Electrical Capacitance Tomography (ECT), as a rapidly developing process tomography technique, has been widely applied in multiphase flow monitoring, industrial process control, and medical imaging. However, due to the “soft-field” effect, the ill-posed nature of the inverse problem, and limitations in data acquisition. Existing reconstruction methods generally suffer from large reconstruction errors, low accuracy, blurred edges, and pronounced artifacts. To address these issues, this paper proposed a reconstruction method based on fractional-order particle filtering and image super-resolution. Firstly, a fractional-order formulation is introduced into the system equation to better characterize the dynamic behavior of the ECT system, by exploiting the short-term memory property of fractional-order systems, thereby accelerating convergence in the search space. Subsequently, the particle filter is employed to effectively alleviate the “soft-field” effect of the ECT system. By directly approximating the posterior probability distribution of the system state through Monte Carlo sampling, more informative particles can be selected, which enhances reconstruction accuracy and stability. Finally, an image super-resolution model based on Lorentzian function fitting is adopted, where the point spread function is estimated and combined with the Lucy–Richardson algorithm to achieve high-resolution imaging and mitigate edge blurring.In the experimental study, a three-dimensional simulation model was established using COMSOL, and a 12-electrode hardware platform was constructed for validation under various typical flow patterns. The results demonstrate that the proposed method achieves superior image quality while maintaining low computational cost (less than 0.3 s), providing an effective solution for ECT imaging in complex industrial environments.

In the oil and gas related fields, three-phase distribution flow in pipelines is one of the important aspects that need to be continuously monitored in the Industrial Pipeline Internet of Things (IoT). The dual-modality system of Electrical Capacitance Tomography (ECT) and Electromagnetic Tomography (EMT) can reconstruct the three-phase distribution within a pipeline’s cross-section. However, due to mutual interference among three-phase flow media and noise effects, the reconstruction quality of ECT and EMT images is degraded. In this paper, an ECT-EMT image fusion method based on cross-sensitive field optimization and super-resolution preprocessing is proposed. Firstly, unimodal ECT image reconstruction generates virtual image in solid-phase fluid regions, leveraging this information, a cross-sensitive field optimization method combined with the YOLOv8 network is proposed to improve the EMT reconstructed image quality. Compared with traditional fixed-sensitivity-field EMT reconstruction methods, the correlation coefficient increased by an average of 3.8%. Then, a Gaussian second-order derivative super-resolution method is introduced, which uses a Gaussian second-order derivative function to fit fuzzy kernel parameters and deconvolves the degradation matrices of ECT and EMT images, solving the edge-blurring problem in their fusion. Compared to traditional deblurring methods such as Wiener filtering and single Gaussian kernel modelling, PSNR improved by an average of 1.4 dB, while SSIM improved by an average of 0.03. Finally, an image fusion method based on Latent Low-Rank Representation is proposed by extracting global and local feature information from super-resolution preprocessed ECT and EMT grayscale matrices, respectively. Compared to mainstream pixel-level fusion methods such as wavelet transforms, it resolves the issue of performance degradation in traditional fusion methods when observational data is insufficient. All validations were conducted using simulation data from COMSOL, with sensitivity field optimisation adapted to the complex flow patterns of three-phase flow scenarios. The super-resolution module addressed reconstructed images exhibiting low signal-to-noise ratios and blurred edges, whilst the LatLRR fusion method maintained stable fusion performance even under small-sample conditions.

This article presents a novel under-sampling method for ECG signals, aimed at reducing the sampling rate and power consumption in IoT-based ECG wearable devices. The key contribution addresses the common issue of model mismatch in existing methods, which negatively impacts signal reconstruction accuracy. Initially, the ECG signal is modeled as a linear combination of several Gaussian second-order derivative functions, which can be efficiently represented with only a few parameters, thus mitigating the problem of large model matching errors. To further enhance reconstruction accuracy, an improved two-channel finite rate of innovation sampling framework is introduced, effectively addressing the nonideal effects caused by the low-pass filter during sampling. Additionally, a modified annihilating filter reconstruction algorithm is proposed, allowing high-precision signal reconstruction using a small number of sampling points to estimate parameters. The validity of the proposed method is confirmed through simulations with real ECG signals from the MIT-BIH arrhythmia database, and a hardware platform is developed to verify its feasibility in a practical system. Experimental results demonstrate that, compared to the existing methods, the proposed approach significantly reduces reconstruction error (achieving a PRD as low as 2.29% and an SRR of 11.77 dB), and exhibits better robustness in noisy environments.

In oil and gas related industries, multiphase flow in pipelines is one of the important elements that the Industrial Pipeline Internet of Things (IoT) should continuously monitor. However, existing reconstruction methods often are limited by low resolution and blurred edges. In this article, an electrical capacitance tomography (ECT) imaging system based on Lorentz deblurring and particle filtering is proposed to suppress ECT image blurring. First, a deblurring model based on Lorentz function fitting is proposed, capable of effectively improving the blurred edges, through point spread function (PSF) estimation and Lucy-Richardson algorithm. Then, in the image reconstruction process, it is reformulated as an iterative search for effective particles and their associated weights in the state space, combined with Lorentz deblurring model for the optimal solution. Finally, the virtual-instrument-based ECT hardware system based on the principle of modularity, creates the synergistic architecture between the ECT hardware and imaging software, which enables real-time visualization of imaging. Simulation experiments demonstrate that the image reconstruction algorithm outperforms existing methods in terms of relative error and correlation coefficient, effectively suppressing image blur. Moreover, the ECT imaging system proposed can enhance the measurement capacitance accuracy.

