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Search Results: 1 - 10 of 422718 matches for " K. S; "
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An investigation of volumetric and corpus callosum dimension to detect brain disorders  [PDF]
S. Prabakar, K. Porkumaran
Journal of Biomedical Science and Engineering (JBiSE) , 2012, DOI: 10.4236/jbise.2012.57047
Abstract: Alzheimer’s disease (AD), Mental retardation, Cerebral Palsy, and other Dementias are the neurogenerative brain disorders which are statistically proven that 2% - 3% of people affected in the world today. The proposed method considered the symptoms which stands distinct for Alzheimer’s disease. Many structural neuroimaging studies have found the atrophy of the Corpus Callosum (CC) and the decrease in brain volume in AD. The measurement, area has been extracted from the gradient mask of the image to characterize the local atrophy of the CC. The result showed decreased area of the CC in AD when compared to the control groups. The volume has also been calculated by volume rendering and voxel size measurement for the same set of control groups and was found to be reduced in the AD patients. These findings confirmed the pathology characteristics in AD of brain disorders. The system’s validity with respect to results obtained with conventional diagnosis has been examined and proved to offer better results.
New Practical Algebraic Public-Key Cryptosystem and Some Related Algebraic and Computational Aspects  [PDF]
S. K. Rososhek
Applied Mathematics (AM) , 2013, DOI: 10.4236/am.2013.47142
Abstract:

The most popular present-day public-key cryptosystems are RSA and ElGamal cryptosystems. Some practical algebraic generalization of the ElGamal cryptosystem is considered-basic modular matrix cryptosystem (BMMC) over the modular matrix ring M2(Zn). An example of computation for an artificially small number n is presented. Some possible attacks on the cryptosystem and mathematical problems, the solution of which are necessary for implementing these attacks, are studied. For a small number n, computational time for compromising some present-day public-key cryptosystems such as RSA, ElGamal, and Rabin, is compared with the corresponding time for the ВММС. Finally, some open mathematical and computational problems are formulated.

Assessment of Natural Radioactivity Levels and Radiation Dose Rate in Some Soil Samples from Historical Area, AL-RAKKAH, Saudi Arabia  [PDF]
K. S. Al Mugren
Natural Science (NS) , 2015, DOI: 10.4236/ns.2015.75027
Abstract: This study aims to determine the activity concentrations of naturally occurring, technically-enhanced levels of radiation and the gamma absorbed dose rates in soil samples collected across the land scape of historical area which discovered in east of Saudi Arabia at 2009 G, Called AL- RAKKAH. By using an HPGe detector gamma-ray spectrometer, the activity concentrations of 226Ra 232Th and 40K were found in surface soil samples ranged from 17. 4 ± 1.2 Bq/kg to 28.3 ± 2.3 Bq/kg with an average value of 23 ± 1.6 Bq/kg, ranging from 1.1 ± 1.8 Bq/kg to 81.0 ± 1.7 Bq/kg with the average value 20 ± 1.4 Bq/kg and from 218 ± 11 Bq/kg to 255 ± 18 Bq/kg, with the mean value of 233 ± 12 Bq/kg respectively. The mean radium equivalent (Raeq) and outdoor radiation hazard index (Hex) for the area under study were determined as 69.52 Bq/kg and 0.16 respectively. The total absorbed dose rate due to three primordial radionuclides lies in the range of 17.74 - 72.24 nGy·h-1 with a mean of 32.69 nGy·h-1, which yields total annual effective dose of 0.37 mSv·y-1. The measured values are comparable with other global radioactivity measurements and are found to be safe for public and environment. The baseline data of this type will almost certainly be of importance in making estimations of population exposure.
