Search results for: ECG beat Classification; Combining Classifiers;Premature Ventricular Contraction (PVC); Multi Layer Perceptrons;Wavelet Transform

Authors: Toktam Zoughi, Reza Boostani

Abstract:

Determining depth of anesthesia is a challenging problem in the context of biomedical signal processing. Various methods have been suggested to determine a quantitative index as depth of anesthesia, but most of these methods suffer from high sensitivity during the surgery. A novel method based on energy scattering of samples in the wavelet domain is suggested to represent the basic content of electroencephalogram (EEG) signal. In this method, first EEG signal is decomposed into different sub-bands, then samples are squared and energy of samples sequence is constructed through each scale and time, which is normalized and finally entropy of the resulted sequences is suggested as a reliable index. Empirical Results showed that applying the proposed method to the EEG signals can classify the awake, moderate and deep anesthesia states similar to BIS.

Keywords: Depth of anesthesia, EEG, BIS, Wavelet transforms.

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Authors: Zeyneb Kurt, Oguzhan Yavuz

Abstract:

The feature of HIV genome is in a wide range because of it is highly heterogeneous. Hence, the infection ability of the virus changes related with different chemokine receptors. From this point, R5 and X4 HIV viruses use CCR5 and CXCR5 coreceptors respectively while R5X4 viruses can utilize both coreceptors. Recently, in Bioinformatics, R5X4 viruses have been studied to classify by using the coreceptors of HIV genome. The aim of this study is to develop the optimal Multilayer Perceptron (MLP) for high classification accuracy of HIV sub-type viruses. To accomplish this purpose, the unit number in hidden layer was incremented one by one, from one to a particular number. The statistical data of R5X4, R5 and X4 viruses was preprocessed by the signal processing methods. Accessible residues of these virus sequences were extracted and modeled by Auto-Regressive Model (AR) due to the dimension of residues is large and different from each other. Finally the pre-processed dataset was used to evolve MLP with various number of hidden units to determine R5X4 viruses. Furthermore, ROC analysis was used to figure out the optimal MLP structure.

Keywords: Multilayer Perceptron, Auto-Regressive Model, HIV, ROC Analysis

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Authors: Md. Nasir Uddin, M. A. Alim, M. M. K. Chowdhury

Abstract:

The effect of mass transfer on MHD mixed convective flow along inclined porous plate with thermodiffusion have been analyzed on the basis of boundary layer approximations. The fluid is assumed to be incompressible and dense, and a uniform magnetic field is applied normal to the direction of the flow. A Similarity transformation is used to transform the problem under consideration into coupled nonlinear boundary layer equations which are then solved numerically using the Runge-Kutta sixth-order integration scheme together with Nachtsheim-Swigert shooting iteration technique. The behavior of velocity, temperature, concentration, local skin-friction, local Nusselt number and local Sherwood number for different values of parameters have been computed and the results are presented graphically, and analyzed thereafter. The validity of the numerical methodology and the results are questioned by comparing the findings obtained for some specific cases with those available in the literature, and a comparatively good agreement is reached.

Keywords: Mass transfer, inclined porous plate, MHD, mixed convection, thermodiffusion.

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Authors: Vesile Evrim, Aliyu Awwal

Abstract:

Today, there is a large number of political transcripts available on the Web to be mined and used for statistical analysis, and product recommendations. As the online political resources are used for various purposes, automatically determining the political orientation on these transcripts becomes crucial. The methodologies used by machine learning algorithms to do an automatic classification are based on different features that are classified under categories such as Linguistic, Personality etc. Considering the ideological differences between Liberals and Conservatives, in this paper, the effect of Personality traits on political orientation classification is studied. The experiments in this study were based on the correlation between LIWC features and the BIG Five Personality traits. Several experiments were conducted using Convote U.S. Congressional- Speech dataset with seven benchmark classification algorithms. The different methodologies were applied on several LIWC feature sets that constituted by 8 to 64 varying number of features that are correlated to five personality traits. As results of experiments, Neuroticism trait was obtained to be the most differentiating personality trait for classification of political orientation. At the same time, it was observed that the personality trait based classification methodology gives better and comparable results with the related work.

Keywords: Politics, personality traits, LIWC, machine learning.

