{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T07:38:10Z","timestamp":1753601890198,"version":"3.37.3"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/S000631\/1"],"award-info":[{"award-number":["EP\/S000631\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Sign Process Syst"],"published-print":{"date-parts":[[2022,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Rapid reconstruction of depth images from sparsely sampled data is important for many applications in machine perception, including robot or vehicle assistance or autonomy. Approximate computing techniques have been widely adopted to reduce resource consumption and increase efficiency in energy and resource constrained systems, especially targeted at FPGA and solid state implementation. Whereas previous work has focused on approximate, but static, representation of data in LiDAR systems, in this paper we show how the flexibility of an arbitrary precision accelerator with fine-grain tuning allows a better trade-off between algorithmic performance and implementation efficiency. A mixed precision framework of <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\ell _1$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mi>\u2113<\/mml:mi>\n                    <mml:mn>1<\/mml:mn>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> solvers is presented, with compact ADMM and PGD, for the lasso problem, enabling compressive depth reconstruction by varying the precision scaling in single bit granularity during the iterative optimization process. Implementing mixed precision <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\ell _1$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mi>\u2113<\/mml:mi>\n                    <mml:mn>1<\/mml:mn>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> solvers on an FPGA with a pipelined architecture for depth image reconstruction across various sensing scenarios, over <jats:bold>74%<\/jats:bold> savings in hardware resources and <jats:bold>60%<\/jats:bold> in power are achieved with only minor reductions in reconstructed depth image quality when compared to single float precision, while over <jats:bold>10%<\/jats:bold> saving in hardware resources and power is achieved compared to relative consistently reduced precision solutions.<\/jats:p>","DOI":"10.1007\/s11265-022-01766-3","type":"journal-article","created":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T18:03:41Z","timestamp":1653329021000},"page":"1083-1099","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Efficient Reconfigurable Mixed Precision $$\\ell _1$$ Solver for Compressive Depth Reconstruction"],"prefix":"10.1007","volume":"94","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9332-2858","authenticated-orcid":false,"given":"Yun","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew M.","family":"Wallace","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jo\u00e3o F.C.","family":"Mota","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"A\u00dfmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brian","family":"Stewart","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,23]]},"reference":[{"key":"1766_CR1","doi-asserted-by":"publisher","unstructured":"Park, K., Kim, S., & Sohn, K. (2018). High-precision depth estimation with the 3D lidar and stereo fusion. In\u00a02018 IEEE International Conference on Robotics and Automation (ICRA)\u00a0(pp. 2156\u20132163).\u00a0https:\/\/doi.org\/10.1109\/ICRA.2018.8461048","DOI":"10.1109\/ICRA.2018.8461048"},{"key":"1766_CR2","doi-asserted-by":"publisher","unstructured":"Latif, R., & Saddik, A. (2019).