Low Density Instability in Asymmetric Nuclear Matter Using Pion Dressing  [PDF]
S. K. Sahu
Journal of Modern Physics (JMP) , 2015, DOI: 10.4236/jmp.2015.69140
Abstract: We study the asymmetric nuclear matter in a nonperturbative manner. The bulk nuclear matter studied by the consistent exchange of σ, ω and π mesons is used to investigate its stability. The equation of state (EOS) at zero temperature is considered to study the symmetry energy, curvature parameter of symmetry energy and asymmetry energy. The effect of the density dependence of the symmetry energy on instability property is investigated and analyzed using proton fraction in the nuclear matter. Here a microscopic density-dependent model of the nucleon-meson coupling is used to reexamine the instability of asymmetric nuclear matter.
Statistical Tests of Hypothesis Based Color Image Retrieval  [PDF]
K. Seetharaman, S. Selvaraj
Journal of Data Analysis and Information Processing (JDAIP) , 2016, DOI: 10.4236/jdaip.2016.42008
Abstract: This paper proposes a novel method based on statistical tests of hypotheses, such as F-ratio and Welch’s t-tests. The input query image is examined whether it is a textured or structured. If it is structured, the shapes are segregated into various regions according to its nature; otherwise, it is treated as textured image and considered the entire image as it is for the experiment. The aforesaid tests are applied regions-wise. First, the F-ratio test is applied, if the images pass the test, then it is proceeded to test the spectrum of energy, i.e. means of the two images. If the images pass both tests, then it is concluded that the two images are the same or similar. Otherwise, they differ. Since the proposed system is distribution-based, it is invariant for rotation and scaling. Also, the system facilitates the user to fix the number of images to be retrieved, because the user can fix the level of significance according to their requirements. These are the main advantages of the proposed system.
Optimized Features Extraction of IRIS Recognition by Using MADLA to Ensure Secure Authentication  [PDF]
S. Pravinthraja, K. Umamaheswari
Circuits and Systems (CS) , 2016, DOI: 10.4236/cs.2016.78167
Abstract: Nowadays, Iris recognition is a method of biometric verification of the person authentication process based on the human iris unique pattern, which is applied to control system for high level security. It is a popular system for recognizing humans and essential to understand it. The objective of this method is to assign a unique subject for each iris image for authentication of the person and provide an effective feature representation of the iris recognition with the image analysis. This paper proposed a new optimization and recognition process of iris features selection by using proposed Modified ADMM and Deep Learning Algorithm (MADLA). For improving the performance of the security with feature extraction, the proposed algorithm is designed and used to extract the strong features identification of iris of the person with less time, better accuracy, improving performance in access control and in security level. The evaluations of iris data are demonstrated the improvement of the recognition accuracy. In this proposed methodology, the recognition of the iris features has been improved and it incorporates into the iris recognition systems.
Testing and Analysis of Induction Motor Electrical Faults Using Current Signature Analysis  [PDF]
K. Prakasam, S. Ramesh
Circuits and Systems (CS) , 2016, DOI: 10.4236/cs.2016.79229
Abstract: The proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) to diagnosis the stator faults of Induction Motors. The performance of the proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) and the Zero-Sequence Voltage Component (ZSVC) to diagnose the stator faults of Induction Motors. The unalleviated study of the robustness of the industrial appliances is obligatory to verdict the fault of the machines at precipitate stages and thwart the machine from brutal damage. For all kinds of industry, a machine failure escorts to a diminution in production and cost increases. The Motor Current Signature Analysis (MCSA) is referred as the most predominant way to diagnose the faults of electrical machines. Since the detailed analysis of the current spectrum, the method will portray the typical fault state. This paper aims to present dissimilar stator faults which are classified under electrical faults using MCSA and the comparison of simulation and hardware results. The magnitude of these fault harmonics analyzes in detail by means of Finite-Element Method (FEM). The anticipated method can effectively perceive the trivial changes too during the operation of the motor and it shows in the results.
