1.Wohlers, T (2013) Additive Manufacturing and 3D Printing State of the Industry. Fort Collins, CO: Wohlers Associates.
2.Ryzhkov, A, Matrosov, SY, Melnikov, V, Zrnic, D, Zhang, P, Cao, Q, Knight, M, Simmer, C and Troemel, S (2015) 3D printed microfluidics for biological applications. Lab Chip 15, 3627–3637.
3.Otter, WJ and Lucyszyn, S (2016) 3-D printing of microwave components for 21st century applications. In 2016 IEEE MTT-S International Microwave Workshop Series on Advanced Material and Processes for RF and THz Applications (IMWSAMP), Chengdu, 2016, pp. 1–3, doi: 10.1109/IMWS-AMP.2016.7588327.
4.Becquaert, M, Cristofani, E, Pandey, G, Vandewal, M, Stiens, J and Deligiannis, N (2016) Compressed sensing mm-wave SAR for non-destructive testing applications using side information. In 2016 IEEE Radar Conference, Philadelphia, PA, 2016, pp. 1–5, doi: 10.1109/RADAR.2016.7485244.
5.Solimene, R, Catapano, I, Gennarelli, G, Cuccaro, A, Dell'Aversano, A and Soldovieri, F (2014) SAR imaging algorithms and some unconventional applications: a unified mathematical overview. Signal Processing Magazine IEEE 31, 90–98.
6.Zhu, J, Wen, J and Zhang, Y (2013) A new algorithm for SAR image despeckling using an enhanced Lee filter and median filter. In 6th International Congress on Image and Signal Processing (CISP), Hangzhou, 2013, pp. 224–228, doi: 10.1109/CISP.2013.6743991.
7.Dike, HU, Zhou, Y, Deveerasetty, KK and Wu, Q (2018) Unsupervised learning based on artificial neural network: a review. In Proceedings of the IEEE International Conference on Cyborgs and Bionic Systems (CBS), Shenzhen, 2018, pp. 322–327, doi: 10.1109/CBS.2018.8612259.
8.Elsaadouny, M, Barowski, J, Jebramcik, J and Rolfes, I (2019) Investigation on scattering characteristics of a 3D-printed sample based on SAR processing. In 2019 European Microwave Conference in Central Europe (EuMCE), Prague, Czech Republic, pp. 273–276.
9.Jaeschke, T, Bredendiek, C, Kueppers, S, Schulz, C, Baer, C and Pohl, N (2016) Cross-polarized multi-channel W-band radar for turbulent flow velocity measurements. In IEEE International Microwave Symposium 2016, San Francisco, CA, USA, pp. 1–4, doi: 10.1109/MWSYM.2016.7540256.
10.Linchen, Z, Jindong, Z and Daiyin, Z (2013) FPGA implementation of polar format algorithm for airborne spotlight SAR processing. In 2013 IEEE 11th International Conference on Dependable, Autonomic and Secure Computing (DASC), Chengdu, 2013, pp. 143–147, doi: 10.1109/DASC.2013.52.
11.Song, X and Yu, W (2017) Processing video-SAR data with the fast backprojection method. IEEE Transactions on Aerospace and Electronic Systems 52, 2838–2848.
12.Gorham, LA and Moore, LJ (2010) SAR image formation toolbox for MATLAB. Proceedings of the SPIE 7699, Volume 7699, id. 769906, April.
13.Zhang, F, Wang, B-n and Xiang, M-s (2010) Accelerating InSAR raw data simulation on GPU using CUDA. In 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA, pp. 2932–2935, doi: 10.1109/IGARSS.2010.5650737.
14.Argenti, F, Lapini, A and Alparone, L (2013) A tutorial on speckle reduction in synthetic aperture radar images. IEEE Geoscience Remote Sensing Magazine 1, 6–35.
15.Loussaief, S and Abdelkrim, A (2016) Machine learning framework for image classification. In 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), Hammamet, 2016, pp. 58–61, doi: 10.1109/SETIT.2016.7939841.
16.Zhang, Y, Li, P, Jin, Y and Choe, Y (2015) A digital liquid state machine with biologically inspired learning and its application to speech recognition. IEEE Transactions on Neural Networks and Learning Systems 26, 2635–2649.
17.Ji, S, Xu, W, Yang, M and Yu, K (2013) 3D convolutional neural networks for human action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 221–231.
18.Shin, H, Roth, HR, Gao, M, Lu, L, Xu, Z, Nogues, I, Yao, J, Mollura, D and Summers, RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging 35, 1285–1298.
19.Syakur, M, Khotimah, B, Rochman, E and Satoto, B (2018) Integration k-means clustering method and elbow method for identification of the best customer profile cluster. In IOP Conference Series: Materials Science and Engineering, vol. 336.
20.Marutho, D, Hendra Handaka, S, Wijaya, E and Muljono, (2018) The determination of cluster number at k-mean using elbow method and purity evaluation on headline news. In 2018 International Seminar on Application for Technology of Information and Communication, Semarang.
21.Gupta, T and Panda, SP (2019) Clustering validation of CLARA and k-means using silhouette & DUNN measures on Iris dataset. In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India, pp. 10–13, doi: 10.1109/COMITCon.2019.8862199.
22.Carreira-Perpinn, MA (2015) A review of mean-shift algorithms for clustering. arXiv preprint arXiv:1503.00687.
23.Nazari, Z, Kang, D, Asharif, MR, Sung, Y and Ogawa, S (2015) A new hierarchical clustering algorithm. In 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Okinawa, pp. 148–152, doi: 10.1109/ICIIBMS.2015.7439517.