Weyl numbers of embeddings of tensor product Besov spaces. - Van Kien Nguyen; Winfried Sickel (J. Approx. Theory, 2015)

A low noise figure and high conversion gain down-conversion mixer for 94 GHz image radar sensors in 90 nm CMOS. - Yo-Sheng Lin; Chih-Chung Chen; Jay-Ming Liu; Yun-Wen Lin; Van Kien Nguyen (RWS, 2016)

Design and implementation of a high-performance CMOS dual balun for millimeter-wave star mixer and four-way power amplifier. - Yo-Sheng Lin; Yun-Wen Lin; Chien-Chin Wang; Chih-Chung Chen; Van Kien Nguyen (RWS, 2016)

A wideband power amplifier with 13.2 dBm Psat and 19.5% PAE for 60∼94 GHz wireless communication systems in 90 nm CMOS. - Yo-Sheng Lin; Van Kien Nguyen; Jia-Wei Gao 001; Chien-Chin Wang; Yun-Wen Lin; Chih-Chung Chen (RWS, 2016)

A 7.2 mW 74∼82 GHz CMOS low-noise amplifier with 17.3±1.5 dB gain and 7.7±0.3 dB NF for automotive radar systems. - Yo-Sheng Lin; Van Kien Nguyen; Hsin-Chen Lin; Chien-Yo Lee; Chien-Chin Wang; Chih-Chung Chen; Yun-Wen Lin (RWS, 2016)

Weyl and Bernstein numbers of embeddings of Sobolev spaces with dominating mixed smoothness. - Van Kien Nguyen (J. Complex., 2016)

Gelfand numbers of embeddings of mixed Besov spaces. - Van Kien Nguyen (J. Complex., 2017)

94-GHz CMOS Power Amplifiers Using Miniature Dual Y-Shaped Combiner With RL Load. - Yo-Sheng Lin; Van Kien Nguyen (IEEE Trans. Circuits Syst. I Regul. Pap., 2017)

Monte Carlo methods for uniform approximation on periodic Sobolev spaces with mixed smoothness. - Glenn Byrenheid; Robert J. Kunsch; Van Kien Nguyen (J. Complex., 2018)

Sparse-grid sampling recovery and deep ReLU neural networks in high-dimensional approximation. - Dinh Dung; Van Kien Nguyen (CoRR, 2020)

Greedy algorithms and Kolmogorov widths in Banach spaces. - Van Kien Nguyen (J. Approx. Theory, 2020)

Unified Analysis of Periodization-Based Sampling Methods for Matérn Covariances. - Markus Bachmayr; Ivan G. Graham; Van Kien Nguyen; Robert Scheichl (SIAM J. Numer. Anal., 2020)

High-dimensional nonlinear approximation by parametric manifolds in Hölder-Nikol'skii spaces of mixed smoothness. - Dinh Dung; Van Kien Nguyen (CoRR, 2021)

Computation complexity of deep ReLU neural networks in high-dimensional approximation. - Dinh Dung; Van Kien Nguyen; Mai Xuan Thao (CoRR, 2021)

Deep ReLU neural network approximation of parametric and stochastic elliptic PDEs with lognormal inputs. - Dinh Dung; Van Kien Nguyen; Duong Thanh Pham (CoRR, 2021)

Deep ReLU neural networks in high-dimensional approximation. - Dinh Dung; Van Kien Nguyen (Neural Networks, 2021)

Analyticity and sparsity in uncertainty quantification for PDEs with Gaussian random field inputs. - Dinh Dung; Van Kien Nguyen; Christoph Schwab; Jakob Zech (CoRR, 2022)

Analyticity of Parametric and Stochastic Elliptic Eigenvalue Problems with an Application to Quasi-Monte Carlo Methods. - Van Kien Nguyen (CoRR, 2022)

Optimal numerical integration and approximation of functions on Rd equipped with Gaussian measure. - Dinh Dung; Van Kien Nguyen (CoRR, 2022)

s-Numbers of embeddings of weighted Wiener algebras. - Van Dung Nguyen; Van Kien Nguyen; Winfried Sickel (J. Approx. Theory, 2022)

Deep ReLU neural network approximation in Bochner spaces and applications to parametric PDEs. - Dinh Dung; Van Kien Nguyen; Duong Thanh Pham (J. Complex., 2023)

Pseudo s-numbers of embeddings of Gaussian weighted Sobolev spaces. - Van Kien Nguyen (J. Approx. Theory, 2025)