The Malacca Empire: Sayings of Prophet Muhammad in Sulalat Al-Salatin

In the fifteenth century, the Malacca Empire emerged as the centre of Islamic civilization in the Malay Archipelago. The history had been recorded in Sulalat Al-Salatin, an important literary source about the genealogy of all Kings in Malacca. The objective of this study was to analyze the understanding of sayings from Prophet Muhammad among Malays in Malacca during the fifteenth century through all of the hadith quoted in Sulalat Al-Salatin. This study used content analysis methodology to validate the sayings where all of them were critically analyzed and compared with the classical hadith sources from prominent Muslim scholars. As a result, only two out of the four quotations were considered as authentic sayings of Prophet Muhammad. This study also showed the importance of the palace as the centre of the Islamic education system and the role played by Muslim preachers from outside of Malacca to propagate Islam in Malacca.

Comparative Analysis of Transient-Fault Tolerant Schemes for Network on Chips

Network on a chip (NoC) has been proposed as a viable solution to counter the inefficiency of buses in the current VLSI on-chip interconnects. However, as the silicon chip accommodates more transistors, the probability of transient faults is increasing, making fault tolerance a key concern in scaling chips. In packet based communication on a chip, transient failures can corrupt the data packet and hence, undermine the accuracy of data communication. In this paper, we present a comparative analysis of transient fault tolerant techniques including end-to-end, node-by-node, and stochastic communication based on flooding principle.

Weight Functions for Signal Reconstruction Based On Level Crossings

Although the level crossing concept has been the subject of intensive investigation over the last few years, certain problems of great interest remain unsolved. One of these concern is distribution of threshold levels. This paper presents a new threshold level allocation schemes for level crossing based on nonuniform sampling. Intuitively, it is more reasonable if the information rich regions of the signal are sampled finer and those with sparse information are sampled coarser. To achieve this objective, we propose non-linear quantization functions which dynamically assign the number of quantization levels depending on the importance of the given amplitude range. Two new approaches to determine the importance of the given amplitude segment are presented. The proposed methods are based on exponential and logarithmic functions. Various aspects of proposed techniques are discussed and experimentally validated. Its efficacy is investigated by comparison with uniform sampling.

A Short Form of the Taiwan Health Literacy Scale (THLS) for Chinese-Speaking Adults

The Taiwan Health Literacy Scale (THLS) was developed to cope with the need of measuring heath literacy of Chinese-speaking adults in Taiwan. Although the scale was proven having good reliability and validity, it was not popularly adopted by the practitioners due to the length, and the time required completing. Based on the THLS, this research further invited healthcare professionals to review the original scale for a possible shorten work. Under the logic of THLS, the research adopted an analytic hierarchy process technique to consolidate the healthcare experts- assessments to shorten the original scale. There are fifteen items out of the original 66 items were identified having higher loadings. Confirmed by the experts and passed a pilot test with 40 undergraduate students, a short form of THLS is then introduced. This research then used 839 samples from the major cities of the Hua-lien county in the eastern part of Taiwan to test the reliability and validity of this new scale. The reliability of the scale is high and acceptable. The current scale is also highly correlated with the original, of which provide evidence for the validity of the scale.

Electric Field and Potential Distributions along Surface of Silicone Rubber Polymer Insulators Using Finite Element Method

This paper presents the simulation the results of electric field and potential distributions along surface of silicone rubber polymer insulators. Near the same leakage distance subjected to 15 kV in 50 cycle salt fog ageing test, alternate sheds silicone rubber polymer insulator showed better contamination performance than straight sheds silicone rubber polymer insulator. Severe surface ageing was observed on the straight sheds insulator. The objective of this work is to elucidate that electric field distribution along straight sheds insulator higher than alternate shed insulator in salt fog ageing test. Finite element method (FEM) is adopted for this work. The simulation results confirmed the experimental data, as well.

