Fruit quality testing plays a crucial role in the advancement of fruit industry, which is related to market competitiveness, consumer satisfaction and production process optimization. In recent years, nondestructive testing technology has become a research hotspot due to its outstanding advantages. In this paper, the principle, application, advantages and disadvantages of optical, acoustic, electromagnetics, dielectric properties research and electronic nose non-destructive testing technology in fruit quality testing are systematically reviewed. These technologies can detect a variety of chemical components of fruit, realize the assessment of maturity, damage degree, disease degree, and are suitable for orchard picking, quality grading, shelf life prediction and other fields. However, there are limitations to these techniques. The optical, acoustic and electronic nose technologies are susceptible to environmental factors, the electromagnetic technology has defects in the detection of complex molecules and fruit internal quality, and the dielectric characteristics are greatly affected by the shape and state of the sample surface. In the future, efforts should be made to enhance the implementation of non-destructive testing technology in the fruit industry through technology integration, optimization algorithm, cost reduction, and expansion of industrial chain application, so as to help the premium growth of the fruit industry.

In order to quickly, nondestructively, and effectively distinguish red jujube varieties, based on the combination of fuzzy theory and improved LDA (iLDA), fuzzy improved linear discriminant analysis (FiLDA) algorithm was proposed to classify near-infrared reflectance (NIR) spectra of red jujube samples. FiLDA shows performs better than iLDA in dealing with NIR spectra containing noise. Firstly, the portable NIR spectrometer was employed to gather the NIR spectra of five kinds of red jujube, and the initial NIR spectra were pretreated by standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky-Golay smoothing (S-G smoothing), mean centering (MC) and Savitzky-Golay filter (S-G filter). Secondly, the high-dimensional spectra were processed for dimension reduction by principal component analysis (PCA). Then, linear discriminant analysis (LDA), iLDA and FiLDA were applied to extract features from the NIR spectra, respectively. Finally, K nearest neighbor (KNN) served as a classifier for the classification of red jujube samples. The highest classification accuracy of this identification system for red jujube, by using FiLDA and KNN, was 94.4%. These results indicated that FiLDA combined with NIR spectroscopy was an available method for identifying the red jujube varieties and this method has wide application prospects.

This paper addresses the issue of broken rotor bar (BRB) fault detection for induction motors (IMs). The widely used fast Fourier transform (FFT) based methods are sensitive to noise and have a low resolution for short-time data; while subspace-type methods suffer from substantial performance degradation due to inappropriate model order choice and spatial smoothing operation. To effectively identify the weak fault sidebands from the rotor current signal, we cast the fault sideband identification problem as a sparse representation (SR) problem, and present a novel sparse Bayesian learning (SBL) approach for the BRB fault diagnosis. The novelties of the proposed approach are three-fold: (i) a three-stage hierarchical sparse prior is combined into the SBL framework to greatly enforce the sparsity of the current spectrum, leading to heavily sharpened fault sidebands; (ii) a new computationally efficient SR model with a truncated spectral grid is introduced to significantly reduce the computational load for Bayesian inference; and (iii) the symmetric pairing structure of fault sidebands is additionally exploited as a priori knowledge to enhance the robustness of the BRB identification performance. Both simulation and experiment results indicate the superiority of the proposed method, especially for the short sampling time and/or high noise level current signal.

Intelligent reflecting surface (IRS) becomes a new promising candidate technology for the next generation wireless communications. When the radar sensing is integrated into the IRS-assisted massive multiple-input multiple-output (MIMO) systems, it is challenging to detect targets-of-interest for the radar sensing, because the target echo signals could be also reflected by IRS, resulting in a fake angle issue for the target localization. To handle the fake angle issue, in this paper, we propose a new sparse Bayesian learning (SBL)-based approach for the target localization with the IRS-assisted massive MIMO system. Taking into account the fact that the positions of IRS and base station (BS) remain unchanged, we novelly utilize the angular reciprocity between the downlink and uplink channels to facilitate the target localization. Then, we present a new hybrid sparse Bayesian framework to characterize the sophisticated structured sparsity brought by the angular reciprocity, where different hierarchical distributions are assigned to the target echo signals and uplink communication signals so as to enforce much sparser common structure corresponding to the fake angles. Finally, we exploit an expectation-maximization (EM)-based algorithm to jointly recover the common and independent sparse signals, which will identify the fake and true angles automatically. Simulation results verify the superiority of the proposed method.

Massive machine-type communication (mMTC) is characterized by sporadic device activities and angular-sparse channels, creating an opportunity for efficient joint device active detection (AD) and channel estimation (CE). However, it is computationally intractable to solve the related 2D-sparse recovery problem. Existing methods often resort to transforming this problem into a 1D-sparse recovery issue, which can result in performance degradation due to energy leakage from the off-grid effect. Moreover, these methods overlook the potential benefits of common sparsity across different frequency bands. To address these limitations, a novel sparse Bayesian learning (SBL) framework for the joint AD and CE is proposed in the paper, and two advanced sparsity structures under the frequency multiplex (corresponding to frequency sparsity and partial common sparsity) are additionally exploited to elevate the sparse recovery performance substantially. The key to the success of the proposed method lies in two crucial factors: (i) the employment of an innovative independent variational Bayesian inference (VBI) factorization technique, effectively decoupling the challenging 2D-sparse recovery problem and mitigating the off-grid mismatch; and (ii) the introduction of a hybrid sparsity prior into the SBL framework, seamlessly integrating additional frequency sparsity and common sparsity across different active frequency bands. Simulation results verify the superiority of the proposed method and indicate that the proposed method can achieve almost 50% NMSE performance enhancement compared with the state-of-the-art methods, attributed to its flexible employment of the sophisticated sparsity structure.

The existing real-valued methods for direction-of-arrival (DOA) estimation usually utilize the orthogonality of signal subspace and noise subspace to compress the dimension of the observation matrix, which, in return, jointly reduces the computational complexity and achieves a noise suppression effect. However, it could suffer a performance loss due to the inaccurately estimated signal subspace. To address this problem, we propose a novel real-valued variational Bayesian inference (VBI)-based method, where we consider the signal subspace matrix as a random variable and adaptively update it by exploiting its row-sparsity. It is challenging to perform the Bayesian inference under the coupling effect caused by the unknown signal subspace matrix. Therefore, a column-independent VBI factorization is further introduced into the VBI framework to decouple the signal of interest. Simulation results demonstrate that our method can significantly improve the DOA estimation performance.