The method for construction adaptive observ-ers (AO) time-varying linear dynamic objects at non-fulfillment of condition excitation constancy (EC) is pro-posed. Synthesis of the adaptive observer is given as the solution of two tasks. The solution first a problem is a choice of the constant matrix decreasing the effect of EC condition. Procedures for obtaining of this matrix are proposed. The matrix specifies restrictions for a vector of parameters AO. The solution of the second problem gives a method of design adaptive multiplicative algorithms in the presence of the obtained restrictions. Procedures for an estimation uncertainty in an object are proposed. They are based on obtaining of static models giving the forecast change of uncertainty. Optimum estimations of the uncertainty are obtained which minimize an error between outputs of the object and AO. An exponential dissipativity of adaptive system is proved. The results of the modeling confirming the effectiveness of designed methods and procedures are presented.

This paper proposes a novel meta-heuristic optimization algorithm inspired by general grass plants fibrous root system, asexual reproduction, and plant development. Grasses search for water and minerals randomly by developing its location, length, primary root, regenerated secondary roots, and small branches of roots called hair roots. The proposed algorithm explore the bounded solution domain globally and locally. Globally using the best grasses survived by the last iteration, and the root system of the best grass obtained so far by the iteration process and locally uses the primary roots, regenerated secondary roots and hair roots of the best global grass. Each grass represents a global candidate solution, while regenerated secondary roots stand for the locally obtained solution. Secondary generated hair roots are equal to the problem dimensions. The performance of the proposed algorithm is tested using seven standard benchmark test functions, comparing it with other meta-heuristic well-known and recently proposed algorithms.

An adaptive neural system which solves a problem of clustering data with missing values in an online mode with a permanent correction of restorable table elements and clusters’ centroids is proposed in this article. The introduced neural system is characterized by both a high speed and a simple numerical implementation. It can process information in a real-time mode.

Most of the models projected in the literature on Emotion Recognition aims at recognizing the emotions from the mobilized persons in noise free environment and is subjected to the emotion recognition of an individual using a single word for testing and training. Literature available to identify the emotions in case of immobilized persons is confined to the results available from the machines only. In this process brain-computer interaction is utilized using neuro-scan machines like Encephalography (EEG), to identify the emotions of immobilized individuals. It uses the physiological signals available from EEG data extracted from the brain signals of immobilized persons and tries to determine the emotions, but these results vary from machine to machine, and there exists no standardization process which can identify the feelings of the brain diseased persons accurately. In this paper a novel method is proposed, Doubly Truncated Gaussian Mixture Model (DT-GMM) to have a complete emotion recognition system which can identify emotions exactly in a noisy environment from both the healthy individuals and sick persons. The results of the proposed system surpassed the accuracy rates of traditional systems.

To detect faults or errors for designing the quality software, software testing tool is used. Testing manually is an expensive and time taking process. To overcome this problem automated testing is used. Test case generation is a vital concept used in software testing which can be derived from requirements specification. Automation of test cases is a method where it can generate the test cases and test data automatically by using search based optimization technique. Model-driven testing is an approach that represents the behavioral model and also encodes the system behavior with certain conditions. Generally, the model consists of a set of objects that defined through variables and object relationships. This piece of work is used to generate the automated optimized test cases or test data with the possible test paths from combinational system graph. A hybrid bee colony algorithm is proposed in this paper for generating and optimizing the test cases from combinational UML diagrams.

Batik is a textile with motifs of Indonesian culture which has been recognized by UNESCO as world cultural heritage. Batik has many motifs which are classified in various classes of batik. This study aims to combine the features of texture and the feature of shapes’ ornament in batik to classify images using artificial neural networks. The value of texture features of images in batik is extracted using a gray level co-occurrence matrices (GLCM) which include Angular Second Moment (ASM) / energy), contrast, correlation, and inverse different moment (IDM). The value of shape features is extracted using a binary morphological operation which includes compactness, eccentricity, rectangularity and solidity. At this phase of the training and testing, we compare the value of a classification accuracy of neural networks in each class in batik with their texture features, their shape, and the combination of texture and shape features. From the three features used in the classification of batik image with artificial neural networks, it was obtained that shape feature has the lowest accuracy rate of 80.95% and the combination of texture and shape features produces a greater value of accuracy by 90.48%. The results obtained in this study indicate that there is an increase in accuracy of batik image classification using the artificial neural network with the combination of texture and shape features in batik image.

In the previous years, wireless sensor networks (WSNs) got lot of attraction from the scientific and industrial society. WSNs are composed of huge number of small resource constrained devices recognized as sensors. Energy is a vital issue in WSN. Energy efficient clustering is an eminent optimization problem which has been studied extensively to prolong the lifetime of the network. This paper demonstrates the programming formulation of this problem followed by a proposed algorithm with particle swarm optimization (PSO) approach. The clustering method is stated by taking into consideration of energy saving of nodes. The proposed algorithm is experimented widely and results are evaluated with existing methods to show their supremacy in term of alive nodes, energy expenditure, packet delivery ratio, and throughput of network. Simulation results shows that our proposed algorithm outperform the other existing algorithms of its category.

The essence of the technology business lies in the improvements and advancements that are continuously taking place in the industry. From vacuum tubes, diodes and transistors to the concepts of nano level designing have by and large created a revolution in the history of mankind. The biggest milestone in this journey has been the CMOS technology which has managed to survive for decades and is still an ongoing research area. However, advancing the technology includes many other dimensions which need to be taken care of. As the devices go on decreasing in size with the improving technology the power dissipation in them becomes a major issue. To counter this, a new logic called reversible logic has come into the pool of research. Further a shift from the transistor based paradigm is being explored to go down to ultra-small structures. A major breakthrough in this can be the Quantum Dot Cellular Automata (QCA) Nanotechnology. In this paper we have given a review about how the reversible logic and QCA nanotechnology together result in ultra-low power designs. Further we have optimized the design of Peres reversible gate using the concepts of explicit interaction of cells in QCA and verified the universal functionality using the optimized designs.