Ameliorative Effect of Calocybe indica, a Tropical Indian Edible Mushroom on Hyperglycemia Induced Oxidative Stress

Mushrooms are a group of fleshy macroscopic fungi. They have been valued throughout the world as both edible and medicine. They are highly nutritious with good amount of quality proteins, vitamins and minerals. An edible mushroom, Calocybe indica was selected to validate its nutritional and medicinal properties. Since tissue damage in hyperglycemia has been related to oxidative stress, we evaluated the enzymatic and non-enzymatic antioxidant status in the serum, liver and kidney since they are the target organs in diabetic complications. From the results, increased oxidative stress and decreased antioxidants might be related to the causation of diabetes mellitus. The treatment in the diabetic rats with the Calocybe indica showed an increase in the antioxidant system and decrease in the production of free radicals. The mushrooms which contain antioxidant phytochemicals has potential free radical scavenging capacity and hence can induce the antioxidant system in the body significantly reduces the generated free radicals thereby maintaining the normal levels of the antioxidants

Compact Er3+-Doped ZBLAN Green Upconversion Fibre Laser

In this paper, a fibre laser at 546 nm has been studied for a signal power of -30 dB. Er3+-doped ZBLAN fibre has been used by upconversion pumping of a 980 nm laser diode. Gain saturation effect has been investigated in detail. Laser performance has also been discussed. An efficiency of 35% has been calculated with a length of 5 mm fibre laser. Results show that Er3+-doped ZBLAN is a promising candidate for optical amplification at 546 nm.

Comparative Analysis and Evaluation of Software Vulnerabilities Testing Techniques

Software and applications are subjected to serious and damaging security threats, these threats are increasing as a result of increased number of potential vulnerabilities. Security testing is an indispensable process to validate software security requirements and to identify security related vulnerabilities. In this paper we analyze and compare different available vulnerabilities testing techniques based on a pre defined criteria using analytical hierarchy process (AHP). We have selected five testing techniques which includes Source code analysis, Fault code injection, Robustness, Stress and Penetration testing techniques. These testing techniques have been evaluated against five criteria which include cost, thoroughness, Ease of use, effectiveness and efficiency. The outcome of the study is helpful for researchers, testers and developers to understand effectiveness of each technique in its respective domain. Also the study helps to compare the inner working of testing techniques against a selected criterion to achieve optimum testing results.

Reducing SAGE Data Using Genetic Algorithms

Serial Analysis of Gene Expression is a powerful quantification technique for generating cell or tissue gene expression data. The profile of the gene expression of cell or tissue in several different states is difficult for biologists to analyze because of the large number of genes typically involved. However, feature selection in machine learning can successfully reduce this problem. The method allows reducing the features (genes) in specific SAGE data, and determines only relevant genes. In this study, we used a genetic algorithm to implement feature selection, and evaluate the classification accuracy of the selected features with the K-nearest neighbor method. In order to validate the proposed method, we used two SAGE data sets for testing. The results of this study conclusively prove that the number of features of the original SAGE data set can be significantly reduced and higher classification accuracy can be achieved.

Service Identification Approach to SOA Development

Service identification is one of the main activities in the modeling of a service-oriented solution, and therefore errors made during identification can flow down through detailed design and implementation activities that may necessitate multiple iterations, especially in building composite applications. Different strategies exist for how to identify candidate services that each of them has its own benefits and trade offs. The approach presented in this paper proposes a selective identification of services approach, based on in depth business process analysis coupled with use cases and existing assets analysis and goal service modeling. This article clearly emphasizes the key activities need for the analysis and service identification to build a optimized service oriented architecture. In contrast to other approaches this article mentions some best practices and steps, wherever appropriate, to point out the vagueness involved in service identification.

