Search results for: low data rate

Authors: W. Triampo, D. Baowan, I.M. Tang, N. Nuttavut, J. Wong-Ekkabut, G. Doungchawee

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In this work, we consider a deterministic model for the transmission of leptospirosis which is currently spreading in the Thai population. The SIR model which incorporates the features of this disease is applied to the epidemiological data in Thailand. It is seen that the numerical solutions of the SIR equations are in good agreement with real empirical data. Further improvements are discussed.

Keywords: Leptospirosis, SIR Model, Deterministic model, Thailand.

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Authors: Tshaudi Motsima

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Class attendance is key at all levels of education. At tertiary level many students develop a tendency of not attending all classes without being aware of the repercussions of not attending all classes. It is important for all students to attend all classes as they can receive first-hand information and they can benefit more. The student who attends classes is likely to perform better academically than the student who does not. The aim of this paper is to assess the relationship between class attendance and academic performance of industrial engineering students. The data for this study were collected through the attendance register of students and the other data were accessed from the Integrated Tertiary Software and the Higher Education Data Analyzer Portal. Data analysis was conducted on a sample of 93 students. The results revealed that students with medium predicate scores (OR = 3.8; p = 0.027) and students with low predicate scores (OR = 21.4, p < 0.001) were significantly likely to attend less than 80% of the classes as compared to students with high predicate scores. Students with examination performance of less than 50% were likely to attend less than 80% of classes than students with examination performance of 50% and above, but the differences were not statistically significant (OR = 1.3; p = 0.750).

Keywords: Class attendance, examination performance, final outcome, logistic regression.

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Authors: Siau-Chuin Liew, Siau-Way Liew, Jasni Mohd Zain

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Digital watermarking in medical images can ensure the authenticity and integrity of the image. This design paper reviews some existing watermarking schemes and proposes a reversible tamper detection and recovery watermarking scheme. Watermark data from ROI (Region Of Interest) are stored in RONI (Region Of Non Interest). The embedded watermark allows tampering detection and tampered image recovery. The watermark is also reversible and data compression technique was used to allow higher embedding capacity.

Keywords: data compression, medical image, reversible, tamperdetection and recovery, watermark.

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Authors: Mouataz Zreika, Maria Estela Varua

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Stock investment decisions are often made based on current events of the global economy and the analysis of historical data. Conversely, visual representation could assist investors’ gain deeper understanding and better insight on stock market trends more efficiently. The trend analysis is based on long-term data collection. The study adopts a hybrid method that combines the Clustering algorithm and Force-directed algorithm to overcome the scalability problem when visualizing large data. This method exemplifies the potential relationships between each stock, as well as determining the degree of strength and connectivity, which will provide investors another understanding of the stock relationship for reference. Information derived from visualization will also help them make an informed decision. The results of the experiments show that the proposed method is able to produced visualized data aesthetically by providing clearer views for connectivity and edge weights.

Keywords: Clustering, force-directed, graph drawing, stock investment analysis.

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Authors: Yung-Chien Sun, O. Grant Clark

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In this paper, the implementation of a rule-based intuitive reasoner is presented. The implementation included two parts: the rule induction module and the intuitive reasoner. A large weather database was acquired as the data source. Twelve weather variables from those data were chosen as the “target variables" whose values were predicted by the intuitive reasoner. A “complex" situation was simulated by making only subsets of the data available to the rule induction module. As a result, the rules induced were based on incomplete information with variable levels of certainty. The certainty level was modeled by a metric called "Strength of Belief", which was assigned to each rule or datum as ancillary information about the confidence in its accuracy. Two techniques were employed to induce rules from the data subsets: decision tree and multi-polynomial regression, respectively for the discrete and the continuous type of target variables. The intuitive reasoner was tested for its ability to use the induced rules to predict the classes of the discrete target variables and the values of the continuous target variables. The intuitive reasoner implemented two types of reasoning: fast and broad where, by analogy to human thought, the former corresponds to fast decision making and the latter to deeper contemplation. . For reference, a weather data analysis approach which had been applied on similar tasks was adopted to analyze the complete database and create predictive models for the same 12 target variables. The values predicted by the intuitive reasoner and the reference approach were compared with actual data. The intuitive reasoner reached near-100% accuracy for two continuous target variables. For the discrete target variables, the intuitive reasoner predicted at least 70% as accurately as the reference reasoner. Since the intuitive reasoner operated on rules derived from only about 10% of the total data, it demonstrated the potential advantages in dealing with sparse data sets as compared with conventional methods.

