The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform
Petko Bogdanov - One of the best experts on this subject based on the ideXlab platform.
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Efficient spectrum summarization using compressed spectrum scans
IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2018Co-Authors: Mariya Zheleva, Timothy Larock, Paul Schmitt, Petko BogdanovAbstract:We present AirPress, a spectrum scan Compression method that leverages wavelet decomposition for lossy Compression of spectrum data and allows up to 64:1 Compression Ratio of power spectral density traces without adversely impacting the spectrum summarization accuracy. We demonstrate the utility of AirPress on real-world spectrum measurements and show that it enables high-accuracy spectrum summarization of real-world transmitters while reducing the corresponding trace by 94%.
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INFOCOM Workshops - Efficient spectrum summarization using compressed spectrum scans
IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2018Co-Authors: Mariya Zheleva, Timothy Larock, Paul Schmitt, Petko BogdanovAbstract:We present AirPress, a spectrum scan Compression method that leverages wavelet decomposition for lossy Compression of spectrum data and allows up to 64:1 Compression Ratio of power spectral density traces without adversely impacting the spectrum summarization accuracy. We demonstrate the utility of AirPress on real-world spectrum measurements and show that it enables high-accuracy spectrum summarization of real-world transmitters while reducing the corresponding trace by 94%.
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AirPress: High-accuracy spectrum summarization using compressed scans
2018 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 2018Co-Authors: Mariya Zheleva, Timothy Larock, Paul Schmitt, Petko BogdanovAbstract:Spectrum summarization is the analysis of a wide-band spectrum scan to determine the number of transmitters, their time-frequency characteristics, approximate modulation and legitimacy of opeRation. Spectrum summarization has emerged as a critical functionality to enable next-geneRation dynamic spectrum access technologies and legislation. Typically, spectrum summarization is performed in a cloud-based manner, requiring full-scan transmission from the spectrum sensors to the cloud. As spectrum scans generate large volumes of data, full-scan transmission quickly incurs prohibitively-high cost in terms of bandwidth and storage requirements. To address this problem we design AirPress, a spectrum scan Compression method that leverages wavelet decomposition for lossy Compression of spectrum data and allows up to 64:1 Compression Ratio of power spectral density traces without adversely impacting the spectrum summarization accuracy. We demonstrate the utility of AirPress on real-world spectrum measurements and show that it enables high-accuracy spectrum summarization of real-world transmitters while reducing the corresponding trace by 94%.
Christos Faloutsos - One of the best experts on this subject based on the ideXlab platform.
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Efficiently supporting ad hoc queries in large datasets of time sequences
ACM SIGMOD Record, 2005Co-Authors: Flip Korn, Hosagrahar Visvesvaraya Jagadish, Christos FaloutsosAbstract:Ad hoc querying is difficult on very large datasets, since it is usually not possible to have the entire dataset on disk. While Compression can be used to decrease the size of the dataset, compressed data is notoriously difficult to index or access. In this paper we consider a very large dataaet compris- ing multiple distinct time sequences. Each point in the sequence is a numerical value. We show how to compress such a dataset into a format that supports ad hoc query- ing, provided that a small error can be tolerated when the data is uncompressed. Experiments on large, real world datasets (AT&T customer calling patterns) show that the proposed method achieves an average of less thau 5% error in any data value after compressing to a mere 2.5% of the original space (i. e., a 40:1 Compression Ratio), with these numbers not very sensitive to dataset size. Experiments on aggregate queries achieved a 0.5% reconstruction error with a space requirement under 2%
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SIGMOD Conference - Efficiently supporting ad hoc queries in large datasets of time sequences
Proceedings of the 1997 ACM SIGMOD international conference on Management of data - SIGMOD '97, 1997Co-Authors: Flip Korn, Hosagrahar Visvesvaraya Jagadish, Christos FaloutsosAbstract:Ad hoc querying is difficult on very large datasets, since it is usually not possible to have the entire dataset on disk. While Compression can be used to decrease the size of the dataset, compressed data is notoriously difficult to index or access. In this paper we consider a very large dataset comprising multiple distinct time sequences. Each point in the sequence is a numerical value. We show how to compress such a dataset into a format that supports ad hoc querying, provided that a small error can be tolerated when the data is uncompressed. Experiments on large, real world datasets ( AT&T customer calling patterns) show that the proposed method achieves an average of less than 5% error in any data value after compressing to a mere 2.5% of the original space ( i.e. , a 40:1 Compression Ratio), with these numbers not very sensitive to dataset size. Experiments on aggregate queries achieved a 0.5% reconstruction error with a space requirement under 2%.
Mariya Zheleva - One of the best experts on this subject based on the ideXlab platform.
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Efficient spectrum summarization using compressed spectrum scans
IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2018Co-Authors: Mariya Zheleva, Timothy Larock, Paul Schmitt, Petko BogdanovAbstract:We present AirPress, a spectrum scan Compression method that leverages wavelet decomposition for lossy Compression of spectrum data and allows up to 64:1 Compression Ratio of power spectral density traces without adversely impacting the spectrum summarization accuracy. We demonstrate the utility of AirPress on real-world spectrum measurements and show that it enables high-accuracy spectrum summarization of real-world transmitters while reducing the corresponding trace by 94%.