Computer Vision Applied to Flower, Fruit and Vegetable Processing

This paper presents the theoretical background and the real implementation of an automated computer system to introduce machine vision in flower, fruit and vegetable processing for recollection, cutting, packaging, classification, or fumigation tasks. The considerations and implementation issues presented in this work can be applied to a wide range of varieties of flowers, fruits and vegetables, although some of them are especially relevant due to the great amount of units that are manipulated and processed each year over the world. The computer vision algorithms developed in this work are shown in detail, and can be easily extended to other applications. A special attention is given to the electromagnetic compatibility in order to avoid noisy images. Furthermore, real experimentation has been carried out in order to validate the developed application. In particular, the tests show that the method has good robustness and high success percentage in the object characterization.

Mitigation of ISI for Next Generation Wireless Channels in Outdoor Vehicular Environments

In order to accommodate various multimedia services, next generation wireless networks are characterized by very high transmission bit rates. Thus, in such systems and networks, the received signal is not only limited by noise but - especially with increasing symbols rate often more significantly by the intersymbol interference (ISI) caused by the time dispersive radio channels such as those are used in this work. This paper deals with the study of the performance of detector for high bit rate transmission on some worst case models of frequency selective fading channels for outdoor mobile radio environments. This paper deals with a number of different wireless channels with different power profiles and different number of resolvable paths. All the radio channels generated in this paper are for outdoor vehicular environments with Doppler spread of 100 Hz. A carrier frequency of 1800 MHz is used and all the channels used in this work are such that they are useful for next generation wireless systems. Schemes for mitigation of ISI with adaptive equalizers of different types have been investigated and their performances have been investigated in terms of BER measured as a function of SNR.

A Novel Fuzzy Logic Based Controller to Adjust the Brightness of the Television Screen with Respect to Surrounding Light

One of the major cause of eye strain and other problems caused while watching television is the relative illumination between the screen and its surrounding. This can be overcome by adjusting the brightness of the screen with respect to the surrounding light. A controller based on fuzzy logic is proposed in this paper. The fuzzy controller takes in the intensity of light surrounding the screen and the present brightness of the screen as input. The output of the fuzzy controller is the grid voltage corresponding to the required brightness. This voltage is given to CRT and brightness is controller dynamically. For the given test system data, different de-fuzzifier methods have been implemented and the results are compared. In order to validate the effectiveness of the proposed approach, a fuzzy controller has been designed by obtaining a test data from a real time system. The simulations are performed in MATLAB and are verified with standard system data. The proposed approach can be implemented for real time applications.

Web Log Mining by an Improved AprioriAll Algorithm

This paper sets forth the possibility and importance about applying Data Mining in Web logs mining and shows some problems in the conventional searching engines. Then it offers an improved algorithm based on the original AprioriAll algorithm which has been used in Web logs mining widely. The new algorithm adds the property of the User ID during the every step of producing the candidate set and every step of scanning the database by which to decide whether an item in the candidate set should be put into the large set which will be used to produce next candidate set. At the meantime, in order to reduce the number of the database scanning, the new algorithm, by using the property of the Apriori algorithm, limits the size of the candidate set in time whenever it is produced. Test results show the improved algorithm has a more lower complexity of time and space, better restrain noise and fit the capacity of memory.

Time-Derivative Estimation of Noisy Movie Data using Adaptive Control Theory

This paper presents an adaptive differentiator of sequential data based on the adaptive control theory. The algorithm is applied to detect moving objects by estimating a temporal gradient of sequential data at a specified pixel. We adopt two nonlinear intensity functions to reduce the influence of noises. The derivatives of the nonlinear intensity functions are estimated by an adaptive observer with σ-modification update law.

Development of Split Air-Conditioning System using Chill Water as the Cooling Medium

Current air conditioning system is using refrigerant as the cooling medium. The main purpose of this study is to develop an air conditioning system using chill water as the cooling medium. In this system, chill water used to replace refrigerant as the cooling medium. This study is focus on the split type unit air conditioning system only. It will be involving some renovation on the indoor unit and freezer. The cooling capability of this system was validate by few series of testing, which conducted at standard 36m3 office room. Result of the testing found that 0.1 m3 of chill water is able to maintain the room temperature within standard up to 4 ~ 8 hours. It expected able to maintain room temperature up to 10 hour with some improvement.