Keywords: Artificial intelligence, intuition, knowledge acquisition, limited certainty.

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Authors: Teerapon Pirom, Ura Pancharoen

Abstract:

Amoxicillin is an antibiotic which is widely used to treat various infections in both human beings and animals. However, when amoxicillin is released into the environment, it is a major problem. Amoxicillin causes bacterial resistance to these drugs and failure of treatment with antibiotics. Liquid membrane is of great interest as a promising method for the separation and recovery of the target ions from aqueous solutions due to the use of carriers for the transport mechanism, resulting in highly selectivity and rapid transportation of the desired metal ions. The simultaneous processes of extraction and stripping in a single unit operation of liquid membrane system are very interesting. Therefore, it is practical to apply liquid membrane, particularly the HFSLM for industrial applications as HFSLM is proved to be a separation process with lower capital and operating costs, low energy and extractant with long life time, high selectivity and high fluxes compared with solid membranes. It is a simple design amenable to scaling up for industrial applications. The extraction and recovery for (Amoxicillin) through the hollow fiber supported liquid membrane (HFSLM) using aliquat336 as a carrier were explored with the experimental data. The important variables affecting on transport of amoxicillin viz. extractant concentration and operating time were investigated. The highest AMOX- extraction percentages of 85.35 and Amoxicillin stripping of 80.04 were achieved with the best condition at 6 mmol/L [aliquat336] and operating time 100 min. The extraction reaction order (n) and the extraction reaction rate constant (kf) were found to be 1.00 and 0.0344 min-1, respectively.

Keywords: Aliquat336, amoxicillin, HFSLM, kinetic.

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Authors: G. B. Hong, H. W. Chen

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Liquid-Liquid Equilibrium (LLE) data are measured for the ternary mixtures of water + 1-butanol + butyl acetate and quaternary mixtures of water + 1-butanol + butyl acetate + glycerol at atmospheric pressure at 313.15 K. In addition, isothermal vapor–liquid–liquid equilibrium (VLLE) data are determined experimentally at 333.15 K. The region of heterogeneity is found to increase as the hydrophilic agent (glycerol) is introduced into the aqueous mixtures. The experimental data are correlated with the NRTL model. The predicted results from the solution model with the model parameters determined from the constituent binaries are also compared with the experimental values.

Keywords: LLE, VLLE, hydrophilic agent, NRTL.

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Authors: Kunyanuth Kularbphettong, Cholticha Tongsiri

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This paper aims to create the model for student in choosing an emphasized track of student majoring in computer science at Suan Sunandha Rajabhat University. The objective of this research is to develop the suggested system using data mining technique to analyze knowledge and conduct decision rules. Such relationships can be used to demonstrate the reasonableness of student choosing a track as well as to support his/her decision and the system is verified by experts in the field. The sampling is from student of computer science based on the system and the questionnaire to see the satisfaction. The system result is found to be satisfactory by both experts and student as well. 

Keywords: Data mining technique, the decision support system, knowledge and decision rules.

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Authors: M. C. Venter, G. Hitge, S. C. Krygsman, J. Thiart

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For decades, South Africa has been planning transportation systems from a supply, rather than a demand side, perspective. In terms of parking, this relates to requiring the minimum parking provision that is enforced by city officials. Newer insight is starting to indicate that South Africa needs to re-think this philosophy in light of a new policy environment that desires a different outcome. Urban policies have shifted from reliance on the private car for access, to employing a wide range of alternative modes. Car dominated travel is influenced by various parameters, of which the availability and location of parking plays a significant role. The question is therefore, what is the right strategy to achieve the desired transport outcomes for SA. The focus of this paper is used to assess this issue with regard to parking provision, and specifically at a tertiary institution. A parking audit was conducted at the Stellenbosch campus of Stellenbosch University, monitoring occupancy at all 60 parking areas, every hour during business hours over a five-day period. The data from this survey was compared with the prescribed number of parking bays according to the Stellenbosch Municipality zoning scheme (requiring a minimum of 0.4 bays per student). The analysis shows that by providing 0.09 bays per student, the maximum total daily occupation of all the parking areas did not exceed an 80% occupation rate. It is concluded that the prevailing parking standards are not supportive of the new urban and transport policy environment, but that it is extremely conservative from a practical demand point of view.