Single Phase Induction Motor Drive with Restrained Speed and Torque Ripples Using Neural Network Predictive Controller  [PDF]
S. Saravanan, K. Geetha
Circuits and Systems (CS) , 2016, DOI: 10.4236/cs.2016.711309
Abstract: In industrial drives, electric motors are extensively utilized to impart motion control and induction motors are the most familiar drive at present due to its extensive performance characteristic similar with that of DC drives. Precise control of drives is the main attribute in industries to optimize the performance and to increase its production rate. In motion control, the major considerations are the torque and speed ripples. Design of controllers has become increasingly complex to such systems for better management of energy and raw materials to attain optimal performance. Meager parameter appraisal results are unsuitable, leading to unstable operation. The rapid intensification of digital computer revolutionizes to practice precise control and allows implementation of advanced control strategy to extremely multifaceted systems. To solve complex control problems, model predictive control is an authoritative scheme, which exploits an explicit model of the process to be controlled. This paper presents a predictive control strategy by a neural network predictive controller based single phase induction motor drive to minimize the speed and torque ripples. The proposed method exhibits better performance than the conventional controller and validity of the proposed method is verified by the simulation results using MATLAB software.
A BP Artificial Neural Network Model for Earthquake Magnitude Prediction in Himalayas, India  [PDF]
S. Narayanakumar, K. Raja
Circuits and Systems (CS) , 2016, DOI: 10.4236/cs.2016.711294
Abstract: The aim of this study is to evaluate the performance of BP neural network techniques in predicting earthquakes occurring in the region of Himalayan belt (with the use of different types of input data). These parameters are extracted from Himalayan Earthquake catalogue comprised of all minor, major events and their aftershock sequences in the Himalayan basin for the past 128 years from 1887 to 2015. This data warehouse contains event data, event time with seconds, latitude, longitude, depth, standard deviation and magnitude. These field data are converted into eight mathematically computed parameters known as seismicity indicators. These seismicity indicators have been used to train the BP Neural Network for better decision making and predicting the magnitude of the pre-defined future time period. These mathematically computed indicators considered are the clustered based on every events above 2.5 magnitude, total number of events from past years to 2014, frequency-magnitude distribution b-values, Gutenberg-Richter inverse power law curve for the n events, the rate of square root of seismic energy released during the n events, energy released from the event, the mean square deviation about the regression line based on the Gutenberg-Richer inverse power law for the n events, coefficient of variation of mean time and average value of the magnitude for last n events. We propose a three-layer feed forward BP neural network model to identify factors, with the actual occurrence of the earthquake magnitude M and other seven mathematically computed parameters seismicity indicators as input and target vectors in Himalayan basin area. We infer through comparing curve as observed from seismometer in Himalayan Earthquake catalogue comprised of all events above magnitude 2.5 mg, their aftershock sequences in the Himalayan basin of year 2015 and BP neural network predicting earthquakes in 2015. The model yields good prediction result for the earthquakes of magnitude between 4.0 and 6.0.
Modeling and Implementation of AC Electrical Capacitance Tomography  [PDF]
K. Manikandan, S. Sathiyamoorthy
Circuits and Systems (CS) , 2016, DOI: 10.4236/cs.2016.711319
Abstract: Electrical Capacitance Tomography (ECT) determines the dielectric permittivity of the interior object depending on the measurements of exterior capacitance. Generally, the electrodes are placed outside the PVC cylinder where the medium to be imaged is present; but in ECT using inter-electrode capacitance measurements can be achieved by placing inside of the dielectric medium. In the proposed ECT system, the ECT sensor is modeled using ANSYS software and the model is implemented in real ECT system. For each step of measurement, a stable AC signal is applied to a pair of electrodes that form a capacitor. The novel system is to measure the capacitance range variation in picofarad and the corresponding voltage ranges?from 1volt to 4volts. The switching speed of all combinational electrodes is implemented using embedded system to achieve higher speed performance of AC ECT system which eliminates the drift and stray capacitance error. This is yielding the original image of unknown multiphase medium inside the electrodes using Lab VIEW. This paper investigates several advantages such as improved overall system performance; simple structure, avoids stray capacitance effect, reduces the drift problem and achieves high signal to noise ratio.
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