Control, Computation and Information Systems

In this paper, we discuss the existence of mild solutions of first-order impulsive functional integro-differential inclusions with scalar multiple delay and infinite delay. The result is obtained by using a fixed point theorem for condensing maps due to Martelli.

In this paper, we consider the problem of delay-dependent robust stability of a class of linear uncertain neutral systems with time-varying delays using Lyapunov-Krasovskii functional approach. By constructing a candidate Lyapunov-Krasovskii (LK) functional, a less conservative stability criterion is derived in terms of linear matrix inequalities (LMIs). In addition to the LK functional candidate, conservativeness in the proposed stability analysis is further reduced by imposing tighter bounding on the time-derivative of the functional using minimal number of slack matrix variables. The proposed analysis, subsequently, yields a stability criterion in convex LMI framework, and is solved non-conservatively at boundary conditions using standard LMI solvers. The effectiveness of the proposed stability criterion is demonstrated through a standard numerical example.

This paper describes the design of a PID Controller for an unstable electronic circuit using various tuning techniques like Ziegler Nichols (Z-N), Chien Hrones Reswick (CHR), and Genetic Algorithm (GA). Further, comparison of various performance parameters for these techniques has been done. The results are verified in MATLAB environment.

The problem of asymptotic stability analysis for a class of stochastic Takagi-Sugeno fuzzy systems with time varying delays is investigated. By employing improved free weighting matrix approach and Lyapunov stability theory, new set of sufficient conditions are derived in terms of linear matrix inequalities which ensure the asymptotic stability of the considered stochastic fuzzy system with time varying delays. Further the result is extended to the delay independent that is, for constant time delay case. Finally, a numerical example is provided to illustrate the effectiveness of the proposed results.

In this paper, delay dependent H∞ control problem of Markovian jumping nonlinear systems (MJNS) with time varying delay based on Takagi-Sugeno (TS) fuzzy modeling is studied. Sufficient criterion for H∞ performance are assured by deriving convex linear matrix inequalities from nonlinear matrix inequalites utilizing novel matrix transforms. Based on the delay decomposition approach, new set of Lyapunov candidates are constructed in order to derive very less conservative and easily testable criterions. Numerical simulation on single link robot arm model is illustrated to adequate the theory.

In this paper two computational systems, one is based on fuzzy logic system and other is based on adaptive neuro fuzzy inference system (ANFIS), are developed for assessing morningness of a group of people and the result is compared for finding the best system in the assessment process. In fuzzy rule based system, the linguistic terms for assessing morningness are quantified by using fuzzy logic and a fuzzy expert system is developed. On the other side, an ANFIS model is generated to assess data for training and testing the model in accordance with a preference scale adopted to quantify the responses of subjects for a reduced version of Morningness-Eveningness Questionnaire (rMEQ). The result reflects that ANFIS is able to assess morningness better than fuzzy expert inference system.

In this paper, the unsteady Navier-Stokes Takagi-Sugeno (T-S) fuzzy equations (UNSTSFEs) are represented as a differential algebraic system of strangeness index one by applying any spatial discretization. Since such differential algebraic systems have a difficulty to solve in their original form, most approaches use some kind of index reduction. While processing this index reduction, it is important to take care of the manifolds contained in the differential algebraic equation (DAE) /singular system (SS) for each fuzzy rule. The Navier-Stokes equations are investigated along the lines of the theoretically best index reduction by using several discretization schemes. Applying this technique, the UNSTSFEs can be reduced into DAE. Optimal control for Navier-Stokes T-S fuzzy system with quadratic performance is obtained by finding the optimal control of singular T-S fuzzy system using Simulink. To obtain the optimal control, the solution of matrix Riccati differential equation (MRDE) is found by solving differential algebraic equation (DAE) using Simulink approach. The solution of Simulink approach is equivalent or very close to the exact solution of the problem. An illustrative numerical example is presented for the proposed method.

This paper describes use of a multiobjective optimization method, AMOSA to the VLSI floorplanning problem. The proposed model provides for decision maker choice from among the different trade-off solutions. The objective is to minimize the area and wirelength with the fixed out line constraint. AMOSA is improved with B*tree and B*tree coded AMOSA is introduced to improve the performance of AMOSA for VLSI floorplanning problem. The MCNC benchmarks are considered as test system. The results are compared and validated.