Keywords: Parking provision, parking requirements, travel behaviour, travel demand management.

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Authors: S. Y. Lin, C. H. Kuan, C. H. She, W. T. Wang

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In this study, ultrasonic assisted machining (UAM) technique is applied in side-surface milling experiment for glass-ceramic workpiece material. The tungsten carbide cutting-tool with diamond coating is used in conjunction with two kinds of cooling/lubrication mediums such as water-soluble (WS) cutting fluid and minimum quantity lubricant (MQL). Full factorial process parameter combinations on the milling experiments are planned to investigate the effect of process parameters on cutting performance. From the experimental results, it tries to search for the better process parameter combination which the edge-indentation and the surface roughness are acceptable. In the machining experiments, ultrasonic oscillator was used to excite a cutting-tool along the radial direction producing a very small amplitude of vibration frequency of 20KHz to assist the machining process. After processing, toolmaker microscope was used to detect the side-surface morphology, edge-indentation and cutting tool wear under different combination of cutting parameters, and analysis and discussion were also conducted for experimental results. The results show that the main leading parameters to edge-indentation of glass ceramic are cutting depth and feed rate. In order to reduce edge-indentation, it needs to use lower cutting depth and feed rate. Water-soluble cutting fluid provides a better cooling effect in the primary cutting area; it may effectively reduce the edge-indentation and improve the surface morphology of the glass ceramic. The use of ultrasonic assisted technique can effectively enhance the surface finish cleanness and reduce cutting tool wear and edge-indentation. 

Keywords: Glass-ceramic, ultrasonic assisted machining, cutting performance, edge-indentation

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Authors: Pilar Rey-del-Castillo, Jesús Cardeñosa

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There are many situations where input feature vectors are incomplete and methods to tackle the problem have been studied for a long time. A commonly used procedure is to replace each missing value with an imputation. This paper presents a method to perform categorical missing data imputation from numerical and categorical variables. The imputations are based on Simpson-s fuzzy min-max neural networks where the input variables for learning and classification are just numerical. The proposed method extends the input to categorical variables by introducing new fuzzy sets, a new operation and a new architecture. The procedure is tested and compared with others using opinion poll data.

Keywords: Classifier, imputation techniques, fuzzy systems, fuzzy min-max neural networks.

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Authors: Nam Lethanh, Bryan T. Adey

Abstract:

In this paper, the potential use of an exponential hidden Markov model to model a hidden pavement deterioration process, i.e. one that is not directly measurable, is investigated. It is assumed that the evolution of the physical condition, which is the hidden process, and the evolution of the values of pavement distress indicators, can be adequately described using discrete condition states and modeled as a Markov processes. It is also assumed that condition data can be collected by visual inspections over time and represented continuously using an exponential distribution. The advantage of using such a model in decision making process is illustrated through an empirical study using real world data.

Keywords: Deterioration modeling, Exponential distribution, Hidden Markov model, Pavement management

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Authors: Sandeep Chandana, Rene V. Mayorga, Christine W. Chan

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This article outlines conceptualization and implementation of an intelligent system capable of extracting knowledge from databases. Use of hybridized features of both the Rough and Fuzzy Set theory render the developed system flexibility in dealing with discreet as well as continuous datasets. A raw data set provided to the system, is initially transformed in a computer legible format followed by pruning of the data set. The refined data set is then processed through various Rough Set operators which enable discovery of parameter relationships and interdependencies. The discovered knowledge is automatically transformed into a rule base expressed in Fuzzy terms. Two exemplary cancer repository datasets (for Breast and Lung Cancer) have been used to test and implement the proposed framework.

