theorem, some new sufficient conditions are established for the existence and uniqueness and globally exponential stability of equilibrium, which generalize and improve the previously known results. One example is given to show the feasibility and effectiveness of our results.

Keywords: Bi-directional associative memory (BAM) neural networks, mixed delays, Lyapunov stability theory, contraction mapping theorem, existence, equilibrium, globally exponential stability.

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Authors: M. Tamilarasi, T.G. Palanivelu

Abstract:

Mobile Ad Hoc Networks (MANETs) are multi-hop wireless networks in which all nodes cooperatively maintain network connectivity. In such a multi-hop wireless network, every node may be required to perform routing in order to achieve end-to-end communication among nodes. These networks are energy constrained as most ad hoc mobile nodes today operate with limited battery power. Hence, it is important to minimize the energy consumption of the entire network in order to maximize the lifetime of ad hoc networks. In this paper, a mechanism involving the integration of load balancing approach and transmission power control approach is introduced to maximize the life-span of MANETs. The mechanism is applied on Ad hoc On-demand Vector (AODV) protocol to make it as energy aware AODV (EA_AODV). The simulation is carried out using GloMoSim2.03 simulator. The results show that the proposed mechanism reduces the average required transmission energy per packet compared to the standard AODV.

Keywords: energy aware routing, load balance, Mobile Ad HocNetworks, MANETs , on demand routing, transmission power control.

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Authors: Jagadish H. Pujar, S. F. Kodad

Abstract:

Recent developments in Soft computing techniques, power electronic switches and low-cost computational hardware have made it possible to design and implement sophisticated control strategies for sensorless speed control of AC motor drives. Such an attempt has been made in this work, for Sensorless Speed Control of Induction Motor (IM) by means of Direct Torque Fuzzy Control (DTFC), PI-type fuzzy speed regulator and MRAS speed estimator strategy, which is absolutely nonlinear in its nature. Direct torque control is known to produce quick and robust response in AC drive system. However, during steady state, torque, flux and current ripple occurs. So, the performance of conventional DTC with PI speed regulator can be improved by implementing fuzzy logic techniques. Certain important issues in design including the space vector modulated (SVM) 3-Ф voltage source inverter, DTFC design, generation of reference torque using PI-type fuzzy speed regulator and sensor less speed estimator have been resolved. The proposed scheme is validated through extensive numerical simulations on MATLAB. The simulated results indicate the sensor less speed control of IM with DTFC and PI-type fuzzy speed regulator provides satisfactory high dynamic and static performance compare to conventional DTC with PI speed regulator.

Keywords: Sensor-less Speed Estimator, Fuzzy Logic Control(FLC), SVM, DTC, DTFC, IM, fuzzy speed regulator.

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Authors: Reza Dinarvand, Mahdi Sadeghian, Somaye Sadeghian

Abstract:

Prediction of the ultimate bearing capacity of piles (Qu) is one of the basic issues in geotechnical engineering. So far, several methods have been used to estimate Qu, including the recently developed artificial intelligence methods. In recent years, optimization algorithms have been used to minimize artificial network errors, such as colony algorithms, genetic algorithms, imperialist competitive algorithms, and so on. In the present research, artificial neural networks based on colonial competition algorithm (ANN-ICA) were used, and their results were compared with other methods. The results of laboratory tests of short piles in clayey soils with parameters such as pile diameter, pile buried length, eccentricity of load and undrained shear resistance of soil were used for modeling and evaluation. The results showed that ICA-based artificial neural networks predicted lateral bearing capacity of short piles with a correlation coefficient of 0.9865 for training data and 0.975 for test data. Furthermore, the results of the model indicated the superiority of ICA-based artificial neural networks compared to back-propagation artificial neural networks as well as the Broms and Hansen methods.

Keywords: Lateral bearing capacity, short pile, clayey soil, artificial neural network, Imperialist competition algorithm.

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

Abstract:

Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: Anomaly detection, autoencoder, data centers, deep learning.

