Comparison of Detrending Methods in Spectral Analysis of Heart Rate Variability

Non-stationary trend in R-R interval series is considered as a main factor that could highly influence the evaluation of spectral analysis. It is suggested to remove trends in order to obtain reliable results. In this study, three detrending methods, the smoothness prior approach, the wavelet and the empirical mode decomposition, were compared on artificial R-R interval series with four types of simulated trends. The Lomb-Scargle periodogram was used for spectral analysis of R-R interval series. Results indicated that the wavelet method showed a better overall performance than the other two methods, and more time-saving, too. Therefore it was selected for spectral analysis of real R-R interval series of thirty-seven healthy subjects. Significant decreases (19.94±5.87% in the low frequency band and 18.97±5.78% in the ratio (p

Analysis of the Loaded Gait Subjected to the Trunk Flexion Change

In the paper, the energetic features of the loaded gait are newly analyzed depending on the trunk flexion change. To investigate the loaded gait, walking experiments are performed for five subjects and, the ground reaction forces and kinematic data are measured. Based on these information, we compute the impulse, momentum and mechanical works done on the center of body mass, through the trunk flexion change. As a result, it is shown that the trunk flexion change does not affect the impulses and momentums during the step-to-step transition as well. However, the direction of the pre-collision momentum does change depending on the trunk flexion change, which is degenerated just after (or during) the collision period.

Robust Artificial Neural Network Architectures

Many artificial intelligence (AI) techniques are inspired by problem-solving strategies found in nature. Robustness is a key feature in many natural systems. This paper studies robustness in artificial neural networks (ANNs) and proposes several novel, nature inspired ANN architectures. The paper includes encouraging results from experimental studies on these networks showing increased robustness.

Long Term Effect of Rice Husk Ash on Strength of Mortar

This paper represents the results of long term strength of mortar incorporating Rice Husk Ash (RHA). For these work mortar samples were made according to ASTM standard C 109/C. OPC cement was partially replaced by RHA at 0, 10, 15, 20, 25 and 30 percent replacement level. After casting all samples were kept in controlled environment and curing was done up to 90 days. Test of mortar was performed on 3, 7, 28, 90, 365 and 700 days. It is noticed that OPC mortar shows better strength at early age than mortar having RHA but at 90 days and onward the picture is different. At 700 days it is observed that mortar containing 20% RHA shows better result than any other samples.

A Method for Modeling Multiple Antenna Channels

In this paper we propose a method for modeling the correlation between the received signals by two or more antennas operating in a multipath environment. Considering the maximum excess delay in the channel being modeled, an elliptical region surrounding both transmitter and receiver antennas is produced. A number of scatterers are randomly distributed in this region and scatter the incoming waves. The amplitude and phase of incoming waves are computed and used to obtain statistical properties of the received signals. This model has the distinguishable advantage of being applicable for any configuration of antennas. Furthermore the common PDF (Probability Distribution Function) of received wave amplitudes for any pair of antennas can be calculated and used to produce statistical parameters of received signals.

2D Human Motion Regeneration with Stick Figure Animation Using Accelerometers

This paper explores the opportunity of using tri-axial wireless accelerometers for supervised monitoring of sports movements. A motion analysis system for the upper extremities of lawn bowlers in particular is developed. Accelerometers are placed on parts of human body such as the chest to represent the shoulder movements, the back to capture the trunk motion, back of the hand, the wrist and one above the elbow, to capture arm movements. These sensors placement are carefully designed in order to avoid restricting bowler-s movements. Data is acquired from these sensors in soft-real time using virtual instrumentation; the acquired data is then conditioned and converted into required parameters for motion regeneration. A user interface was also created to facilitate in the acquisition of data, and broadcasting of commands to the wireless accelerometers. All motion regeneration in this paper deals with the motion of the human body segment in the X and Y direction, looking into the motion of the anterior/ posterior and lateral directions respectively.

Binarization of Text Region based on Fuzzy Clustering and Histogram Distribution in Signboards

In this paper, we present a novel approach to accurately detect text regions including shop name in signboard images with complex background for mobile system applications. The proposed method is based on the combination of text detection using edge profile and region segmentation using fuzzy c-means method. In the first step, we perform an elaborate canny edge operator to extract all possible object edges. Then, edge profile analysis with vertical and horizontal direction is performed on these edge pixels to detect potential text region existing shop name in a signboard. The edge profile and geometrical characteristics of each object contour are carefully examined to construct candidate text regions and classify the main text region from background. Finally, the fuzzy c-means algorithm is performed to segment and detected binarize text region. Experimental results show that our proposed method is robust in text detection with respect to different character size and color and can provide reliable text binarization result.