In this paper, we prove the approximate controllability for a class of semilinear delay control systems of fractional order of the form

${{d^\alpha y(t)}\over{dt^\alpha}}=Ay(t)+v(t)+f(t,y_t,v(t)), t\in [0,\tau];\\ y_0(\theta)=\phi(\theta),\theta\in[-h,0],$

where

A

is linear operators and

f

is a nonlinear operator defined on appropriate Banach space.

This article presents a fuzzy goal programming (FGP) procedure for modeling and solving multilevel programming (MLP) problems by using genetic algorithm (GA) in a large hierarchical decision making system.

In the proposed approach, an GA scheme is introduced first for searching of solutions at different stages and thereby solving the problem and making decision in the order of hierarchy of execution of decision powers of the decision makers (DMs) located at different hierarchical levels.

In the proposed GA scheme, Roulette-wheel selection scheme, single point crossover and random mutation are adopted to search a satisfactory solution in the hierarchical decision system.

To illustrate the potential use of the approach, a numerical example is solved.

We develop the Hutchinson-Barnsley theory for generating the intuitionistic fuzzy fractals through iterated function system, which consists of finite number of intuitionistic fuzzy contractive mappings on intuitionistic fuzzy metric space. For that, we prove the existence and uniqueness of fractals in a complete intuitionistic fuzzy metric space and a compact intuitionistic fuzzy metric space by using the intuitionistic fuzzy Banach contraction theorem and the intuitionistic fuzzy Edelstein contraction theorem respectively.

To solve the contradiction in using Dempster’s method for the combination of highly conflicting evidences, an improved evidence combination method is presented based on Mahalanobis distance weight coefficients. First of all, the similarities between evidences are used as an approach to judge whether conflict exists. If there are more than 3 evidences consisting of conflicts, the Mahalanobis Distance algorithm can be used to calculate the distance between each evidence and the others to obtain the evidences’ weight coefficients, which could be transformed into BPA functions by means of the coefficients, and finally the Dempster’s method is used for the combination. Stimulation results show that this method can deal with conflicting evidences effectively, calculate faster than traditional algorithms, reduce more uncertainty in recognizing results, and retain the advantages of Dempster’s method in tackling non-conflicting evidences.

Image compression plays a vital role in many online applications like Video Conferencing, High Definition Television, Satellite Communication and other applications that demand fast and massive transmission of images. In this paper, we propose a Mode based K-means method that combines K-Means and Residual Vector Quantization(RVQ) for compressing images. Three processes are involved in this approach; Partitioning and Clustering, Pruning and Construction of Master codebook and Residual vector Quantization. Extensive experiments show that this method obtains a fast solution with better compression rate and comparable PSNR than conventional K-Means algorithm.

In this work Lossy methods are used for the approximation studies, since it is suitable for natural images such as photographs. When comparing the compression codecs, it used as an approximation to human perception of reconstruction quality. In numerical analysis and approximation theory, basis functions are also called blending functions, because of their use in interpolation. Fuzzy Logic Systems are blending functions and it has been used to develop a Modified Fuzzy Basis Function (MFBF) and also its approximation capability has been proved. An iterative formula (Lagrange Equation) for the fuzzy optimization has been adopted in this paper. This formula closely relates with the membership function in the RGB colour space. By maximizing the weight in the objective function the noise in the image reduced, so that the filtered image approximates the original image.

Template matching is the process for determining the presence and location of a certain reference in the scene sub image. The conventional approach like spatial correlation, cross correlation is computationally expensive and error prone when the object in the image is rotated or translated. The conventional approach gives erroneous result in the presence of variation in illumination of the scene sub image. In this paper, an algorithm for a rotation, scale and illumination invariant template matching approach is proposed, which is based on the combination of multi-resolution wavelet technique, projection method and Zernike moments. It is ideally suited for highlighting local feature points in the decomposed sub images, and the result is computationally saving in the matching process. As demonstrated in the result, the wavelet decomposition along with Zernike moments makes the template matching scheme feasible and efficient for detecting objects in arbitrary orientation and rotations.

