Design and Development of Variable Fiber Optic Coupler

This thesis presents the research works on design, development and analysis of variable fiber optic coupler. The fiber couplers are devices that distribute light from a main fiber into one or more branches of fibers which can be used as a power divider, wavelength-division multiplexer (WDM) and optical switches. These couplers are important in all fiber optic networks as they are used in long haul, metro, distribution and access fiber networks. With the widely increasing deployment of metropolitan area network, fiberto- the-home (FTTH) and cable TV networks, the market potential for the fiber couplers is becoming larger. However, most of the fiber couplers can only provide a fixed optical coupling ratio which is unable to provide flexibility, reliability and scalability. The evolution of the telecommunications networks has created an increasing demand for the variable fiber couplers, but there are some limitations of the existing variable fiber couplers such as unstable, small tuning range of coupling ratio, high in loss and costly. Hence, this gives rise to the need for searching new design of developing better performance variable fiber couplers. The new design utilize a fix coupling ratio Fused Biconical Tapered (FBT) coupler that is involved low loss coupling process and low in cost. This coupler is known as a Variable Fiber Optic Coupler (VFOC). There are two approaches used in this thesis that are simulation and experimentation. The simulation is important in designing the VFOCs, as through the simulation process, the characteristic of the design VFOCs can be determined. The successful simulation is then realized through the experimentation, which is included the fabrication of the VFOCs. However, the results obtained from the experiments are slightly different from the simulation results. These are due to the losses in the experiments and the ideal environment in the simulation. In the experiment, the variable coupling ratio is achieved by applying deflection to the fabricated FBT couplers. The dependency of the variable coupling ratio on the deflection is elaborated. It is found that in the range of 0.8 mm deflection, the coupling ratio can be varied from 11% to 92% with the excess loss less than 0.1 dB

Face Recognition from Face Signatures

This thesis presents techniques for detecting and recognizing faces under various imaging conditions. In particular, it presents a system that combines several methods for face detection and recognition. Initially, the faces in the images are located using the Viola-Jones method and each detected face is represented by a subimage. Then, an eye and mouth detection method is used to identify the coordinates of the eyes and mouth, which are then used to update the subimages so that the subimages contain only the face area. After that, a method based on Bayesian estimation and a fuzzy membership function is used to identify the actual faces on both subimages (obtained from the first and second steps). Then, a face similarity measure is used to locate the oval shape of a face in both subimages. The similarity measures between the two faces are compared and the one with the highest value is selected. In the recognition task, the Trace transform method is used to extract the face signatures from the oval shape face. These signatures are evaluated using the BANCA and FERET databases in authentication tasks. Here, the signatures with discriminating ability are selected and were used to construct a classifier. However, the classifier was shown to be a weak classifier. This problem is tackled by constructing a boosted assembly of classifiers developed by a Gentle Adaboost algorithm. The proposed methodologies are evaluated using a family album database

Boosted features for face authentication

Boosted features developed using face signatures in combination with Gentle Adaboost algorithm offer alternative features for face authentication and face recognition. Face signatures are face representations extracted from Trace transform and Gentle Adaboost is used to enhance the performance of the features extracted from the face signatures. In this paper, we demonstrate the usefulness of the constructed features with experiments on BANCA database

Prediction of quality attributes and ripeness classification of bananas using optical properties

Consumers consider the ripeness of fruit as a very important factor in making a choice at the time of purchase. Ripeness in fruit generally affects the eating quality and market price of fruit. This study investigated the potential of using the optical properties of banana such as absorption, reduced scattering and effective attenuation coefficients extracted from backscattered images captured at five different wavelengths of 532, 660, 785, 830, and 1060 nm for predicting the quality attributes of the fruits. It was observed that there was a very strong correlation between the optical properties investigated and the banana ripening stages at wavelengths 532, 660 and 785 nm. Absorption and effective attenuation coefficients showed a negative correlation with ripening stages while the reduced scattering coefficient exhibited a positive correlation with ripening stages. Prediction and classification models were developed using an artificial neural network to build both prediction and classification models. The visible wavelength region of 532, 660 and 785 nm gave the highest correlation coefficient (R) range of 0.9768–0.9807 for chlorophyll prediction and 0.9553–0.9759 for elasticity prediction, while the near infrared region of 830 and 1060 nm gave an R range of 0.9640–0.9801 for prediction of the soluble solids content (SSC) when the absorption and reduced scattering coefficients were used. For the classification of banana into ripening stages 2–7, the visible wavelength region gave the highest classification accuracy of 97.53%. This study has shown that the optical properties of banana can be employed for non-destructive prediction and classification of banana into different ripening stages