Keywords: Knowledge Extraction, Fuzzy Sets, Rough Sets, Neuro–Fuzzy Systems, Databases

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Authors: S. Kotsiantis, A. Kostoulas, S. Lykoudis, A. Argiriou, K. Menagias

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Estimates of temperature values at a specific time of day, from daytime and daily profiles, are needed for a number of environmental, ecological, agricultural and technical applications, ranging from natural hazards assessments, crop growth forecasting to design of solar energy systems. The scope of this research is to investigate the efficiency of data mining techniques in estimating minimum, maximum and mean temperature values. For this reason, a number of experiments have been conducted with well-known regression algorithms using temperature data from the city of Patras in Greece. The performance of these algorithms has been evaluated using standard statistical indicators, such as Correlation Coefficient, Root Mean Squared Error, etc.

Keywords: regression algorithms, supervised machine learning.

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Authors: Farshad Arvin, Shyamala Doraisamy

Abstract:

This paper presents a new hardware interface using a microcontroller which processes audio music signals to standard MIDI data. A technique for processing music signals by extracting note parameters from music signals is described. An algorithm to convert the voice samples for real-time processing without complex calculations is proposed. A high frequency microcontroller as the main processor is deployed to execute the outlined algorithm. The MIDI data generated is transmitted using the EIA-232 protocol. The analyses of data generated show the feasibility of using microcontrollers for real-time MIDI generation hardware interface.

Keywords: Signal processing, MIDI, Microcontroller, EIA-232.

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Authors: B. Elshafei, X. Mao

Abstract:

The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models.

Keywords: Data fusion, Gaussian process regression, signal denoise, temporal extrapolation.

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Authors: Edwaldo R. B. Monteiro, Patricia D. M. Plentz, Edson R. De Pieri

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Mobile robotics is gaining an increasingly important role in modern society. Several potentially dangerous or laborious tasks for human are assigned to mobile robots, which are increasingly capable. Many of these tasks need to be performed within a specified period, i.e, meet a deadline. Missing the deadline can result in financial and/or material losses. Mechanisms for predicting the missing of deadlines are fundamental because corrective actions can be taken to avoid or minimize the losses resulting from missing the deadline. In this work we propose a simple but reliable deadline missing prediction mechanism for mobile robots through the use of historical data and we use the Pioneer 3-DX robot for experiments and simulations, one of the most popular robots in academia.

Keywords: Deadline missing, historical data, mobile robots, prediction mechanism.

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Authors: L. A. Muhammad, M. S. Argungu

Abstract:

Educational data mining (EDM) has recorded substantial considerations. Techniques of data mining in one way or the other have been proposed to dig out out-of-sight knowledge in educational data. The result of the study got assists academic institutions in further enhancing their process of learning and methods of passing knowledge to students. Consequently, the performance of students boasts and the educational products are by no doubt enhanced. This study adopted a student performance prediction model premised on techniques of data mining with Students' Essential Features (SEF). SEF are linked to the learner's interactivity with the e-learning management system. The performance of the student's predictive model is assessed by a set of classifiers, viz. Bayes Network, Logistic Regression, and Reduce Error Pruning Tree (REP). Consequently, ensemble methods of Bagging, Boosting, and Random Forest (RF) are applied to improve the performance of these single classifiers. The study reveals that the result shows a robust affinity between learners' behaviors and their academic attainment. Result from the study shows that the REP Tree and its ensemble record the highest accuracy of 83.33% using SEF. Hence, in terms of the Receiver Operating Curve (ROC), boosting method of REP Tree records 0.903, which is the best. This result further demonstrates the dependability of the proposed model.

Keywords: Ensemble, bagging, Random Forest, boosting, data mining, classifiers, machine learning.