Histogram Equalization (HE) based methods are the commonly used image enhancement techniques to improve the contrast of an input image by changing the intensity level of the pixels, based on its intensity distribution. This paper presents a novel image contrast and edge enhancement technique using constrained histogram equalization. A new transformation function for histogram equalization is devised and a set of constraints are applied over the histogram of an image prior to the equalization process and then, unsharp masking is applied on the resultant image. The proposed method ensures better contrast and edge enhancement, which is measured in terms of discrete entropy.

The optimal placement of Thyristor Controlled Series Compensators (TCSCs) in transmission systems is formulated as a multi-objective optimization problem with the objective of maximizing transmission system loadability, minimizing transmission loss and minimizing the cost of TCSCs. The thermal limits of transmission lines and voltage limits of load buses are considered as security constraints. Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Comprehensive Learning Particle Swarm Optimizer (MOCLPSO) are applied to solve this problem. The proposed approach has been successfully tested on IEEE 14 bus system and IEEE 118 bus system. From the results it is inferred that MOCLPSO can obtain an evenly distributed pareto front. A multi-criterion decision-making tool, known as TOPSIS, has been employed to arrive at the best trade-off solution.

An analysis is presented to consider the problem of torsional body forces within a Kelvin-Voigt type visco- elastic material half space. Hankel & Fourier transform are used to calculate the disturbance for a body force located at a constant depth from the free surface. Numerical results are obtained and the natures are shown graphically.

This paper describes a new approach for medical image binarization based on square wave representation. A square wave is a type of wave form, where the input signal has two levels +1 (foreground) and -1 (background). The signal switches between these levels based on the threshold value computed at that level with the specified time interval. In this method, a local threshold value is calculated at every interval using the current intensity value. Then, the image pixel is assigned with a value +1 or -1 using this local threshold value. The experimental results show that the proposed method reduces the complexity and increases the seperability factor in medical image segmentation. The result obtained by our method is comparable to or better than Otsu’s thresholding method.

A new method is proposed for solving a two-stage transportation problem, which is based on zero point method [5]. This method is very simple, easy to understand and apply and also, provides more than one solution to the two-stage transportation problem. The proposed method helps the decision makers in the logistics related issues by aiding them in the decision making process and providing an optimal solution in a simple and effective manner.

The fractal properties and temporal chracteristics of tumors reveal interesting results. In this paper, we describe a mathematical model for tumor growth in the space-time domain using fractal based density. This model is an alternative for the fractal dimension approach. Determination of the temporal fractal density

D

t

(

t

) and the scaling factor

a

t

(

t

) for the tumor growth in the fractal space-time domain is presented.

This paper describes how the fuzzy goal programming (FGP) can be efficiently used for modelling and solving land allocation problems having chance constraints for optimal production of seasonal crops in agricultural system.

In the proposed model, utilization of total cultivable land, different farming resources, achievement of the aspiration levels of production of seasonal crops are fuzzily described. The water supply as a productive resource and certain socio-economic constraints are described probabilistically in the decision making environment.

In the solution process, achievement of the highest membership value (unity) of the membership goals defined for the fuzzy goals of the problem to the extent possible on the basis of the needs and desires of the decision maker (DM) is taken into account in the decision making horizon. The potential use of the approach is demonstrated by a case example of the Nadia District, West Bengal (W. B.), INDIA.

This paper develops a fuzzy goal programming methodology for solving chance constrained programming problem involving fuzzy numbers and fuzzy random variables which follow standard normal distribution. In the model formulation process, the problem is converted into an equivalent fuzzy programming problem by applying chance constrained programming technique. Then the problem is divided into equivalent sub problems by considering the tolerance limit of the fuzzy numbers relating to the system constraint having different forms of membership functions in the decision making context. Afterwards the objective and the constraints resulting from chance constraints are converted into fuzzy goals by assigning some imprecise aspiration levels. In the decision process, a fuzzy goal programming methodology is introduced to find the most satisfactory solution in the decision making environment. To demonstrate the potentiality of the proposed approach, an illustrative example, studied previously, is solved and is compared with the existing methodologies.

Fractal geometry is an exciting area of interest with diverse applications in various disciplines of engineering and applied sciences. There is a plethora of papers on its versatility in the literature. The basic aim of this paper is to study the pattern of attractors of the iterated function systems (IFS) through Ishikawa iterative scheme. Several recent results are also obtained as special cases. The results obtained are illustrated through figures generated by Matlab programs.

