The development of the Internet of Things (IoTs) has empowered revolution in almost all walks of life. Although substantial effort has been made to bring IoT into manufacturing, there are still technical challenges to provide solutions of real-time pervasive multisensing, quality evaluation, and boundaryless information sharing, which put people at risk of deteriorated food products and food adulteration. In this article, we present an industrial IoT-based system for food product quality assessment and prediction. This research completes the real-time food quality assessment via radiofrequency identification (RFID) based multisensor fusion for the first time. Also, a novel concept of shelf-life prediction is proposed. The RFID-powered sensors provide a new idea for nondestructive food product and environment sensing. A five-layer architecture, considering sensing, controlling, communication, interfaces, and data analysis, is highlighted. A novel RFID metadata structure is first proposed to achieve multidimensional information traceability and global data sharing. Machine-learning-based multisensor fusion is proposed to provide the accurate quality assessment and prediction. The proposed system is implemented as a smart-shelf system to demonstrate its feasibility and advantages. The system is of great significance in improving food safety, reducing food waste, and providing powerful information support for food manufacturing line and supply chain management.

Radio frequency identification (RFID) is well known as an identification, track, and trace approach and is considered to be the key physical layer technology for the industrial internet of things (IIoT). However, IIoT systems have to introduce additional complex sensor networks for pervasive monitoring, and there are still challenges related to item-level sensing and data recording. To overcome the shortage, this work proposes an artificial intelligence (AI)-assisted RFID-based multi-sensing technology. Both passive and semi-passive RFID tag-integrated multi-sensors are developed. The main contributions and the novelty of this investigation are as follows. A UHF RFID tag-integrated multi-sensor with a boosted charge pump is proposed; it enables high RF signal sensitivity and a long operational range. The whole hardware design, including the antenna and energy harvester, are studied. Moreover, a demonstration with real-world ham product sensing data is conducted. This work also proposes and successfully demonstrates the integration of machine learning algorithms, specifically the NARX neural network, with RFID sensing data for food product quality assessment and sensing (QAS). This application of machine learning to RFID-generated data for quality assessment is also a novel aspect of the research. The deployment of an autoregressive model with an exogenous input (NARX) neural network model, tailored for nonlinear processes, emerges as the most effective, achieving a root mean square error (RMSE) of 0.007 and an R-squared value of 0.99 for ham product QAS. By deploying the technology, low-cost, timely, and flexible product QAS can be achieved in manufacturing industries, which helps product quality improvement and the optimization of the manufacturing line and supply chain.

This letter presents a loop antenna array for ultra-high frequency near-field and far-field radio frequency identification (RFID) reader applications. The antenna array consists of several microstrip collinear loop antennas, and each loop antenna is composed of several periodic radiating units. By deploying microstrip collinear radiating units, a novel loop antenna with in-phase radiating currents is realized, which generates a strong and uniform magnetic field over a large interrogation area and a horizontally polarized omnidirectional radiation. By configuring the array antenna, the magnetic fields distribute on the entire antenna array and create an enlarged near-field reading area. The far-field coverage is enlarged by the superposition of omnidirectional radiation. The antenna array is simulated and validated with a four-element prototype made on FR-4 substrates. The antenna array is measured to have a -10 dB impedance bandwidth from 802 MHz to 963 MHz with an enlarged near-field reading volume of 280 mm × 280 mm× 540 mm and a far-field gain of 6.66 dBi at the UK RFID band.

Ultra-wideband (UWB) vital detection radar plays an important role in post-disaster search and rescue, but the vital signal acquired in practice is often submerged in noise. In this paper, an advanced signal processing algorithm based on low-rank block-sparse representation is proposed to enhance the vital signal in life detection radar applications. The preprocessed echo signal can be decomposed into low-rank and block-sparse parts. The alternate direction method (ADM) is employed to obtain the block-sparse part containing the desired vital signal. We solve the subproblems involved in the ADM method using the Douglas/Peaceman–Rachford (DR) monotone operator splitting method. The projection method is applied to accelerate the calculation. Simulation and experimental results show that the proposed algorithm outperforms existing methods in terms of output signal-to-noise ratio (SNR).

Ground-penetrating radar (GPR) has been widely used in estimating the permittivity of mediums. The radar echo amplitude method is an important method used by GPR in this estimation, the basic step of which is to deduce the magnitude of the permittivity according to the relationship between the reflection coefficient and the permittivity. Based on the basic principle of the radar echo amplitude method, this paper proposes a full-wave inversion real-time permittivity estimation method that can be used for stepped-frequency GPR (SFGPR), which offers high efficiency, accuracy, and generalization ability. The characteristics of this method are mainly reflected in the following four aspects: Using the SFGPR system and introducing a layered media detection model, we can complete waveform compensation optimization with high precision. The distance between the antenna and the surface of the reflective medium is extracted from the time domain waveform without manual measurement, avoiding human measurement errors. The inversion of the total reflection waveform at the required height works under the principle of an electromagnetic field, eliminating the need for repeated metal plate calibration experiments and improving work efficiency and waveform accuracy. In a continuous measurement line, the total reflection waveform inversion on each measurement point can be efficiently completed, and the change of permittivity on the measurement line can be obtained. To evaluate the feasibility of the proposed method, we performed experiments on a wall of known thickness, and the permittivity estimation was basically consistent with that of the dielectric probe, physical model calculation, and wall penetration. We also successfully applied this method to the dielectric property analysis of adobe samples. The results indicate that the proposed method can help grasp the condition of a measured medium, which can ensure the accuracy of detection and improve subsequent data processing efficiency.

Ultra-wideband (UWB) radars play a significant role in non-contact vital sign monitoring, especially in post-disaster search and rescue. However, practical applications are hindered by weak physiological signals and reduced detection reliability when human targets are oriented away from the radar. In this paper, a multi-channel fusion method for minimum distortion vital signal detection is proposed to solve the above problems. Using one transmitting and five receiving antennas, the method combines imaging algorithms to locate potential human targets. After preprocessing, the echo signals are separated into vital and noise components. Firstly, a spatial-temporal prediction approach is employed to determine a set of optimal prediction transforms between different channels by minimizing the residual noise while ensuring minimal signal distortion approaches zero. Additionally, the optimal noise filter is then generated using these transforms. The noise correlation matrix is estimated from the inanimate signal part. Target realism is validated using output results and the band energy ratio (BER) parameter. Simulation and experimental results show that when the detected target does not directly face the radar, the target frequency can still be accurately obtained. The proposed method effectively enhances the vital signs in the echo signal, providing more robust target detection capabilities.