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Authors: T. T. Manikandan, Rajeev Sukumaran

Abstract:

For maintaining the biodiversity of many ecosystems the existence of coral reefs play a vital role. But due to many factors such as pollution and coral mining, coral reefs are dying day by day. One way to protect the coral reefs is to farm them in a carefully monitored underwater environment and restore it in place of dead corals. For successful farming of corals in coral farms, different parameters of the water in the farming area need to be monitored and maintained at optimal level. Sensing underwater parameters using wireless sensor nodes is an effective way for precise and continuous monitoring in a highly dynamic environment like oceans. Here the sensed information is of varying importance and it needs to be provided with desired Quality of Service(QoS) guarantees in delivering the information to offshore monitoring centers. The main interest of this research is Stochastic Network Calculus (SNC) based modeling of network layer design for underwater wireless sensor communication. The model proposed in this research enforces differentiation of service in underwater wireless sensor communication with the help of buffer sizing and link scheduling. The delay and backlog bounds for such differentiated services are analytically derived using stochastic network calculus.

Keywords: Underwater Coral Farms, SNC, differentiated service, delay bound, backlog bound.

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Authors: John Hadden, Ashutosh Tiwari, Rajkumar Roy, Dymitr Ruta

Abstract:

The aim of this paper is to identify the most suitable model for churn prediction based on three different techniques. The paper identifies the variables that affect churn in reverence of customer complaints data and provides a comparative analysis of neural networks, regression trees and regression in their capabilities of predicting customer churn.

Keywords: Churn, Decision Trees, Neural Networks, Regression.

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Authors: A. A. Hussen, I. N. Jleta

Abstract:

Micro-electromechanical system (MEMS) accelerometers and gyroscopes are suitable for the inertial navigation system (INS) of many applications due to low price, small dimensions and light weight. The main disadvantage in a comparison with classic sensors is a worse long term stability. The estimation accuracy is mostly affected by the time-dependent growth of inertial sensor errors, especially the stochastic errors. In order to eliminate negative effects of these random errors, they must be accurately modeled. In this paper, the Allan variance technique will be used in modeling the stochastic errors of the inertial sensors. By performing a simple operation on the entire length of data, a characteristic curve is obtained whose inspection provides a systematic characterization of various random errors contained in the inertial-sensor output data.

Keywords: Allan variance, accelerometer, gyroscope, stochastic errors.

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Authors: N. Ojaroudi Parchin, H. Jahanbakhsh Basherlou, Y. Al-Yasir, A. M. Abdulkhaleq, R. A. Abd-Alhameed, P. S. Excell

Abstract:

A compact design of multi-sector patch antenna array for 60 GHz applications is presented and discussed in details. The proposed design combines five 1x8 linear patch antenna arrays, referred to as sectors, in a multi-sector configuration. The coaxial-fed radiation elements of the multi-sector array are designed on 0.2 mm Rogers RT5880 dielectrics. The array operates in the frequency range of 58-62 GHz and provides switchable directional/omnidirectional radiation beams with high gain and high directivity characteristics. The designed multi-sector array exhibits good performances and could be used in the fifth generation (5G) cellular networks.

Keywords: MM-wave communications, multi-sector array, patch antenna, small cell networks.

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Authors: Samia Salah, M’hamed Hadj Sadok, Abderrezak Guessoum

Abstract:

In this paper, we propose a new modular approach called neuroglial consisting of two neural networks slow and fast which emulates a biological reality recently discovered. The implementation is based on complex multi-time scale systems; validation is performed on the model of the asynchronous machine. We applied the geometric approach based on the Gerschgorin circles for the decoupling of fast and slow variables, and the method of singular perturbations for the development of reductions models.

This new architecture allows for smaller networks with less complexity and better performance in terms of mean square error and convergence than the single network model.

Keywords: Gerschgorin’s Circles, Neuroglial Network, Multi time scales systems, Singular perturbation method.

