Dual-wavelength thulium fluoride fiber laser based on SMF-TMSIF-SMF interferometer as potential source for microwave generationin 100-GHz region

A dual-wavelength thulium-doped fluoride fiber (TDFF) laser is presented. The generation of the TDFF laser is achieved with the incorporation of a single modemultimode- single mode (SMS) interferometer in the laser cavity. The simple SMS interferometer is fabricated using the combination of two-mode step index fiber and single-mode fiber. With this proposed design, as many as eight stable laser lines are experimentally demonstrated. Moreover, when a tunable bandpass filter is inserted in the laser cavity, a dual-wavelength TDFF laser can be achieved in a 1.5-μm region. By heterodyning the dual-wavelength laser, simulation results suggest that the generated microwave signals can be tuned from 105.678 to 106.524 GHz with a constant step of �0.14 GHz. The presented photonics-based microwave generation method could provide alternative solution for 5G signal sources in 100-GHz region

Red blood cell segmentation and classification method using MATLAB

Red blood cells (RBCs) are the most important kind of blood cell. Its diagnosis is very important process for early detection of related disease such as malaria and anemia before suitable follow up treatment can be proceed. Some of the human disease can be showed by counting the number of red blood cells. Red blood cell count gives the vital information that help diagnosis many of the patient’s sickness. Conventional method under blood smears RBC diagnosis is applying light microscope conducted by pathologist. This method is time-consuming and laborious. In this project an automated RBC counting is proposed to speed up the time consumption and to reduce the potential of the wrongly identified RBC. Initially the RBC goes for image pre-processing which involved global thresholding. Then it continues with RBCs counting by using two different algorithms which are the watershed segmentation based on distance transform, and the second one is the artificial neural network (ANN) classification with fitting application depend on regression method. Before applying ANN classification there are step needed to get feature extraction data that are the data extraction using moment invariant. There are still weaknesses and constraints due to the image itself such as color similarity, weak edge boundary, overlapping condition, and image quality. Thus, more study must be done to handle those matters to produce strong analysis approach for medical diagnosis purpose. This project build a better solution and help to improve the current methods so that it can be more capable, robust, and effective whenever any sample of blood cell is analyzed. At the end of this project it conducted comparison between 20 images of blood samples taken from the medical electronic laboratory in Universiti Tun Hussein Onn Malaysia (UTHM). The proposed method has been tested on blood cell images and the effectiveness and reliability of each of the counting method has been demonstrated

Improved synapse detection for mGRASP-assisted brain connectivity mapping

Motivation: A new technique, mammalian green fluorescence protein (GFP) reconstitution across synaptic partners (mGRASP), enables mapping mammalian synaptic connectivity with light microscopy. To characterize the locations and distribution of synapses in complex neuronal networks visualized by mGRASP, it is essential to detect mGRASP fluorescence signals with high accuracy

Computational Methods on Study of Differentially Expressed Proteins in Maize Proteomes Associated with Resistance to Aflatoxin Accumulation

Plant breeders have focused on improving maize resistance to Aspergillus flavus infection and aflatoxin accumulation by breeding with genotypes having the desirable traits. Various maize inbred lines have been developed for the breeding of resistance. Identification of differentially expressed proteins among such maize inbred lines will facilitate the development of gene markers and expedite the breeding process. Computational biology and proteomics approaches on the investigation of differentially expressed proteins were explored in this research. The major research objectives included 1) application of computational methods in homology and comparative modeling to study 3D protein structures and identify single nucleotide polymorphisms (SNPs) involved in changes of protein structures and functions, which can in turn increase the efficiency of the development of DNA markers; 2) investigation of methods on total protein profiling including purification, separation, visualization, and computational analysis at the proteome level. Special research goals were set on the development of open source computational methods using Matlab image processing tools to quantify and compare protein expression levels visualized by 2D protein electrophoresis gel techniques

