Davud Asemani

Surface defect detection in tiling Industries using digital image processing methods: analysis and evaluation.

Ceramic and tile industries should indispensably include a grading stage to quantify the quality of products. Actually, human control systems are often used for grading purposes. An automatic grading system is essential to enhance the quality control and marketing of the products. Since there generally exist six different types of defects originating from various stages of tile manufacturing lines with distinct textures and morphologies, many image processing techniques have been proposed for defect detection. In this paper, a survey has been made on the pattern recognition and image processing algorithms which have been used to detect surface defects. Each method appears to be limited for detecting some subgroup of defects. The detection techniques may be divided into three main groups: statistical pattern recognition, feature vector extraction and texture/image classification. The methods such as wavelet transform, filtering, morphology and contourlet transform are more effective for pre-processing tasks. Others including statistical methods, neural networks and model-based algorithms can be applied to extract the surface defects. Although, statistical methods are often appropriate for identification of large defects such as Spots, but techniques such as wavelet processing provide an acceptable response for detection of small defects such as Pinhole. A thorough survey is made in this paper on the existing algorithms in each subgroup. Also, the evaluation parameters are discussed including supervised and unsupervised parameters. Using various performance parameters, different defect detection algorithms are compared and evaluated.

A robust adaptive algorithm of moving object detection for video surveillance

In visual surveillance of both humans and vehicles, a video stream is processed to characterize the events of interest through the detection of moving objects in each frame. The majority of errors in higher-level tasks such as tracking are often due to false detection. In this paper, a novel method is introduced for the detection of moving objects in surveillance applications which combines adaptive filtering technique with the Bayesian change detection algorithm. In proposed method, an adaptive structure firstly detects the edges of motion objects. Then, Bayesian algorithm corrects the shape of detected objects. The proposed method exhibits considerable robustness against noise, shadows, illumination changes, and repeated motions in the background compared to earlier works. In the proposed algorithm, no prior information about foreground and background is required and the motion detection is performed in an adaptive scheme. Besides, it is shown that the proposed algorithm is computationally efficient so that it can be easily implemented for online surveillance systems as well as similar applications.

A TUNABLE MULTIBAND CMOS LNA FOR MOBILE WIMAX WITH HIGH LINEARITY

In this paper, a multiband low-noise amplifier (LNA) with capability of band tuning is proposed to support the Mobile WiMAX (IEEE 802.16e) standard associated with a large IIP3 at RF frequencies us...

A tunable multi-band CMOS LNA for mobile WiMAX

In this paper, we present the design and implementation of a tunable multi-band LNA for the mobile WiMAX (802.16e) standard using 0.13μm CMOS process. The target frequency bands include 2.5~2.9GHz, 3.4~3.6GHz, and 5.2~5.9GHz. The proposed LNA uses coupling capacitors to select frequency band. This technique leads to an improvement in linearity and frequency dependency of linearity as well as to a reduction in chip area through eliminating additional band tuning inductors. The simulated IIP3 for this LNA is +2.2 dBm, +3.2 dBm and +1.43 dBm at the lower, middle and upper bands respectively. It provides a power gain of 10 dB, a noise figure below 2 dB and S11 of 10.5 dB at all three bands. The proposed tunable multiband LNA dissipates 7.5 mW considering a power supply of 1.2 V.

OPTIMIZATION OF MDL-BASED WAVELET DENOISING FOR FMRI DATA ANALYSIS

Denoising is an important preprocessing step to remove the signal noise with minimum effect on informative part. Wavelet transform is usually used for denoising through some criteria such as Minimum Description Length (MDL) which provides a suitable thresholding value for denoising. In this paper, the wavelet denoising via MDL is optimized in terms of wavelet function, decomposition level and noise type for HRF estimation as well as activation detection in vision region of task-based fMRI data. Simulations show that the MDL-based denoising performance is independent from the noise type for both Refined- and Crude- MDLs. According to simulations, it is necessary to select a scaling function being the most similar to Hemodynamic Response Function (HRF) involved in the experimental fMRI data. Besides, R-MDL can lead to optimum denoising at lower decomposition level compared to C-MDL. Applying MDL-based denoising to fMRI data as a preprocessing step, a larger set of activated voxels for vision tasks has been obtained which appear to be more realistic in comparison to earlier works.