Improving Air Temperature Prediction with Artificial Neural Networks

The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. Previous work established that the Ward-style artificial neural network (ANN) is a suitable tool for developing such models. The current research focused on developing ANN models with reduced average prediction error by increasing the number of distinct observations used in training, adding additional input terms that describe the date of an observation, increasing the duration of prior weather data included in each observation, and reexamining the number of hidden nodes used in the network. Models were created to predict air temperature at hourly intervals from one to 12 hours ahead. Each ANN model, consisting of a network architecture and set of associated parameters, was evaluated by instantiating and training 30 networks and calculating the mean absolute error (MAE) of the resulting networks for some set of input patterns. The inclusion of seasonal input terms, up to 24 hours of prior weather information, and a larger number of processing nodes were some of the improvements that reduced average prediction error compared to previous research across all horizons. For example, the four-hour MAE of 1.40°C was 0.20°C, or 12.5%, less than the previous model. Prediction MAEs eight and 12 hours ahead improved by 0.17°C and 0.16°C, respectively, improvements of 7.4% and 5.9% over the existing model at these horizons. Networks instantiating the same model but with different initial random weights often led to different prediction errors. These results strongly suggest that ANN model developers should consider instantiating and training multiple networks with different initial weights to establish preferred model parameters.

Efficient Hardware Realization of Truncated Multipliers using FPGA

Truncated multiplier is a good candidate for digital signal processing (DSP) applications including finite impulse response (FIR) and discrete cosine transform (DCT). Through truncated multiplier a significant reduction in Field Programmable Gate Array (FPGA) resources can be achieved. This paper presents for the first time a comparison of resource utilization of Spartan-3AN and Virtex-5 implementation of standard and truncated multipliers using Very High Speed Integrated Circuit Hardware Description Language (VHDL). The Virtex-5 FPGA shows significant improvement as compared to Spartan-3AN FPGA device. The Virtex-5 FPGA device shows better performance with a percentage ratio of number of occupied slices for standard to truncated multipliers is increased from 40% to 73.86% as compared to Spartan- 3AN is decreased from 68.75% to 58.78%. Results show that the anomaly in Spartan-3AN FPGA device average connection and maximum pin delay have been efficiently reduced in Virtex-5 FPGA device.

Machine Learning Methods for Environmental Monitoring and Flood Protection

More and more natural disasters are happening every year: floods, earthquakes, volcanic eruptions, etc. In order to reduce the risk of possible damages, governments all around the world are investing into development of Early Warning Systems (EWS) for environmental applications. The most important task of the EWS is identification of the onset of critical situations affecting environment and population, early enough to inform the authorities and general public. This paper describes an approach for monitoring of flood protections systems based on machine learning methods. An Artificial Intelligence (AI) component has been developed for detection of abnormal dike behaviour. The AI module has been integrated into an EWS platform of the UrbanFlood project (EU Seventh Framework Programme) and validated on real-time measurements from the sensors installed in a dike.

Energy Consumptions of Different Building Heating Systems for Various Meteorological Regions of Iran: A Comparison Study

To simulate heating systems in buildings, a research oriented computer code has been developed in Sharif University of Technology in Iran where the climate, existing heating equipment in buildings, consumer behavior and their interactions are considered for simulating energy consumption in conventional systems such as heaters, radiators and fan-coils. In order to validate the computer code, the available data of five buildings was used and the computed consumed energy was compared with the estimated energy extracted from monthly bills. The initial heating system was replaced by the alternative system and the effect of this change was observed on the energy consumption. As a result, the effect of changing heating equipment on energy consumption was investigated in different climates. Changing heater to radiator renders energy conservation up to 50% in all climates and changing radiator to fan-coil decreases energy consumption in climates with cold and dry winter.