[{"oa":1,"month":"06","_id":"9828","status":"public","oa_version":"Preprint","doi":"10.1109/TSP.2021.3087899","scopus_import":"1","intvolume":"        69","author":[{"orcid":"0000-0002-7758-2016","last_name":"Gabrielaitis","full_name":"Gabrielaitis, Mantas","first_name":"Mantas","id":"4D5B0CBC-F248-11E8-B48F-1D18A9856A87"}],"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2102.04832"}],"volume":69,"date_created":"2021-08-08T22:01:31Z","publisher":"Institute of Electrical and Electronics Engineers","article_type":"original","day":"09","article_processing_charge":"No","department":[{"_id":"GaTk"}],"year":"2021","language":[{"iso":"eng"}],"page":"4039 - 4054","corr_author":"1","publication_identifier":{"issn":["1053-587X"],"eissn":["1941-0476"]},"arxiv":1,"title":"Fast and accurate amplitude demodulation of wideband signals","publication_status":"published","date_updated":"2024-10-09T21:00:43Z","isi":1,"external_id":{"isi":["000682123900002"],"arxiv":["2102.04832"]},"date_published":"2021-06-09T00:00:00Z","abstract":[{"lang":"eng","text":"Amplitude demodulation is a classical operation used in signal processing. For a long time, its effective applications in practice have been limited to narrowband signals. In this work, we generalize amplitude demodulation to wideband signals. We pose demodulation as a recovery problem of an oversampled corrupted signal and introduce special iterative schemes belonging to the family of alternating projection algorithms to solve it. Sensibly chosen structural assumptions on the demodulation outputs allow us to reveal the high inferential accuracy of the method over a rich set of relevant signals. This new approach surpasses current state-of-the-art demodulation techniques apt to wideband signals in computational efficiency by up to many orders of magnitude with no sacrifice in quality. Such performance opens the door for applications of the amplitude demodulation procedure in new contexts. In particular, the new method makes online and large-scale offline data processing feasible, including the calculation of modulator-carrier pairs in higher dimensions and poor sampling conditions, independent of the signal bandwidth. We illustrate the utility and specifics of applications of the new method in practice by using natural speech and synthetic signals."}],"quality_controlled":"1","type":"journal_article","acknowledgement":"The author thanks his colleagues K. Huszár and G. Tkačik for valuable discussions and comments on the manuscript.","citation":{"ama":"Gabrielaitis M. Fast and accurate amplitude demodulation of wideband signals. <i>IEEE Transactions on Signal Processing</i>. 2021;69:4039-4054. doi:<a href=\"https://doi.org/10.1109/TSP.2021.3087899\">10.1109/TSP.2021.3087899</a>","ieee":"M. Gabrielaitis, “Fast and accurate amplitude demodulation of wideband signals,” <i>IEEE Transactions on Signal Processing</i>, vol. 69. Institute of Electrical and Electronics Engineers, pp. 4039–4054, 2021.","ista":"Gabrielaitis M. 2021. Fast and accurate amplitude demodulation of wideband signals. IEEE Transactions on Signal Processing. 69, 4039–4054.","mla":"Gabrielaitis, Mantas. “Fast and Accurate Amplitude Demodulation of Wideband Signals.” <i>IEEE Transactions on Signal Processing</i>, vol. 69, Institute of Electrical and Electronics Engineers, 2021, pp. 4039–54, doi:<a href=\"https://doi.org/10.1109/TSP.2021.3087899\">10.1109/TSP.2021.3087899</a>.","chicago":"Gabrielaitis, Mantas. “Fast and Accurate Amplitude Demodulation of Wideband Signals.” <i>IEEE Transactions on Signal Processing</i>. Institute of Electrical and Electronics Engineers, 2021. <a href=\"https://doi.org/10.1109/TSP.2021.3087899\">https://doi.org/10.1109/TSP.2021.3087899</a>.","apa":"Gabrielaitis, M. (2021). Fast and accurate amplitude demodulation of wideband signals. <i>IEEE Transactions on Signal Processing</i>. Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/TSP.2021.3087899\">https://doi.org/10.1109/TSP.2021.3087899</a>","short":"M. Gabrielaitis, IEEE Transactions on Signal Processing 69 (2021) 4039–4054."