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Authors: L. Yu, W. K. Li, S. K. Ong, A. Y. C. Nee

Abstract:

In this paper, a scalable augmented reality framework for handheld devices is presented. The presented framework is enabled by using a server-client data communication structure, in which the search for tracking targets among a database of images is performed on the server-side while pixel-wise 3D tracking is performed on the client-side, which, in this case, is a handheld mobile device. Image search on the server-side adopts a residual-enhanced image descriptors representation that gives the framework a scalability property. The tracking algorithm on the client-side is based on a gravity-aligned feature descriptor which takes the advantage of a sensor-equipped mobile device and an optimized intensity-based image alignment approach that ensures the accuracy of 3D tracking. Automatic content streaming is achieved by using a key-frame selection algorithm, client working phase monitoring and standardized rules for content communication between the server and client. The recognition accuracy test performed on a standard dataset shows that the method adopted in the presented framework outperforms the Bag-of-Words (BoW) method that has been used in some of the previous systems. Experimental test conducted on a set of video sequences indicated the real-time performance of the tracking system with a frame rate at 15-30 frames per second. The presented framework is exposed to be functional in practical situations with a demonstration application on a campus walk-around.

Keywords: Augmented reality framework, server-client model, vision-based tracking, image search.

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Authors: Nawin Raj, Zhenquan Li

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Streamtube is used to visualize expansion, contraction and various properties of the fluid flow. These are useful in fluid mechanics, engineering and geophysics. The streamtube constructed in this paper only reveals the flow expansion rate along streamline. Based on the mass conservative streamline, we will show how to construct the streamtube.

Keywords: Flow visualization, mass conservative, streamline, streamtube.

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Authors: Rachid Ahdid, Khaddouj Taifi, Said Safi, Bouzid Manaut

Abstract:

Automatic detection of facial feature points plays an important role in applications such as facial feature tracking, human-machine interaction and face recognition. The majority of facial feature points detection methods using two-dimensional or three-dimensional data are covered in existing survey papers. In this article chosen approaches to the facial features detection have been gathered and described. This overview focuses on the class of researches exploiting facial feature points detection to represent facial surface for two-dimensional or three-dimensional face. In the conclusion, we discusses advantages and disadvantages of the presented algorithms.

Keywords: Facial feature points, face recognition, facial feature tracking, two-dimensional data, three-dimensional data.

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Authors: S. K. Pillai, M. K. Jeyakumar

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Estimation of model parameters is necessary to predict the behavior of a system. Model parameters are estimated using optimization criteria. Most algorithms use historical data to estimate model parameters. The known target values (actual) and the output produced by the model are compared. The differences between the two form the basis to estimate the parameters. In order to compare different models developed using the same data different criteria are used. The data obtained for short scale projects are used here. We consider software effort estimation problem using radial basis function network. The accuracy comparison is made using various existing criteria for one and two predictors. Then, we propose a new criterion based on linear least squares for evaluation and compared the results of one and two predictors. We have considered another data set and evaluated prediction accuracy using the new criterion. The new criterion is easy to comprehend compared to single statistic. Although software effort estimation is considered, this method is applicable for any modeling and prediction.

Keywords: Software effort estimation, accuracy, Radial Basis Function, linear least squares.

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Authors: M.Suganthi, M.Madheswaran

Abstract:

This paper presents a novel approach to finding a priori interesting regions in mammograms. In order to delineate those regions of interest (ROI-s) in mammograms, which appear to be prominent, a topographic representation called the iso-level contour map consisting of iso-level contours at multiple intensity levels and region segmentation based-thresholding have been proposed. The simulation results indicate that the computed boundary gives the detection rate of 99.5% accuracy.

Keywords: Breast Cancer, Mammogram, and Segmentation.

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Authors: Ahmed N. Shmroukh, Gamal Tag Abdel-Jaber, Rashed D. Aldughpassi

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The feasibility of improving the performance of the proposed solar still unit which operated in very hot climate is investigated numerically and verified with experimental data. This solar desalination unit with proposed auxiliary device as seawater preheating system using petrol based textherm oil was used to produce pure fresh water from seawater. The effective evaporation area of basin is about 1 m². The unit was tested in two main operation modes which are normal and with seawater preheating system. The results showed that, there is good agreement between the theoretical data and the experimental data; this means that the numerical model can be accurately dependable for predicting the proposed solar still performance and design parameters. The results also showed that the fresh water productivity of the solar still in the modified preheating case which is higher than normal case, leads to an increase in productivity of 42%.

