RF Front End Receiver for WiMAX Application

This paper presents the design of a high gain, low noise direct conversion Radio frequency(RF) front-end receiver system. The Front end receiver is designed to operate at 5.8 GHz compliant with IEEE 802.16 WIiMAX standard. The system consists of a low noise amplifier (LNA), a radio frequency amplifier (RFA), a power divider and two band pass filters. The overall performance of the RF front-end receiver system produced a gain of 52.4 dB. A cascaded LNA designed for the system produced a high gain of 36.8 dB. The RFA contributed an extra gain 15.6dB. The overall noise figure achieved for the system is 3.7 dB. The return loss achieved is -25.5 dB for the RFA. The radio frequency bandwidth recorded for the system is above 1120 MHz. The measured power divider insertion loss is 2.80 dB. Using microstrip technology for designing the Chebyshev filter, the insertion loss is 3.00 dB and the channel bandwidth recorded is 107 MHz which can accommodate 4 sub channels IEEE WiMAX standard

Construction of plasmids expressing specific genes of Mycobacterium tuberculosis: towards the development of vaccine against tuberculosis

The objective of the project was to oonstrud plasmid(s) expressing specific genes of Mycobacterium tuberculosis (MTB): towards the development of potential DNA vaccine candidates against tuberculosis.Several T-cell epitopes from various specific genes of MTB were selected which include ESAT-6, MTP40, MPT64 and 38k0a.In order to construct the synthetic gene consists of those selected epitopes, the innovative PCR technology known as 'Assembly PCR' was employed. In this project, the synthetic gene (consist of multiple epitopes of M. tuberculosis) was successfully constructed and the gene is designated as vacll.The vacll was then cloned into plasmid DNA vector, pJW4303.The recombinant plasmid containing the vacll designated as pJWvacll was obtained.The DNA sequence of the cloned gene(vacll) or the insert was confinned by DNA sequencing method.So, in this project,we have constructed one DNA vaccine candidate for tuberculosis. Further studies that to be done to evaluate this vaccine candidate include immunogenecity and protection efficacy in animal model.In this project, the immunogenicity of pJWvacll was studied in mice in order to determine its ability to induce various immune responses. Mice were immunised with the pJWvacll and control plasmid (pJW blank vector). At the end of the immunisation protocol, the spleenic lymhocytes were prepared from the two groups of mice. Lymphocyte responses in vitro were determined by Flow Cytometric analyses, Lymphocyte Transformation Test and IFN gamma production {by ELISA) after culturing them with several peptides derived from the vacll construct.Results of Flow Cytometry which measure lymphocyte activation status did not show any significant different between the control and test group. But, results of LTT and IFN gamma production showed that some mice vaccinated with pJWvacll were positive when compared to the control group. These results indicated that the DNA vaccine candidate (pJWvacll) is immunogenic in mice ie able to induce immune response. The ability of pJWvacll to induce IFN gamma production suggested that the DNA vaccine construct stimulated the Th1 type of immune response which is essential or immunity to TB. However, more studies need to be done to confirm these prilimanary findings. Such studies include protection or challenge studies in mice but this part of the study can be done here due to lack of facility required

Land use detection in Nusajaya using higher-order modified geodesic active contour model

Urban development is a global phenomenon. In Johor, especially Nusajaya is one of the most rapidly developing cities. This is due to the increase of land demand and population growth. Moreover, land-use changes are considered to be one of the major components of current environmental monitoring strategies. In this context, image segmentation and mathematical model offers essential tools that can be used to analyze land use detection. The image segmentation process is known as the most important and difficult task in image analysis. Nonlinear fourth-order models had shown to have a good achievement in recovering smooth regions. Therefore, these motivate us to propose a fourth-order modified geodesic active contour (GAC) model. In the proposed model, a modified signed pressure force (SPF) function has been defined to segment the inhomogeneous satellite images. The simulations of the fourth-order modified GAC model through some numerical methods based on the higher-order finite difference method (FDM) have been illustrated. Matlab R2015a software in Windows 7 Ultimate on Intel (R) Core (TM) i5-3230M @ 2.60GHz CPU with 8 GB RAM has been considered as a computational platform for the simulation. Qualitative and quantitative differences between modified SPF functions and other SPF functions have been shown as a comparison. Hence land use detection is very useful for local governments and urban planners to enhance the future sustainable development plans of Nusajaya

Modeling of Indoor Wave Propagation Models from 1 G Hz to 10 G Hz

This paper presents the modeling of indoor wave propagation from 1 GHz to 10 GHz. Several obstacles such as partition, whiteboard, and door have been tested and measured their losses compare to line of sight. Horn antennas have been used as transmitter and receiver. An average attenuation between of this obstacle between 1.5 dB to 3.0 dB has been observed. Prediction model for this indoor propagation has been presented using suitable software. Finally, results, discussion, conclusions and further work are given