Image deblurring is a technique employed to reduce or eliminate degradation resulting from the effects of impulse response and atmospheric turbulence during remote sensing image acquisition. However, the issue of low recovery accuracy due to inadequate point spread function (PSF) matching is a significant challenge in current deblurring methods. In this letter, a remote sensing image deblurring method based on the differential Lorentzian PSF model is proposed. First, a differential Lorentzian PSF model is proposed, which is able to model the line spread function (LSF) of a real image as a series of pulsewidth variable Lorentzian functions and their differential function combinations. Subsequently, a model parameter estimation algorithm based on the improved zeroing filter is proposed. This algorithm is able to reconstruct the target parameters by utilizing the edge information of the actual image, with the objective of eliminating the matching error of the PSF model in the actual complex scene and improving the parameter estimation accuracy. Finally, a remote sensing image deblurring method is proposed by using the differential Lorenz point diffusion function model combined with the L-R algorithm. The results of the simulation experiments demonstrate that the method proposed in this letter is more effective than existing remote sensing image deblurring methods in terms of image recovery accuracy.

Ground penetrating radar (GPR) full waveform inversion (FWI) can make full use of kinematics information and dynamics information to achieve the highest theoretical resolution, serving as a promising tool for reconstructing subsurface structures and the physical properties of the medium. However, conventional FWI is constrained by strong nonlinearity, easily falls into the local minimum, and requires multiple forward simulations coupled with intensive adjoint wavefield calculations, which cannot satisfy the requirements of engineering exploration. To mitigate the nonlinearity of the inversion and improve computational efficiency, this paper designs a FWI framework based on deep learning, featuring a multi-frequency and multiscale fusion strategy. Utilizing a multi-output convolutional neural network (CNN) constructed by the hybrid dilated convolution, the receptive field is expanded without incurring additional computational complexity and memory consumption. The dilated CNN predicts multiple sets of available low-frequency data from its respective higher-frequency components of GPR data and integrates the multi-frequency strategy to guide FWI to converge the global minimum. The sizes of computational models are selected according to distinct electromagnetic wave frequencies, and the very deep super-resolution (VDSR) model facilitates the automatic mapping of grids at different scales which reduces unnecessary calculation and boosts inversion efficiency. The synthetic and field cases prove that the proposed framework significantly enhances the spatial resolution, robustness, and efficiency of FWI. The dilated CNN and VDSR constructed have demonstrated robust generalization and noise tolerance abilities, which are suitable for geophysical tasks.

Unmanned aerial vehicle-aided (UAV-aided) mobile edge computing (MEC) network can greatly reduce the data growth pressure of Internet of Things (IoT) and expand the wireless communication coverage. However, there is a risk of eavesdropping on the offloading information of terminal users (TUs) because of UAV light-of-sight (LoS) transmission. In this paper, we propose a Dinkelbach-guided deep reinforcement learning (DRL) scheme for secure communication in the UAV-aided MEC network. Specifically, the security calculating efficiency of the network is maximized by optimizing offloading decision and resource allocation under the condition of the data queue stability and minimum calculating requirement. The problem is intractable due to the fractional structure and binary constraint. Firstly, we deal with the fractional structure by taking advantage of Dinkelbach optimization. Then, offloading decision is generated based on DRL and the resource is allocated by successive convex approximation (SCA). Simulation results show that the proposed Dinkelbach-guided DRL scheme efficiently improves the security calculating efficiency of the network.

Internet of Things (IoT) is able to provide various physical objects to exchange their information through the 6G wireless communication network. However, with the large increasing number of the IoT devices (IoDs), the deployment of IoDs faces two basic challenges, i.e., spectrum scarcity and energy limitation. Cooperative spectrum sharing and simultaneous wireless information and power transfer (SWIPT) provide effective ways to improve the spectrum and energy efficiency. In this article, two SWIPT cooperative spectrum sharing methods are proposed to improve the energy and spectrum efficiency for 6G-enabled cognitive IoT network, in which IoDs access to the primary spectrum by serving as orthogonal frequency-division multiplexing (OFDM) relay with the energy harvested from the received radio-frequency (RF) signal. Specifically, in phase1, the IoDs transmitter (DT) in the cognitive IoT network performs information decoding and energy harvesting with the received RF signal. In phase2, DT transmits the signals of the primary system and itself to the corresponding receiver by utilizing orthogonal subcarriers with the harvested energy to avoid the interference. Achievable rates of the cognitive IoT system with amplify-and-forward (AF) and decode-and-forward (DF) relaying mode are maximized through joint power and subcarrier optimization, while ensuring the target rate of the primary system. Simulation results are performed to illustrate the improvement of the spectrum and energy efficiency.

