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Weifeng Han

Publications and source records attributed to Weifeng Han.

2 recordsLinked to original sources

Breaking the trade-off between invisibility and sensitivity in electromagnetic sensing

Weak electromagnetic signals demand highly sensitive sensors, yet increasing a sensor's sensitivity inevitably strengthens its interaction with the surrounding field, producing scattering that perturbs the very signals being measured. Conversely, existing cloaking strategies suppress scattering only by isolating the sensor from incident waves, thereby compromising signal reception. Resolving this long-standing trade-off between invisibility and sensitivity has remained an outstanding challenge. Here we overcome this dilemma through an integrated transformation-optical architecture that co-designs the entire sensing system, including the electrically large sensor body, the subwavelength sensing probe, and their electrical interconnection. The proposed multifunctional core-shell structure guides incident waves around the sensor body while simultaneously concentrating them into the sensing region without disturbing the external electromagnetic field. A deep-subwavelength aperture preserves electrical connectivity without degrading either cloaking or field concentration, enabling invisible sensing within a single platform. A microwave prototype based on practical optic-null-medium metamaterials experimentally demonstrates broadband scattering suppression exceeding 3 dB together with an average sixfold enhancement of the detected signal over 4.9-5.1 GHz. By simultaneously eliminating measurement-induced field perturbation and amplifying the local sensing field, our approach establishes a general framework for invisible yet highly responsive electromagnetic sensors, opening new opportunities for weak-signal detection in biomedical diagnostics, secure communications, quantum technologies, and deep-space exploration.

physics.optics

Deep Neural Network-Based Quantized Signal Reconstruction for DOA Estimation

For a massive multiple-input-multiple-output (MIMO) system using intelligent reflecting surface (IRS) equipped with radio frequency (RF) chains, the multi-channel RF chains are expensive compared to passive IRS, especially, when the high-resolution and high-speed analog to digital converters (ADC) are used in each RF channel. In this letter, a direction of angle (DOA) estimation problem is investigated with low-cost ADC in IRS, and we propose a deep neural network (DNN) as a recovery method for the low-resolution sampled signal. Different from the existing denoising convolutional neural network (DnCNN) for Gaussian noise, the proposed DNN with fully connected (FC) layers estimates the quantization noise caused by the ADC. Then, the denoised signal is subjected to the DOA estimation, and the recovery performance for the quantized signal is evaluated by DOA estimation. Simulation results show that under the same training conditions, the better reconstruction performance is achieved by the proposed network than state-of-the-art methods. The performance of the DOA estimation using 1-bit ADC is improved to exceed that using 2-bit ADC.

eess.SP