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[IEEE 2018 20th International Conference on Transparent Optical Networks (ICTON) - Bucharest (2018.7.1-2018.7.5)] 2018 20th International Conference on Transparent Optical Networks (ICTON) - Multi-Section Semiconductor Optical Amplifiers for Data Centre Networks
摘要： The ever-growing Internet based services and applications, involving a huge amount of computing and storage resources, have been powered by data centres (DC) [1]. The storage and movement of data within the DCs is driving the requirement for cost-effective, higher capacity inter- and intra- next generation DC networks [2]. Within the context of these next generation DC networks, the ability to transmit very high data rates (100 – 400 Gb/s) over both short and long distances (intra or inter DC fibre links) is one of the main challenges (within the optical sector. The modulation format that is currently touted as the most suitable for such high capacities is 4-level pulse amplitude modulation (PAM4), which carries 2 bits per symbol. Optical amplification is needed for reach extension for inter- and intra- DC communications, Semiconductor optical amplifiers (SOAs) are needed to realize a low cost amplification solution. SOAs possess many advantages, including low power consumption, small footprint, wide bandwidth, being integrateable, and the ability to accommodate wavelength ranges beyond the scope of Erbium doped fibre amplifiers. However, the use of SOAs for linear amplification of C-band optical signals is still relatively limited, mainly due to the relatively large noise figure (NF) associated with them compared to erbium doped fiber amplifiers and low saturation powers of about 10 mW. Multi-section SOAs are known to possess superior NFs and larger saturation powers than an equivalent single-section SOA [3], and hence may provide performance benefit for reach extension for DC networks. In this work, we examine the use of a multi-section semiconductor optical amplifier (MS-SOA) [3] to provide an improvement in its use as a linear amplifier compared to a single section SOA. The MS-SOAs have been shown to have superior noise and linearity performance compared with single section SOAs. We configure the MS-SOA to operate in the low-NF mode with high saturation power mode and the equivalent single-section SOA. We compare the input power dynamic range for the MS-SOA and equivalent single-section SOA. We expect an improvement in the input power dynamic range of at least 3 dB [4]. The combination of a lower NF and higher saturation power enables crosstalk-free amplification of simultaneous multi-wavelength channels using the same SOA device.
关键词： multi-section semiconductor optical amplifier,advanced modulation formats,data centre networks
更新于2025-09-04 15:30:14
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Sea Ice Classification with Convolutional Neural Networks Using Sentinel-L Scansar Images
摘要： In this paper, the Sentinel-1 ScanSAR IR GRD products are used for sea ice mapping using convolutional neural networks (CNN). The sea and ice are classified as 2 types and 4 types respectively according to their SAR image textures. They are smooth sea, rough sea, granular ice, massive ice, smooth ice and striped ice. The Sentinel-1 SAR images are firstly pre-processed using ESA SNAP software. Then the classes are interpreted manually for chip preparation and annotations. Chips with 3 spatial scales (32x32, 64x64, 128x128) are used for training input of the CNN. The trained CNN is then used for generation of sea ice map from the ScanSAR image. The results are promising. Further work is still going on.
关键词： sea ice,classification,convolutional neural networks,deep learning,SAR
更新于2025-09-04 15:30:14
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A near-infrared spectroscopy routine for unambiguous identification of cryptic ant species
摘要： Species identification—of importance for most biological disciplines—is not always straightforward as cryptic species hamper traditional identification. Fibre-optic near-infrared spectroscopy (NIRS) is a rapid and inexpensive method of use in various applications, including the identification of species. Despite its efficiency, NIRS has never been tested on a group of more than two cryptic species, and a working routine is still missing. Hence, we tested if the four morphologically highly similar, but genetically distinct ant species Tetramorium alpestre, T. caespitum, T. impurum, and T. sp. B, all four co-occurring above 1,300 m above sea level in the Alps, can be identified unambiguously using NIRS. Furthermore, we evaluated which of our implementations of the three analysis approaches, partial least squares regression (PLS), artificial neural networks (ANN), and random forests (RF), is most efficient in species identification with our data set. We opted for a 100% classification certainty, i.e., a residual risk of misidentification of zero within the available data, at the cost of excluding specimens from identification. Additionally, we examined which strategy among our implementations, one-vs-all, i.e., one species compared with the pooled set of the remaining species, or binary-decision strategies, worked best with our data to reduce a multi-class system to a two-class system, as is necessary for PLS. Our NIRS identification routine, based on a 100% identification certainty, was successful with up to 66.7% of unambiguously identified specimens of a species. In detail, PLS scored best over all species (36.7% of specimens), while RF was much less effective (10.0%) and ANN failed completely (0.0%) with our data and our implementations of the analyses. Moreover, we showed that the one-vs-all strategy is the only acceptable option to reduce multi-class systems because of a minimum expenditure of time. We emphasise our classification routine using fibre-optic NIRS in combination with PLS and the one-vs-all strategy as a highly efficient pre-screening identification method for cryptic ant species and possibly beyond.