Assessment of near infrared LED radiation pattern using Otsu thresholding

This paper describes the use of Otsu thresholding method in assessing the radiation pattern emitted by near infrared (NIR) LED. The NIR LED configured in this paper is intended to be used as illumination source for the development of a NIR palm vein image acquisition device. The experiment is conducted using a single board computer (SBC) to promote a real-time embedded system development that can be readily integrated as a vein viewing device. Based on the Otsu thresholded image obtained, it is observed that the NIR LED radiation pattern can be accessed subjectively through the thresholding process. The resulted thresholded image can be used as preliminary assessment of the radiation pattern in developing a NIR image acquisition system that fully utilizes the NIR LED properties

Wavelet-based medical image fusion via a non-linear operator

Medical image fusion has been extensively used to aid medical diagnosis by combining images of various modalities such as Computed Tomography (CT) and Magnetic Resonance Image (MRI) into a single output image that contains salient features from both inputs. This paper proposes a novel fusion algorithm through the use of a non-linear fusion operator, based on the low sub-band coefficients of the Discrete Wavelet Transform (DWT). Rather than employing the conventional mean rule for approximation sub-bands, a modified approach is taken by the introduction of a non-linear fusion rule that exploits the multimodal nature of the image inputs by prioritizing the stronger coefficients. Performance evaluation of CT-MRI image fusion datasets based on a range of wavelet filter banks shows that the algorithm boasts improved scores of up to 92% as compared to established methods. Overall, the non-linear fusion rule holds strong potential to help improve image fusion applications in medicine and indeed other fields

Automated road marking detection system for autonomous car

In recent years, road markings detection has received great attention and has been widely explored due to the aim of producing a system that is able to detect various shape of road markings on the images that are captured under various imaging conditions. Generally, the road images are captured using a camera, which has been placed inside the vehicle at a fixed position. However, the quality of the resulting images decreases if the camera position has been changed accidentally, due to the movement of the car. Hence, in this paper, a road markings detection system that tackle the problems of detecting road markings on the images captured under various camera positions and illumination conditions is proposed. The system consists of a graph cut segmentation method, which is used to detect the road, an inverse perspective transform method, which is used to convert the image into a bird's-eye view image, an image normalization method, which is CLAHE and a connected component analysis that is used to remove the background. We demonstrate the usefulness of the constructed algorithm by performing experiments on a database that consists of 400 road images

A real time road marking detection system on large variability road images database

For no less than two decades, the development of autonomous systems has led to the development of embedded applications permitting to enhance the driving comfort and limit the hazard level of dangerous zones. One of the first embedded system is a lane detection system, which was implemented using road marking detection algorithms with the aim to produce a system that is able to detect various shapes of road markings on the images that are captured under various imaging conditions. Generally, the road images were captured using a camera, which has been placed inside a vehicle at a fixed position. In this paper, a road markings detection system that tackles the problems of detecting road markings on the images captured under various weather and illumination conditions is proposed. The proposed system consists of inverse perspective transform method, which is used to convert an image into a bird’s-eye view image, an image normalization method, namely CLAHE that tackle various illumination conditions and Sobel edge detection method for identifying the road marker. We demonstrate the usefulness of the constructed algorithm by performing experiments on our Large Variability Road Images database (LVRI) that consists of 22,500 road images with the accuracy of 96.53%

Integrated face and facial components detection

This paper presents an algorithm that detects faces and facial features (eyes, nose and mouth) on images captured by CCTV system under various imaging conditions, such as variation in poses, scale, illumination and occlusion. The system detects face, nose and mouth using three different classifiers, which were created based on the Viola-Jones method [1] and the eyes were detected using an Eye Detection method that consists of resolution reduction, identification of the eye candidates using eye filter [2] and eyes localization based on mean comparison. Experimented on 500 images, the algorithm produced 98.4% accuracy for face, 98.8% for nose, 95.6% for mouth and 94.8% for eyes

Application and potential of backscattering imaging techniques in agricultural and food processing – a review

This review covers the application of backscattering imaging as a non-invasive technique for monitoring the quality of agricultural and food products. The review enumerates and discusses the concepts and various applications of laser light backscattering imaging (LLBI), multispectral laser backscattering imaging (MBI) and hyperspectral laser backscattering imaging (HBI). All the methods make use of laser light which varies in spectrum from visible up to near-infrared to detect changes in the quality of fresh produce. Emphasis is placed on applications which demonstrate promising potential for agricultural and food applications under various conditions. A critical review of the limitations is also given