M.H. Moradi | AmirKabir University Of Technology (original) (raw)

Papers by M.H. Moradi

British Poultry Science, 2020

ABSTRACT 1. Theoretically, haplotype blocks might be a more suitable alternative to SNP genotypes... more ABSTRACT 1. Theoretically, haplotype blocks might be a more suitable alternative to SNP genotypes as they are usually better at capturing multi-allelic QTL effects, compared to individual SNP genotypes in genome-wide association studies. The objectives of this study were to identify genomic regions related to egg weight traits by Bayesian methods (BayesA, BayesB, and BayesN) that fit fixed-length haplotypes using GenSel software. 2. Genotypes at 294,705 SNPs, that were common on a 600K Affymetrix chip, were phased for an egg-laying hen population of 1,063 birds. Recorded traits included first egg weight (FEW) and average egg weight at 28, 36, 56, 66, 72 and 80 weeks of age. 2. Fitting 1Mb haplotypes from BayesB resulted in the highest proportion of genetic variance explained for the egg weight traits. Based on the trait, the genetic variance explained by each marker ranged from 27% to 76%. 3. Different haplotype windows associated with egg weight traits only explained a small percentage of the genetic variance. 4. The top one 1-Mb window on GGA1 explained approximately 4.05% of total genetic variance for the FEW. Candidate genes, including PRKAR2B, HMGA2, LEMD3, GRIP1, EHBP1, MAP3K7, and MYH were identified for egg weight traits. 5. Several genomic regions, potentially associated with egg weight traits, were identified, some of which overlapped with known genes and previously reported QTL regions for egg production traits.

Advances in Adaptive Data Analysis, 2009

The installation of long-term structural health monitoring (SHM) system on super-tall buildings, ... more The installation of long-term structural health monitoring (SHM) system on super-tall buildings, long span bridges and large space structures has become a worldwide trend since last decade to monitor loading conditions, to detect damage, to assess structural safety and to guide maintenance during their service life. The core part of an SHM system is the function of data processing and structural parameter/damage identification that extracts useful information from huge amount of raw data and provides reliable knowledge for proper decision. Recently emerged data processing technique empirical mode decomposition (EMD) in conjunction with Hilbert transform (HT) provides a more better and powerful tool for SHM. This paper summarizes some research experience gained from application of EMD + HT in SHM with focuses on pre-processing raw data, structural parameter identification and damage detection. In particular, EMD is applied to determining time varying mean wind speed for wind data and...

The conventional assessment of human semen specially sperm movement characteristics, is a highly ... more The conventional assessment of human semen specially sperm movement characteristics, is a highly sub jective assessment, with considerable intra- and inter-technician variability. Computer-assisted sperm analysis systems provide a rapid and automated assessment of the parameters of sperm motion, together with impro ved standardization and quality control. Then this system should have better precisi on than human expert. In this paper, we have proposed a powerful algorithm for image enhanc ement. The goal of the algorithm is increasing of sperm segmentation.

In this paper a new idea is suggested for designing an appropriate bio-impedance sensor in the fo... more In this paper a new idea is suggested for designing an appropriate bio-impedance sensor in the form of a biopsy forceps to measure the electrical properties of the tissues inside the body. First, by analytically solving the Laplace equation for wedge-shaped tissue in the mouth of the forceps, the relationship between electric potential (results from excitation current) in different points on the tissue surface and the electrical properties of the tissue are obtained. Then, to evaluate the designed bioimpedance forceps using the finite element method and the experimental data obtained for different tissues by Gabriel et al., modeling and simulation were done and it was found that the voltages obtained for all of the tissues inside the mouth of the forceps at different frequencies from 50 Hz to 5 MHz, are consistent with that of the analytical method. To investigate the influence of the opening angle of the forceps, measurements were done at different angles and it was found that for ...