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INFOCOM Workshops - Efficient spectrum summarization using compressed spectrum scans
IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2018Co-Authors: Mariya Zheleva, Timothy Larock, Paul Schmitt, Petko BogdanovAbstract:We present AirPress, a spectrum scan Compression method that leverages wavelet decomposition for lossy Compression of spectrum data and allows up to 64:1 Compression Ratio of power spectral density traces without adversely impacting the spectrum summarization accuracy. We demonstrate the utility of AirPress on real-world spectrum measurements and show that it enables high-accuracy spectrum summarization of real-world transmitters while reducing the corresponding trace by 94%.
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AirPress: High-accuracy spectrum summarization using compressed scans
2018 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 2018Co-Authors: Mariya Zheleva, Timothy Larock, Paul Schmitt, Petko BogdanovAbstract:Spectrum summarization is the analysis of a wide-band spectrum scan to determine the number of transmitters, their time-frequency characteristics, approximate modulation and legitimacy of opeRation. Spectrum summarization has emerged as a critical functionality to enable next-geneRation dynamic spectrum access technologies and legislation. Typically, spectrum summarization is performed in a cloud-based manner, requiring full-scan transmission from the spectrum sensors to the cloud. As spectrum scans generate large volumes of data, full-scan transmission quickly incurs prohibitively-high cost in terms of bandwidth and storage requirements. To address this problem we design AirPress, a spectrum scan Compression method that leverages wavelet decomposition for lossy Compression of spectrum data and allows up to 64:1 Compression Ratio of power spectral density traces without adversely impacting the spectrum summarization accuracy. We demonstrate the utility of AirPress on real-world spectrum measurements and show that it enables high-accuracy spectrum summarization of real-world transmitters while reducing the corresponding trace by 94%.
Flip Korn - One of the best experts on this subject based on the ideXlab platform.
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Efficiently supporting ad hoc queries in large datasets of time sequences
ACM SIGMOD Record, 2005Co-Authors: Flip Korn, Hosagrahar Visvesvaraya Jagadish, Christos FaloutsosAbstract:Ad hoc querying is difficult on very large datasets, since it is usually not possible to have the entire dataset on disk. While Compression can be used to decrease the size of the dataset, compressed data is notoriously difficult to index or access. In this paper we consider a very large dataaet compris- ing multiple distinct time sequences. Each point in the sequence is a numerical value. We show how to compress such a dataset into a format that supports ad hoc query- ing, provided that a small error can be tolerated when the data is uncompressed. Experiments on large, real world datasets (AT&T customer calling patterns) show that the proposed method achieves an average of less thau 5% error in any data value after compressing to a mere 2.5% of the original space (i. e., a 40:1 Compression Ratio), with these numbers not very sensitive to dataset size. Experiments on aggregate queries achieved a 0.5% reconstruction error with a space requirement under 2%
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SIGMOD Conference - Efficiently supporting ad hoc queries in large datasets of time sequences
Proceedings of the 1997 ACM SIGMOD international conference on Management of data - SIGMOD '97, 1997Co-Authors: Flip Korn, Hosagrahar Visvesvaraya Jagadish, Christos FaloutsosAbstract:Ad hoc querying is difficult on very large datasets, since it is usually not possible to have the entire dataset on disk. While Compression can be used to decrease the size of the dataset, compressed data is notoriously difficult to index or access. In this paper we consider a very large dataset comprising multiple distinct time sequences. Each point in the sequence is a numerical value. We show how to compress such a dataset into a format that supports ad hoc querying, provided that a small error can be tolerated when the data is uncompressed. Experiments on large, real world datasets ( AT&T customer calling patterns) show that the proposed method achieves an average of less than 5% error in any data value after compressing to a mere 2.5% of the original space ( i.e. , a 40:1 Compression Ratio), with these numbers not very sensitive to dataset size. Experiments on aggregate queries achieved a 0.5% reconstruction error with a space requirement under 2%.
Gert Cauwenberghs - One of the best experts on this subject based on the ideXlab platform.
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Image sensor with focal plane change event driven video Compression
2008 IEEE International Symposium on Circuits and Systems, 2008Co-Authors: Ralph Etienne-cummings, Gert CauwenberghsAbstract:An image sensor with focal plane based hardware acceleRation of video Compression is presented. The 90 x 90 pixel CMOS image sensor provides in-pixel professing of intensity changes, serving as an analog memory and processor for temporal image difference computation. Surveillance quality videos of up to a 48:1 Compression Ratio via a temporally compensated DCT based Compression algorithm is attained with just an 18.5 MHz micro-controller. Power consumption is 225 mW during full opeRation and 6mVV during full sleep mode that continuously monitors for change events to trigger encoding.