\u00a0Slam algorithms implementation in a UAV, based on a heterogeneous system: A survey. In\u00a02019 4th World Conference on Complex Systems (WCCS) (pp. 1\u20136).\u00a0https:\/\/doi.org\/10.1109\/ICoCS.2019.8930783","DOI":"10.1109\/ICoCS.2019.8930783"},{"issue":"1","key":"1766_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TMRB.2019.2957061","volume":"2","author":"L Qian","year":"2020","unstructured":"Qian, L., Wu, J. Y., DiMaio, S. P., Navab, N., & Kazanzides, P. (2020). A review of augmented reality in robotic-assisted surgery. IEEE Transactions on Medical Robotics and Bionics, 2(1), 1\u201316. https:\/\/doi.org\/10.1109\/TMRB.2019.2957061","journal-title":"IEEE Transactions on Medical Robotics and Bionics"},{"key":"1766_CR4","doi-asserted-by":"publisher","unstructured":"Liu, M., Hou, Z., Sun, Z., Yin, N., Yang, H., Wang, Y., Chu, Z., & Kong, H. (2019) Campus guide: A lidar-based mobile robot. In\u00a02019 European Conference on Mobile Robots (ECMR) (pp. 1\u20136).\u00a0https:\/\/doi.org\/10.1109\/ECMR.2019.8870916","DOI":"10.1109\/ECMR.2019.8870916"},{"issue":"12","key":"1766_CR5","doi-asserted-by":"publisher","first-page":"2214","DOI":"10.1109\/JPROC.2020.3030121","volume":"108","author":"W Liu","year":"2020","unstructured":"Liu, W., Gu, C., O\u2019Neill, M., Qu, G., Montuschi, P., & Lombardi, F. (2020). Security in approximate computing and approximate computing for security: Challenges and opportunities. Proceedings of the IEEE, 108(12), 2214\u20132231. https:\/\/doi.org\/10.1109\/JPROC.2020.3030121","journal-title":"Proceedings of the IEEE"},{"key":"1766_CR6","doi-asserted-by":"publisher","unstructured":"Roy, K., & Raghunathan, A. (2015).\u00a0Approximate computing: An energy-efficient computing technique for error resilient applications. In\u00a02015 IEEE Computer Society Annual Symposium on VLSI (pp. 473\u2013475).\u00a0https:\/\/doi.org\/10.1109\/ISVLSI.2015.130","DOI":"10.1109\/ISVLSI.2015.130"},{"key":"1766_CR7","doi-asserted-by":"publisher","unstructured":"Pandey, P., He, Q., Pompili, D., & Tron, R. (2018).\u00a0Light-weight object detection and decision making via approximate computing in resource-constrained mobile robots. In\u00a02018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)\u00a0(pp. 6776\u20136781).\u00a0https:\/\/doi.org\/10.1109\/IROS.2018.8594200","DOI":"10.1109\/IROS.2018.8594200"},{"key":"1766_CR8","doi-asserted-by":"publisher","unstructured":"Ibrahim, A., Osta, M., Alameh, M., Saleh, M., Chible, H., & Valle, M. (2018)\u00a0Approximate computing methods for embedded machine learning. In\u00a02018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS) (pp. 845\u2013848).\u00a0https:\/\/doi.org\/10.1109\/ICECS.2018.8617877","DOI":"10.1109\/ICECS.2018.8617877"},{"key":"1766_CR9","doi-asserted-by":"crossref","unstructured":"Agrawal, A., Choi, J., Gopalakrishnan, K., Gupta, S., Nair, R., Oh, J., Prener, D.A., Shukla, S., Srinivasan, V., & Sura, Z. (2016)\u00a0Approximate computing: Challenges and opportunities. 2016 IEEE International Conference on Rebooting Computing (ICRC)\u00a01\u20138.\u00a0","DOI":"10.1109\/ICRC.2016.7738674"},{"issue":"11","key":"1766_CR10","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1109\/LED.2012.2214760","volume":"33","author":"EAG Webster","year":"2012","unstructured":"Webster, E. A. G., Grant, L. A., & Henderson, R. K. (2012). A high-performance single-photon avalanche diode in 130-nm CMOS imaging technology. IEEE Electron Device Letters, 33(11), 1589\u20131591. https:\/\/doi.org\/10.1109\/LED.2012.2214760","journal-title":"IEEE Electron Device Letters"},{"key":"1766_CR11","doi-asserted-by":"publisher","unstructured":"A\u00dfmann, A., Stewart, B., Mota, J. F. C., & Wallace, A. M. (2019)\u00a0Compressive super-pixel lidar for high-framerate 3D depth imaging. In\u00a02019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)\u00a0(pp. 1\u20135).