Symmetric 10G-EPON ONU transceiver integrated with newly-developed burst-mode pre-bias timing control IC
We developed symmetric 10G-EPON ONU transceiver fully compliant with IEEE802.3av PR30. Output power of +5 dBm, sensitivity of -32.4 dBm, and fast LD turn-on/off time of 10 ns/7 ns were successfully achieved by newly-developed burst-mode pre-bias timing control IC.
Digital signal processing optical receivers for the mitigation of physical layer impairments in dynamic optical networks
IT IS generally believed by the research community that the introduction of complex 
network functions—such as routing—in the optical domain will allow a better network 
utilisation, lower cost and footprint, and a more efficiency in energy usage. The new optical 
components and sub-systems intended for dynamic optical networking introduce 
new kinds of physical layer impairments in the optical signal, and it is of paramount 
importance to overcome this problem if dynamic optical networks should become a 
reality. Thus, the aim of this thesis was to first identify and characterise the physical 
layer impairments of dynamic optical networks, and then digital signal processing 
techniques were developed to mitigate them. 
The initial focus of this work was the design and characterisation of digital optical 
receivers for dynamic core optical networks. Digital receiver techniques allow for complex 
algorithms to be implemented in the digital domain, which usually outperform 
their analogue counterparts in performance and flexibility. An AC-coupled digital receiver 
for core networks—consisting of a standard PIN photodiode and a digitiser that 
takes samples at twice the Nyquist rate—was characterised in terms of both bit-error 
rate and packet-error rate, and it is shown that the packet-error rate can be optimised by 
appropriately setting the preamble length. Also, a realistic model of a digital receiver 
that includes the quantisation impairments was developed. Finally, the influence of 
the network load and the traffic sparsity on the packet-error rate performance of the 
receiver was investigated. 
Digital receiver technologies can be equally applied to optical access networks, 
which share many traits with dynamic core networks. A dual-rate digital receiver, capable 
of detecting optical packets at 10 and 1.25 Gb/s, was developed and characterised. 
The receiver dynamic range was extended by means of DC-coupling and non-linear 
signal clipping, and it is shown that the receiver performance is limited by digitiser 
noise for low received power and non-linear clipping for high received power.
Application of SVM on satellite images to detect hotspots in Jharia coal field region of India
The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.