1,483 research outputs found
Layout-aware variability analysis, yield prediction, and optimization in photonic integrated circuits
No full textEffect of fabrication imperfections on the performance of silicon-on-insulator arrayed waveguide gratings
No full textIntensity guided cost metric for fast stereo matching under radiometric variations
Full text linkReliable and efficient stereo matching is a challenging task due to the presence of multiple radiometric variations. In stereo matching, correspondence between left and right images can become hard owing to low correlation between radiometric changes in left and right images. Previously presented cost metrics are not robust enough against intensive radiometric variations and/or are computationally expensive. In this work, we propose a new similarity metric coined as Intensity Guided Cost Metric (IGCM). IGCM turns out to significantly contribute to the depth accuracy by rejecting outliers and reducing the edge-fattening effect in object boundaries. IGCM is further combined explicitly with a color formation model to handle various radiometric changes that occur between stereo images. Experimental results on Middlebury dataset show 13.8%, 22.8%, 20.9%, 19.5 % and 9.1% decrease in average error rate compared to Adaptive Normalized Cross-Correlation (ANCC), Dense Adaptive Self-Correlation (DASC), Adaptive Descriptor(AD), Fast Cost Volume Filtering (FCVF) and Iterative Guided Filter (IGF)-based methods, respectively. Moreover, using integral images IGCM can achieve a speedup of 20x, 6x, 41x, 25x and 45x compared to the aforementioned methods. (c) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Parameter extraction, variability analysis and yield prediction of the photonic integrated circuits
No full textA reappraisal of attitudes to the 'People of the Book' in the Qur'an and hadith, with particular reference to Muslim fiscal policy and the covenant of 'Umar
Full text linkEThOS - Electronic Theses Online ServiceGBUnited Kingdo
Smart Provisioning of Sliceable Bandwidth Variable Transponders in Elastic Optical Networks
Full text linkPrior provisioning of optical source technologies have techno-economic importance for the operator during the design and planning of optical network architectonics. Advancement towards the latest technology paradigm such as Elastic Optical Networks (EONs) and Software Defined Networking (SDN) open a gateway for a flexible and re-configurable optical network architecture. In order to achieve the required degree of flexibility, a flexible and dynamic behaviour is required both at the control and data plane. In this regards, SDN-enabled flexible optical transceivers are proposed to provide the required degree of flexibility. Sliceable Bandwidth Variable Transponders (SBVTs) is one of the recent type of flexible optical transceivers. Based on the type/technology of optical carrier source, the SBVTs are categorized into two types; Multi-Laser SBVT (ML-SBVT) and Multi-wavelength SBVT (MW-SBVT). Both architectures have their own pros and cons when it comes to accommodate traffic request. In this paper, we propose a selection model for the SBVTs before its actual deployment in the network. The selection model consider various design and planning phase network characteristics. In addition to this selection model, the comparison of centralized Flex-OCSM architecture is also presented with the already discussed SBVT types. The analysis in this work is performed on random network (20 nodes) and the German Network (17 nodes)
Optimal control of Beneš optical networks assisted by machine learning
Full text linkOptimal control of Beneˇs optical networks
assisted by machine learning
Ihtesham Khana, Lorenzo Tunesia, Muhammad Umar Masooda, Enrico Ghillinob,
Paolo Bardellaa, Andrea Carenaa, and Vittorio Curria
aPolitecnico di Torino, Corso Duca degli Abruzzi 24, Torino, Italy
bSynopsys Inc., Executive Blvd 101, Ossining, New York, USA
ABSTRACT
Beneˇs networks represent an excellent solution for the routing of optical telecom signals in integrated, fully
reconfigurable networks because of their limited number of elementary 2x2 crossbar switches and their non-
blocking properties. Various solutions have been proposed to determine a proper Control State (CS) providing
the required permutation of the input channels; since for a particular permutation, the choice is not unique, the
number of cross-points has often been used to estimate the cost of the routing operation. This work presents an
advanced version of this approach: we deterministically estimate all (or a reasonably large number of) the CSs
corresponding to the permutation requested by the user. After this, the retrieved CSs are exploited by a data-
driven framework to predict the Optical Signal to Noise Ratio (OSNR) penalty for each CS at each output port,
finally selecting the CS providing minimum OSNR penalty. Moreover, three different data-driven techniques are
proposed, and their prediction performance is analyzed and compared.
The proposed approach is demonstrated using 8x8 Beneˇs architecture with 20 ring resonator-based crossbar
switches. The dataset of 1000 OSNRs realizations is generated synthetically for random combinations of the
CSs using Synopsys® OptsimTM simulator. The computational cost of the proposed scheme enables its real-time
operation in the field
Hybrid Discriminator With Correlative Autoencoder for Anomaly Detection
No full textAdvances in deep neural networks (DNNs) have led to impressive results and in recent years many works have exploited DNNs for anomaly detection. Among others, generative/reconstruction model-based methods have been frequently used for anomaly detection because they do not require any labels for training. The anomaly detection performance of these methods, however, varies a lot, due to the change of the intra-class variance and the difference in complexity of input samples. In addition, most previous state-of-the-art works on anomaly detection have empirically adjusted several hyperparameters to heighten their performance of anomaly detection. These sorts of procedures are known to be impractical and create obstacles in real world anomaly detection. To solve these problems, we propose a hybrid discriminator with a correlative autoencoder for anomaly detection. In the proposed framework, the discriminator implicitly estimates the conditional probability density function and the autoencoder has improved ability to control the reconstruction error. We provide theoretical foundation of our method and verify it through various experiments. We also confirm practical benefits of our interpretation of the conditional expectation and the proposed framework by comparing our results with other state-of-the-art methods.
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