\u00a0https:\/\/doi.org\/10.1109\/GlobalSIP45357.2019.8969177","DOI":"10.1109\/GlobalSIP45357.2019.8969177"},{"key":"1766_CR12","doi-asserted-by":"publisher","unstructured":"Nguyen, M. U., Dao, T. T., & Tang, V. H. (2018).\u00a0Efficient depth image reconstruction using accelerated proximal gradient method. In\u00a02018 10th International Conference on Knowledge and Systems Engineering (KSE)\u00a0(pp. 1\u20136).\u00a0https:\/\/doi.org\/10.1109\/KSE.2018.8573361","DOI":"10.1109\/KSE.2018.8573361"},{"key":"1766_CR13","doi-asserted-by":"publisher","unstructured":"A\u00dfmann A., Wu, Y., Stewart, B., & Wallace, A. M. (2021).\u00a0Accelerated 3D image reconstruction for resource constrained systems. In\u00a02020 28th European Signal Processing Conference (EUSIPCO) (pp. 565\u2013569). https:\/\/doi.org\/10.23919\/Eusipco47968.2020.9287749","DOI":"10.23919\/Eusipco47968.2020.9287749"},{"key":"1766_CR14","doi-asserted-by":"publisher","first-page":"4268","DOI":"10.1109\/TSP.2020.3010355","volume":"68","author":"NM G\u00fcrel","year":"2020","unstructured":"G\u00fcrel, N. M., Kara, K., Stojanov, A., Smith, T., Lemmin, T., Alistarh, D.,\u00a0P\u00fcschel, 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\u20134282. https:\/\/doi.org\/10.1109\/TSP.2020.3010355","journal-title":"IEEE Transactions on Signal Processing"},{"key":"1766_CR15","doi-asserted-by":"crossref","unstructured":"Wills, A., Mills, A., & Ninness, B. (2011).\u00a0FPGA implementation of an interior-point solution for linear model predictive control. 18th IFAC World Congress.","DOI":"10.3182\/20110828-6-IT-1002.02857"},{"key":"1766_CR16","doi-asserted-by":"publisher","unstructured":"Wu, Y., Mota, J. F. C., & Wallace, A. M. (2020).\u00a0Approximate lasso model predictive control for resource constrained systems. In\u00a02020 Sensor Signal Processing for Defence Conference (SSPD) (pp. 1\u20135).\u00a0https:\/\/doi.org\/10.1109\/SSPD47486.2020.9272000","DOI":"10.1109\/SSPD47486.2020.9272000"},{"key":"1766_CR17","doi-asserted-by":"publisher","unstructured":"Wu, Y., Assmann, A., Stewart, B., & Wallace, A. M. (2021).\u00a0Energy efficient approximate 3d image reconstruction. IEEE Transactions on Emerging Topics in Computing, 1.\u00a0https:\/\/doi.org\/10.1109\/TETC.2021.3116471","DOI":"10.1109\/TETC.2021.3116471"},{"issue":"4","key":"1766_CR18","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1177\/10943420211003313","volume":"35","author":"A Abdelfattah","year":"2021","unstructured":"Abdelfattah, A., Anzt, H., Boman, E. G., Carson, E., Cojean, T., Dongarra, J.,\u00a0Fox, A., Gates, M., Higham, N. J., Li, X. S., Loe, J., Luszczek, P., Pranesh, S., Rajamanickam, S., Ribizel, T., Smith, B. F., Swirydowicz, K., Thomas, S., Tomov, S., Tsai, Y. M., & Yang, U. M.