关键词： Random forests,Ants,Species identification tool,One-vs-all strategy,Formicidae,Neural networks,Cryptic-species complex,Partial least squares regression,Tetramorium
更新于2025-09-04 15:30:14
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An effective image denoising using PPCA and classification of CT images using artificial neural networks
摘要： The main aim of denoising is to remove the noise while recollecting as much possible important signal features. This appears to be very simple when considered under practical situations, where the type of images and noises are all variable parameters. This paper deals with removal of combination of noises from image and classification of normal and abnormal images. At first phase, median filter is used to remove the noises present in the images. To improve the denoised output, we are using PSM and PPCA with morphological operations, filter and region props. In the second phase, to analyse the denoised output, neural network-based classification is proposed. The use of artificial intelligent techniques for classification shows a great potential in this field. Hence the performance of neural network classifier is estimated in terms of training performance and classification accuracy and is compared with the existing method to show the system is effective.
关键词： GLCM,median filter,Gaussian noise,pixel surge model,CT images,neural networks,image denoising,PPCA
更新于2025-09-04 15:30:14
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Phase behavior of main-chain liquid crystalline polymer networks synthesized by alkyne–azide cycloaddition chemistry
摘要： Liquid crystalline polymer networks (LCNs) couple polymer chain organization to molecular ordering, the switching of which has been shown to impart stimuli-responsive properties, including actuation and one-way shape memory, to the networks. While LCNs have long been proposed as artificial muscles, recent reports have also suggested potential as dynamic biomaterial substrates. In contrast to many existing LCNs synthesized using hydrophobic spacers, this work investigates networks synthesized using more hydrophilic spacers to promote interaction with water. A challenge with such materials is liquid crystalline phases could be disrupted in hydrated networks. This work thus investigates the impact of polyether spacers and mesogen composition on the phase behavior of LCNs. Main-chain LCNs were synthesized using alkyne–azide cycloaddition ("click" chemistry), where two different mesogens (5yH and 5yMe) and a non-LC monomer (5yTe) were coupled with one of two different polyether spacers, poly(ethylene glycol) and poly(propylene glycol), and a crosslinker. The chemistry led to high gel fraction materials, the workup of which resulted in networks that displayed no difference in cellular toxicity due to leachable components compared to tissue culture plastic control. Calorimetric analysis, dynamic mechanical analysis, and X-ray scattering revealed the LC microstructure and temperature-responsive properties of the networks. The use of low molecular weight polyether spacers was found to prevent their crystallization within the LC network, and adjusting mesogen composition to enhance its LC phase stability allowed the use of spacers with larger molecular weights and pendant groups. Hydrated networks were found to rearrange their structure compared to dry networks, while maintaining their LC phases. Like other crosslinked LC materials, the networks display shape changes (actuation) that are tied to changes in LC ordering. The result is a new synthetic approach for polydomain networks that form stable LC phases that are tailorable using polyether spacers and may enable future application as hydrated, stimuli-responsive materials.
关键词： Liquid crystalline polymer networks,Phase behavior,Alkyne–azide cycloaddition,Stimuli-responsive materials,Polyether spacers
更新于2025-09-04 15:30:14