Automatic Detection of Optic Disc Based on PCA and Mathematical Morphology

The algorithm proposed in this paper allows to automatically segment the optic disc from a fundus image. The goal is to facilitate the early detection of certain pathologies and to fully automate the process so as to avoid specialist intervention. The method proposed for the extraction of the optic disc contour is mainly based on mathematical morphology along with principal component analysis (PCA). It makes use of different operations such as generalized distance function (GDF), a variant of the watershed transformation, the stochastic watershed, and geodesic transformations. The input of the segmentation method is obtained through PCA. The purpose of using PCA is to achieve the grey-scale image that better represents the original RGB image. The implemented algorithm has been validated on five public databases obtaining promising results. The average values obtained (a Jaccard s and Dice s coefficients of 0.8200 and 0.8932, respectively, an accuracy of 0.9947, and a true positive and false positive fractions of 0.9275 and 0.0036) demonstrate that this method is a robust tool for the automatic segmentation of the optic disc. Moreover, it is fairly reliable since it works properly on databases with a large degree of variability and improves the results of other state-of-the-art methods.This work was supported in part by the project IMIDTA/2010/47 and in part by projects Consolider-C (SEJ2006-14301/PSIC), "CIBER of Physiopathology of Obesity and Nutrition, an initiative of ISCIII" and Excellence Research Program PROMETEO (Generalitat Valenciana. Conselleria de Educacion, 2008-157).Morales Martínez, S.; Naranjo Ornedo, V.; Angulo Lopez, J.; Alcañiz Raya, ML. (2013). Automatic Detection of Optic Disc Based on PCA and Mathematical Morphology. IEEE Transactions on Medical Imaging. 32(4):786-796. https://doi.org/10.1109/TMI.2013.2238244S78679632

Nuclei/Cell Detection in Microscopic Skeletal Muscle Fiber Images and Histopathological Brain Tumor Images Using Sparse Optimizations

Nuclei/Cell detection is usually a prerequisite procedure in many computer-aided biomedical image analysis tasks. In this thesis we propose two automatic nuclei/cell detection frameworks. One is for nuclei detection in skeletal muscle fiber images and the other is for brain tumor histopathological images. For skeletal muscle fiber images, the major challenges include: i) shape and size variations of the nuclei, ii) overlapping nuclear clumps, and iii) a series of z-stack images with out-of-focus regions. We propose a novel automatic detection algorithm consisting of the following components: 1) The original z-stack images are first converted into one all-in-focus image. 2) A sufficient number of hypothetical ellipses are then generated for each nuclei contour. 3) Next, a set of representative training samples and discriminative features are selected by a two-stage sparse model. 4) A classifier is trained using the refined training data. 5) Final nuclei detection is obtained by mean-shift clustering based on inner distance. The proposed method was tested on a set of images containing over 1500 nuclei. The results outperform the current state-of-the-art approaches. For brain tumor histopathological images, the major challenges are to handle significant variations in cell appearance and to split touching cells. The proposed novel automatic cell detection consists of: 1) Sparse reconstruction for splitting touching cells. 2) Adaptive dictionary learning for handling cell appearance variations. The proposed method was extensively tested on a data set with over 2000 cells. The result outperforms other state-of-the-art algorithms with F1 score = 0.96

A switched-beam antenna for cellular communication

Wireless communication has created a continuing demand for increased bandwidth and better quality of services. Smart antenna arrays are one of the ways to accommodate this demand which can provide numerous benefits to service provider and the customer. Switched-beam antenna was chosen for this project due to its easier implementation and lower cost compared to adaptive array. Switched-beam antenna is one of smart antenna technique which comprises a number of predefined beams. The control system switches among the beams that provide the maximum signal response. Through the investigation and study on this system, found that, the 1200 sectorization with three monopole antenna elements suited for prototype construction. The initial stage to design this system is by using MA TLAB simulation to identify the antenna characteristic and the parameters involved. The second stage is about the construction of the prototype switched-beam antenna used to measure the antenna gain and relative power level which displayed using CASSY program