Keywords: Improving productivity, seawater desalination, solar stills, theoretical model.

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Authors: Sanjay Singh, M Sathish Kumar, H. S Mruthyunjaya

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Multicarrier transmission system such as Orthogonal Frequency Division Multiplexing (OFDM) is a promising technique for high bit rate transmission in wireless communication system. OFDM is a spectrally efficient modulation technique that can achieve high speed data transmission over multipath fading channels without the need for powerful equalization techniques. However the price paid for this high spectral efficiency and less intensive equalization is low power efficiency. OFDM signals are very sensitive to nonlinear effects due to the high Peak-to-Average Power Ratio (PAPR), which leads to the power inefficiency in the RF section of the transmitter. This paper investigates the effect of PAPR reduction on the performance parameter of multicarrier communication system. Performance parameters considered are power consumption of Power Amplifier (PA) and Digital-to-Analog Converter (DAC), power amplifier efficiency, SNR of DAC and BER performance of the system. From our analysis it is found that irrespective of PAPR reduction technique being employed, the power consumption of PA and DAC reduces and power amplifier efficiency increases due to reduction in PAPR. Moreover, it has been shown that for a given BER performance the requirement of Input-Backoff (IBO) reduces with reduction in PAPR.

Keywords: BER, Crest Factor (CF), Digital-to-Analog Converter(DAC), Input-Backoff (IBO), Orthogonal Frequency Division Multiplexing(OFDM), Peak-to-Average Power Ratio (PAPR), PowerAmplifier efficiency, SNR

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Authors: A. Gandomkar, R. Dehghani

Abstract:

Climate change is a phenomenon has been based on the available evidence from a very long time ago and now its existence is very probable. The speed and nature of climate parameters changes at the middle of twentieth century has been different and its quickness more than the before and its trend changed to some extent comparing to the past. Climate change issue now regarded as not only one of the most common scientific topic but also a social political one, is not a new issue. Climate change is a complicated atmospheric oceanic phenomenon on a global scale and long-term. Precipitation pattern change, fast decrease of snowcovered resources and its rapid melting, increased evaporation, the occurrence of destroying floods, water shortage crisis, severe reduction at the rate of harvesting agricultural products and, so on are all the significant of climate change. To cope with this phenomenon, its consequences and events in which public instruction is the most important but it may be climate that no significant cant and effective action has been done so far. The present article is included a part of one surrey about climate change in Fars. The study area having annually mean temperature 14 and precipitation 320 mm .23 stations inside the basin with a common 37 year statistical period have been applied to the meteorology data (1974-2010). Man-kendal and change factor methods are two statistical methods, applying them, the trend of changes and the annual mean average temperature and the annual minimum mean temperature were studied by using them. Based on time series for each parameter, the annual mean average temperature and the mean of annual maximum temperature have a rising trend so that this trend is clearer to the mean of annual maximum temperature.

Keywords: Climate change, Coefficient Variation, Fars province, Man-Kendal method.

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Authors: U. Waqas, M. F. Ahmed, A. Mehmood, M. A. Rashid

Abstract:

In geophysical exploration surveys, the quality of acquired data holds significant importance before executing the data processing and interpretation phases. In this study, 2D seismic reflection survey data of Fort Abbas area, Cholistan Desert, Pakistan was taken as test case in order to assess its quality on statistical bases by using normalized root mean square error (NRMSE), Cronbach’s alpha test (α) and null hypothesis tests (t-test and F-test). The analysis challenged the quality of the acquired data and highlighted the significant errors in the acquired database. It is proven that the study area is plain, tectonically least affected and rich in oil and gas reserves. However, subsurface 3D modeling and contouring by using acquired database revealed high degrees of structural complexities and intense folding. The NRMSE had highest percentage of residuals between the estimated and predicted cases. The outcomes of hypothesis testing also proved the biasness and erraticness of the acquired database. Low estimated value of alpha (α) in Cronbach’s alpha test confirmed poor reliability of acquired database. A very low quality of acquired database needs excessive static correction or in some cases, reacquisition of data is also suggested which is most of the time not feasible on economic grounds. The outcomes of this study could be used to assess the quality of large databases and to further utilize as a guideline to establish database quality assessment models to make much more informed decisions in hydrocarbon exploration field.