High Gain Cascaded Low Noise Amplifier Using T Matching Network

This project presents a design of high gain cascaded low noise amplifier (LNA), which operates at 5.8 GHz frequency for WiMAX application. The LNA designed used T matching network consist of lump reactive elements and microstrip at the input and output impedance. A cascaded LNA is developed in this project contributes a high gain of 36.8 dB with overall noise figure of 1.3 dB. The overall measured bandwidth measures is 1.240 GHz with S parameters S11, S12 and S22 measured are -11.4dB, -39.1dB and -12.3dB respectively. The input sensitivity of the LNA is -80dBm which compliant with the IEEE 802.16 WiMAX application. The LNA used FET transistor FX 76 LP from Eudina In

5.8 GHz Radio Frequency Amplifier with 3 dB Π Network Attenuator

This paper presents a design of radio frequency amplifier (RFA), which operates at 5.8 GHz frequency for WiMAX application. The RFA designed used T matching network consist of lump reactive elements, 3 dB attenuator and microstrip line at the input and output impedance. The RFA developed in this project contributes a gain of 15.6 dB with overall noise figure of 2.4 dB. The overall measured bandwidth measures is 1.240 GHz with S parameters S11, S12 and S22 measured are -12.4 dB, -25.5 dB and -12.3 dB respectively. The RFA used FET transistor EPA018A from Excelics Semiconductor Inc

High Gain Cascaded Low Noise Amplifier using T-Matching Network

This project presents a design of high gain cascaded low noise amplifier (LNA), which operates at 5.8 GHz frequency for WiMAX application. The LNA designed used T-matching network consisting of lump reactive elements and microstrip at the input and the output matching load uses quarter wavelength techniques. A cascaded LNA is developed in this project contribute a high gain of 36.8 dB with overall noise figure of 1.3 dB. The overall measured bandwidth measures is 1.240 GHz with S parameters S11, S12 and S22 measured are -11.4dB, -39.1dB and -12.3dB respectively. The input sensitivity of the LNA is -80dBm which compliant with the IEEE 802.16 WiMAX application. The LNA used FET transistor FHX 76 LP from Eudina Inc

Audio deformation based data augmentation for convolution neural network in vibration analysis

Audio deformations in audio processing have proved ability in preserve semantic meaning for audio signal. Convolution Neural Network (CNN) is among deep learning model that requires huge dataset during training for excellence performance Thus, data augmentation (DA) method is used to overcome the problem of limited dataset number for vibration analysis. Several signal processing phases including segmentation and image converting need to be performed before the vibration signal can be used as input for CNN. In this research, audio-deformation based DA is proposed in generating the additional vibration signal dataset. The proses is start by encoding the raw vibration signal to audio signal format to enable the audio deformation process performing, then decoding back into new vibration signal. Speed and amplify transformation are selected for audio deformation process. The new vibration data set of bearing fault detection problem are used for training CNN to validate the proposed approach. The results obtained from 13 experiments setting have shown that the proposed DA able to increase the accuracy of training for CNN until 13% compared with the previous DA method

Low Noise, High Gain RF Front End Receiver at 5.8GHz for WiMAX Application

This paper presents the design of a high gain, low noise direct conversion Radio frequency(RF) front-end receiver system. The Front end receiver is designed to operate at 5.8 GHz in compliant with IEEE 802.16 WiMAX standard. The system consists of a low noise amplifier (LNA), a radio frequency amplifier (RFA), a power divider and two band pass filters. The design process involved the use of software such as ADS 2000A, Ansoft Designer and MathCad. FET FHX76 LP is used in the design of the LNA due to its low noise figure and high impedance input. As for the RFA design,FET EPA018A was used. The LNA and the RFA used T lumped reactive element network and microstrip line matching network. Two 3 dBπ -attenuators were inserted at the input and output of the RFA to isolate the system from the reflected load power. A Wilkinson power divider is developed for two equal power structures using impedance microstrip line technique. Microstrip technology was used for designing the Chebyshev filter. The result of each module for the front end is presented

An application of hybrid swarm intelligence algorithms for dengue outbreak prediction

Dengue fever is a hazardous infectious disease which is channeled by Aedes mosquito. A serious infection of dengue may lead to a potentially lethal complication, known as severe dengue, which includes Dengue Haemorrhagic Fever and shock syndrome. In recent decades, this disease becomes a global burden which has grown dramatically around the world. Unfortunately, until today, a specific anti-viral medicine for dengue is still undiscovered. Therefore, it is a huge responsibility to the community in finding an effective solution to prevent a widespread of this disease in advance. Concerning this matter, this study presents an application of hybrid Swarm Intelligence (SI) algorithms for a dengue outbreak prediction. For simulation purposes, a monthly dengue cases time series data in the area of Indonesia were employed, which are fed to four hybrid SI algorithms, namely Moth Flame Optimization (MFO), Grey Wolf Optimizer (GWO), Firefly Algorithm (FA) and Artificial Bee Colony (ABC) algorithm. These algorithms are individually hybrid with Least Squares Support Vector Machines. Guided by Mean Square Error (MSE) and Root Mean Square Percentage Error (RMSPE), findings of the study indicate that the identified hybrid algorithms were able to produce competitive result, with a slightly favor to ABCLSSVM