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Authors: Saeideh Hesam, Alireza Nazemi, Ahmad Haghbin

Abstract:

This paper presents the approximate analytical solution of a Zakharov-Kuznetsov ZK(m, n, k) equation with the help of the differential transform method (DTM). The DTM method is a powerful and efficient technique for finding solutions of nonlinear equations without the need of a linearization process. In this approach the solution is found in the form of a rapidly convergent series with easily computed components. The two special cases, ZK(2,2,2) and ZK(3,3,3), are chosen to illustrate the concrete scheme of the DTM method in ZK(m, n, k) equations. The results demonstrate reliability and efficiency of the proposed method.

Keywords: Zakharov-Kuznetsov equation, differential transform method, closed form solution.

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Authors: Ehab F. Badran, Guoxiang Gu

Abstract:

We consider optimal channel equalization for MIMO (multi-input/multi-output) time-varying channels in the sense of MMSE (minimum mean-squared-error), where the observation noise can be non-stationary. We show that all ZF (zero-forcing) receivers can be parameterized in an affine form which eliminates completely the ISI (inter-symbol-interference), and optimal channel equalizers can be designed through minimization of the MSE (mean-squarederror) between the detected signals and the transmitted signals, among all ZF receivers. We demonstrate that the optimal channel equalizer is a modified Kalman filter, and show that under the AWGN (additive white Gaussian noise) assumption, the proposed optimal channel equalizer minimizes the BER (bit error rate) among all possible ZF receivers. Our results are applicable to optimal channel equalization for DWMT (discrete wavelet multitone), multirate transmultiplexers, OFDM (orthogonal frequency division multiplexing), and DS (direct sequence) CDMA (code division multiple access) wireless data communication systems. A design algorithm for optimal channel equalization is developed, and several simulation examples are worked out to illustrate the proposed design algorithm.

Keywords: Channel equalization, Kalman filtering, Time-varying systems.

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Authors: Mutuku Muvengei, John Kihiu

Abstract:

In this paper, a bond graph dynamic model for a valvecontrolled hydraulic cylinder has been developed. A simplified bond graph model of the inter-actuator interactions in a multi-cylinder hydraulic system has also been presented. The overall bond graph model of a valve-controlled hydraulic cylinder was developed by combining the bond graph sub-models of the pump, spool valve and the actuator using junction structures. Causality was then assigned in order to obtain a computational model which could be simulated. The causal bond graph model of the hydraulic cylinder was verified by comparing the open loop state responses to those of an ODE model which had been developed in literature based on the same assumptions. The results were found to correlate very well both in the shape of the curves, magnitude and the response times, thus indicating that the developed model represents the hydraulic dynamics of a valve-controlled cylinder. A simplified model for interactuator interaction was presented by connecting an effort source with constant pump pressure to the zero-junction from which the cylinders in a multi-cylinder system are supplied with a constant pressure from the pump. On simulating the state responses of the developed model under different situations of cylinder operations, indicated that such a simple model can be used to predict the inter-actuator interactions.

Keywords: Bond graphs, Inter-actuator interactions, Valvecontrolledhydraulic cylinder.

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Authors: Hooman Hoodat, Hassan Rashidi

Abstract:

Despite various methods that exist in software risk management, software projects have a high rate of failure. When complexity and size of the projects are increased, managing software development becomes more difficult. In these projects the need for more analysis and risk assessment is vital. In this paper, a classification for software risks is specified. Then relations between these risks using risk tree structure are presented. Analysis and assessment of these risks are done using probabilistic calculations. This analysis helps qualitative and quantitative assessment of risk of failure. Moreover it can help software risk management process. This classification and risk tree structure can apply to some software tools.

Keywords: Risk analysis, risk assessment, risk classification, risk tree.

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Authors: T.G. Basavaraju, Subir Kumar Sarkar, C Puttamadappa

Abstract:

Mobile Ad hoc Networks is an autonomous system of mobile nodes connected by multi-hop wireless links without centralized infrastructure support. As mobile communication gains popularity, the need for suitable ad hoc routing protocols will continue to grow. Efficient dynamic routing is an important research challenge in such a network. Bandwidth constrained mobile devices use on-demand approach in their routing protocols because of its effectiveness and efficiency. Many researchers have conducted numerous simulations for comparing the performance of these protocols under varying conditions and constraints. Most of them are not aware of MAC Protocols, which will impact the relative performance of routing protocols considered in different network scenarios. In this paper we investigate the choice of MAC protocols affects the relative performance of ad hoc routing protocols under different scenarios. We have evaluated the performance of these protocols using NS2 simulations. Our results show that the performance of routing protocols of ad hoc networks will suffer when run over different MAC Layer protocols.

