Magnetic Particle Imaging (MPI) is an innovative medical modality, providing nanomolar-scale in vivo sensitivity and radiation-free dynamic real-time detection for precision medicine. However, MPI faces a challenging problem in accurately visualizing nanoparticle distributions, where the reconstructed images with unidirectional scanning exhibit anisotropy. The anisotropy in spatial resolution leads to distortion and blurred image boundaries. Existing deep learning methods for anisotropy calibration are only limited to simulation data due to lacking of real-world MPI datasets. To address the aforementioned problems, we spent over three years designing and constructing a real-world MPI anisotropic image datasets (20,156 images) with diverse phantoms (sensitivity, resolution, vessel, shape) and animal scanning. Then, we introduce a novel Mamba-based method, MPI-Mamba, for anisotropic image calibration. Specifically, we propose a latent feature fusion state space model (LFF-SSM) block for feature fusion and leverage conditional latent diffusion model (CL-DM) branch for feature extraction. The CL-DM is performed to extract latent features in a highly compressed latent space for guiding the calibration and deblurring process. Next, we exploit the LFF-SSM to fully fuse the extracted multi-scale features to capture contextual information from the image structure, enabling the model to learn the overall distribution of signal concentration. We evaluate our method and competing methods on simulation dataset and our constructed diverse real-world MPI datasets. The results show that our proposed approach outperforms competing methods for anisotropic image calibration and deblurring.

A core challenge in survival analysis is to model the distribution of time-to-event data, where the event of interest may be a death, failure, or occurrence of a specific event. Previous studies have showed that ranking and maximum likelihood estimation loss functions are widely-used learning approaches for survival analysis. However, ranking loss only focus on the ranking of survival time and does not consider potential effect of samples’ exact survival time values. Furthermore, the maximum likelihood estimation is unbounded and easily subject to outliers (e.g., censored data), which may cause poor performance of modeling. To handle the complexities of learning process and exploit valuable survival time values, we propose a time-adaptive coordinate loss function, TripleSurv, to achieve adaptive adjustments by introducing the differences in the survival time between sample pairs into the ranking, which can encourage the model to quantitatively rank relative risk of pairs, ultimately enhancing the accuracy of predictions. Most importantly, the TripleSurv is proficient in quantifying the relative risk between samples by ranking ordering of pairs, and consider the time interval as a trade-off to calibrate the robustness of model over sample distribution. Our TripleSurv is evaluated on three real-world survival datasets and a public synthetic dataset. The results show that our method outperforms the state-of-the-art methods and exhibits good model performance and robustness on modeling various sophisticated data distributions with different censor rates.

Magnetic particle imaging (MPI) uses nonlinear response signals to noninvasively detect magnetic nanoparticles in space, and its quantitative properties hold promise for future precise quantitative treatments. In reconstruction, the system matrix based method necessitates suitable regularization terms, such as Tikhonov or non-negative fused lasso (NFL) regularization, to stabilize the solution. While NFL regularization offers clearer edge information than Tikhonov regularization, it carries a biased estimate of thel1penalty, leading to an underestimation of the reconstructed concentration and adversely affecting the quantitative properties. In this paper, a new nonconvex regularization method including min-max concave (MC) and total variation (TV) regularization is proposed. This method utilized MC penalty to provide nearly unbiased sparse constraints and adds the TV penalty to provide a uniform intensity distribution of images. By combining the alternating direction multiplication method (ADMM) and the two-step parameter selection method, a more accurate quantitative MPI reconstruction was realized. The performance of the proposed method was verified on the simulation data, the Open-MPI dataset, and measured data from a homemade MPI scanner. The results indicate that the proposed method achieves better image quality while maintaining the quantitative properties, thus overcoming the drawback of intensity underestimation by the NFL method while providing edge information. In particular, for the measured data, the proposed method reduced the relative error in the intensity of the reconstruction results from 28% to 8%.