2010 17th Iranian Conference of Biomedical Engineering (ICBME), 2010

A Brain Computer Interface (BCI) utilizes signals derived from electroencephalography (EEG) to es... more A Brain Computer Interface (BCI) utilizes signals derived from electroencephalography (EEG) to establish a connection between a person&amp;#x27;s state of mind and a computer-based signal processing system which interprets the EEG signals. Extracting appropriate features from available EEG signals is essential for good BCI communication and an acceptable level of accuracy. Till now, many different feature extraction techniques have been

In recent years, an increasing number of researches have been focused on bio-inspired algorithms ... more In recent years, an increasing number of researches have been focused on bio-inspired algorithms to solve the elaborate engineering problems. Artificial Immune System (AIS) is an artificial intelligence technique which has potential of solving problems in various fields. The immune system, due to self-regulating nature, has been an inspiration source of unsupervised learning methods for pattern recognition task. The purpose of this study is to apply the AIS to pre-process the lie-detection dataset to promote the recognition of guilty and innocent subjects. A new Unsupervised AIS (UAIS) was proposed in this study as a pre-processing method before classification. Then, we applied three different classifiers on pre-processed data for Event Related Potential (ERP) assessment in a P300-based Guilty Knowledge Test (GKT). Experiment results showed that UAIS is a successful pre-processing method which is able to improve the classification rate. In our experiments, we observed that the classification accuracies for three different classifiers: K-Nearest Neighbourhood (KNN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) were increased after applying UAIS pre-processing. Using of scattering criterion to assessment the features before and after pre-processing proved that our proposed method was able to perform data mapping from a primary feature space to a new area where the data separability was improved significantly.

2010 5th Cairo International Biomedical Engineering Conference, 2010

Electroencephalography-based brain computer interface is the most appropriate way to translate hu... more Electroencephalography-based brain computer interface is the most appropriate way to translate human thoughts into commands. Motor imagery activities appear as changes in μ and/or β rhythms which varies extremely from one subject to another. ERD/ERS patterns is the most common feature that represent these rhythmic information which are hidden in time, frequency, and space in the sense of brain's topographic modulations. In this paper we present most recent and powerful techniques of single trial motor imagery classification of optimization the spatial and spectral filters simultaneously, and apply their multiclass extension to a 4class motor imagery data from BCI Competition III. Our results show a significant improvement in comparison with winner results of that competition. These are: Common Spatial Patterns (CSP) and its two extensions to the Common Spatio-Spectral Patterns (CSSP), Common Sparse Spectral Spatial Patterns (CSSSP), and also the frequency tuned version of CSP, i...

Iranian Journal of Medical Physics, 2007

Introduction: Amajor problem in the treatment of cancer is the lack of an appropriate method for ... more Introduction: Amajor problem in the treatment of cancer is the lack of an appropriate method for the early diagnosis of the disease. The chemical reaction within an organ may be reflected in the form of proteomic patterns in the serum, sputum, or urine. Laser mass spectrometry is a valuable tool for extracting the proteomic patterns from biological samples. A major challenge in extracting such patterns is the optimum selection of feature subset from mass spectrum data. Materials and Methods: In this research, the data corresponding to proteomic patterns of serum from patients with ovarian cancer was analyzed in two independent groups. Using a mathematical model, the baseline and electrical noises were eliminated in the preprocessing stage with subsequent normalization of mass spectra. The proposed method uses a hybrid algorithm based on a statistical test and Bhattacharyya distance measure. Using the final prediction error criteria, the best feature subset was selected from 15154 da...

IFMBE Proceedings, 2009

cDNA micro arrays are more and more frequently used in molecular biology as they can give insight... more cDNA micro arrays are more and more frequently used in molecular biology as they can give insight into the relation of an organism's metabolism and its genome. The process of imaging a micro array sample can introduce a great deal of noise and bias into the data with higher variance than the original signal which may swamp the useful information. As imperfections and fabrication artifacts often impair our ability to measure accurately the quantities of interest in micro array images, image processing for analysis of these images is an important and challenging problem. How to eliminate the effect of the noise imposes a challenging problem in micro array analysis. In this paper we implemented a novel algorithm for image sifting which could remove objective noise and simply could remove impulse noise from micro array images. This method could remove objects that smaller than size of grid. We used regular moving grids to sift and remove impulse noise and obtained denoised. In the other hand, this paper describes image processing methods for automatic spotted microarray image analysis. Automatic gridding is important to achieve constant data quality and is, therefore, especially interesting for large-scale experiments as well as for integration of microarray expression data from different sources. In this paper we have proposed a new method for automatic gridding of micro array images using sum of intensity of columns and rows, and we have developed it to gain a flexible algorithm to determine cells which contain spots of micro array images. By biorthogonal wavelet transform, we increase signal to nose ratio (SNR) of each cells locally. Then with universal threshold we convert the image of each cell to binary image. At last via labeling each of these binary images we have detected pixels of the spots. Lastly, we used Stanford microarray images database as our database and compared results with Genepix and achieved significant improvement.

