Relaxed constraints support vector machines for noisy data

Real-world data collected for computer-based applications are frequently impure. Differentiation of outliers and noisy data from normal ones is a major task in data mining applications. On the other hand, elimination of noisy and outlier data from training samples of a dataset may lead to over-fitting or information loss. A fuzzy support vector machine (FSVM) provides an effective means to deal with this problem. It reduces the effect of the noisy data and outliers by using a fuzzy membership functions. In this paper, a new formation for SVMs is introduced that considers importance degrees for training samples. The constraints of the SVM are converted to fuzzy inequalities. The proposed method, RSVM, shows better efficiency in the classification of data in different domains. Especially, using the proposed RSVM for multi-class classification of arrhythmia disease is presented at the end of this paper as a practical case study to show the effectiveness of the proposed system.     

Prediction of phosphorus content of electroless nickel–phosphorous coatings using artificial neural network modeling

A multilayer feedforward neural network with two hidden layers was designed and developed for prediction of the phosphorus content of electroless Ni–P coatings. The input parameters of the network were the pH, metal turnover, and loading of an electroless bath. The output parameter was the phosphorus content of the electroless Ni–P coatings. The temperature and molar rate of the bath were constant ( 91^° \textC, 0.4 \textNi^{\text + +} /\textH₂ \textPO₂ ^{- -} 91^\circ {\text{C}},\:0.4\,{\text{Ni}}^{{{\text{ + + }}}} /{\text{H}}_{2} {\text{PO}}_{2}^{{ - - }} ). The network was trained and tested using the data gathered from our own experiments. The goal of the study was to estimate the accuracy of this type of neural network in prediction of the phosphorus content. The study result shows that this type of network has high accuracy even when the number of hidden neurons is very low. Some comparison between the network’s predictions and own experimental data are given.     

Video transmission enhancement in presence of misbehaving nodes in MANETs

Mobile Ad-hoc NETworks (MANET) are infrastructureless networks where self-configuring mobile nodes are connected by wireless links. Because of its decentralized operation, these nodes rely on each other to store and forward packets. Video transmission over MANETs is more challenging than over conventional wireless networks due to rapid topology changes and lack of central administration. Most of the proposed MANET protocols assume that all nodes are working within a cooperative and friendly network context. However, misbehaving nodes that exhibit abnormal behaviors can disrupt the network operation and affect the network availability by refusing to cooperate to route packets due to their selfish or malicious behavior. In this paper, we examine the effect of packet dropping attacks on video transmission over MANETs. We also study the effects of mitigation using intrusion detection systems to MANET in presence of video traffic. To the best of our knowledge, this is the first attempt to study multimedia over such environments. We propose a novel intrusion detection system, which is an adaptive acknowledgment scheme (AACK) with the ability to detect misbehaved nodes and avoid them in other transmissions. The aim of AACK scheme is to overcome watchdog weaknesses due to collisions and limited transmission power and also to improve TWOACK scheme. To demonstrate the performance of our proposed scheme, simulation experiments are performed. The results of our experiments show that MPEG4 is more suitable for our simulation environment than H264 video traffic. The simulation results show that AACK scheme provides better network performance with less overhead than other schemes; it also shows that AACK outperforms both TWOACK and watchdog in video transmission applications in the presence of misbehaving nodes.     

User Activities Outliers Detection; Integration of Statistical and Computational Intelligence Techniques

In this article, a hybrid technique for user activities outliers detection is introduced. The hybrid technique consists of a two‐stage integration of principal component analysis and fuzzy rule‐based systems. In the first stage, the Hamming distance is used to measure the differences between different activities. Principal component analysis is then applied to the distance measures to find two indices of Hotelling's T² and squared prediction error. In the second stage of the process, the calculated indices are provided as inputs to the fuzzy rule‐based systems to model them heuristically. The model is used to identify the outliers and classify them. The proposed system is tested in real home environments, equipped with appropriate sensory devices, to identify outliers in the activities of daily living of the user. Three case studies are reported to demonstrate the effectiveness of the proposed system. The proposed system successfully identifies the outliers in activities distinguishing between the normal and abnormal behavioral patterns.     

Energy Consumption Optimization for Mobile Robots Motion Using Predictive Control

Operation of mobile robots in off-road environment requires the attention to the torque saturation problem that occurs in the wheels DC motors while climbing hills. In the present work, off-road conditions are utilized to benefit while avoiding torque saturation. Energy optimization algorithm using predictive control is implemented on a two-DC motor-driven wheels mobile robot while crossing a ditch. The predictive control algorithm is simulated and compared with the PID control and the open-loop control. Predictive control showed more capability to avoid torque saturation and noticeable reduction in the energy consumption. Furthermore, using the wheels motors armature current instead of the supply voltage as control variable in the predictive control showed more efficient speed control. Simulation results showed that in case of known ditch dimensions ahead of time, the developed algorithm is feasible. Experimental examination of the developed energy optimization algorithm is presented. The experimental results showed a good agreement with the simulation results. The effects of the road slope and the prediction horizon length on the consumed energy are evaluated. The analytical study showed that the energy consumption is reduced by increasing the prediction horizon until it reaches a limit at which no more energy reduction is obtained. This limit is proportional to the width of the ditch in front of the mobile robot. Curve fitting is applied to the obtained results to address further the effect of the parameters on the energy consumption.     

