VFPred: A Fusion of Signal Processing and Machine Learning techniques in Detecting Ventricular Fibrillation from ECG Signals

Ventricular Fibrillation (VF), one of the most dangerous arrhythmias, is responsible for sudden cardiac arrests. Thus, various algorithms have been developed to predict VF from Electrocardiogram (ECG), which is a binary classification problem. In the literature, we find a number of algorithms based on signal processing, where, after some robust mathematical operations the decision is given based on a predefined threshold over a single value. On the other hand, some machine learning based algorithms are also reported in the literature; however, these algorithms merely combine some parameters and make a prediction using those as features. Both the approaches have their perks and pitfalls; thus our motivation was to coalesce them to get the best out of the both worlds. Hence we have developed, VFPred that, in addition to employing a signal processing pipeline, namely, Empirical Mode Decomposition and Discrete Time Fourier Transform for useful feature extraction, uses a Support Vector Machine for efficient classification. VFPred turns out to be a robust algorithm as it is able to successfully segregate the two classes with equal confidence (Sensitivity = 99.99%, Specificity = 98.40%) even from a short signal of 5 seconds long, whereas existing works though requires longer signals, flourishes in one but fails in the other

Quantum Nonthermal Radiation of Kerr-anti-de Sitter Black Holes

We examine the properties of Quantum nonthermal radiation of a Kerr-anti-de Sitter (KAdS) black holes. Assuming that a crossing of the positive and negative Dirac energy levels occurs in a region near the event horizon of the hole, and spontaneous quantum nonthermal radiation takes place in the overlap region. We solve the biquadratic equation governing the location of the event horizon of the KAdS black holes and present closed analytic expression for the radii of the horizons.Comment: 13 page

Predicting and Forecasting the Price of Constituents and Index of Cryptocurrency Using Machine Learning

At present, cryptocurrencies have become a global phenomenon in financial sectors as it is one of the most traded financial instruments worldwide. Cryptocurrency is not only one of the most complicated and abstruse fields among financial instruments, but it is also deemed as a perplexing problem in finance due to its high volatility. This paper makes an attempt to apply machine learning techniques on the index and constituents of cryptocurrency with a goal to predict and forecast prices thereof. In particular, the purpose of this paper is to predict and forecast the close (closing) price of the cryptocurrency index 30 and nine constituents of cryptocurrencies using machine learning algorithms and models so that, it becomes easier for people to trade these currencies. We have used several machine learning techniques and algorithms and compared the models with each other to get the best output. We believe that our work will help reduce the challenges and difficulties faced by people, who invest in cryptocurrencies. Moreover, the obtained results can play a major role in cryptocurrency portfolio management and in observing the fluctuations in the prices of constituents of cryptocurrency market. We have also compared our approach with similar state of the art works from the literature, where machine learning approaches are considered for predicting and forecasting the prices of these currencies. In the sequel, we have found that our best approach presents better and competitive results than the best works from the literature thereby advancing the state of the art. Using such prediction and forecasting methods, people can easily understand the trend and it would be even easier for them to trade in a difficult and challenging financial instrument like cryptocurrency.Comment: main article along with the supplement article at the en

Waves in General Relativistic Two-fluid Plasma around a Schwarzschild Black Hole

Waves propagating in the relativistic electron-positron or ions plasma are investigated in a frame of two-fluid equations using the 3+1 formalism of general relativity developed by Thorne, Price and Macdonald (TPM). The plasma is assumed to be freefalling in the radial direction toward the event horizon due to the strong gravitational field of a Schwarzschild black hole. The local dispersion relations for transverse and longitudinal waves have been derived, in analogy with the special relativistic formulation as explained in an earlier paper, to take account of relativistic effects due to the event horizon using WKB approximationComment: 7 pages; Astrophys Space Sci (2012). arXiv admin note: substantial text overlap with arXiv:1008.483

New universality class in percolation on multifractal scale-free planar stochastic lattice

We investigate site percolation on a weighted planar stochastic lattice (WPSL) which is a multifractal and whose dual is a scale-free network. Percolation is typically characterized by percolation threshold $p_c$ and by a set of critical exponents $\beta$, $\gamma$, $\nu$ which describe the critical behavior of percolation probability $P(p)\sim (p_c-p)^\beta$, mean cluster size $S\sim (p_c-p)^{-\gamma}$ and the correlation length $\xi\sim (p_c-p)^{-\nu}$. Besides, the exponent $\tau$ characterizes the cluster size distribution function $n_s(p_c)\sim s^{-\tau}$ and the fractal dimension $d_f$ the spanning cluster. We obtain an exact value for $p_c$ and for all these exponents. Our results suggest that the percolation on WPSL belong to a new universality class as its exponents do not share the same value as for all the existing planar lattices.Comment: 5 pages, 5 figure

Universality class of site and bond percolation on multi-multifractal scale-free planar stochastic lattice