\u00a0(2021). A survey of numerical linear algebra methods utilizing mixed-precision arithmetic. The International Journal of High Performance Computing Applications, 35(4), 344\u2013369. https:\/\/doi.org\/10.1177\/10943420211003313","journal-title":"The International Journal of High Performance Computing Applications"},{"key":"1766_CR19","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1137\/17M1140819","volume":"40","author":"E Carson","year":"2018","unstructured":"Carson, E., & Higham, N. (2018). Accelerating the solution of linear systems by iterative refinement in three precisions. SIAM Journal on Scientific Computing, 40, 817\u2013847. https:\/\/doi.org\/10.1137\/17M1140819","journal-title":"SIAM Journal on Scientific Computing"},{"issue":"3","key":"1766_CR20","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1145\/1816038.1815968","volume":"38","author":"R Hameed","year":"2010","unstructured":"Hameed, R., Qadeer, W., Wachs, M., Azizi, O., Solomatnikov, A., Lee, B. C.,\u00a0Richardson, S., Kozyrakis, C., & Horowitz, M.\u00a0(2010). Understanding sources of inefficiency in general-purpose chips. SIGARCH Computer Architecture News, 38(3), 37\u201347. https:\/\/doi.org\/10.1145\/1816038.1815968","journal-title":"SIGARCH Computer Architecture News"},{"issue":"12","key":"1766_CR21","doi-asserted-by":"publisher","first-page":"2170","DOI":"10.1109\/TPAMI.2007.1122","volume":"29","author":"S Hern\u00e1ndez-Mar\u00edn","year":"2007","unstructured":"Hern\u00e1ndez-Mar\u00edn, S., Wallace, A. M., & Gibson, G. J. (2007). Bayesian analysis of lidar signals with multiple returns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(12), 2170\u20132180. https:\/\/doi.org\/10.1109\/TPAMI.2007.1122","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1766_CR22","doi-asserted-by":"publisher","unstructured":"Wallace, A. M., Ye, J., Krichel, N. J., McCarthy, A., Collins, R. J., & Buller, G. S. (2010).\u00a0Full waveform analysis for long-range 3D imaging laser radar. EURASIP Journal on Advances in Signal Processing.\u00a0https:\/\/doi.org\/10.1155\/2010\/896708","DOI":"10.1155\/2010\/896708"},{"key":"1766_CR23","doi-asserted-by":"publisher","unstructured":"Halimi, A., Tobin, R., McCarthy, A., McLaughlin, S., & Buller, G. S. (2017) Restoration of multilayered single-photon 3D Lidar images. In 25th IEEE European Signal Processing Conference (EUSIPCO)\u00a0(pp. 708\u2013712).\u00a0https:\/\/doi.org\/10.23919\/EUSIPCO.2017.8081299","DOI":"10.23919\/EUSIPCO.2017.8081299"},{"issue":"1","key":"1766_CR24","doi-asserted-by":"publisher","first-page":"4984","DOI":"10.1038\/s41467-019-12943-7","volume":"10","author":"J Tachella","year":"2019","unstructured":"Tachella, J., Altmann, Y., Mellado, N., McCarthy, A., Tobin, R., Buller, G. S., Tourneret, J.-Y., & McLaughlin, S. (2019). Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers. Nature Communications, 10(1), 4984. https:\/\/doi.org\/10.1038\/s41467-019-12943-7","journal-title":"Nature Communications"},{"key":"1766_CR25","unstructured":"Patanwala, S. M., Gyongy, I., Dutton, N. A. W., Rae, B. R., & Henderson, R .K. (2019).\u00a0A reconfigurable 40nm CMOS SPAD array for lidar receiver validation. In International Image Sensor Workshop (IISW)."},{"key":"1766_CR26","doi-asserted-by":"publisher","unstructured":"Henderson, R. K., Johnston, N., Hutchings, S. W., Gyongy, I., Abbas, T. A., Dutton, N., Tyler, M., Chan, S., & Leach, J. (2019)\u00a05.7 a 256$$\\times$$256 40nm\/90nm CMOS 3D-stacked 120db dynamic-range reconfigurable time-resolved SPAD imager. In\u00a02019 IEEE International Solid- State Circuits Conference - (ISSCC)\u00a0(pp. 106\u2013108).