Keywords: AODV, DSR, DSDV, MAC, MANETs, relativeperformance

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Authors: A. Borji, M. Hamidi

Abstract:

In this paper we examine the use of global texture analysis based approaches for the purpose of Persian font recognition in machine-printed document images. Most existing methods for font recognition make use of local typographical features and connected component analysis. However derivation of such features is not an easy task. Gabor filters are appropriate tools for texture analysis and are motivated by human visual system. Here we consider document images as textures and use Gabor filter responses for identifying the fonts. The method is content independent and involves no local feature analysis. Two different classifiers Weighted Euclidean Distance and SVM are used for the purpose of classification. Experiments on seven different type faces and four font styles show average accuracy of 85% with WED and 82% with SVM classifier over typefaces

Keywords: Persian font recognition, support vector machine, gabor filter.

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Authors: Benjamin Y. M. Kwan, Jennifer Y. Y. Kwan, Hon Keung Kwan

Abstract:

In the paper, the relative performances on spectral classification of short exon and intron sequences of the human and eleven model organisms is studied. In the simulations, all combinations of sixteen one-sequence numerical representations, four threshold values, and four window lengths are considered. Sequences of 150-base length are chosen and for each organism, a total of 16,000 sequences are used for training and testing. Results indicate that an appropriate combination of one-sequence numerical representation, threshold value, and window length is essential for arriving at top spectral classification results. For fixed-length sequences, the precisions on exon and intron classification obtained for different organisms are not the same because of their genomic differences. In general, precision increases as sequence length increases.

Keywords: Exons and introns classification, Human genome, Model organism genome, Spectral analysis

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Authors: Mahesh Pal

Abstract:

This paper proposes to use ETM+ multispectral data and panchromatic band as well as texture features derived from the panchromatic band for land cover classification. Four texture features including one 'internal texture' and three GLCM based textures namely correlation, entropy, and inverse different moment were used in combination with ETM+ multispectral data. Two data sets involving combination of multispectral, panchromatic band and its texture were used and results were compared with those obtained by using multispectral data alone. A decision tree classifier with and without boosting were used to classify different datasets. Results from this study suggest that the dataset consisting of panchromatic band, four of its texture features and multispectral data was able to increase the classification accuracy by about 2%. In comparison, a boosted decision tree was able to increase the classification accuracy by about 3% with the same dataset.

Keywords: Internal texture; GLCM; decision tree; boosting; classification accuracy.

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Authors: H. Ifassiouen, H. Medromi, N. E. Radhy

Abstract:

In this paper, we present the recently implemented approach allowing dynamics systems to plan its actions, taking into account the environment perception changes, and to control their execution when uncertainty and incomplete knowledge are the major characteristics of the situated environment [1],[2],[3],[4]. The control distributed architecture has three modules and the approach is related to hierarchical planning: the plan produced by the planner is further refined at the control layer that in turn supervises its execution by a functional level. We propose a new intelligent distributed architecture constituted by: Multi-Agent subsystem of the sensor, of the interpretation and representation of environment [9], of the dynamic localization and of the action. We tested this distributed architecture with dynamic system in the known environment. The autonomous for Rotor Mini Rotorcraft task is described by the primitive actions. The distributed controlbased on multi-agent system is in charge of achieving each task in the best possible way taking into account the context and sensory feedback.

Keywords: Autonomous four rotors helicopter, Control system, Hierarchical planning, Intelligent Distributed Architecture.

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Authors: R. Ziaie Moayed, V. Rashidian, E. Izadi

Abstract:

This paper utilizes a finite element analysis to study the bearing capacity of ring footings on a two-layered soil. The upper layer, that the footing is placed on it, is soft clay and the underneath layer is a cohesionless sand. For modeling soils, Mohr–Coulomb plastic yield criterion is employed. The effects of two factors, the clay layer thickness and the ratio of internal radius of the ring footing to external radius of the ring, have been analyzed. It is found that the bearing capacity decreases as the value of ri / ro increases. Although, as the clay layer thickness increases the bearing capacity was alleviated gradually.