},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publication":"IEEE Transactions on Signal Processing"},{"month":"07","oa":1,"_id":"8268","oa_version":"Preprint","status":"public","intvolume":"        68","doi":"10.1109/TSP.2020.3010355","scopus_import":"1","volume":68,"date_created":"2020-08-16T22:00:56Z","author":[{"first_name":"Nezihe Merve","full_name":"Gurel, Nezihe Merve","last_name":"Gurel"},{"full_name":"Kara, Kaan","last_name":"Kara","first_name":"Kaan"},{"last_name":"Stojanov","full_name":"Stojanov, Alen","first_name":"Alen"},{"last_name":"Smith","full_name":"Smith, Tyler","first_name":"Tyler"},{"first_name":"Thomas","last_name":"Lemmin","full_name":"Lemmin, Thomas"},{"last_name":"Alistarh","full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian"},{"first_name":"Markus","full_name":"Puschel, Markus","last_name":"Puschel"},{"last_name":"Zhang","full_name":"Zhang, Ce","first_name":"Ce"}],"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1802.04907"}],"day":"20","publisher":"IEEE","article_type":"original","department":[{"_id":"DaAl"}],"article_processing_charge":"No","year":"2020","language":[{"iso":"eng"}],"page":"4268-4282","publication_identifier":{"issn":["1053-587X"],"eissn":["1941-0476"]},"arxiv":1,"isi":1,"date_published":"2020-07-20T00:00:00Z","external_id":{"arxiv":["1802.04907"],"isi":["000562044500001"]},"title":"Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications","publication_status":"published","date_updated":"2025-07-10T11:55:10Z","abstract":[{"text":"Modern scientific instruments produce vast amounts of data, which can overwhelm the processing ability of computer systems. Lossy compression of data is an intriguing solution, but comes with its own drawbacks, such as potential signal loss, and the need for careful optimization of the compression ratio. In this work, we focus on a setting where this problem is especially acute: compressive sensing frameworks for interferometry and medical imaging. We ask the following question: can the precision of the data representation be lowered for all inputs, with recovery guarantees and practical performance Our first contribution is a theoretical analysis of the normalized Iterative Hard Thresholding (IHT) algorithm when all input data, meaning both the measurement matrix and the observation vector are quantized aggressively. We present a variant of low precision normalized IHT that, under mild conditions, can still provide recovery guarantees. The second contribution is the application of our quantization framework to radio astronomy and magnetic resonance imaging. We show that lowering the precision of the data can significantly accelerate image recovery. We evaluate our approach on telescope data and samples of brain images using CPU and FPGA implementations achieving up to a 9x speedup with negligible loss of recovery quality.","lang":"eng"}],"quality_controlled":"1","type":"journal_article","acknowledgement":"The authors would like to thank Dr. Michiel Brentjens at the Netherlands Institute for Radio Astronomy (ASTRON) for providing radio interferometer data and Dr. Josip Marjanovic and Dr. Franciszek Hennel at the Magnetic Resonance Technology of ETH Zurich for providing their insights on the experiments. CZ and the DS3Lab gratefully acknowledge the support from the Swiss Data Science Center, Alibaba, Google Focused Research Awards, Huawei, MeteoSwiss, Oracle Labs, Swisscom, Zurich Insurance, Chinese Scholarship Council, and the Department of Computer Science at ETH Zurich.","citation":{"ama":"Gurel NM, Kara K, Stojanov A, et al. Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. <i>IEEE Transactions on Signal Processing</i>. 2020;68:4268-4282. doi:<a href=\"https://doi.org/10.1109/TSP.2020.3010355\">10.1109/TSP.2020.3010355</a>","ieee":"N. M. Gurel <i>et al.</i>, “Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications,” <i>IEEE Transactions on Signal Processing</i>, vol. 68. IEEE, pp. 4268–4282, 2020.","ista":"Gurel NM, Kara K, Stojanov A, Smith T, Lemmin T, Alistarh D-A, Puschel M, Zhang C. 2020. Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. IEEE Transactions on Signal Processing. 68, 4268–4282.","mla":"Gurel, Nezihe Merve, et al. “Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications.” <i>IEEE Transactions on Signal Processing</i>, vol. 68, IEEE, 2020, pp. 4268–82, doi:<a href=\"https://doi.org/10.1109/TSP.2020.3010355\">10.1109/TSP.2020.3010355</a>.","apa":"Gurel, N. M., Kara, K., Stojanov, A., Smith, T., Lemmin, T., Alistarh, D.-A., … Zhang, C. (2020). Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. <i>IEEE Transactions on Signal Processing</i>. IEEE. <a href=\"https://doi.org/10.1109/TSP.2020.3010355\">https://doi.org/10.1109/TSP.2020.3010355</a>","chicago":"Gurel, Nezihe Merve, Kaan Kara, Alen Stojanov, Tyler Smith, Thomas Lemmin, Dan-Adrian Alistarh, Markus Puschel, and Ce Zhang. “Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications.” <i>IEEE Transactions on Signal Processing</i>. IEEE, 2020. <a href=\"https://doi.org/10.1109/TSP.2020.3010355\">https://doi.org/10.1109/TSP.2020.3010355</a>.","short":"N.M. Gurel, K. Kara, A. Stojanov, T. Smith, T. Lemmin, D.-A. Alistarh, M. Puschel, C. Zhang, IEEE Transactions on Signal Processing 68 (2020) 4268–4282."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"IEEE Transactions on Signal Processing"},{"year":"2018","language":[{"iso":"eng"}],"page":"1676-1690","issue":"7","day":"01","publisher":"IEEE","article_processing_charge":"No","intvolume":"        66","extern":"1","doi":"10.1109/tsp.2018.2791945","scopus_import":"1","volume":66,"date_created":"2024-10-09T07:45:08Z","author":[{"first_name":"Raja","full_name":"Giryes, Raja","last_name":"Giryes"},{"last_name":"Eldar","full_name":"Eldar, Yonina C.","first_name":"Yonina C."},{"first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","orcid":"0000-0001-9699-8730","last_name":"Bronstein","full_name":"Bronstein, Alexander"},{"last_name":"Sapiro","full_name":"Sapiro, Guillermo","first_name":"Guillermo"}],"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.1605.09232","open_access":"1"}],"month":"04","oa":1,"_id":"18278","status":"public","oa_version":"Preprint","citation":{"ama":"Giryes R, Eldar YC, Bronstein AM, Sapiro G. Tradeoffs between convergence speed and reconstruction accuracy in inverse problems. <i>IEEE Transactions on Signal Processing</i>. 2018;66(7):1676-1690. doi:<a href=\"https://doi.org/10.1109/tsp.2018.2791945\">10.1109/tsp.2018.2791945</a>","ieee":"R. Giryes, Y. C. Eldar, A. M. Bronstein, and G. Sapiro, “Tradeoffs between convergence speed and reconstruction accuracy in inverse problems,” <i>IEEE Transactions on Signal Processing</i>, vol. 66, no. 7. IEEE, pp. 1676–1690, 2018.","mla":"Giryes, Raja, et al. “Tradeoffs between Convergence Speed and Reconstruction Accuracy in Inverse Problems.” <i>IEEE Transactions on Signal Processing</i>, vol. 66, no. 7, IEEE, 2018, pp. 1676–90, doi:<a href=\"https://doi.org/10.1109/tsp.2018.2791945\">10.1109/tsp.2018.2791945</a>.","ista":"Giryes R, Eldar YC, Bronstein AM, Sapiro G. 2018. Tradeoffs between convergence speed and reconstruction accuracy in inverse problems. IEEE Transactions on Signal Processing. 66(7), 1676–1690.","apa":"Giryes, R., Eldar, Y. C., Bronstein, A. M., &#38; Sapiro, G. (2018). Tradeoffs between convergence speed and reconstruction accuracy in inverse problems. <i>IEEE Transactions on Signal Processing</i>. IEEE. <a href=\"https://doi.org/10.1109/tsp.2018.2791945\">https://doi.org/10.1109/tsp.2018.2791945</a>","chicago":"Giryes, Raja, Yonina C. Eldar, Alex M. Bronstein, and Guillermo Sapiro. “Tradeoffs between Convergence Speed and Reconstruction Accuracy in Inverse Problems.” <i>IEEE Transactions on Signal Processing</i>. IEEE, 2018. <a href=\"https://doi.org/10.1109/tsp.2018.2791945\">https://doi.org/10.1109/tsp.2018.2791945</a>.","short":"R. Giryes, Y.C. Eldar, A.M. Bronstein, G. Sapiro, IEEE Transactions on Signal Processing 66 (2018) 1676–1690."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","publication":"IEEE Transactions on Signal Processing","type":"journal_article","external_id":{"arxiv":["1605.09232"]},"date_published":"2018-04-01T00:00:00Z","title":"Tradeoffs between convergence speed and reconstruction accuracy in inverse problems","publication_status":"published","date_updated":"2024-12-18T11:53:16Z","abstract":[{"lang":"eng","text":"Solving inverse problems with iterative algorithms is popular, especially for large data. Due to time constraints, the number of possible iterations is usually limited, potentially affecting the achievable accuracy. Given an error one is willing to tolerate, an important question is whether it is possible to modify the original iterations to obtain faster convergence to a minimizer achieving the allowed error without increasing the computational cost of each iteration considerably. Relying on recent recovery techniques developed for settings in which the desired signal belongs to some low-dimensional set, we show that using a coarse estimate of this set may lead to faster convergence at the cost of an additional reconstruction error related to the accuracy of the set approximation. Our theory ties to recent advances in sparse recovery, compressed sensing, and deep learning. Particularly, it may provide a possible explanation to the successful approximation of the ℓ 1 -minimization solution by neural networks with layers representing iterations, as practiced in the learned iterative shrinkage-thresholding algorithm."