In this paper, a new method known as Single Term Haar Wavelet Series (STHWS) has been presented to obtain the solution for linear and non-linear singular systems. This new approach provides a better effectiveness to find discrete solutions of linear and non-linear singular systems for any length of time t. This is a direct method and can be easily implemented in a digital computer.

Personnel identification by ID images is the main authentication source of the security system. For multiple authentication unique biometric authentication features like finger print, signature and personal identification marks can also be used. Instead of sending these features additionally, they can be embedded in the personal ID images itself. In this paper we propose an algorithm for image fusion for personal ID image using blind and zero watermarking. By using the secret key the embedded information can be extracted. Experimental result shows that the algorithm is robust against various attacks like JPEG compression and few image processing methods.

Paddy is a staple food for more than 93 countries and it will stay of life for future generations. Harvesting is one of the vital operations in crop production and timely harvesting is essential for getting maximum yield. Moisture content and forward speed are the two factors to overcome the post harvest losses and minimise the quantitative losses. In this paper, an artificial neural network is introduced to assess the grain losses in the field condition. The simulation result shows that the ANN method is appropriate and feasible to assess the grain losses. However, model results that, an error of (RMSE) 0.1582 for cutter bar loss, for threshing loss was 0.1299 and for separation loss was 0.1321. Hence, the ANN model help the operator / farmer to decide the time of harvest. It also minimizes the post harvest grain losses by considering crop and machine parameters.

Dynamic power management is a technique used to save power when the system is idle. Earlier it was assumed that the prediction can be done only in long range dependent systems. But a single user will not work similarly the next time, so a single assumption will not hold good. To overcome the above assumptions, we propose an Elman Model which uses Moving Average, Elman Backprop network and random walk model to predict the idle period. Here we use Artificial Neural Network (ANN) in which we train the neurons in a particular way the user desires, replacing neurons by time series we can calculate how much power is saved. This model utilizes both long range dependency and central tendency to predict the past idle periods, by which we predict the future idle period. By simulation we can show that this method achieves higher power saving compared to other methods.

The automatic diagnosis of breast cancer is an important, real-world medical problem. A major class of problems in Medical Science involves the diagnosis of disease, based upon various tests performed upon the patient. When several tests are involved, the ultimate diagnosis may be difficult to obtain, even for a medical expert. This has given rise, over the past few decades, to computerized diagnostic tools, intended to aid the Physician in making sense out of the confusion of data. This Paper carried out to generate and evaluate both fuzzy and neural network models to predict malignancy of breast tumor, using Wisconsin Diagnosis Breast Cancer Database (WDBC). Our objectives in this Paper are: (i) to compare the diagnostic performance of fuzzy and neural network models in distinction between malignance and benign patterns, (ii) to reduce the number of benign cases sent for biopsy using the best model as a supportive tool, and (iii) to validate the capability of each model to recognize new cases.

Providing efficient security method to support the distribution of multimedia multicast is a challenging issue, since the group membership in such applications requires dynamic key generation and updation which takes more computation time. Moreover, the key must be sent securely to the group members. In this paper, we propose a new Key Distribution Protocol that provides more security and also reduces computation complexity. To achieve higher level of security, we use Euler’s Totient Function

ϕ

(

n

) and

gcd

(

ϕ

(

n

)) in the key distribution protocol. Therefore, it increases the key space while breaking the re-keying information. Two major operations in this scheme are joining and leaving operations for managing group memberships. An N-ary tree is used to reduce number of multiplications needed to perform the member leave operation. Using this tree, we reduce the computation time when compared with the existing key management schemes.

In this paper, we propose a security-enhanced method for the visual cryptography schemes using recursion. Visual cryptography is interesting because decryption can be done with no prior knowledge of cryptography and can be performed without any cryptographic computations. The proposed method, the secret image is encoded into shares and subshares in a recursive way. By using recursion for the visual cryptography schemes, the security and reliability of the secret image can be improved than in the case of existing visual cryptography schemes.