Keywords: Bearing capacity, Ring footing, Two-layered soil

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Authors: Ehsan Amid, Sina Rezaei Aghdam

Abstract:

In this paper, a novel method for recognition of musical instruments in a polyphonic music is presented by using an embedded hidden Markov model (EHMM). EHMM is a doubly embedded HMM structure where each state of the external HMM is an independent HMM. The classification is accomplished for two different internal HMM structures where GMMs are used as likelihood estimators for the internal HMMs. The results are compared to those achieved by an artificial neural network with two hidden layers. Appropriate classification accuracies were achieved both for solo instrument performance and instrument combinations which demonstrates that the new approach outperforms the similar classification methods by means of the dynamic of the signal.

Keywords: hidden Markov model (HMM), embedded hidden Markov models (EHMM), MFCC, musical instrument.

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Authors: S. A. Naeini, H. R. Rahmani, M. Hossein Zade

Abstract:

Many classical bearing capacity theories assume that the natural soil's layers are homogenous for determining the bearing capacity of the soil. But, in many practical projects, we encounter multi-layer soils. Geosynthetic as reinforcement materials have been extensively used in the construction of various structures. In this paper, numerical analysis of the Plate Load Test (PLT) using of ABAQUS software in double-layered soils with different thicknesses of sandy and gravelly layers reinforced with geogrid was considered. The PLT is one of the common filed methods to calculate parameters such as soil bearing capacity, the evaluation of the compressibility and the determination of the Subgrade Reaction module. In fact, the influence of the geogrid layers on the bearing capacity of the layered soils is investigated. Finally, the most appropriate mode for the distance and number of reinforcement layers is determined. Results show that using three layers of geogrid with a distance of 0.3 times the width of the loading plate has the highest efficiency in bearing capacity of double-layer (sand and gravel) soils. Also, the significant increase in bearing capacity between unreinforced and reinforced soil with three layers of geogrid is caused by the condition that the upper layer (gravel) thickness is equal to the loading plate width.

Keywords: Bearing capacity, reinforcement, geogrid, plate load test, layered soils.

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Authors: Walid Cherif

Abstract:

Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: Data mining, knowledge discovery, machine learning, similarity measurement, supervised classification.

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Authors: Yasser F. Hassan

Abstract:

Deep learning structure is a branch of machine learning science and greet achievement in research and applications. Cellular neural networks are regarded as array of nonlinear analog processors called cells connected in a way allowing parallel computations. The paper discusses how to use deep learning structure for representing neural cellular automata model. The proposed learning technique in cellular automata model will be examined from structure of deep learning. A deep automata neural cellular system modifies each neuron based on the behavior of the individual and its decision as a result of multi-level deep structure learning. The paper will present the architecture of the model and the results of simulation of approach are given. Results from the implementation enrich deep neural cellular automata system and shed a light on concept formulation of the model and the learning in it.

Keywords: Cellular automata, neural cellular automata, deep learning, classification.

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Authors: Joe Walsh, Mike Climstein, Ian Timothy Heazlewood, Stephen Burke, Jyrki Kettunen, Kent Adams, Mark DeBeliso

Abstract:

Thousands of masters athletes participate quadrennially in the World Masters Games (WMG), yet this cohort of athletes remains proportionately under-investigated. Due to a growing global obesity pandemic in context of benefits of physical activity across the lifespan, the prevalence of obesity in this unique population was of particular interest. Data gathered on a sub-sample of 535 football code athletes, aged 31-72 yrs ( =47.4, s =±7.1), competing at the Sydney World Masters Games (2009) demonstrated a significantly (p<0.001), reduced classification of obesity using Body Mass Index (BMI) when compared to data on the Australian national population. This evidence of improved classification in one index of health (BMI<30) implies there are either improved levels of this index of health due to adherence to sport or possibly the reduced BMI is advantageous and contributes to this cohort adhering (or being attracted) to masters sport. Given the worldwide focus on the obesity epidemic and the need for a multi-faceted solution to this problem, demonstration of these middle to older aged adults having improved BMI over the general population is of particular interest.

