The structure of the technical accident prevention subsystem for the smart home system has been developed in the article. The subsystem model based on Petri network, model based on neural network and physical model using the Arduino microcontroller have been realized in the development process. The subsystem research results with the use of the developed models, soft- and hardware tools are also presented.

The DNA microarray technology enables the biologists to observe the expressions of multiple thousands of genes in parallel fashion. However, processing and gaining knowledge from the voluminous microarray gene data is serious issue. It is necessary for the biologists to retrieve the required data in a reasonable time. In order to address this issue, this work presents a gene retrieval system, which is based on feature dimensionality minimization and classification of the microarray gene data. The feature dimensionality minimization is achieved by Local Fisher Discriminant Analysis (LFDA), which inherits the merits of both Fisher Discriminant Analysis (FDA) and Locality Preserving Projection (LPP). Support Vector Machine (SVM) is employed as the classifier to classify between the genes. The LFDA is chosen for reducing the dimensionality of the features, owing to its better performance on multimodal data. The SVM is trained with the feature dimensionality reduced microarray gene data, which improves the efficiency and overthrows the computational complexity. The performance of the proposed approach is compared with the LPP and FDA. Additionally, the performance of SVM is compared with the k-Nearest Neighbour (k-NN) classifier. The combination of LFDA and SVM serves better in terms of accuracy, sensitivity and specificity.

Time Series data is large in volume, highly dimensional and continuous updating. Time series data analysis for forecasting, is one of the most important aspects of the practical usage. Accurate rainfall forecasting with the help of time series data analysis will help in evaluating drought and flooding situations in advance. In this paper, Artificial Neural Network (ANN) technique has been used to develop one-month and two-month ahead forecasting models for rainfall prediction using monthly rainfall data of Northern India. In these model, Feed Forward Neural Network (FFNN) using Back Propagation Algorithm and Levenberg- Marquardt training function has been used. The performance of both the models has been assessed based on Regression Analysis, Mean Square Error (MSE) and Magnitude of Relative Error (MRE). Proposed ANN model showed optimistic results for both the models for forecasting and found one month ahead forecasting model perform better than two months ahead forecasting model. This paper also gives some future directions for rainfall prediction and time series data analysis research.

The forced oscillations of the damping mechanical system of solids "Ball Vibration Absorber (BVA) with linearly viscous resistance – a movable carrier body" under the influence of external harmonic excitation are considered. Based on Appell's formalism, the dynamic equations for the joint motion of a heavy ball without sliding into a spherical cavity of a carrier body are formulated and numerically studied. The amplitude-frequency characteristic of the damping mechanical system and the curves of the dependences of the maximum amplitude of the oscillations of the carrier body on the values of the radius of the spherical cavity and the coefficient of viscous resistance of the BVA are obtained. The conditions and restrictions on the rolling of a heavy ball in the spherical recess of the absorber without sliding are determined.

Mutation testing is a structural testing technique in which the effectiveness of a test suite is measured by the suite ability to detect seeded faults. One fault is seeded into a copy of the program, called mutant, leading to a large number of mutants with a high cost of compiling and running the test suite against the mutants. Moreover, many of the mutants produce the same output as the original program (called equivalent mutants), such mutants need to be minimized to produce accurate results. Higher order mutation testing aims at solving these problems by allowing more than one fault to be seeded in the mutant. Recent work in higher order mutation show promising result in reducing the cost of mutation testing and increasing the approach effectiveness. In this paper, we present an approach for generating higher order mutants using a genetic algorithm. The aim of the proposed approach is to produce subtle and harder to kill mutants, and reduce the percentage of produced equivalent mutants. A Java tool has been developed, called HOMJava (Higher Order Mutation for Java), which implements the proposed approach. An experimental study was performed to evaluate the effectiveness of the proposed approach. The results show that the approach was able to produce subtle higher order mutants, the fitness of mutants improved by almost 99% compared with the first order mutants used in the experiment. The percentage of produced equivalent mutants was about 4%.

Mobility management is one of the most important challenges in Next Generation Wireless Networks (NGWNs) as it enables users to move across geographic boundaries of wireless networks. Nowadays, mobile communications has heterogeneous wireless networks offering variable coverage and Quality of Service (QoS). The availability of alternatives generates a problem of occurrence of unnecessary handoff that results in wastage of network resources. To avoid this, an efficient algorithm needs to be developed to minimize the unnecessary handoffs. Conventionally, whenever a Wireless Local Area Network (WLAN) connectivity is available, the mobile node switch from the cellular network to wireless local area network to gain maximum use of high bandwidth and low cost of wireless local area network as much as possible. But to maintain call quality and minimum number of call failure, a considerable proportion of these handovers should be determined. Our algorithm makes the handoff to wireless local area network only when the Predicted Received Signal Strength (PRSS) falls below a threshold value and travelling distance inside the wireless local area networkis larger than a threshold distance. Through MATLAB simulation, we show that our algorithm minimizes the probability of unnecessary handoff, and probability of handoff failure. Hence, the proposed algorithm is able to improve handover performance.

This paper illustrates the design and performance evaluation of few algorithms used for analysing the medical image volumes on the massive parallel graphics processing unit (GPU) with compute unified device architecture (CUDA). These algorithms are selected from the general framework, devised for computer aided diagnostic (CAD) system. The CAD system used for analysing large medical image datasets are usually a pipeline processing that includes a variety of image processing operations. A MRI scanner captures the 3D human head into a series of 2D images. Considerable time spent in pre and post processing of these images. Noise filters, segmentation, image diffusion and enhancement are few such methods. The algorithms are chosen for study requires local information, available in few pixels or global information available in the entire image. These problems are best candidates for GPU implementation, since the parallelism is naturally provided by the proposed Per-Pixel Threading (PPT) or Per-Slice Threading (PST) operations. In this paper implement the algorithms for adaptive filtering, anisotropic diffusion, bilateral filtering, non-local means (NLM) filtering, K-Means segmentation and feature extraction in 1536 core’s NVIDIA GPU and estimated the speed up gained. Our experiments show that the GPU based implementation achieved typical speedup values in the range of 3-338 times compared to conventional central processing unit (CPU) processor in PPT model and up to 30 times in PST model.

The messages routing in a DTN network is a complicated challenge, due on the one hand of intermittent connection between the nodes, the lack of the end-to-end path between source / destination and on the other hand, the constraints related to the capacity of the buffer and the battery. To ensure messages delivery in such an environment, the proposed routing protocols use multiple copies of each message in order to increase the delivery ratio. Most of these routing protocols do not take into account the remaining energy of nodes and the history on the relays that have already received a copy of the message in order to select the nodes that will participate in the message routing. This paper proposes a new approach named EERPFAnt inspired by the ant colony intelligence and improved by the fuzzy logic technique to select the best relay by combining the energy level of the nodes, as well as the information on the relay that have already received a copy of the message to estimate intelligently, the energy level of the nodes at the time of encounter with the desired destination. Simulation results will show that the proposed approach performances are better than those of Epidemic routing protocols, Spray and Wait and ProPHET.