Evaluation of the Contribution of Starting Pitchers in a Professional Baseball Team by Grey Relational Analysis

The evaluation of the contribution of professional baseball starting pitchers is a complex decision-making problem that includes several quantitative attributes. It is considered a type of multi-attribute or multi-criteria decision making (MADM/MCDM) problem. This study proposes a model using the Grey Relational Analysis (GRA) to evaluate the starting pitcher contribution for teams of the Chinese Professional Baseball League. The GRA calculates the individual grey relational degree of each alternative to the positive ideal alternative. An empirical analysis was conducted to show the use of the model for the starting pitcher contribution problem. The results demonstrate the effectiveness and feasibility of the proposed model.

Stability of New Macromycetes Phytases under Room, Cooling and Freezing Temperatures of Storage

Phytases are enzymes used as an important component in monogastric animals feeds in order to improve phosphorous availability, since it is not readily assimilated by these animals in the form of the phytate presented in plants and grains. As these enzymes are used in industrial activities, they must retain its catalytic activities during a certain storage period. This study presents information about the stability of 4 different phytases, produced by four macromycetes fungi through solid-state fermentation (SSF). There is a lack of data in literature concerning phytase from macromycetes shelf-life in storage conditions at room, cooling and freezing temperatures. The 4 phytases from macromycetes still had enzymatic activities around 100 days of storage at room temperature. At cooling temperature in 146 days of studies, the phytase from G. stipitatum was the most stable with 44% of the initial activity, in U.gds (units per gram of dried fermented substrate). The freezing temperature was the best condition storage for phytases from G. stipitatum and T. versicolor. Each condition provided a study for each mushroom phytase, totalizing 12 studies. The phytases showed to be stable for a long period without the addition of additives.

The Impact of Product Package Information on Consumer Behavior toward Genetically Modified Foods

Genetically modified (GM) technology in food production continued to generate controversies. Consumers were concerned with the GM foods about the healthy and environmental risks. While consumers- acceptance was a critical factor affecting how widely this technology be used. According to the research review, consumers- lack of information was one of the reasons to explain consumers- low acceptance toward GM foods. The objective for this study wanted to find out would informative product package affect consumers- behavior toward GM foods. An experiment was designed to investigate consumer behavior toward different product package information. The results indicated that the product package information influenced consumer product trust toward GM foods. Compared with the traceability production system information, the information about the GM rice was approved by authorized organizations could increase consumers product trust in GM foods. Consumers in Taiwan saw the information provided by authorized organizations more credible than other information.

Nonconforming Control Charts for Zero-Inflated Poisson Distribution

This paper developed the c-Chart based on a Zero- Inflated Poisson (ZIP) processes that approximated by a geometric distribution with parameter p. The p estimated that fit for ZIP distribution used in calculated the mean, median, and variance of geometric distribution for constructed the c-Chart by three difference methods. For cg-Chart, developed c-Chart by used the mean and variance of the geometric distribution constructed control limits. For cmg-Chart, the mean used for constructed the control limits. The cme- Chart, developed control limits of c-Chart from median and variance values of geometric distribution. The performance of charts considered from the Average Run Length and Average Coverage Probability. We found that for an in-control process, the cg-Chart is superior for low level of mean at all level of proportion zero. For an out-of-control process, the cmg-Chart and cme-Chart are the best for mean = 2, 3 and 4 at all level of parameter.

Meta-analysis of Performance: Summarizing Research for Implementation of Reconfigurability

The aim of this study is to identify the conditions of implementation for reconfigurability in summarizing past flexible manufacturing systems (FMS) research by drawing overall conclusions from many separate High Performance Manufacturing (HPM) studies. Meta-analysis will be applied to links between HPM programs and their practices related to FMS and manufacturing performance with particular reference to responsiveness performance. More specifically, an application of meta-analysis will be made with reference to two of the main steps towards the development of an empirically-tested theory: testing the adequacy of the measurement of variables and testing the linkages between the variables.

Dynamic Clustering using Particle Swarm Optimization with Application in Unsupervised Image Classification

A new dynamic clustering approach (DCPSO), based on Particle Swarm Optimization, is proposed. This approach is applied to unsupervised image classification. The proposed approach automatically determines the "optimum" number of clusters and simultaneously clusters the data set with minimal user interference. The algorithm starts by partitioning the data set into a relatively large number of clusters to reduce the effects of initial conditions. Using binary particle swarm optimization the "best" number of clusters is selected. The centers of the chosen clusters is then refined via the Kmeans clustering algorithm. The experiments conducted show that the proposed approach generally found the "optimum" number of clusters on the tested images.

Anomaly Detection and Characterization to Classify Traffic Anomalies Case Study: TOT Public Company Limited Network

This paper represents four unsupervised clustering algorithms namely sIB, RandomFlatClustering, FarthestFirst, and FilteredClusterer that previously works have not been used for network traffic classification. The methodology, the result, the products of the cluster and evaluation of these algorithms with efficiency of each algorithm from accuracy are shown. Otherwise, the efficiency of these algorithms considering form the time that it use to generate the cluster quickly and correctly. Our work study and test the best algorithm by using classify traffic anomaly in network traffic with different attribute that have not been used before. We analyses the algorithm that have the best efficiency or the best learning and compare it to the previously used (K-Means). Our research will be use to develop anomaly detection system to more efficiency and more require in the future.