Research article: Modeling of tumor growth in dendritic cell-based immunotherapy using artificial neural networks

Exposure-response modeling and simulation is especially useful in oncology as it permits to predict and design un-experimented clinical trials as well as dose selection. Dendritic cells (DC) are the most effective immune cells in the regulation of immune system. To activate immune system, DCs may be matured by many factors like bacterial CpG-DNA, Lipopolysaccharaide (LPS) and other microbial products. In this paper, a model based on artificial neural network (ANN) is presented for analyzing the dynamics of antitumor vaccines using empirical data obtained from the experimentations of different groups of mice treated with DCs matured by bacterial CpG-DNA, LPS and whole lysate of a Gram-positive bacteria Listeria monocytogenes. Also, tumor lysate was added to DCs followed by addition of maturation factors. Simulations show that the proposed model can interpret the important features of empirical data. Owing to the nonlinearity properties, the proposed ANN model has been able not only to describe the contradictory empirical results, but also to predict new vaccination patterns for controlling the tumor growth. For example, the proposed model predicts an exponentially increasing pattern of CpG-matured DC to be effective in suppressing the tumor growth.

An efficient adaptive algorithm for motion detection

Detecting moving objects in video sequences is a vital task in many computer vision applications. Many different algorithms have been proposed to detect moving objects in successive frames. Gaussian Mixture Model (GMM) is a well-known algorithm that is robust against repetitive motions, illumination changes and long-term scene changes. Adaptive Noise Cancelation (ANC) is another algorithm that has significant robustness against shadow, noise, lighting changes, etc. In this paper, a background is made for each frame by GMM method that is used instead of previous frame in ANC algorithm. This background is much similar to the real background than previous frame is used by ANC. Simulation results show that proposed algorithm detects motions much efficiently than other algorithms.

Design and implementation of digital image processing techniques in pulse-domain

Image pulse sensors provide pixels information with a series of pulse train considering Pulse Frequency Modulation (PFM). PFM sensors are often used in vision chips considering related advantages. In this paper, edge detection and contrast enhancement algorithms are analyzed and simulated using pulse domain techniques of suppression and promotion. Comparing with classical methods, pulse-domain-based algorithms show a better performance as well as simpler implementation in circuit level. Designing and implementing the pulse-domain algorithms on FPGA, a simple and fast realization of image processing methods for edge detection and contrast enhancement is presented. The integration of proposed design in cameras is discussed and evaluated in terms of circuit complexity as well as computational load.

Effects of ageing and Alzheimer disease on haemodynamic response function: a challenge for event-related fMRI

Functional magnetic resonance imaging (fMRI) can generate brain images that show neuronal activity due to sensory, cognitive or motor tasks. Haemodynamic response function (HRF) may be considered as a biomarker to discriminate the Alzheimer disease (AD) from healthy ageing. As blood-oxygenation-level-dependent fMRI signal is much weak and noisy, particularly for the elderly subjects, a robust method is necessary for HRF estimation to efficiently differentiate the AD. After applying minimum description length wavelet as an extra denoising step, deconvolution algorithm is here employed for HRF estimation, substituting the averaging method used in the previous works. The HRF amplitude peaks are compared for three groups HRF of young, non-demented and demented elderly groups for both vision and motor regions. Prior works often reported significant differences in the HRF peak amplitude between the young and the elderly. The authors’ experimentations show that the HRF peaks are not significantly different comparing the young adults with the elderly (either demented or non-demented). It is here demonstrated that the contradictory findings of the previous studies on the HRF peaks for the elderly compared with the young are originated from the noise contribution in fMRI data.

Analysis and evaluation of somatotopic mapping and rs-fMRI parcellation for healthy aging and alzheimer’s disease

Task-based functional Magnetic Resonance Imaging (fMRI) has exhibited a thriving potential in the extraction of somatotopic maps of the human sensorimotor cortex representing the correspondence of cortex regions with the sensorimotor functions. Several studies have explored the correspondence of cortex divisions in the absence of motor functions utilizing resting-state fMRI (rs-fMRI) with the somatotopic maps using successive task and rest sessions. In this paper, it is shown that the mentioned similarity between the somatotopic divisions (task fMRI) and the clusters due to rsfMRI parcellation holds for the independent task and rest experimentations as well. Also, it is found that the motor cortices of BA3 and BA4 exhibit no significant change in the somatotopic divisions for both healthy aging and Alzheimer Disease (AD) cases, though AD has been shown to affect the voxel activities in BA3 and BA4. Then, the clusters of sensorimotor cortices are shown to remain unchanged in the AD compared with the healthy aging. There exist a close association with the somatotopic maps of the young for both the AD and healthy aging as well.