Keywords: BMI, masters athlete, rugby union, soccer, touch football

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Authors: Yi-Pin Hsu, Shin-Yu Lin

Abstract:

In order to protect original data, watermarking is first consideration direction for digital information copyright. In addition, to achieve high quality image, the algorithm maybe can not run on embedded system because the computation is very complexity. However, almost nowadays algorithms need to build on consumer production because integrator circuit has a huge progress and cheap price. In this paper, we propose a novel algorithm which efficient inserts watermarking on digital image and very easy to implement on digital signal processor. In further, we select a general and cheap digital signal processor which is made by analog device company to fit consumer application. The experimental results show that the image quality by watermarking insertion can achieve 46 dB can be accepted in human vision and can real-time execute on digital signal processor.

Keywords: watermarking, digital signal processor, embedded system

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Authors: Liudmyla Koliechkina, Olena Dvirna

Abstract:

The statement of the multi-objective optimization problem on combinatorial configurations is formulated, and the approach to its solution is proposed. The problem is of interest as a combinatorial optimization one with many criteria, which is a model of many applied tasks. The approach to solving the multi-objective optimization problem on combinatorial configurations consists of two stages; the first is the reduction of the multi-objective problem to the single criterion based on existing multi-objective optimization methods, the second stage solves the directly replaced single criterion combinatorial optimization problem by the horizontal combinatorial method. This approach provides the optimal solution to the multi-objective optimization problem on combinatorial configurations, taking into account additional restrictions for a finite number of steps.

Keywords: Discrete set, linear combinatorial optimization, multi-objective optimization, multipermutation, Pareto solutions, partial permutation set, permutation, structural graph.

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Authors: Jiakai Li, Gursel Serpen, Steven Selman, Matt Franchetti, Mike Riesen, Cynthia Schneider

Abstract:

This paper presents the development of a Bayesian belief network classifier for prediction of graft status and survival period in renal transplantation using the patient profile information prior to the transplantation. The objective was to explore feasibility of developing a decision making tool for identifying the most suitable recipient among the candidate pool members. The dataset was compiled from the University of Toledo Medical Center Hospital patients as reported to the United Network Organ Sharing, and had 1228 patient records for the period covering 1987 through 2009. The Bayes net classifiers were developed using the Weka machine learning software workbench. Two separate classifiers were induced from the data set, one to predict the status of the graft as either failed or living, and a second classifier to predict the graft survival period. The classifier for graft status prediction performed very well with a prediction accuracy of 97.8% and true positive values of 0.967 and 0.988 for the living and failed classes, respectively. The second classifier to predict the graft survival period yielded a prediction accuracy of 68.2% and a true positive rate of 0.85 for the class representing those instances with kidneys failing during the first year following transplantation. Simulation results indicated that it is feasible to develop a successful Bayesian belief network classifier for prediction of graft status, but not the graft survival period, using the information in UNOS database.

Keywords: Bayesian network classifier, renal transplantation, graft survival period, United Network for Organ Sharing

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Authors: Mohammad H. Fattahi

Abstract:

Chaotic analysis has been performed on the river flow time series before and after applying the wavelet based de-noising techniques in order to investigate the noise content effects on chaotic nature of flow series. In this study, 38 years of monthly runoff data of three gauging stations were used. Gauging stations were located in Ghar-e-Aghaj river basin, Fars province, Iran. Noise level of time series was estimated with the aid of Gaussian kernel algorithm. This step was found to be crucial in preventing removal of the vital data such as memory, correlation and trend from the time series in addition to the noise during de-noising process.

Keywords: Chaotic behavior, wavelet, noise reduction, river flow.

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Authors: Alan C. Lin, Tzu-Kuan Lin, Tsong Der Lin

Abstract:

This paper proposes a new method to find the equations of transformation matrix for the rotation angles of the two rotational axes and the coordinates of the three linear axes of an orthogonal multi-axis milling machine. This approach provides intuitive physical meanings for rotation angles of multi-axis machines, which can be used to evaluate the accuracy of the conversion from CL data to NC data.

Keywords: CAM, multi-axis milling machining.

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Authors: Durga C. Dalal, Swapan K. Pandit

Abstract:

In this paper, a numerical study has been made to analyze the transient 2-D flows of a viscous incompressible fluid through channels with forward or backward constriction. Problems addressed include flow through sudden contraction and sudden expansion channel geometries with rounded and increasingly sharp reentrant corner. In both the cases, numerical results are presented for the separation and reattachment points, streamlines, vorticity and flow patterns. A fourth order accurate compact scheme has been employed to efficiently capture steady state solutions of the governing equations. It appears from our study that sharpness of the throat in the channel is one of the important parameters to control the strength and size of the separation zone without modifying the general flow patterns. The comparison between the two cases shows that the upstream geometry plays a significant role on vortex growth dynamics.

