The Experts below are selected from a list of 2499 Experts worldwide ranked by ideXlab platform
K. Oysted - One of the best experts on this subject based on the ideXlab platform.
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A fully integrated CMOS-MEMS pressure sensor with on-chip /spl Delta/-/spl Sigma/ analog-to-digital converter
IEEE International Workshop on Biomedical Circuits and Systems 2004., 2004Co-Authors: Dag T. Wisland, K. OystedAbstract:This paper describes a fully integrated CMOS-MEMS pressure sensor implemented in a standard 0.6 /spl mu/m process from AMS. The fabricated chip includes all necessary components from pressure sensor to A/D-converter and decimation filter. The pressure sensitive diaphragm is fabricated using one single Postprocessing Step. Theoretical background is provided along with simulated results laying the fundament for the implemented CMOS design. Measured results will be available in short time.
Helmut Schmid - One of the best experts on this subject based on the ideXlab platform.
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qcri mes submission at wmt13 using transliteration mining to improve statistical machine translation
Workshop on Statistical Machine Translation, 2013Co-Authors: Hassan Sajjad, Svetlana Smekalova, Nadir Durrani, Alexander Fraser, Helmut SchmidAbstract:This paper describes QCRI-MES’s submission on the English-Russian dataset to the Eighth Workshop on Statistical Machine Translation. We generate improved word alignment of the training data by incorporating an unsupervised transliteration mining module to GIZA++ and build a phrase-based machine translation system. For tuning, we use a variation of PRO which provides better weights by optimizing BLEU+1 at corpus-level. We transliterate out-of-vocabulary words in a Postprocessing Step by using a transliteration system built on the transliteration pairs extracted using an unsupervised transliteration mining system. For the Russian to English translation direction, we apply linguistically motivated pre-processing on the Russian side of the data.
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WMT@ACL - QCRI-MES Submission at WMT13: Using Transliteration Mining to Improve Statistical Machine Translation
2013Co-Authors: Hassan Sajjad, Svetlana Smekalova, Nadir Durrani, Alexander Fraser, Helmut SchmidAbstract:This paper describes QCRI-MES’s submission on the English-Russian dataset to the Eighth Workshop on Statistical Machine Translation. We generate improved word alignment of the training data by incorporating an unsupervised transliteration mining module to GIZA++ and build a phrase-based machine translation system. For tuning, we use a variation of PRO which provides better weights by optimizing BLEU+1 at corpus-level. We transliterate out-of-vocabulary words in a Postprocessing Step by using a transliteration system built on the transliteration pairs extracted using an unsupervised transliteration mining system. For the Russian to English translation direction, we apply linguistically motivated pre-processing on the Russian side of the data.
Michael Davies - One of the best experts on this subject based on the ideXlab platform.
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Sparsity-based autofocus for undersampled synthetic aperture radar
IEEE Transactions on Aerospace and Electronic Systems, 2014Co-Authors: Shaun I. Kelly, Mehrdad Yaghoobi, Michael DaviesAbstract:Motivated by the field of compressed sensing and sparse recovery, nonlinear algorithms have been proposed for the reconstruction of synthetic-aperture-radar images when the phase history is undersampled. These algorithms assume exact knowledge of the system acquisition model. In this paper we investigate the effects of acquisition-model phase errors when the phase history is undersampled. We show that the standard methods of autofocus, which are used as a Postprocessing Step on the reconstructed image, are typically not suitable. Instead of applying autofocus in Postprocessing, we propose an algorithm that corrects phase errors during the image reconstruction. The performance of the algorithm is investigated quantitatively and qualitatively through numerical simulations on two practical scenarios where the phase histories contain phase errors and are undersampled.
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Auto-focus for under-sampled synthetic aperture radar
Sensor Signal Processing for Defence (SSPD 2012), 2012Co-Authors: Shaun I. Kelly, Mehrdad Yaghoobi, Michael DaviesAbstract:We investigate the effects of phase errors on undersampled synthetic aperture radar (SAR) systems. We show that the standard methods of auto-focus, which are used as a Postprocessing Step, are typically not suitable. Instead of applying auto-focus as a post-processor we propose using a stable algorithm, which is based on algorithms from the dictionary learning literature, that corrects phase errors during the reconstruction and is found empirically to recover sparse SAR images. (5 pages)
Luo Dai-sheng - One of the best experts on this subject based on the ideXlab platform.
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Method for cell image segmentation based on scale space analysis and probability relaxation
Computer Engineering, 2005Co-Authors: Luo Dai-shengAbstract:A hybrid method was proposed for cell image segmentation based on histogram analysis using scale space approach and probability relaxation. Firstly, according to the properties of the histogram scale space of an original image and the result of the multiscale filtering, optimal thresholds were determined for classifying the cells image. Then an iterative probability relaxation operation is applied in order to optimize the coarse segmentation. In the Postprocessing Step, overlapped cells were spilt. To compare with maximum deviation method and region growing method, some results of segmentation were given. The results show that the proposed method is more effective.
Peyman Milanfar - One of the best experts on this subject based on the ideXlab platform.
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ICASSP (4) - Improved spectral analysis of nearby tones using local detectors
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 1Co-Authors: Morteza Shahram, Peyman MilanfarAbstract:This paper concerns the problem of resolvability power in the frequency domain. The canonical case of interest is to distinguish whether the received noise-corrupted signal is a single-frequency sinusoid or a two-frequency sinusoid, where the amplitudes, phases and frequencies are unknown to the receiver. Using a model-based hypothesis testing approach, we quantify a measure of attainable resolution between sinusoids with nearby frequencies, in the presence of noise. An explicit relationship is derived for the minimum detectable difference between the frequencies of two tones, for any particular false alarm and detection rate, and at a given SNR. An associated algorithm is proposed that produces significantly better performance compared to the standard subspace-based methods like MUSIC and can be effectively used in practice as a Postprocessing Step for the existing spectral estimation methods.