Adaptive synchronization between two non-identical BAM neural networks with unknown parameters and time-varying delays

International Journal of Control, Automation and Systems - In this paper, the synchronization of two non-identical bidirectional associative memory (BAM) neural networks with unknown parameters and...     

Reconfigurable computing: design methodology and hardware tasks scheduling for real-time image processing

Technology evolution makes possible the integration of heterogeneous components as programmable elements (processors), hardware dedicated blocks, hierarchical memories and buses. Furthermore, an optimized reconfigurable logic core embedded within a System-on-Chip will associate the performances of dedicated architecture and the flexibility of programmable ones. In order to increase performances, some of the applications are carried out in hardware, using dynamically reconfigurable logic, rather than software, using programmable elements. This approach offers a suitable hardware support to design malleable systems able to adapt themselves to a specific application. This article makes a synthesis of the Ardoise project. The first objective of Ardoise project was to design and to produce a dynamically reconfigurable platform based on commercial FPGAs. The concept of dynamically reconfigurable architecture depends partially on new design methodologies elaboration as well as on the programming environment. The platform architecture was designed to be suitable for real-time image processing. The article outlines mainly the Ardoise tools aspect: development environment and real-time management of the hardware tasks. The proposed methodology is based on a dynamic management of tasks according to an application scenario written using C++ language.

Performance evaluation of TCP connections in ideal and non-ideal network environments

In this paper, we study the performance of TCP in both ideal and non-ideal network environments. For the ideal environments, we develop a simple analytical model for the throughput and transfer time of TCP as a function of the file size and TCP parameters. Our simulation measurements demonstrate that this model can accurately predict the throughput for ideal TCP connections characterized by no packet loss due to congestion or bit errors. If these ideal conditions are not met, the model gives an upper bound for throughput and lower bound for transfer time. For the non-ideal environments, we concentrate on wireless links. While our ideal model provides an easy to use tool to calculate bounds on the performance of all TCP implementations in such environments, we also show through simulation the relative performance of four well-known TCP implementations. We also present simulation results that demonstrate the dominant factors affecting the performance of wireless TCP.     

Selection of regression models for predicting strength and deformability properties of rocks using GA

Recently, many regression models have been presented for prediction of mechanical parameters of rocks regarding to rock index properties. Although statistical analysis is a common method for developing regression models, but still selection of suitable transformation of the independent variables in a regression model is difficult. In this paper, a genetic algorithm (GA) has been employed as a heuristic search method for selection of best transformation of the independent variables (some index properties of rocks) in regression models for prediction of uniaxial compressive strength (UCS) and modulus of elasticity (E). Firstly, multiple linear regression (MLR) analysis was performed on a data set to establish predictive models. Then, two GA models were developed in which root mean squared error (RMSE) was defined as fitness function. Results have shown that GA models are more precise than MLR models and are able to explain the relation between the intrinsic strength/elasticity properties and index properties of rocks by simple formulation and accepted accuracy.     

Four degree of freedom liquid dispenser for direct write capillary self-assembly with sub-nanoliter precision

Capillary forces provide a ubiquitous means of organizing micro- and nanoscale structures on substrates. In order to investigate the mechanism of capillary self-assembly and to fabricate complex ordered structures, precise control of the meniscus shape is needed. We present a precision instrument that enables deposition of liquid droplets spanning from 2 nl to 300 μl, in concert with mechanical manipulation of the liquid-substrate interface with four degrees of freedom. The substrate has sub-100 nm positioning resolution in three axes of translation, and its temperature is controlled using thermoelectric modules. The capillary tip can rotate about the vertical axis while simultaneously dispensing liquid onto the substrate. Liquid is displaced using a custom bidirectional diaphragm pump, in which an elastic membrane is hydraulically actuated by a stainless steel syringe. The syringe is driven by a piezoelectric actuator, enabling nanoliter volume and rate control. A quantitative model of the liquid dispenser is verified experimentally, and suggests that compressibility in the hydraulic line deamplifies the syringe stroke, enabling sub-nanoliter resolution control of liquid displacement at the capillary tip. We use this system to contact-print water and oil droplets by mechanical manipulation of a liquid bridge between the capillary and the substrate. Finally, we study the effect of droplet volume and substrate temperature on the evaporative self-assembly of monodisperse polymer microspheres from sessile droplets, and demonstrate the formation of 3D chiral assemblies of micro-rods by rotation of the capillary tip during evaporative assembly.