Opto-VLSI based WDM multifunction device

The tremendous expansion of telecommunication services in the past decade, in part due to the growth of the Internet, has made the development of high-bandwidth optical net-works a focus of research interest. The implementation of Dense-Wavelength Division Multiplexing (DWDM) optical fiber transmission systems has the potential to meet this demand. However, crucial components of DWDM networks – add/drop multiplexers, filters, gain equalizers as well as interconnects between optical channels – are currently not implemented as dynamically reconfigurable devices. Electronic cross-connects, the traditional solution to the reconfigurable optical networks, are increasingly not feasible due to the rapidly increasing bandwidth of the optical channels. Thus, optically transparent, dynamically reconfigurable DWDM components are important for alleviating the bottleneck in telecommunication systems of the future. In this study, we develop a promising class of Opto-VLSI based devices, including a dynamic multi-function WDM processor, combining the functions of optical filter, channel equalizer and add-drop multiplexer, as well as a reconfigurable optical power splitter. We review the technological options for all optical WDM components and compare their advantages and disadvantages. We develop a model for designing Opto-VLSI based WDM devices, and demonstrate experimentally the Opto-VLSI multi-function WDM device. Finally, we discuss the feasibility of Opto-VLSI WDM components in meeting the stringent requirements of the optical communications industry

Plant discrimination by Support Vector Machine classifier based on spectral reflectance

Support Vector Machine (SVM) algorithms are developed for weed-crop discrimination and their accuracies are compared with a conventional data-aggregation method based on the evaluation of discrete Normalised Difference Vegetation Indices (NDVIs) at two different wavelengths. A testbed is especially built to collect the spectral reflectance properties of corn (as a crop) and silver beet (as a weed) at 635 nm, 685 nm, and 785 nm, at a speed of 7.2 km/h. Results show that the use of the Gaussian-kernel SVM method, in conjunction with either raw reflected intensities or NDVI values as inputs, provides better discrimination accuracy than that attained using the discrete NDVI-based aggregation algorithm. Experimental results carried out in laboratory conditions demonstrate that the developed Gaussian SVM algorithms can classify corn and silver beet with corn/silver-beet discrimination accuracies of 97%, whereas the maximum accuracy attained using the conventional NDVI-based method does not exceed 70%