Control and manipulation of entanglement between two coupled qubits by fast pulses

We have investigated the analytical and numerical dynamics of entanglement for two qubits that interact with each other via Heisenberg XXX-type interaction and subject to local time-specific external kick and Gaussian pulse-type magnetic fields in $x$ – $y$ plane. The qubits have been assumed to be initially prepared in different pure separable and maximally entangled states and the effect of the strength and the direction of external fast pulses on concurrence has been investigated. The carefully designed kick or pulse sequences are found to enable one to obtain constant long-lasting entanglement with desired magnitude. Moreover, the time ordering effects are found to be important in the creation and manipulation of entanglement by external fields.     

A Novel Approach to Steganography: Enhanced Least Significant Bit Substitution Algorithm Integrated with Self-Determining Encryption Feature

One of the most well-known and used algorithms for Steganography is Least Significant Bit (LSB) substitution. Although LSB has several advantages such as simplicity, efficiency, and easy-to-do implementation, it has some distinct disadvantages such as it openness to miscellaneous attacks. In this study, we aim to improve the traditional LSB algorithm by eliminating its main disadvantage, being easy to detect, and this way propose an enhanced LSB algorithm called E-LSB. We mainly aim to minimize differences which are due to encryption and image hiding steps in LSB algorithm and make it more difficult to notice that some text has been hidden in the original cover image.
As most of the researchers and practitioners in security field argue, stenographic techniques alone are not sufficient for protecting sensitive information and thus must be used together with encryption algorithms. Therefore, the proposed approach integrates E-LSB with an encryption algorithm. E-LSB does not modify the file size and allows the flexibility of choosing one of well-known encryption algorithms including RSA, AES and CAESAR, but others can be implemented in it. With a set of experiments, the proposed approach is compared with the traditional LSB based embedding approach, and its efficiency and usability is analyzed. A set of performance evaluations realized with the developed software tool based on E-LSB algorithm show that E-LSB is better than the traditional LSB algorithm from security point of view.     

Ferromagnetic Behavior of Fe+ Implanted Si(100) Semiconductor

Fe ions have been implanted into Si (100) single crystals using ion implantation technique. The Fe ions have been accelerated to 45 keV with a dose of 5×10¹⁷ ion/cm² at room temperature. The ions have been sent to the substrate??s surface at normal incidence. The temperature dependence of magnetization measurement was explored at the temperature range of 10?C300 K. The implanted Si substrate was studied with Ferromagnetic Resonance (FMR) technique and Vibrating Sample Magnetometer (VSM). The FMR spectra were recorded by applying external magnetic field in different experimental geometries. FMR spectra were analyzed and the magnetic properties, which are the g-factor, effective magnetization and uniaxial anisotropy parameter, were estimated by simulation of the experimental data. The sample showed two-fold magnetic anisotropic symmetry. By fitting the Si-2p region obtained through XPS measurements it is observed that Fe and Fe compounds are present in the material.     

Diagnosis of valvular heart disease through neural networks ensembles

In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the valvular heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS Base Software 9.1.3 for diagnosing of the valvular heart disease. A neural networks ensemble method is in the centre of the proposed system. The ensemble-based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with proposed tool. We obtained 97.4% classification accuracy from the experiments made on data set containing 215 samples. We also obtained 100% and 96% sensitivity and specificity values, respectively, in valvular heart disease diagnosis.     

IoHT-based deep learning controlled robot vehicle for paralyzed patients of smart cities

The Journal of Supercomputing - Paralysis caused by physical trauma is a common disease today, with approximately 30% of paralysis caused by this trauma. The disease in question both physically...     

A new automatic target recognition system based on wavelet extreme learning machine