Magnetic particle imaging (MPI) is an emerging technique for determining magnetic nanoparticle distributions in biological tissues. Although system-matrix (SM)-based image reconstruction offers higher image quality than the X-space-based approach, the SM calibration measurement is time-consuming. Additionally, the SM should be recalibrated if the tracer's characteristics or the magnetic field environment change, and repeated SM measurement further increase the required labor and time. Therefore, fast SM calibration is essential for MPI. Existing calibration methods commonly treat each row of the SM as independent of the others, but the rows are inherently related through the coil channel and frequency index. As these two elements can be regarded as additional multimodal information, we leverage the transformer architecture with a self-attention mechanism to encode them. Although the transformer has shown superiority in multimodal fusion learning across several fields, its high complexity may lead to overfitting when labeled data are scarce. Compared with labeled SM (i.e., full size), low-resolution SM data can be easily obtained, and fully using such data may alleviate overfitting. Accordingly, we propose a pseudo-label-based progressive pretraining strategy to leverage unlabeled data. Our method outperforms existing calibration methods on a public real-world OpenMPI dataset and simulation dataset. Moreover, our method improves the resolution of two in-house MPI scanners without requiring full-size SM measurements. Ablation studies confirm the contributions of modeling SM inter-row relations and the proposed pretraining strategy.

Magnetic particle imaging (MPI) is a radiation-free vessel- and target-imaging modality that can sensitively detect nanoparticles. A static magnetic gradient field, referred to as a selection field, is required in MPI to provide a field-free region (FFR) for spatial encoding. The image resolution of MPI is closely related to the size of the FFR, which is determined by the selection field gradient amplitude. Because of the limitations of existing gradient coil hardware, the image resolution of MPI cannot satisfy the clinical requirements of human in vivo imaging. Pulsed excitation has been confirmed to improve the image resolution of MPI by breaking down the 'relaxation wall.' This work proposes the use of a pulsed waveform magnetic gradient from magnetic resonance imaging to further improve the image resolution of MPI. Through alignment of the gradient direction along the field-free line (FFL), each location on the FFL is able to have a unique excitation field strength that generates a specific relaxation-induced decay signal. Through excitation of nanoparticles on the FFL with many gradient profiles, a high-resolution, one-dimensional (1D) image can be reconstructed on the FFL. For larger magnetic nanoparticles, simulation results revealed that a pulsed excitation field with a greater flat portion generates a 1D bar pattern phantom image with a higher correlation and spatial resolution. With parallel FFL and gradient coil movements, high-resolution, two-dimensional (2D) Shepp-Logan phantom and brain vessel maps were reconstructed through repetition of the spatially resolved measurement of magnetic nanoparticles on the FFL.

As an emerging solution to mitigate the problems of the shortage of power resources, electric vehicles (EVs) have advocated to provide safety and convenient driving recently. However, with the ever increasing number of EVs and the new demand of services, how to optimally schedule the charging services becomes a challenge. Therefore, in this paper we present a novel pricing mechanism to optimally schedule the charging demands with user utilities. Firstly, a framework with a nonpreemptive priority charging service is shown for users to queue up. Secondly, based on queuing theory, a novel pricing mechanism is designed to balance the load of charging station by considering the characteristics of different regions and the status of queue. Thirdly, the user utility is studied according to the distance, waiting time as well as the expense, in order to improve the user utility. Finally, simulation results show that the proposed scheme can optimally distribute the charging demand and improve the user utility more efficiently than other conventional methods.

Mobile charging stations (MCSs) can provide electric vehicles (EVs) with better charging services than the fixed charging stations, as the flexible and efficient charging sites can be available. However, how to schedule the tasks from the EVs and optimally place the MCSs becomes a new challenge. Therefore, in this paper we present a novel approach to help EVs' charging with MCSs through heterogeneous networks. Firstly, a novel heterogeneous network model is presented to improve the communication between EVs and MCSs by using macro cells and small cells. Next, a novel model is developed to make optimal decisions for MCSs to schedule the tasks from EVs. Then, a chaotic evolution particle swarm optimization (CEPSO) algorithm is presented to determine the optimal placement of MCSs based on the charging demand and the maintenance cost. Finally, the simulation experiments prove that the proposed approach can outperform the conventional methods.