Smart agriculture is able to optimize the information resources of agriculture, which can improve the quality and productivity of agricultural products. Wireless sensor networks (WSNs) provide smart agriculture with effective solutions for collecting, transmitting, and processing of information. However, the large number of sensor networks consume too much energy that violates the principle of green communication. Simultaneous wireless information and power transfer (SWIPT) technology utilizes radio-frequency signals to transmit information and provide energy to WSNs, which can extend the lifetime of WSNs effectively. In this article, an architecture design of smart agriculture is first proposed by exploiting the SWIPT. Then, an energy efficiency optimization scheme is studied to achieve green communication, in which the subcarriers' pairing and power allocation are jointly optimized. The process of communication is divided into two phases. Specifically, in the first phase, source sensor sends information to relay sensor and destination sensor. Relay sensor utilizes a part of the subcarriers to receive the information, and utilizes the remaining subcarriers to collect energy. Destination sensor uses all the subcarriers to receive the information. In the second phase, relay sensor utilizes the energy collected in the first phase to forward the information to destination sensor. An effective iterative optimization algorithm is proposed to resolve the proposed optimization problem through Lagrangian dual function. Simulation results validate that the performance of the algorithm can improve energy efficiency of the system effectively.

In this paper we study interference reducing and resource allocation in Unmanned aerial vehicle (UAV) wireless powered communication system with two UAVs and two ground nodes (GNs). In existing scenarios interference exists at the receiver because multiple GNs transmit information at the same time. In order to reduce interference at the receiver, a new scenario is proposed in this paper. In the proposed scenario, one GN transmit information while another is receiving energy. Minimum uplink throughput is maximized by optimizing trajectory of UAVs and resource allocation. The optimization problem is decomposed into three subproblems which are approximated to convex optimization problems. Simulation results show that the new scenario achieves larger minimum uplink throughput than original scenario.

Wireless sensor networks (WSNs) have the disadvantages of short lifetime due to the limited energy of the energy storage batteries of the sensor nodes and scarcity of spectrum resources as the number of sensor nodes increasing. Simultaneous wireless information and power transfer (SWIPT) can make WSNs solve the problem of short lifetime through sensor nodes harvest energy from radio-frequency (RF) signals. Cognitive radio(CR) can make WSNs solve the problem of the scarcity of spectrum resources through sensor nodes sense and access free licensed spectrum. This paper mainly investigates the performance of an underlay cognitive sensor network (CSN). The sensor nodes in the underlay CSN can communicate with each other through the help of energy harvesting (EH) relay sensor node (RSN) by using amplify-and-forward (AF) relaying protocol. To maximize the throughput of CSN, we propose a algorithm through optimizing the transmit power of sensor nodes. Simulation results show the algorithm is correct and has good performance.

In this letter, we propose new sub-Nyquist sampling schemes for multiple sinusoids, which require fewer number of samples than previous works. Since it is impossible to resolve the frequency ambiguity using a single sub-Nyquist sample sequence, an additional sampling channel is used to determine the correct frequencies. First, a time-staggered sampling system, with the staggered time less than or equal to the Nyquist sampling interval, is proposed. This approach requires only 3K samples to estimate the K frequency components in the signal. However, aliasing can occur when the differences between some frequencies are integer multiples of the sampling rate. Then, another sampling strategy that makes use of feedback is proposed to prevent aliasing. We demonstrate that using two sampling channels and with feedback, 4K samples suffice to resolve both frequency ambiguity and image frequency aliasing. Simulation results are provided to demonstrate the effectiveness of the proposed systems.

Multi-pulse signals are composed of finite pulse streams of arbitrary pulse shape. With the pulse shape known, a multichannel sampling scheme for multi-pulse signals can operate at the rate of innovation, which is much lower than the Nyquist rate. The sampling system is based on low-pass filters, oscillators and integrators. By now there is no hardware to practice the approach. In this paper, we design a hardware system and discover that the non-idealities of low-pass filters will lead to failing in signal reconstruction. We research how the low-pass filters affect the reconstruction and solve the problem by channel calibration. The experiments show that channel calibration compensates most of the errors induced by low-pass filters, and this approach can achieve better estimation of time-delays and amplitudes of multi-pulse signals with a known pulse shape.

In the last decade, it was shown that it is possible to reconstruct signals with finite rate of innovation (FRI signals) from the samples of their filtered versions. However, when noise is present, the present reconstruction algorithms tend to be low accuracy. In this work, a new sparsity-based reconstruction method for FRI signals is put forward. The streams of Diracs and exponential reproducing kernel are considered. Firstly, the analog time axis is quantified and aligned to grids. Secondly, selecting a finite subset of time delay parameters, the measurement vector is represented as a sparse linear combination of the amplitude parameters. Finally, the sparse solution is calculated by solving an optimization problem under L0 norm. The position of non-zero elements is approximation to the time delays, and the value of non-zero elements is the amplitude. Extensive numerical simulations demonstrate the accuracy and robustness of our method.