Keywords: Forward and backward constriction, HOC scheme, Incompressible viscous flows, Separation and reattachment points.

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Authors: H. Khorashadi-Zadeh, M. R. Aghaebrahimi

Abstract:

A novel application of neural network approach to fault classification and fault location of Medium voltage cables is demonstrated in this paper. Different faults on a protected cable should be classified and located correctly. This paper presents the use of neural networks as a pattern classifier algorithm to perform these tasks. The proposed scheme is insensitive to variation of different parameters such as fault type, fault resistance, and fault inception angle. Studies show that the proposed technique is able to offer high accuracy in both of the fault classification and fault location tasks.

Keywords: Artificial neural networks, cable, fault location andfault classification.

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Authors: N. Nacereddine, M. Tridi, S. S. Belaïfa, M. Zelmat

Abstract:

For the characterization of the weld defect region in the radiographic image, looking for features which are invariant regarding the geometrical transformations (rotation, translation and scaling) proves to be necessary because the same defect can be seen from several angles according to the orientation and the distance from the welded framework to the radiation source. Thus, panoply of geometrical attributes satisfying the above conditions is proposed and which result from the calculation of the geometrical parameters (surface, perimeter, etc.) on the one hand and the calculation of the different order moments, on the other hand. Because the large range in values of the raw features and taking into account other considerations imposed by some classifiers, the scaling of these values to lie between 0 and 1 is indispensable. The principal component analysis technique is used in order to reduce the number of the attribute variables in the aim to give better performance to the further defect classification.

Keywords: Geometric parameters, invariant attributes, principal component analysis, weld defect image.

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Authors: S. Esakkirajan, T. Veerakumar, V. Senthil Murugan, P. Navaneethan

Abstract:

The existing image coding standards generally degrades at low bit-rates because of the underlying block based Discrete Cosine Transform scheme. Over the past decade, the success of wavelets in solving many different problems has contributed to its unprecedented popularity. Due to implementation constraints scalar wavelets do not posses all the properties such as orthogonality, short support, linear phase symmetry, and a high order of approximation through vanishing moments simultaneously, which are very much essential for signal processing. New class of wavelets called 'Multiwavelets' which posses more than one scaling function overcomes this problem. This paper presents a new image coding scheme based on non linear approximation of multiwavelet coefficients along with multistage vector quantization. The performance of the proposed scheme is compared with the results obtained from scalar wavelets.

Keywords: Image compression, Multiwavelets, Multi-stagevector quantization.

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Authors: Afaf Alharbi, Qianni Zhang

Abstract:

The identification of malignant tissue in histopathological slides holds significant importance in both clinical settings and pathology research. This paper presents a methodology aimed at automatically categorizing cancerous tissue through the utilization of a multiple instance learning framework. This framework is specifically developed to acquire knowledge of the Bernoulli distribution of the bag label probability by employing neural networks. Furthermore, we put forward a neural network-based permutation-invariant aggregation operator, equivalent to attention mechanisms, which is applied to the multi-instance learning network. Through empirical evaluation on an openly available colon cancer histopathology dataset, we provide evidence that our approach surpasses various conventional deep learning methods.

Keywords: Attention Multiple Instance Learning, Multiple Instance Learning, transfer learning, histopathological slides, cancer tissue classification.

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Authors: K. A. Navas, S. Archana Thampy, M. Sasikumar

Abstract:

Medical image data hiding has strict constrains such as high imperceptibility, high capacity and high robustness. Achieving these three requirements simultaneously is highly cumbersome. Some works have been reported in the literature on data hiding, watermarking and stegnography which are suitable for telemedicine applications. None is reliable in all aspects. Electronic Patient Report (EPR) data hiding for telemedicine demand it blind and reversible. This paper proposes a novel approach to blind reversible data hiding based on integer wavelet transform. Experimental results shows that this scheme outperforms the prior arts in terms of zero BER (Bit Error Rate), higher PSNR (Peak Signal to Noise Ratio), and large EPR data embedding capacity with WPSNR (Weighted Peak Signal to Noise Ratio) around 53 dB, compared with the existing reversible data hiding schemes.

Keywords: Biomedical imaging, Data security, Datacommunication, Teleconferencing.

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