\u00a0https:\/\/doi.org\/10.1109\/ISSCC.2019.8662355","DOI":"10.1109\/ISSCC.2019.8662355"},{"issue":"3","key":"1766_CR27","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1088\/0266-5611\/23\/3\/008","volume":"23","author":"E Cand\u00e8s","year":"2007","unstructured":"Cand\u00e8s, E., & Romberg, J. (2007). Sparsity and incoherence in compressive sampling. Inverse Problems, 23(3), 969\u2013985. https:\/\/doi.org\/10.1088\/0266-5611\/23\/3\/008","journal-title":"Inverse Problems"},{"issue":"4","key":"1766_CR28","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","volume":"52","author":"DL Donoho","year":"2006","unstructured":"Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289\u20131306. https:\/\/doi.org\/10.1109\/TIT.2006.871582","journal-title":"IEEE Transactions on Information Theory"},{"key":"1766_CR29","doi-asserted-by":"publisher","unstructured":"Tibshirani, R. (1996).\u00a0Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) 58, 267\u2013288.\u00a0https:\/\/doi.org\/10.2307\/2346178","DOI":"10.2307\/2346178"},{"key":"1766_CR30","doi-asserted-by":"publisher","unstructured":"Boyd, S., Parikh, N., Chu, E., & Peleato, B. (2011)\u00a0Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 3(1), 1\u2013122\u00a0arXiv:0307085 [cond-mat].\u00a0https:\/\/doi.org\/10.1561\/2200000016","DOI":"10.1561\/2200000016"},{"issue":"3","key":"1766_CR31","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1561\/2400000003","volume":"1","author":"N Parikh","year":"2014","unstructured":"Parikh, N., & Boyd, S. (2014). Proximal algorithms, 1(3), 127\u2013239. https:\/\/doi.org\/10.1561\/2400000003","journal-title":"Proximal algorithms"},{"key":"1766_CR32","doi-asserted-by":"publisher","unstructured":"Flegar, G., Scheidegger, F., Novakovi\u0107, V., Mariani, G., Tom\u2019s, A. E., Malossi, A. C. I., & Quintana-Ort\u00ed, E. S. (2019).\u00a0Floatx: A c++ library for customized floating-point arithmetic. ACM Transactions on Mathematical Software (TOMS), 45(4).\u00a0https:\/\/doi.org\/10.1145\/3368086","DOI":"10.1145\/3368086"},{"key":"1766_CR33","doi-asserted-by":"publisher","unstructured":"Muller, J.-M., Brunie, N., de Dinechin, F., Jeannerod, C.-P., Joldes, M., Lefvre, V., Melquiond, G., Revol, N., & Torres, S. (2018).\u00a0Handbook of floating-point arithmetic. Birkh\u00e4user.\u00a0https:\/\/doi.org\/10.1007\/978-0-8176-4705-6","DOI":"10.1007\/978-0-8176-4705-6"},{"key":"1766_CR34","doi-asserted-by":"publisher","unstructured":"Langou, J., Langou, J., Luszczek, P., Kurzak, J., Buttari, A., & Dongarra, J. (2006)\u00a0Exploiting the performance of 32 bit floating point arithmetic in obtaining 64 bit accuracy (revisiting iterative refinement for linear systems). In Proceedings of the 2006 ACM\/IEEE Conference on Supercomputing. SC \u201906\u00a0(p. 113). Association for Computing Machinery, New York, NY, USA.\u00a0https:\/\/doi.org\/10.1145\/1188455.1188573","DOI":"10.1145\/1188455.1188573"},{"key":"1766_CR35","doi-asserted-by":"crossref","unstructured":"Yu, H., Han, Q., Li, J., Shi, J., Cheng, G.-L., & Fan, B. (2020)\u00a0Search what you want: Barrier panelty NAS for mixed precision quantization. ArXiv abs\/2007.10026.","DOI":"10.1007\/978-3-030-58545-7_1"},{"key":"1766_CR36","unstructured":"Smith, S. W. (1997).\u00a0The scientist and engineer\u2019s guide to digital signal processing. California Technical Publishing, USA."},{"key":"1766_CR37","doi-asserted-by":"publisher","unstructured":"Boyd, S., & Vandenberghe, L. (2004).\u00a0Convex optimization. Cambridge University Press.\u00a0https:\/\/doi.org\/10.1017\/CBO9780511804441","DOI":"10.1017\/CBO9780511804441"},{"key":"1766_CR38","doi-asserted-by":"publisher","unstructured":"Beck, A., & Teboulle, M. (2009).\u00a0Gradient-based algorithms with applications to signal-recovery problems. Cambridge University Press.\u00a0https:\/\/doi.org\/10.1017\/CBO9780511804458.003","DOI":"10.1017\/CBO9780511804458.003"},{"key":"1766_CR39","unstructured":"Xilinx. (2020).