Broad-Tuning, Dichroic Metagrating Fabry-Perot Filter Based on Liquid Crystal for Spectral Imaging
Dynamic structural color can empower devices with additional functions like spectrum and polarization detection beyond display or imaging. However, present methods suffer from narrow tuning ranges, low throughput, or bulky volumes. In this work, a tunable filter composed of a dichroic metagrating Fabry-Perot cavity and liquid crystal (LC) material is proposed and investigated. By modulating the polarization of the incident light with the LC, the color response can change from blue to green and deep red due to the `mode jumping' effect, with a tuning range of around 300 nm. Besides, we experimentally demonstrate the use of this device as a spectral imager in the visible range. Experimental results show that spectral resolvability can be around 10 nm, with the largest peak wavelength in accuracy of ~5 nm. This approach shows superior performance over traditional liquid crystal tunable filters in low light conditions and indicates the potential of dynamic structural color for miniaturized spectroscopic applications.
Variational Bayesian Learning for the Modelling of Indoor Broadband Powerline Communication Impulsive Noise
Thomas Joachim Odhiambo Afullo
Powerline communication (PLC) noise is the main cause of reduced performance and reliability of the communication channel. The major source of these noise bursts, which distort and degrade the communication signal, is the arbitrary plugging in and unplugging of electric devices from the electrical network. It is therefore important to perform statistical modelling of the PLC noise characteristics to enable the development and optimisation of reliable PLC systems. This paper presents the Variational Bayesian (VB) Gaussian Mixture (GM) modelling of the amplitude distribution of the indoor broadband PLC noise. In the proposed model, a fully Bayesian treatment is employed where the parameters of the GM model are assumed to be random variables. Consequently, prior distributions over the parameters are introduced. The VB criterion is used to determine the optimal number of components where the Bayesian information criterion emerges as a limiting case. To find the parameters of the GM components, the variational-expectation maximisation algorithm is employed. Measurements from different indoor PLC environments are then used to validate the model. Thereafter, performance analysis is carried out, and the VB framework is compared to the Maximum Likelihood (ML) estimate method. It is observed that while the ML model performs better when the amplitude distribution contains multiple peaks, the VB framework offers high accuracy and good generalization to the measured data and is thus effective in modelling the amplitude distribution of the PLC noise.
A Shape-Based Approach for Recognition of Hidden Objects Using Microwave Radar Imaging System
Akhilendra Pratap Singh
Microwave imaging radar systems are often required for the recognition of hidden objects at various job sites. Most existing imaging methods that these systems employ, such as beamforming, diffraction tomography, and compressed sensing, which operate on synthetic aperture radar, produce highly distorted radar images due to the limitation of the frequency range, size of the array, and attenuation during the propagation, and thereby become hard to interpret the description of the object. Several methods explored for the recognition of hidden objects are based on deep neural network models with millions of parameters and high computational costs that render them unusable in portable devices. Moreover, most methods have been evaluated on datasets of microwave radar images of hidden objects with the same relative permittivity, orientation, size, and position. In real-time scenarios, objects may not have similar relative permittivity, orientation, size, and position. Due to variation in the object's relative permittivity, orientation, size, and position, there will also be variation in the reflectivity. Consequently, it is hard to say if those algorithms will be robust in real-world conditions. This paper presents a novel shape-based approach for recognizing hidden objects which combines delay-and-sum beamforming with an artificial neural network. The merit of this proposed method is its ability to simultaneously recognize and reconstruct the object's actual shape from distorted microwave radar images irrespective of any variation in relative permittivity, orientation, size, and position of hidden object. The performance of the developed technique was tested on a dataset of microwave radar images of various hidden objects having different relative permittivities, sizes, orientations, and positions. The proposed method yielded an average reconstruction rate of 91.6%. The proposed method is appropriate for evaluating occluded objects such as utility infrastructure, assets, and weapons detection and interpretation, which have regular shapes and sizes of the cross-section at various construction, archaeological and forensic sites.
Dual-Band Metasurface Antenna Based on Characteristic Mode Analysis
A dual-band metasurface antenna is designed consisting of three-layer metal patches and two-layer dielectric substrates. To facilitate the modal analysis of the metasurface, Characteristic Mode Analysis (CMA) is used to analyze the metasurface antenna with 4×4 rectangular patches, and the performance of the antenna is optimized based on the Modal Significance (MS) curves. In order to excite the current of different characteristic modes at certain frequencies, the symmetric resonant arms and cross-shaped impedance matching converters are used in the feeding structure. The measured results are consistent with the simulated values, and the designed antenna can yield the gains of 7.67 dBi at 3.5 GHz and 7.28 dBi at 4.9 GHz, which provides the potential applications in 5G and other wireless communications.
Frequency Diverse Arc Array Beampattern Synthesis Analysis with Nonlinear Frequency Offset
Frequency diversity array (FDA) can generate distance and angle dependent ``S'' beam patterns, but there is a problem of distance and angle coupling, which can be well solved by using nonlinear frequency offset in recent years' research. The rotational symmetry of the arc-shaped structure brings the beam scanning capability of the array antenna within a range of 360°, which can realize the all-round monitoring of the target position, and provides a more flexible method for radar communication. In this paper, a nonlinear frequency offset based frequency diversity arc array (FDAA) beam scanning method is proposed, which activates the selection matrix according to the target direction. In order to form equal phase plane beam scanning, phase compensation between array elements is carried out, and three kinds of nonlinear frequency bias are introduced to simulate beampattern synthesis. Compared with the traditional linear frequency offset FDAA, the numerical simulation results verify the feasibility and effectiveness of the scheme.