NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, A- MUHANDISI -I BARQ
Year:2011 | Volume:8 | Issue:4|Papers Count:10

In this paper, a complementary metal–oxide semiconductor (CMOS) based Laboratory-on-Chip platform is presented for bacteria growth monitoring. This platform integrates a 0.18 μm CMOS chip with two microfluidic channels. The proposed CMOS chip manufactured by Taiwan Semiconductor Manufacturing Company (TSMC) features a differential capacitive sensor along with two reference and sensing interdigitized electrodes. Two microfluidic channels are thereafter implemented atop the electrodes through a direct-write assembly technique. These microchannels are filled with pure Luria-Bertani (LB) medium and Escherichia Coli (E. Coli) bacteria suspended in the LB medium, respectively. We demonstrate and discuss the experimental results by using two different bacteria concentrations in the order of 106 and 107 per 1 mL in the LB medium.

The images of steel surfaces are generally textural images. There are different texture analysis methods to extract features from these images. In those methods using multi-scale/multi-directional analysis, Gabor filters are used for feature extraction. In this paper, we extract texture features using the optimum Gabor filter bank. This filter bank is designed in a way that diverse filtering frequency and orientation will allow it to extract considerable amounts of texture information from the input images. We also introduce a new method called Gabor composition for segmentation and defect detection of steel surfaces. In this method, using two different algorithms, the input image is decomposed into detail images using an appropriate Gabor filter bank and then selected detail images are re composed. The created feature map illustrates the defective areas well. By calculating data distribution of detail images and comparing them, the second method of Gabor composition can accomplish segmentation without needing the normal images and the number of detail images to re-compose. Furthermore, we did different tests towards optimizing of segmentation by means of classifiers. Using a K-means classifier and adding gray levels to the extracted features, complete the segmentation procedure. The experimental results show that the Gabor composition method in most of the tests has got better defect detection performance than the ordinary K-means classifier and the standard wavelet method; also the Second method of Gabor composition has got the best performance over all.

In recent years, chaotic signals have created a new area in the designation of wideband communication systems. Most of the activity has focused on DS-CDMA systems, in which the conventional pseudo-noise sequences will be replaced by binary chaotic sequences. Unfortunately, despite the advantages of chaotic systems such as aperiodicity, low cost generation and noise-like spectrum, the performance of most of such designs is not still suitable for multiuser wireless channels. In this paper, we propose a novel method based on singular value decomposition for adaptive multiuser detection in chaos-based DS-CDMA systems. We also propose a new genetic algorithm-based method for the optimal generation of chaotic sequences in such systems. Simulation results show that our proposed nonlinear receiver with optimized chaotic sequences outperforms the conventional DS-CDMA systems with "maximal length" codes as well as non-optimized chaos-based DS-CDMA systems in all channel condition, particularly for under-loaded CDMA condition, which the number of active users is less than processing gain.

This article, an efficient system for texture defect detection based on curvelet transform is presented. The main idea is to model the defects in the texture image as one-dimensional discontinuities.Based on this idea, the curvelet transform is the most efficient method for describing defects. First, in the learning phase, training samples of intact and defected blocks of the texture image are collected and transformed to the curvelet domain. Next, for each block a feature vector based on curvelet sub-bands is extracted and using a proposed method some important and effective features are determined for the desired texture. Then, a proper threshold for detecting defected from intact blocks is determined. In the performance phase, a vector containing the important features from each block of the texture is extracted and then the block by is classified. The results of simulation show that the proposed system is superior to the mean shift method in detecting defected texture blocks, and is less sensitive to the type of texture.

training of the network. Error back propagation is the most usual training method of neural networks that late convergence and stopping in local optimum points are its weakness. New approach in neural networks training is the usage of heuristic algorithms. This paper suggests a new learning method namely gravitational search algorithm (GSA) in training of neural network for data classification.GSA method is the latest and the most novel version of swarm intelligence optimization methods. This algorithm is inspired fby the law of Newtonian gravity and mass concept in nature. In this paper, a MLP neural network is trained for classification of five benchmark data set by GSA method.Also, the proposed method efficiency in training and testing of neural network compared with those of two training methods error back propagation and particle swarm optimization. Final results showed the GSA method extraordinary performance for data correct classification in most of cases. Also, in these experiments the GSA method produced stable results in all of cases. In addition, the run time of GSA method is shorter than that of the PSO.

In speaker verification, a system investigates a person's identity and decides whether the person is a true client or an imposter. In this paper, genetic programming (GP) is used as a method for speaker modeling. When GP is used for construction of models for speakers, due to long training time to train GP models, training data compression is proposed in this paper. This idea reduced training time for 20 times. Training of several GP trees as a speaker's model is another idea presented in this paper to improve the speaker verification performance. In this method, training data are separated to a few clusters. Then a GP tree is trained for each cluster. Therefore, a speaker is modeled by several genetic programming trees. The verification performance increased from 50% to about 92% using the proposed method. Genetic programming performance was compared to some other discriminative methods such as Multi-Layer Perceptron neural network and Learning Vector quantization, and generative methods such as K-Means, GMM and LBG, GMM-UBM and VQ-MAP. Experiments show that Genetic programming is more effective than the other methods.

In this paper, robust controllers for a new hyperchaotic system are investigated in the presence of uncertainty, disturbance and nonlinear control inputs. The controllers are designed by considering two major goals: first to stabilize the hyperchaotic system in the presence of uncertainties, disturbance and nonlinear control inputs; and second, to guarantee the prescribed disturbance attenuation, considering the defined performance index for it. Sliding mode control by defining three proportional integral switching surfaces is used to reach mentioned goals. Numerical simulations are used to exhibit the feasibility and performance of the proposed method.