This paper presents a dual-band four-element multiple-input-multiple-output (MIMO) array for the fifth generation (5G) mobile communication. The proposed antenna is composed of an open-loop ring resonator feeding element and a T-shaped radiating element. The utilization of the open-loop ring resonator not only reduces the size of the antenna element, but also provides positive cross-coupling. The dimension of a single antenna element is 14.9 mm × 7 mm (0.27λ × 0.13λ, where λ is the wavelength of 5.5 GHz). The MIMO antenna exhibits a dual-band feature from 3.3 to 3.84 GHz and 4.61 to 5.91 GHz, which can cover 5G New Radio N78 (3.3–3.8 GHz), 5G China Band N79 (4.8–5 GHz), and IEEE 802.11 ac (5.15–5.35 GHz, 5.725–5.85 GHz). The measured total efficiency and isolation are better than 70% and 15 dB, respectively. The calculated envelope correlation coefficient (ECC) is less than 0.02. The measured results are in good agreement with the simulated results.

Egg freshness is of great importance to daily nutrition and food consumption. In this work, visible near-infrared (vis-NIR) spectroscopy combined with the sparsity of interval partial least square regression (iPLS) were carried out to measure the egg’s freshness by semi-transmittance spectral acquisition. A fiber spectrometer with a spectral range of 550-985 nm was embedded in the developed spectral scanner, which was designed with rich light irradiation mode from another two reflective surfaces. The semi-transmittance spectra were collected from the waist of eggs and monitored every two days. Haugh unit (HU) is a key indicator of egg’s freshness, and ranged 56–91 in 14 days after delivery. The profile of spectra was analyzed the relation to the changes of egg’s freshness. A series of iPLS models were constructed on the basis of spectral intervals at different divisions of the spectral region to predict the egg’s HU, and then the least absolute shrinkage and selection operator (Lasso) was used to sparse the number of iPLS member models acting as a role of model selection and fusion regression. By optimization of the number of spectral intervals in the range of 1 to 40, the 26^th fusion model obtained the best performance with the minimum root mean of squared error of prediction (RMSEP) of 5.161, and performed the best among the general PLS model and other intervals-combined PLS models. This study provided a new, rapid, and reliable method for the non-destructive and in-site determination of egg’s freshness.

The recently-emerging compressed sensing (CS) theory makes GNSS signal processing at a sub-Nyquist rate possible if it has a sparse representation in certain domain. The previously proposed code-domain compression acquisition algorithms have high computational complexity and low acquisition accuracy under high dynamic conditions. In this paper, a GNSS signal acquisition algorithm based on two-stage compression of the code-frequency domain is proposed. The algorithm maps the incoming signal of the same interval to multiple carrier frequency bins and overlaps the mapped signal that belongs to the same code phase. Meanwhile, the code domain compression is introduced to the preprocessed signal, replacing circular correlation with compressed reconstruction to obtain Doppler frequency and code phase. Theoretical analyses and simulation results show that the proposed algorithm can improve the frequency search accuracy and reduce the computational complexity by about 50% in high dynamics.

The recognition of pavement cracks is crucial in road engineering and airport maintains. In order to successfully apply image processing technique for automatic crack detection, the first and hardest task is to recognize crack images in a huge number of pavement images. To do this, the image processing technique and Fracture mechanics are combined first time in this area, the studied method includes four steps: (1) The pavement crack image shrinking is carried out by a proposed multi-scale analysis algorithm, which is more effective for both preserving weak valley edges and reducing computing cost; (2) Then, a so called valley edge detection algorithm based on Fractional differential for finding local dark line/curve is studied for tracing crack segments, it considers template size, weighted average gray level value in each line in four different directions, the output can be a gradient magnitude image or a binary image; (3) In the binary image, the crack segments are refined based on a number of post processing functions to remove noise and fill segment gaps; and (4) After that, to quickly judge if the image has cracks, Fracture mechanics is applied to calculate the judgment parameter T, which is directly proportion to the image edge density, and the ratio between the average gradient magnitude value and the average gray level value in the candidate crack segment. In experiments, more than 400 pavement images (the resolution is 4096 × 2048 pixels) are tested, and the crack identification accuracy is up to 97%.

Integrating Tiny Machine Learning (TinyML) with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level. Constrained devices efficiently implement a Binary Neural Network (BNN) for road feature extraction, utilizing quantization and compression through a pruning strategy. The modifications resulted in a 28-fold decrease in memory usage and a 25% enhancement in inference speed while only experiencing a 2.5% decrease in accuracy. It showcases its superiority over conventional detection algorithms in different road image scenarios. Although constrained by computer resources and training datasets, our results indicate opportunities for future research, demonstrating that quantization and focused optimization can significantly improve machine learning models’ accuracy and operational efficiency. ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device: Advanced machine learning in edge computing. The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing, suggesting ways to improve smart city frameworks in road network assessment, traffic management, and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.

In some cases, near-infrared spectra (NIRS) make the prediction of quantitative models unreliable, and the choice of a suitable number of latent variables (LVs) for partial least square (PLS) is difficult. In this case, a strategy of fusing member models with important information is gradually becoming valued in recent research. In this work, a series of PLS regression models were developed with an increasing number of LVs as member models. Then, the least absolute shrinkage and selection operator (Lasso) was employed as the model’s selection access to sparse uninformative ones among these PLS member models. Deviation weighted fusion (DW-F), partial least squares regression coefficient fusion (PLS-F), and ridge regression coefficient fusion (RR-F) were comparatively used further to fuse the above sparsed member models, respectively. Three spectral datasets, including six attributes in NIR data of corn, apple, and marzipan, respectively, were applied in order to validate the feasibility of this fusion algorithm. Six fusion models of the above attributes performed better than the general optimal PLS model, with a noticeable enhancement of root mean errors squared of prediction (RMSEP) arriving at its highest at 80%. It also reduced more than half of the spectral bands; the DW-F especially showed its excellent fusing capacity and obtained the best performance. Results show that the preferred strategy of DW-F model combined with Lasso selection can make full use of spectral information, and significantly improve the prediction accuracy of fusion models.