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Authors: Martin Doran, Roy Sterritt, George Wilkie

Abstract:

NASA, ESA, and NSSC space agencies have plans to put planetary rovers on Mars in 2020. For these future planetary rovers to succeed, they will heavily depend on sensors to detect obstacles. This will also become of vital importance in the future, if rovers become less dependent on commands received from earth-based control and more dependent on self-configuration and self-decision making. These planetary rovers will face harsh environments and the possibility of hardware failure is high, as seen in missions from the past. In this paper, we focus on using Autonomic principles where self-healing, self-optimization, and self-adaption are explored using the MAPE-K model and expanding this model to encapsulate the attributes such as Awareness, Analysis, and Adjustment (AAA-3). In the experimentation, a Pioneer P3-DX research robot is used to simulate a planetary rover. The sonar sensors on the P3-DX robot are used to simulate the sensors on a planetary rover (even though in reality, sonar sensors cannot operate in a vacuum). Experiments using the P3-DX robot focus on how our software system can be adapted with the loss of sonar sensor functionality. The autonomic manager system is responsible for the decision making on how to make use of remaining ‘enabled’ sonars sensors to compensate for those sonar sensors that are ‘disabled’. The key to this research is that the robot can still detect objects even with reduced sonar sensor capability.

Keywords: Autonomic, self-adaption, self-healing, self-optimization.

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Authors: Hsu-Yung Cheng, Victor Gau, Chih-Wei Huang, Jenq-Neng Hwang, Chih-Chang Yu

Abstract:

In order to achieve better road utilization and traffic efficiency, there is an urgent need for a travel information delivery mechanism to assist the drivers in making better decisions in the emerging intelligent transportation system applications. In this paper, we propose a relayed multicast scheme under heterogeneous networks for this purpose. In the proposed system, travel information consisting of summarized traffic conditions, important events, real-time traffic videos, and local information service contents is formed into layers and multicasted through an integration of WiMAX infrastructure and Vehicular Ad hoc Networks (VANET). By the support of adaptive modulation and coding in WiMAX, the radio resources can be optimally allocated when performing multicast so as to dynamically adjust the number of data layers received by the users. In addition to multicast supported by WiMAX, a knowledge propagation and information relay scheme by VANET is designed. The experimental results validate the feasibility and effectiveness of the proposed scheme.

Keywords: Intelligent Transportation Systems, RelayedMulticast, WiMAX, Vehicular Ad hoc Networks (VANET).

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Authors: Muhammad R. Alrabeiah, Ibrahim S. Alnomay

Abstract:

The need in cognitive radio system for a simple, fast, and independent technique to sense the spectrum occupancy has led to the energy detection approach. Energy detector is known by its dependency on noise variation in the system which is one of its major drawbacks. In this paper, we are aiming to improve its performance by utilizing a weighted collaborative spectrum sensing, it is similar to the collaborative spectrum sensing methods introduced previously in the literature. These weighting methods give more improvement for collaborative spectrum sensing as compared to no weighting case. There is two method proposed in this paper: the first one depends on the channel status between each sensor and the primary user while the second depends on the value of the energy measured in each sensor.

Keywords: Cognitive radio, Spectrum sensing, Collaborative sensors, Weighted Decisions.

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Authors: Yasser F. Hassan

Abstract:

The reduction or removal of noise in a color image is an essential part of image processing, whether the final information is used for human perception or for an automatic inspection and analysis. This paper describes the modeling system based on the rough neural network model to adaptive cellular automata for various image processing tasks and noise remover. In this paper, we consider the problem of object processing in colored image using rough neural networks to help deriving the rules which will be used in cellular automata for noise image. The proposed method is compared with some classical and recent methods. The results demonstrate that the new model is capable of being trained to perform many different tasks, and that the quality of these results is comparable or better than established specialized algorithms.

Keywords: Rough Sets, Rough Neural Networks, Cellular Automata, Image Processing.

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