FPGA implementation and performance evaluation of a high throughput crypto coprocessor

This paper describes the FPGA implementation of FastCrypto, which extends a general-purpose processor with a crypto coprocessor for encrypting/decrypting data. Moreover, it studies the trade-offs between FastCrypto performance and design parameters, including the number of stages per round, the number of parallel Advance Encryption Standard (AES) pipelines, and the size of the queues. Besides, it shows the effect of memory latency on the FastCrypto performance. FastCrypto is implemented with VHDL programming language on Xilinx Virtex V FPGA. A throughput of 222 Gb/s at 444 MHz can be achieved on four parallel AES pipelines. To reduce the power consumption, the frequency of four parallel AES pipelines is reduced to 100 MHz while the other components are running at 400 MHz. In this case, our results show a FastCrypto performance of 61.725 bits per clock cycle (b/cc) when 128-bit single-port L2 cache memory is used. However, increasing the memory bus width to 256-bit or using 128-bit dual-port memory, improves the performance to 112.5 b/cc (45 Gb/s at 400 MHz), which represents 88% of the ideal performance (128 b/cc).     

A new homotopy perturbation method for solving systems of partial differential equations

In this paper, a new homotopy perturbation method (NHPM) is introduced for obtaining solutions of systems of non-linear partial differential equations. Theoretical considerations are discussed. To illustrate the capability and reliability of the method three examples are provided. Comparison of the results of applying NHPM with those of applying HPM reveal the effectiveness and convenience of the new technique.     

Recognition of Arabic (Indian) bank check digits using log-gabor filters

In this paper we present a technique for the automatic recognition of Arabic (Indian) bank check digits based on features extracted by using the Log Gabor filters. The digits are classified by using the K-Nearest Neighbor (K-NN), Hidden Markov Models (HMM) and Support Vector Machines (SVM) classifiers. An extensive experimentation is conducted on the CENPARMI data, a database consisting of 7390 samples of Arabic (Indian) digits for training and 3035 samples for testing extracted from real bank checks. The data is normalized to a height of 64 pixels, maintaining the aspect ratio. Log Gabor filters with several scales and orientations are used. In addition, the filtered images are segmented into different region sizes for feature extraction. Recognition rates of 98.95%, 98.75%, 98.62%, 97.21% and 94.43% are achieved with SVM, 1-NN, 3-NN, HMM and NM classifiers, respectively. These results significantly outperform published work using the same database. The misclassified digits are evaluated subjectively and results indicate that human subjects misclassified 1/3 of these digits. The experimental results, including the subjective evaluation of misclassified digits, indicate the effectiveness of the selected Log Gabor filters parameters, the implemented image segmentation technique, and extracted features for practical recognition of Arabic (Indian) digits.     

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.     

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.

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.