\u00a0Vivado design suite user guide: High-level synthesis. Accessed on 11\/10\/2020\u00a0https:\/\/www.xilinx.com\/support\/documentation\/sw_manuals\/xilinx2020_1\/ug902-vivado-high-level-synthesis.pdf"},{"key":"1766_CR40","doi-asserted-by":"crossref","unstructured":"Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012).\u00a0Indoor segmentation and support inference from RGBD images. In A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, & C. Schmid (Eds.), Computer Vision \u2013 ECCV 2012\u00a0(pp. 746\u2013760). Springer, Berlin, Heidelberg.","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"1766_CR41","doi-asserted-by":"publisher","unstructured":"Wang,\u00a0Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing,\u00a013(4), 600\u2013612.\u00a0https:\/\/doi.org\/10.1109\/TIP.2003.819861","DOI":"10.1109\/TIP.2003.819861"},{"key":"1766_CR42","doi-asserted-by":"publisher","unstructured":"Gaidon, A., Wang, Q., Cabon, Y., & Vig, E. (2016).\u00a0Virtualworlds as proxy for multi-object tracking analysis. In\u00a02016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)\u00a0(pp. 4340\u20134349). IEEE Computer Society, Los Alamitos, CA, USA.\u00a0https:\/\/doi.org\/10.1109\/CVPR.2016.470","DOI":"10.1109\/CVPR.2016.470"},{"key":"1766_CR43","doi-asserted-by":"publisher","unstructured":"Chhabra, P., Maccarone, A., McCarthy, A., Buller, G., & Wallace, A. (2016).\u00a0Discriminating underwater LiDAR target signatures using sparse multi-spectral depth codes. In\u00a02016 Sensor Signal Processing for Defence, SSPD 2016.\u00a0https:\/\/doi.org\/10.1109\/SSPD.2016.7590595","DOI":"10.1109\/SSPD.2016.7590595"},{"key":"1766_CR44","doi-asserted-by":"publisher","unstructured":"Chhabra, P., Maccarone, A., McCarthy, A., Buller, G., & Wallace, A. (2016).\u00a0Discriminating underwater lidar target signatures using sparse multi-spectral depth codes. In\u00a02016 Sensor Signal Processing for Defence (SSPD)\u00a0(pp. 1\u20135).\u00a0https:\/\/doi.org\/10.1109\/SSPD.2016.7590595","DOI":"10.1109\/SSPD.2016.7590595"}],"container-title":["Journal of Signal Processing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11265-022-01766-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11265-022-01766-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11265-022-01766-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,9]],"date-time":"2022-09-09T19:40:15Z","timestamp":1662752415000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11265-022-01766-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,23]]},"references-count":44,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["1766"],"URL":"https:\/\/doi.org\/10.1007\/s11265-022-01766-3","relation":{},"ISSN":["1939-8018","1939-8115"],"issn-type":[{"type":"print","value":"1939-8018"},{"type":"electronic","value":"1939-8115"}],"subject":[],"published":{"date-parts":[[2022,5,23]]},"assertion":[{"value":"16 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 May 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There are no human or animal related experiments conducted in this research. All the authors are agreed with this submission. This work is invited extension from previous SiPS 2021 paper entitled \u201cMixed Precision 1 Solver for Compressive Depth Reconstruction: An ADMM Case Study\u201d. Our related previous work are in reference [, , ]. We declare that this work fulfills the standard requirement of invited extended publication with over  new materials than its previous conference version. All the outcomes are not published elsewhere at the same time of this submission.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}