Vinay Kumar Deolia

Backstepping Control of Discrete-Time Nonlinear System Under Unknown Dead-zone Constraint

This paper proposes the adaptive back stepping controller for a class of nonlinear discrete-time systems in strict-feedback form with unknown dead-zone using neural networks. The control design is attained by introducing the dead-zone nonlinearity and using it in the controller design with back stepping technique. A dead-zone inverse is developed to compensate the dead-zone effect in nonlinear systems. In this scheme, Chebyshev Neural Network (CNN) is used to approximate the unknown nonlinear functions and also used to compensate the dead-zone nonlinearity. New weight updates laws are derived to guarantee uniform ultimate boundedness (UUB) for all signals in closed loop system.

Digital Image Watermarking Using Least Significant Bit Technique in Different Bit Positions

Watermarking is a method to protect the data and to authenticate the digital content. Watermarking is required due to the emergence of usage of internet in ones day to day life. As the usage of digital content is growing rapidly, there are many instances where data is insecure. Watermarking is a process to hide data for authorization purpose. Watermarking is the best way to secure the digital content. Watermarking can be done by various methods. Least Significant Bit Watermarking (LSBW) method is one of them. In this method, the pixel values of the image are converted in to binary and the information is concealed in the bits of the pixel values. This paper presents and compares the LSBW method using different bit positions. Comparison for these bit positions is done on the basis of various parameters like Mean Square Error, Peak Signal to Noise Ratio and Normalized Cross Correlation. These parameters are evaluated for various attacks like Gaussian Noise, Poisson Noise, Salt & Pepper Noise and Speckle Noise.

Comparative Analysis of Digital Watermarking Techniques

In this paper various techniques used for digital watermarking such as least significant bit (LSB) technique, discrete cosine transform (DCT), discrete wavelet transform (DWT), and back propagation neural network (BPN) algorithm have been compared. These techniques are used to embed and extract a watermark of an image. The performance of these algorithms is evaluated using various parameters such as mean square error, peak signal-to-noise ratio (PSNR), and normalized correlation (NC). Parameters for each technique are compared for various noises like Gaussian noise, Poisson noise, salt-and-pepper noise, and speckle noise. Based on comparison it is suggested that BPN gives better result in terms of PSNR and NC.

Performance Boosting in IDMA System Using Helical Interleaver and Gold Codes

In Interleave Division Multiple Access (IDMA) scheme, interleavers have significant importance in the performance of whole system. In recent years interleavers have been proved to give the best performance in terms of data security and noise immunity, but huge memory requirement and complexity are major concerns for specifying the interleaver pattern. A new helical interleaver based IDMA system has already proposed due to some distinct advantages such as low system complexity and significant reduction in memory requirement as well. In this paper the performance of layer specific helically interleaved IDMA (HIDMA) system has been compared to randomly interleave and tree based IDMA with BPSK coding and with Gold Codes. Evaluation is done on the basis of mathematics and results are simulated in MATLAB.

Performance analysis of modified tent map interleaver in IDMA systems

Abstract In the last years, Interleave Division Multiple Access (IDMA) has been presented as a potential alternate of Code Division Multiple Access (CDMA) system. In IDMA systems, the interleavers are used to separate the users of the system in multiple access environments. Random interleaver is popular and basic taxonomy, which scrambles information bits of craving users with different patterns. However the indispensable characteristics of a random interleaver such as bandwidth requirement, computational complexity, and memory restraint at both transmitter and receiver end is uttermost. Further, it has also been observed that the study of role of chaos in interleaver design is very limited in literature. Hence, in this paper, a low complexity chaos based interleaver named as modified Tent map interleaver is designed for further performance improvement of IDMA system and the characteristic parameters are compared with the random interleaver. The IDMA system model uses a BPSK modulation and repetition coder with a code rate of 1/2. The system is simulated in MATLAB and results show that the better BER performance without the need of extra memory resources.

A Discrete-Time Sliding Mode Controller for Time-Delay Linear Time-Invariant Systems with Uncertainty

This paper presents the Linear time invariant system with discrete time sliding mode control with modified switching function. The method is capable of handling both the systems with delayed states and control input delay. The simulation results are demonstrated with better asymptotic convergence in presence of static disturbance and robustness is justified.

Control of Discrete-Time Nonlinear Systems Using Backstepping Technique in the Presence of Saturation and Dead-Zone Constraints

This paper proposes the work on the back stepping controller for the class of Discrete-time nonlinear system under amplitude & rate saturation, dead-zone constraints. A robust adaptive neural network (NN) control is developed for a general class of uncertain single-input-single output (SISO) discrete time nonlinear system with unknown system dynamics and input nonlinear ties. Here ( ) function is employed to saturate the amplitude and rate of system input. For input nonlinear ties like saturation and dead zone, discrete time SISO nonlinear system in combination with back stepping and Lyapunov synthesis is proposed for adaptive NN design with guaranteed stability. To reduce the effect of dead-zone a dead-zone compensator or dead-zone inverse is designed. Chebyshev Neural Networks (CNNs) are used to approximate the unknown nonlinear functions in system dynamics. New weight update laws are derived to make this scheme adaptive and show the stability of this scheme. Finally simulation results are presented in this paper to show the effectiveness of proposed scheme.

An LMI Approach for the Stabilization of Discrete-Time Delay Nonlinear Systems Using Backstepping

In this paper, an-LMI approach is utilized for the stabilization of Discrete-Time Delay Nonlinear Systems (DTDNS). The delay provided is state-delay. The control criterion is derived in terms of linear matrix inequalities (LMIs). The controller is designed by making use of back stepping technique. By choosing an appropriate Lyapunov function, some mathematical manipulations have been carried out to deduce simple LMI condition for asymptotic stabilization. The effectiveness and the applicability of the proposed control law is verified through simulation.

Cooperative relay beamforming in IDMA communication networks

Abstract In this paper, a new combination of Interleave division multiple access (IDMA) and spatial diversity offered by cooperative relay aided distributed beam forming is proposed. In the offered scheme communication strategy consists two steps. All users broadcast their message to relays in the first step and then relays amplifies and forward the information to the desired destination. IDMA, which is popular non-orthogonal multiple access (NOMA) technique is used to combat the effect of multiple access interference (MAI) at relay as well as destination nodes. Each relay processed the signal to maintain the QoS of destination. The goal of this work is to find the appropriate beam forming weights by minimising the transmit power and without compromising the QoS in terms of SINR. However power minimization is not the convex problem, so semi-definite relaxation is used to modify the problem in to semi-definite programming (SDP) problem and the conventional SDP problem solver CVX is used for solution. The numerical explanation and simulation experiment of the proposed scheme shows the performance improvements in terms of bit error rate.

Sliding Mode Control of Uncertain Nonlinear Discrete Delayed Time System Using Chebyshev Neural Network

This paper investigates a Chebyshev Neural Network (CNN) sliding mode controller for stabilization of time-delayed version of system with uncertainty and nonlinearity. The nonlinearity in the system is unknown but bounded and has been approximated with the help of CNN. The input delay has been balanced and further converted into regular form and the original system is converted into a delayed free version with the help of Smith Predictor. Now, the predicted states of the system and “Gao’s reaching law” are used to derive the robust control law. Further, to prove the stability analysis Lyapunov–Krasovskii candidates has been chosen according to the proposed system. A numerical example is provided to illustrate the stability of the system in the presence of uncertainty, time delay and nonlinearity.