}],"quality_controlled":"1","publication_identifier":{"eissn":["1941-0476"],"issn":["1053-587X"]},"arxiv":1},{"intvolume":"        64","doi":"10.1109/tsp.2016.2546221","extern":"1","scopus_import":"1","volume":64,"related_material":{"link":[{"url":"https://doi.org/10.1109/TSP.2019.2961228","relation":"erratum"}]},"date_created":"2024-10-15T11:20:55Z","author":[{"last_name":"Giryes","full_name":"Giryes, Raja","first_name":"Raja"},{"full_name":"Sapiro, Guillermo","last_name":"Sapiro","first_name":"Guillermo"},{"orcid":"0000-0001-9699-8730","last_name":"Bronstein","full_name":"Bronstein, Alexander","first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6"}],"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.1504.08291","open_access":"1"}],"month":"07","oa":1,"_id":"18419","oa_version":"Preprint","status":"public","year":"2016","language":[{"iso":"eng"}],"page":"3444-3457","issue":"13","day":"01","publisher":"IEEE","article_processing_charge":"No","date_published":"2016-07-01T00:00:00Z","external_id":{"arxiv":["1504.08291"]},"title":"Deep neural networks with random Gaussian weights: A universal classification strategy?","publication_status":"published","date_updated":"2024-12-18T11:49:18Z","abstract":[{"text":"Three important properties of a classification machinery are i) the system preserves the core information of the input data; ii) the training examples convey information about unseen data; and iii) the system is able to treat differently points from different classes. In this paper, we show that these fundamental properties are satisfied by the architecture of deep neural networks. We formally prove that these networks with random Gaussian weights perform a distance-preserving embedding of the data, with a special treatment for in-class and out-of-class data. Similar points at the input of the network are likely to have a similar output. The theoretical analysis of deep networks here presented exploits tools used in the compressed sensing and dictionary learning literature, thereby making a formal connection between these important topics. The derived results allow drawing conclusions on the metric learning properties of the network and their relation to its structure, as well as providing bounds on the required size of the training set such that the training examples would represent faithfully the unseen data. The results are validated with state-of-the-art trained networks.","lang":"eng"}],"quality_controlled":"1","publication_identifier":{"issn":["1053-587X"],"eissn":["1941-0476"]},"arxiv":1,"citation":{"apa":"Giryes, R., Sapiro, G., &#38; Bronstein, A. M. (2016). Deep neural networks with random Gaussian weights: A universal classification strategy? <i>IEEE Transactions on Signal Processing</i>. IEEE. <a href=\"https://doi.org/10.1109/tsp.2016.2546221\">https://doi.org/10.1109/tsp.2016.2546221</a>","chicago":"Giryes, Raja, Guillermo Sapiro, and Alex M. Bronstein. “Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?” <i>IEEE Transactions on Signal Processing</i>. IEEE, 2016. <a href=\"https://doi.org/10.1109/tsp.2016.2546221\">https://doi.org/10.1109/tsp.2016.2546221</a>.","short":"R. Giryes, G. Sapiro, A.M. Bronstein, IEEE Transactions on Signal Processing 64 (2016) 3444–3457.","ieee":"R. Giryes, G. Sapiro, and A. M. Bronstein, “Deep neural networks with random Gaussian weights: A universal classification strategy?,” <i>IEEE Transactions on Signal Processing</i>, vol. 64, no. 13. IEEE, pp. 3444–3457, 2016.","ama":"Giryes R, Sapiro G, Bronstein AM. Deep neural networks with random Gaussian weights: A universal classification strategy? <i>IEEE Transactions on Signal Processing</i>. 