Improving Cache Memory Utilization

In this paper, an efficient technique is proposed to manage the cache memory. The proposed technique introduces some modifications on the well-known set associative mapping technique. This modification requires a little alteration in the structure of the cache memory and on the way by which it can be referenced. The proposed alteration leads to increase the set size virtually and consequently to improve the performance and the utilization of the cache memory. The current mapping techniques have accomplished good results. In fact, there are still different cases in which cache memory lines are left empty and not used, whereas two or more processes overwrite the lines of each other, instead of using those empty lines. The proposed algorithm aims at finding an efficient way to deal with such problem.

Analysis and Categorization of e-Learning Activities Based On Meaningful Learning Characteristics

Learning is the acquisition of new mental schemata, knowledge, abilities and skills which can be used to solve problems potentially more successfully. The learning process is optimum when it is assisted and personalized. Learning is not a single activity, but should involve many possible activities to make learning become meaningful. Many e-learning applications provide facilities to support teaching and learning activities. One way to identify whether the e-learning system is being used by the learners is through the number of hits that can be obtained from the e-learning system's log data. However, we cannot rely solely to the number of hits in order to determine whether learning had occurred meaningfully. This is due to the fact that meaningful learning should engage five characteristics namely active, constructive, intentional, authentic and cooperative. This paper aims to analyze the e-learning activities that is meaningful to learning. By focusing on the meaningful learning characteristics, we match it to the corresponding Moodle e-learning activities. This analysis discovers the activities that have high impact to meaningful learning, as well as activities that are less meaningful. The high impact activities is given high weights since it become important to meaningful learning, while the low impact has less weight and said to be supportive e-learning activities. The result of this analysis helps us categorize which e-learning activities that are meaningful to learning and guide us to measure the effectiveness of e-learning usage.

Finite Volume Model to Study The Effect of Voltage Gated Ca2+ Channel on Cytosolic Calcium Advection Diffusion

Mathematical and computational modeling of calcium signalling in nerve cells has produced considerable insights into how the cells contracts with other cells under the variation of biophysical and physiological parameters. The modeling of calcium signaling in astrocytes has become more sophisticated. The modeling effort has provided insight to understand the cell contraction. Main objective of this work is to study the effect of voltage gated (Operated) calcium channel (VOC) on calcium profile in the form of advection diffusion equation. A mathematical model is developed in the form of advection diffusion equation for the calcium profile. The model incorporates the important physiological parameter like diffusion coefficient etc. Appropriate boundary conditions have been framed. Finite volume method is employed to solve the problem. A program has been developed using in MATLAB 7.5 for the entire problem and simulated on an AMD-Turion 32-bite machine to compute the numerical results.

Pseudo-Homogeneous Kinetic of Dilute-Acid Hydrolysis of Rice Husk for Ethanol Production: Effect of Sugar Degradation

Rice husk is a lignocellulosic source that can be converted to ethanol. Three hundreds grams of rice husk was mixed with 1 L of 0.18 N sulfuric acid solutions then was heated in an autoclave. The reaction was expected to be at constant temperature (isothermal), but before that temperature was achieved, reaction has occurred. The first liquid sample was taken at temperature of 140 0C and repeated every 5 minute interval. So the data obtained are in the regions of non-isothermal and isothermal. It was observed that the degradation has significant effects on the ethanol production. The kinetic constants can be expressed by Arrhenius equation with the frequency factors for hydrolysis and sugar degradation of 1.58 x 105 1/min and 2.29 x 108 L/mole/min, respectively, while the activation energies are 64,350 J/mole and 76,571 J/mole. The highest ethanol concentration from fermentation is 1.13% v/v, attained at 220 0C.

Daily and Seasonal Changes of Air Pollution in Kuwait

This paper focuses on assessment of air pollution in Umm-Alhyman, Kuwait, which is located south to oil refineries, power station, oil field, and highways. The measurements were made over a period of four days in March and July in 2001, 2004, and 2008. The measured pollutants included methanated and nonmethanated hydrocarbons (MHC, NMHC), CO, CO2, SO2, NOX, O3, and PM10. Also, meteorological parameters were measured, which includes temperature, wind speed and direction, and solar radiation. Over the study period, data analysis showed increase in measured SO2, NOX and CO by factors of 1.2, 5.5 and 2, respectively. This is explained in terms of increase in industrial activities, motor vehicle density, and power generation. Predictions of the measured data were made by the ISC-AERMOD software package and by using the ISCST3 model option. Finally, comparison was made between measured data against international standards.