In this paper, we suggest an intelligent surveillance system that detects an intruder, using images received in real-time, and offer accurate and adaptive alarm services based on context-aware technology. A surveillance system, based on only image processing, may detect a target other than an intruder as a moving object, providing users unnecessary information. In addition, face recognition may not be accurate or possible. Therefore, we designed a system that detects an intruder through context information such as positions of a camera, directions of movement, time, space and so on, as well as image processing so as to offer adaptive alarm services.

In this paper, we have proposed a modified run-length encoding (RLE) method for binary patterns. This method of encoding considers only the run-length of ‘1’ bit sequences. The proposed distance algorithm considers binary patterns in the proposed encoded form and computes the distance between patterns. Handwritten digit data in the binary sequence is encoded using our proposed encoding method and the proposed distance algorithm is used to classify them in the encoded form itself. It has been observed that, there is a significant reduction in the computation time, and the amount of memory (Run dimension) required for the proposed work.

The Fingerprint recognition system involves several steps. In such recognition systems, the orientation of the fingerprint image has influence on fingerprint image enhancement phase, minutia detection phase and minutia matching phase of the system. The fingerprint image rotation, translation, and registration are the commonly used techniques, to minimize the error in all these stages of fingerprint recognition. Generally, image-processing techniques such as rotation, translation and registration will consume more time and hence impact the overall performance of the system. In this work, we proposed fuzzy c-means and k-mean clustering based fingerprint image rotation algorithm to improve the performance of the fingerprint recognition system. This rotation algorithm can be applied as a pre-processing step before minutia detection and minutia matching phase of the system. Hence, the result will be better detection of minutia as well as better matching with improved performance in terms of time.

In this paper, Neural Network algorithm is used to solve Lorenz System. The solution obtained using neural network is compared with Runge-Kutta Butcher (RK Butcher) method and it is found that neural network algorithm is efficient than RK method.

Mobile solutions using LBS (Location based Service) technology have significantly affected development of various application programs. Particularly, application technology using GPS-based location information and load map is essential to smartphone application. In this study, we designed a context-aware processing module using location and environment information in order to apply weather information to LBS-based services. This research is expected to provide various application services according to personal weather environments.

In this paper, we present a new dimension reduction method using Wilk’s Lambda Average Threshold (WLAT) for classifying the masses present in mammogram. According to Breast Imaging Reporting and Data System (BIRADS) benign and malignant can be differentiated using its shape, size and density, which is how radiologist visualize the mammograms. Measuring regular and irregular shapes mathematically is found to be a difficult task, since there is no single measure to differentiate various shapes. Various shape and margin features of masses are extracted, which are effective in differentiating regular from irregular polygon shapes. It has been found that not that all the features are equally important in classifying the masses. In our experiment, we have used Digital Database for Screening Mammography database (DDSM) database and the classification accuracy obtained for WLAT selected features is better than Principal Component Analysis (PCA) and Image Factoring dimension reduction methods. The main advantage of proposed WLAT method are i) the features selected can be reused when the database size increases or decreases, without the need of extracting components each and every time; ii) WLAT method considers grouping class variable for finding the important features for dimension reduction.

Image encryption is different from text encryption due to some inherent features of image such as bulk data capacity and high correlation among pixels, which are generally difficult to handle by conventional methods. The desirable cryptographic properties of chaotic maps such as initial conditions and random-like behavior can be used to develop new encryption algorithms. The chaos – based cryptographic algorithms have suggested new ways to develop efficient image encryption schemes. The random-like nature of chaos is effectively spread into encrypted images. The proposed method transforms the statistical characteristic of original image information. So, it increases the difficulty of an unauthorized individual to break the encryption. The proposed symmetric image encryption algorithm provides an effective way for real-time applications and transmission.

One of the main obstacles in capturing semantic of images present in the Web document is that it is difficult to describe the semantics. The high-level textual information of images can be extracted and associated with the textual keywords for narrowing the search space and improving the precision of retrieval. In this paper, we propose a weighted matrix approach for associating keywords and high-level semantics of images to use their complementing strengths. The high-level semantics of the image is described in the HTML documents in the form of image name, optional description and caption. A weighted matrix is constructed using both textual keywords and image information for measuring the association between the keywords and the HTML page. The frequency of occurrence of words can be calculated and used for measuring the relevance of keywords to images. The retrieval performance is compared with various recently proposed techniques and is found to be encouraging.