2016;64(13):3444-3457. doi:<a href=\"https://doi.org/10.1109/tsp.2016.2546221\">10.1109/tsp.2016.2546221</a>","ista":"Giryes R, Sapiro G, Bronstein AM. 2016. Deep neural networks with random Gaussian weights: A universal classification strategy? IEEE Transactions on Signal Processing. 64(13), 3444–3457.","mla":"Giryes, Raja, et al. “Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?” <i>IEEE Transactions on Signal Processing</i>, vol. 64, no. 13, IEEE, 2016, pp. 3444–57, doi:<a href=\"https://doi.org/10.1109/tsp.2016.2546221\">10.1109/tsp.2016.2546221</a>."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","publication":"IEEE Transactions on Signal Processing","type":"journal_article"},{"publication_identifier":{"eissn":["1941-0476"],"issn":["1053-587X"]},"month":"06","_id":"18418","oa_version":"None","status":"public","title":"Quasi maximum likelihood MIMO blind deconvolution: Super- and sub-Gaussianity versus consistency","publication_status":"published","extern":"1","doi":"10.1109/tsp.2005.849221","date_updated":"2024-12-12T12:11:49Z","scopus_import":"1","date_published":"2005-06-20T00:00:00Z","intvolume":"        53","author":[{"orcid":"0000-0001-9699-8730","last_name":"Bronstein","full_name":"Bronstein, Alexander","first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6"},{"first_name":"M.M.","last_name":"Bronstein","full_name":"Bronstein, M.M."},{"first_name":"M.","full_name":"Zibulevsky, M.","last_name":"Zibulevsky"}],"quality_controlled":"1","abstract":[{"lang":"eng","text":"In this correspondence, we consider the problem of multi-input multi output (MIMO) quasi maximum likelihood (QML) blind deconvolution. We examine two classes of estimators, which are commonly believed to be suitable for super and sub-Gaussian sources. We state the consistency conditions and demonstrate a source distribution, for which the studied estimators are unsuitable, in the sense that they are inconsistent."}],"volume":53,"date_created":"2024-10-15T11:20:55Z","publisher":"Institute of Electrical and Electronics Engineers (IEEE)","day":"20","issue":"7","article_processing_charge":"No","type":"journal_article","citation":{"short":"A.M. Bronstein, M.M. Bronstein, M. Zibulevsky, IEEE Transactions on Signal Processing 53 (2005) 2576–2579.","apa":"Bronstein, A. M., Bronstein, M. M., &#38; Zibulevsky, M. (2005). Quasi maximum likelihood MIMO blind deconvolution: Super- and sub-Gaussianity versus consistency. <i>IEEE Transactions on Signal Processing</i>. Institute of Electrical and Electronics Engineers (IEEE). <a href=\"https://doi.org/10.1109/tsp.2005.849221\">https://doi.org/10.1109/tsp.2005.849221</a>","chicago":"Bronstein, Alex M., M.M. Bronstein, and M. Zibulevsky. “Quasi Maximum Likelihood MIMO Blind Deconvolution: Super- and Sub-Gaussianity versus Consistency.” <i>IEEE Transactions on Signal Processing</i>. Institute of Electrical and Electronics Engineers (IEEE), 2005. <a href=\"https://doi.org/10.1109/tsp.2005.849221\">https://doi.org/10.1109/tsp.2005.849221</a>.","ista":"Bronstein AM, Bronstein MM, Zibulevsky M. 2005. Quasi maximum likelihood MIMO blind deconvolution: Super- and sub-Gaussianity versus consistency. IEEE Transactions on Signal Processing. 53(7), 2576–2579.","mla":"Bronstein, Alex M., et al. “Quasi Maximum Likelihood MIMO Blind Deconvolution: Super- and Sub-Gaussianity versus Consistency.” <i>IEEE Transactions on Signal Processing</i>, vol. 53, no. 7, Institute of Electrical and Electronics Engineers (IEEE), 2005, pp. 2576–79, doi:<a href=\"https://doi.org/10.1109/tsp.2005.849221\">10.1109/tsp.2005.849221</a>.","ama":"Bronstein AM, Bronstein MM, Zibulevsky M. Quasi maximum likelihood MIMO blind deconvolution: Super- and sub-Gaussianity versus consistency. <i>IEEE Transactions on Signal Processing</i>. 2005;53(7):2576-2579. doi:<a href=\"https://doi.org/10.1109/tsp.2005.849221\">10.1109/tsp.2005.849221</a>","ieee":"A. M. Bronstein, M. M. Bronstein, and M. Zibulevsky, “Quasi maximum likelihood MIMO blind deconvolution: Super- and sub-Gaussianity versus consistency,” <i>IEEE Transactions on Signal Processing</i>, vol. 53, no. 7. Institute of Electrical and Electronics Engineers (IEEE), pp. 2576–2579, 2005."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","year":"2005","language":[{"iso":"eng"}],"publication":"IEEE Transactions on Signal Processing","page":"2576-2579"}]