In this letter, a three-dimensional (3-D) wide passband frequency selective surface (FSS) is proposed. Compared with planar wide passband FSSs, 3-D structures usually present superior roll-off characteristics, especially under a lower frequency. However, 3-D wideband FSSs typically have greater insertion loss in the passband, and the transmission characteristic is sensitive to the angle of incident electromagnetic wave. The proposed FSS, by concurrently applying the design methods of planar FSSs and 3-D FSSs, provides sharp double roll-off sidebands while maintaining excellent angular stability and low insertion loss in a wide passband. The simulation results show that the designed FSS can achieve a passband range from 5.86 to 13.4 GHz with an insertion loss of less than 1 dB, and it can retain a stable transmission characteristic with the incident angle ranging from 0° to 45°. The proposed FSS is fabricated and measured in an anechoic chamber, and the measured results show that the fabricated FSS achieves a −3 dB passband from 5.33 to 14.5 GHz. Under different incident angles ranging from 0° to 45°, the FSS also features the lower and upper roll-off transition bandwidths of 6.4% and 4.7%, respectively.

The new generation of 5G mobile communication systems is using millimeter wave (mmWave) active phased arrays (APA) which have up to hundreds of individual analog transmitter and receiver chains and antennas. For these highly integrated systems linearization of each analog path is very challenging. Therefore a single input single output (SISO) system in combination with over the air (OTA) measurement is considered as an efficient approach for linearization. However, the knowledge about the dependency of the total SISO nonlinearity on the contributions from different blocks in the antenna array, as well as the linearization trade-offs is still missing. In this paper, an overview of the possible linearization trade-offs in an OTA setup with a mmWave APA is provided. The linearization technique is applied to a 4x4 active phased array containing up-conversion of a sub 6 GHz LTE10 signal to an RF frequency of 28 GHz. Through measurements, the effects on adjacent channel power ratio (ACPR) and error vector magnitude (EVM) have been investigated for the following scenarios: i. impact from the up-converter, ii. impact of the steering angle due to antenna crosstalk and iii. a linearity comparison between a linearized and a backed-off system.

A novel humidity sensor based on polymer-salt complexes was developed in this research. Poly(ethylene glycol) (PEG) segmented poly(ether ether ketone) copolymers (PEEK-co-PEG) has been successfully synthesized via condensation polymerization and the structure of this copolymer was confirmed by ¹ H NMR. Humidity sensors based on SPEEK/PEEK-co-PEG composites were prepared, and then humidity sensitive characteristics were systematically studied. Comparing with pure SPEEK, PEEK-co-PEG composites can significantly reduce the hysteresis effect of humidity sensors with the proportion of introduced PEG flexible segments increasing. Furthermore, different contents of LiCl were doped into polymer composites and their humidity sensing characteristics were also investigated. The humidity sensor based on SPEEK/PEEK-co-PEG/LiCl with [Li ⁺ ]:[O] ratio of 1:2 had excellent sensitivity, linearity, negligible hysteresis and quick response. Moreover, we proposed that the humidity sensing mechanism of sensors based on polymer-salt complexes is related to the combination of both directional motion of Li ⁺ on PEG backbone under low RH and ionization (SPEEK polyelectrolyte, Li ⁺ and Cl ^- ) under high RH. On the basis of these humidity sensing characteristics and mechanism, this work provides an approach to integrating polymeric and inorganic complexes as the promising materials for humidity sensors.

Signal source number detection is an essential issue for the direction of arrival (DOA) estimation in satellite communication systems. The performances of conventional and deep-learning-based signal source number detection methods will deteriorate when the signal-to-noise ratio is low or coherent signals exist. This paper proposes a DOA detection network (DTN) combined with the root weighted subspace fitting (root-WSF) method to tackle this challenge. The DTN uses the constructed deep neural networks (DNN) to denoise the received signals and captures the nonlinear mapping relationship between the received signals and the number of signal sources. The received signals in the complex-valued domain are directly treated as DTN’s input, and the label of DTN is the one-hot encoding of the source number. It solves the issue that the classifier cannot well-handle the coherent signals and extends the values of discrete features to Euclidean space. Accordingly, the trained DTN can detect the signal source numbers with an average detection accuracy of 96.6%, and the root-WSF algorithm is applied as the rear stage of DOA estimation. Compared with the traditional DOA methods, the proposed DTN incorporated with the root-WSF algorithm features superior robustness, high DOA estimation accuracy, and enhanced resolution.

Direction-of-arrival (DOA) estimation plays a vital role in the field of array signal processing. However, the need for heavy computing tasks in most traditional DOA algorithms, e.g., multiple signal classification (MUSIC), makes their engineering practicality significantly compromised in satellite communication systems. The neuroevolution of augmenting topologies (NEAT) can quickly search for appropriate topologies and weights of neural network functions, but its computational complexity is still too high for satellite systems. This paper proposes a modified NEAT architecture featuring a recurrent structure (RNEAT) that only needs a small number of phase components of the received signal covariance matrix as inputs to reduce the complexity and simplify the neural network architecture. The proposed RNEAT incorporated with multiple signal classification (RNEAT-MUSIC) features low complexity to achieve high resolution and low complexity simultaneously. Validation has been done by applying the proposed method in a two-dimensional direction of arrival estimation (2D-DOA) problem. Results show that the proposed RNEAT-MUSIC efficiently restricts the scanning region before forwarding the covariance matrix to the MUSIC stage. Consequently, the computational workload is reduced by 3/4 compared with the traditional 2D-MUSIC algorithm while maintaining satisfactory DOA resolution.

Monitoring bone healing after osteosynthesis in the lower limb provides insight into the need for additional medical intervention. Electrical impedance spectroscopy (EIS), via an implantable device, has recently been studied as a remote and radiation-free surveillance method for fracture patients. To address ambiguities in conventional EIS characterizations of physiological and pathological features, this article presents a machine learning (ML)-assisted EIS interpretation method, tailored specifically for cases where an intramedullary nail (IM nail) is applied. Building on the equivalent circuit model method, we propose modeling the electrical properties of the tissue under test (TUT) using generic distributed elements. A convolutional neural network (CNN) is then deployed to prefit the measured EIS with the aim of identifying the dominating elements, addressing the issue of initial guesswork, and revealing their physiological meaning. This method is demonstrated through the analysis of in vivo EIS measurements of the rabbit tibia postsurgery. By aligning fitting curves to closely match the measured spectra, the relevant components in the equivalent circuit model can be consistently identified, and their local correlation with the bone states is characterized. Our method exhibits potential for the quantitative analysis of EIS and could pave the way for its future research and application.