2012 19th Iranian Conference of Biomedical Engineering (ICBME), 2012

ABSTRACT Background: While consciousness and top-down attention seem to be inextricably connected... more ABSTRACT Background: While consciousness and top-down attention seem to be inextricably connected, recent evidence has suggested that these processes can be present in the absence of the other. Recent studies show that observers can pay attention to an invisible stimulus (unconscious), and that a stimulus can be clearly seen in the absence of attention. We used a novel psychophysics task to explore the neural correlates of top-down attention and consciousness. Method: The task is meant to confirm that these two processes are independent from one another. EEG were recorded during the task from 45 subjects in occipital, Parietal and frontal lobes. Target-locked ERPs for masked and unmasked condition were constructed. Time features corresponding to P100, ,200 and P300 components (i.e. correlate candidates of consciousness and attention) were extracted for all eight channels separately. Results: The results indicate that some of the mentioned components are increased when attention or consciousness occurs. By comparing difference waves in each condition separably, we found that increase in positivity in P100 window is the only ERP correlate of consciousness and decrease in negativity in N100,200 window and increase in positivity in P300 window are ERP correlates of attention in O1, O2, PO7 and PO8 which are relevant channels. Conclusions: Our task could separate attention and consciousness successfully through their neural correlates. Our results introduce new ERP correlates of attention and consciousness. The results also suggest that these ERP components are meaningful features for the distinction between these two concepts. To our knowledge, this is the first time that these correlates of consciousness and specially attention are introduced in separable method.

2006 8th international Conference on Signal Processing, 2006

In this paper, we use three gene ranking techniques including two new methods based on a single g... more In this paper, we use three gene ranking techniques including two new methods based on a single gene score approach and Support Vector Machine (SVM) method based on Recursive Feature Elimination (RFE). These methods have been evaluated on colon cancer dataset by Gene Ontology. Experimental results showed that these methods select genes that do not have any GO. So, great parts of data that have GO are deposited. With having GO, we can gain a better understanding of the data than just applying statistical analysis because biological significance does not necessarily have to be statistically significant. We propose to enter biological knowledge into gene selection process. Using GO prevents from producing spurious results.

2005 IEEE 7th Workshop on Multimedia Signal Processing, 2005

In this paper we introduce an effective ECG compression algorithm based on two dimensional multiw... more In this paper we introduce an effective ECG compression algorithm based on two dimensional multiwavelet transform. Multi-wavelets offer simultaneous orthogonality, symmetry and short support, which is not possible with scalar two-channel wavelet systems. These features are known to be important in signal processing. Thus multiwavelet offers the possibility of superior performance for image processing applications. The SPIHT algorithm has achieved notable success in still image coding. We suggested applying SPIHT algorithm to 2-D multi-wavelet transform of 2-D arranged ECG signals. Experiments on selected records of ECG from MIT-BIH arrhythmia database revealed that the proposed algorithm is significantly more efficient in comparison with previously proposed ECG compression schemes.

Petroleum Science and Technology, 2014

The accuracy of the Content should not be relied upon and should be independently verified with p... more The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden.

Neurophysiologie Clinique/Clinical Neurophysiology, 2012

s 65 insights about emotional saliency coding as a function of visual space. doi:10.1016/j.neucli... more s 65 insights about emotional saliency coding as a function of visual space. doi:10.1016/j.neucli.2011.11.037

Journal of Applied Sciences, 2008

Applied Soft Computing, 2011

In this paper, an efficient strategy is proposed to design the altitude hold mode autopilot for a... more In this paper, an efficient strategy is proposed to design the altitude hold mode autopilot for a UAV which is non-minimum phase, and its model includes both parametric uncertainties and unmodeled nonlinear dynamics. This work has been motivated by the challenge of developing and implementing an autopilot that is robust with respect to these uncertainties. By combination of classic controller as the principal section of the autopilot and the fuzzy logic controller to increase the robustness in a single loop scheme, it is tried to exploit both methods advantages. The multi-objective genetic algorithm is used to mechanize the optimal determination of fuzzy logic controller parameters based on an efficient cost function that comprises undershoot, overshoot, rise time, settling time, steady state error and stability. Simulation results show that the proposed strategy performances are desirable in terms of the time response characteristics for both phugoid mode and short period mode, the robustness, and the adaptation of itself with respect to the large commands.