In this paper, an automatic system is presented for target recognition using target echo signals of High Resolution Range (HRR) radars. This paper especially deals with combination of the feature extraction and classification from measured real target echo signal waveforms by using X-band pulse radar. The past studies in the field of radar target recognition have shown that the learning speed of feedforward neural networks is in general much slower than required and it has been a major disadvantage. There are two key reasons forth is status of feedforward neural networks: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms (Feng et al., 2009, Huang and Siew, 2004, Huang and Chen, 2007, Huang and Chen, 2008, Huang et al., 2006, Huang et al., 2010, Huang et al., 2004, Huang et al., 2005, Huang et al., 2012, Huang et al., 2008, Huang and Siew, 2005, Huang et al., 2011, Huang et al., 2006, Huang et al., 2006a, Huang et al., 2006b, Lan et al., 2009, Li et al., 2005, Liang et al., 2006, Liang et al., 2006, Rong et al., 2009, Wang and Huang, 2005, Wang et al., 2011, Yeu et al., 2006, Zhang et al., 2007, Zhu et al., 2005). To resolve these disadvantages of feedforward neural networks for automatic target recognition area in this paper suggested a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) (Feng et al., 2009, Huang and Siew, 2004, Huang and Chen, 2007, Huang and Chen, 2008, Huang et al., 2006, Huang et al., 2010, Huang et al., 2004, Huang et al., 2005, Huang et al., 2012, Huang et al., 2008, Huang and Siew, 2005, Huang et al., 2011, Huang et al., 2006, Huang et al., 2006a, Huang et al., 2006b, Lan et al., 2009, Li et al., 2005, Liang et al., 2006, Liang et al., 2006, Rong et al., 2009, Wang and Huang, 2005, Wang et al., 2011, Yeu et al., 2006, Zhang et al., 2007, Zhu et al., 2005) which randomly choose hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. Moreover, the Discrete Wavelet Transform (DWT) and wavelet entropy is used for adaptive feature extraction in the time-frequency domain in feature extraction stage to strengthen the premium features of the ELM in this study. The correct recognition performance of this new system is compared with feedforward neural networks. The experimental results show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks.     

Effective diagnosis of heart disease through neural networks ensembles

In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS base software 9.1.3 for diagnosing of the heart disease. A neural networks ensemble method is in the centre of the proposed system. This ensemble based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with the proposed tool. We obtained 89.01% classification accuracy from the experiments made on the data taken from Cleveland heart disease database. We also obtained 80.95% and 95.91% sensitivity and specificity values, respectively, in heart disease diagnosis.     

Effect of chromium addition on the structural,morphological and magnetic properties of nano-crystalline cobalt ferrite system

Nano-crystalline chromium doped cobalt ferrite powders have been synthesized by PEG assisted hydrothermal route. The structural, morphological and magnetic properties of the products were determined by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray (EDX) spectroscopy and vibrating sample magnetometer (VSM). X-ray analysis showed that the samples were cubic spinel. SEM images reveal that the samples' surfaces exhibit well-defined crystalline nanoparticles of spherical shapes with small agglomeration. The addition of Cr³⁺ ions caused a decrease in the average crystallite size, magnetization and the coercive field of the sample. The observed decreases in saturation magnetization and coercivity are explained on the bases of exchange interactions.     

Synthesis, characterization and superparamagnetic resonance studies of ZnFe2O4 nanoparticles

Superparamagnetic nanoparticles of zinc ferrite (ZnFe2O4) were produced by a microwave induced combustion synthesis method. XRD, FT-IR, SEM, VSM and ESR were used for the structural, morphological, and magnetic investigation of the product, respectively. Average particle size of the nanoparticles was estimated by the Schérrer equation using the full-width at half maximum (FWHM) of the most intense XRD peak and found as 41 nm. Magnetization measurements have shown that the samples have a blocking temperature of 72 K which indicates a superparamagnetic behavior. Superparamagnetic resonance (SPR) spectra at room temperature show a broad line with a Landé g-factor, g(eff) approximately 2. We used a theoretical formalism based on a distribution of diameters of the nanoparticles following lognormal proposed by Berger et al. The nanoparticles behave as single magnetic domains with random orientations of magnetic moments which are subject to thermal fluctuations. A Landau-Lifshitz line shape function presents adequate results which are in good agreement with the experimental ones. At high temperatures, the SPR line shape is governed by the core anisotropy and the thermal fluctuations. By decreasing the temperature, the magnetic susceptibility of shell spins increases. As a result of this, the surface spins produce an effective field on the core leading to a decrease of resonance field, B(r). Also, the effective anisotropy increases as the shell spins begin to order. So, the results are interpreted by a simple model, in which each single-domain nanoparticle is considered as a core-shell system, with magneto-crystalline anisotropy on the core and surface anisotropy on the shell.