In this article, we investigate both site and bond percolation on a weighted planar stochastic lattice (WPSL) which is a multi-multifractal and whose dual is a scale-free network. The characteristic properties of percolation is that it exhibits threshold phenomena as we find sudden or abrupt jump in spanning probability across $p_c$ accompanied by the divergence of some other observable quantities which is reminiscent of continuous phase transition. Indeed, percolation is characterized by the critical behavior of percolation strength $P(p)\sim (p_c-p)^\beta$, mean cluster size $S\sim (p_c-p)^{-\gamma}$ and the system size $L\sim (p_c-p)^{-\nu}$ which are known as the equivalent counterpart of the order parameter, susceptibility and correlation length respectively. Moreover, the cluster size distribution function $n_s(p_c)\sim s^{-\tau}$ and the mass-length relation $M\sim L^{d_f}$ of the spanning cluster also provide useful characterization of the percolation process. We obtain an exact value for $p_c$ and for all the exponents such as $\beta, \nu, \gamma, \tau$ and $d_f$. We find that, except $p_c$, all the exponents are exactly the same in both bond and site percolation despite the significant difference in the definition of cluster and other quantities. Our results suggest that the percolation on WPSL belongs to a new universality class as its exponents do not share the same value as for all the existing planar lattices and like other cases its site and bond belong to the same universality class.Comment: 12 pages, 7 figures, 1 tabl

Intrinsic Cutoff and Acausality for Massive Spin 2 Fields Coupled to Electromagnetism

We couple a massive spin 2 particle to electromagnetism. By introducing new, redundant degrees of freedom using the Stueckelberg formalism, we extract an intrinsic, model independent UV cutoff of the effective field theory describing this system. The cutoff signals both the onset of a strongly interacting dynamical regime and a finite size for the spin 2 particle. We show that the existence of a cutoff is strictly connected to other pathologies of interacting high-spin fields, such as the Velo-Zwanziger acausality. We also briefly comment on implications of this result for the detection of high spin states and on its possible generalization to arbitrary spin.Comment: 14 pages, to appear in Nuclear Physics

A Noble Methodology for Users Work Process Driven Software Requirements for Smart Handheld Devices

Requirement engineering is a key ingredient for software development to be effective. Apart from the traditional software requirement which is not much appropriate for new emerging software such as smart handheld device based software. In many perspectives of requirement engineering, traditional and new emerging software are not similar. Whereas requirement engineering of traditional software needs more research, it is obvious that new emerging software needs methodically and in-depth research for improved productivity, quality, risk management and validity. In particular, the result of this paper shows that how effective requirement engineering can improve in project negotiation, project planning, managing feature creep, testing, defect, rework and product quality. This paper also shows a new methodology which is focused on users work process applicable for eliciting the requirement of traditional software and any new type software of smart handheld device such as iPad. As an example, the paper shows how the methodology will be applied as a software requirement of iPad-based software for play-group students.Comment: 18 pages, 9 figure

Microcomputer Aided Selection Of Robot Manipulators

This paper presents two programs for microcomputer aided assessment of the performance of robot manipulators. The first program automatically generates robot models based on user-supplied kinematic parameters. The program also derives a kinematic model that relates the motion of manipulator end-effector to the motion of the joints using the inverse kinematic approach. The approach uses a robust inversion technique that can handle singular conditions as well as joint redundancy. A user can optionally select evaluation of kinematic capabilities of the robot manipulator, such as the ability of the end-effector to reach a specified position and orientation in space or the evaluation of the work space. The second program generates dynamic variables, such as forces and torques, based on user-supplied dynamic parameters and equations of motion of the various joints. Both programs are written for implementation on personal computers. Several runs were carried out to demonstrate the capability and execution times of the two program

Modulational instability, rogue waves, and envelope solitons in opposite polarity dusty plasmas

Dust-acoustic (DA) waves (DAWs) and their modulational instability (MI) have been investigated theoretically in a plasma system consisting of inertial opposite polarity (positively and negatively) warm adiabatic charged dust particles as well as inertialess non-extensive $q$-distributed electrons and non-thermal ions. A nonlinear Schr\"{o}dinger (NLS) equation is derived by using the reductive perturbation method. It has been observed from the analysis of NLS equaion that the modulationally stable solitary DAWs give rise to the existence of dark envelope solitons, and that the modulationally unstable solitary DAWs give rise to the existence of bright envelope solitons or rogue structures. It is also observed for the fast mode of DAWs that the basic features (viz. stability of the DAWs, MI growth rate, amplitude and width of the DA rogue waves, etc.) are significantly modified by the related plasma parameters (viz. dust masses, dust charge state, non-extensive parameter $q$, and non-thermal parameter $\alpha$). The results of our present investigation might be useful for understanding different nonlinear electrostatic phenomena in both space (viz. ionosphere and mesosphere) and laboratory plasmas (viz. high intensity laser irradiation and hot cathode discharge).Comment: 11 pages; 11 figure