Keywords: Data quality, null hypothesis, seismic lines, seismic reflection survey.

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Authors: Alaa A. Shamaun Al-Abboodi, Yunis A. A. Bapeer

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400 one-day-old male broiler chicks (Ross-308) randomly divided to 2 main groups, 1st main group (GA) was feeding basal diet with medium chain fatty acid (MCFA) at rate of 0.15% and divided to four subgroups, 3 subgroups vaccinated with different routes with Newcastle Disease Virus (NDV) and non-vaccinated group. The 2nd main group (GB) feeding basal diet without MCFA and divided the same as 1st main group. The parameters used in this study included: ND antibody titers at 1, 10, 21, 28, 35 and 42 days of age and values of CD4 and CD8 at 1, 20, 30 and 42 days of age. This experiment detected increase in ND antibodies titers in (G1, G2, G3) groups were fed on basal diet MCFA comparing to groups were fed without adding MCFA (G5, G6, G7) and control groups (G4, G8). The results of cellular immune response (CD4 and CD8) T-cells in broiler chicks indicated that there was obviously significant relationship between dietary Fatty Acid (FA) versus the diet without FA on the level of CD4 parameter, for the entire experimental period. The effect of different ages was statistically significant in creating different values of CD4 level, whereas the CD4 level decreases markedly with age. However, analyzing the data of different vaccination methods, oculonasal method of vaccination led to the highest value of CD4 compared with the oral, S/C and control groups. There were statistical differences in CD8 values due to supplementation of FA versus the basal diet and due to the effect of different age periods. As for the age effect, the CD8 value at 20 days of age was significantly higher than at 42 and 30 days.

Keywords: Broiler, CD4 and CD8, fatty acids, Newcastle disease.

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Authors: Sofien Chtourou, Mohamed Chtourou, Omar Hammami

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Embedded systems need to respect stringent real time constraints. Various hardware components included in such systems such as cache memories exhibit variability and therefore affect execution time. Indeed, a cache memory access from an embedded microprocessor might result in a cache hit where the data is available or a cache miss and the data need to be fetched with an additional delay from an external memory. It is therefore highly desirable to predict future memory accesses during execution in order to appropriately prefetch data without incurring delays. In this paper, we evaluate the potential of several artificial neural networks for the prediction of instruction memory addresses. Neural network have the potential to tackle the nonlinear behavior observed in memory accesses during program execution and their demonstrated numerous hardware implementation emphasize this choice over traditional forecasting techniques for their inclusion in embedded systems. However, embedded applications execute millions of instructions and therefore millions of addresses to be predicted. This very challenging problem of neural network based prediction of large time series is approached in this paper by evaluating various neural network architectures based on the recurrent neural network paradigm with pre-processing based on the Self Organizing Map (SOM) classification technique.

Keywords: Address, data set, memory, prediction, recurrentneural network.

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Authors: Ahmed Elrewainy

Abstract:

Mixing in the hyperspectral imaging occurs due to the low spatial resolutions of the used cameras. The existing pure materials “endmembers” in the scene share the spectra pixels with different amounts called “abundances”. Unmixing of the data cube is an important task to know the present endmembers in the cube for the analysis of these images. Unsupervised unmixing is done with no information about the given data cube. Sparsity is one of the recent approaches used in the source recovery or unmixing techniques. The l₁-norm optimization problem “basis pursuit” could be used as a sparsity-based approach to solve this unmixing problem where the endmembers is assumed to be sparse in an appropriate domain known as dictionary. This optimization problem is solved using proximal method “iterative thresholding”. The l₁-norm basis pursuit optimization problem as a sparsity-based unmixing technique was used to unmix real and synthetic hyperspectral data cubes.

Keywords: Basis pursuit, blind source separation, hyperspectral imaging, spectral unmixing, wavelets.

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