Massive Multi-input Multi-output (MIMO) technique shows great potentials in improving energy and spectral efficiency while suffers high costs of the requirement for large amount of radio frequency (RF) chains. In this paper, we develop an l1/2-regularity based downlink transmit antenna selection scheme for massive MIMO systems with limited RF chains. With the objective to minimize the transmission power at the given number of RF-chains and transmission quality, we formulate and decompose the original l1/2-norm optimization problem into two separated problems with l1/2-sparsity and signal-interference-to-noise-ratio (SINR) requirements, respectively. An iterative algorithm based on coordinate descent and feasible set projection are then developed. This scheme provides an efficient way to evaluate the minimum required RF chains for the deployment of a massive MIMO system. Simulation results show that the proposed scheme achieves higher energy efficiency compared with the lp(p > 1/2)-norm based antenna selection methods, especially for large numbers of correlated antennas.

With the widespread application of wireless networks and the requirements of different user equipments (UEs), energy has become a scarcer resource as well as spectrum. In this paper, considering the actual scenarios of imperfect channel state information (CSI), we study a resource allocation scheme in the downlink orthogonal frequency division multiple access (OFDMA) systems. To balance between energy efficiency (EE) and proportional fairness (PF), the problem is formulated as maximizing average achievable EE with the constraints of PF of users and QoS assurance. To solve the optimal problem, we divide it into two layers. The sub-problem P1 of inner layer is solved to maximize PF, with the parameter of total transmit power which is updated by the sub-problem P2 of outer layer. In outer layer, with the allocation scheme from P1, a gradient-based adaptation resource allocation algorithm is proposed to achieve the maximum EE with total transmit power updated in every gradient iteration. Moreover, the impacts of the imperfect CSI on EE and PF are analysed. Simulation results are presented to show the superior performance of the proposed algorithms and verify the analytical findings.

Secure relay and jammer selection for physical-layer security is studied in a wireless network with multiple intermediate nodes and eavesdroppers, where each intermediate node either helps to forward messages as a relay, or broadcasts noise as a jammer. We derive a closed-form expression for the secrecy outage probability (SOP), and we develop two relay and jammer selection methods for SOP minimization. In both methods a selection vector and a corresponding threshold are designed and broadcast by the destination to ensure each intermediate node knows its own role while knowledge of the relay and jammer set is kept secret from all eavesdroppers. Simulation results show the SOP of the proposed methods are very close to that obtained by an exhaustive search, and that maintaining the privacy of the selection result greatly improves the SOP performance.

In this paper the performance of decode-and-forward (DF) opportunistic relaying systems with limited feedback is studied. Firstly an approximate expression of outage probability for limited-feedback opportunistic relaying is derived. Secondly the diversity gain with limited feedback is proved to be only 2 regardless of the number of potential relays or feedback accuracy, which differs from the full-diversity gain achieved by the ideal opportunistic DF relaying. Moreover, further analysis shows that with the increasing accuracy of feedback, the outage probability will approach to that of the ideal opportunistic relaying. Based on the theoretical results, a feedback method is developed to determine the minimum number of feedback bits with the outage loss under control. Simulation results confirm the theoretical analyses.

Antenna selection provides a practical way to decrease system complexity and the hardware cost of radio frequency (RF) chains in MIMO system. In this paper, We propose an antenna selection scheme in the regenerative MIMO relaying scenario,which can achieve full diversity order of MIMO single relay network, and derive the diversity-multiplexing tradeoff(DMT) performance of the scheme. The algorithm has a lower complexity , and the simulation results show that its performance is relatively good.

Prolonging lifetime is of paramount importance for an energy-constrained wireless network to ensure uninterrupted information exchange. Cooperative communication technology, which can achieve high energy-efficiency by exploiting spatial diversity of distributed nodes, has become an attractive way for lifetime extension of wireless networks. In this paper, a novel relay selection method is developed for lifetime extension in an Amplify-and-Forward (AF) cooperative network where the transmission power is subject to a signal-to-noise ratio (SNR) requirement at the destination. In the proposed method firstly the average transmission power for every relay to forward the message is derived through the knowledge of channel statistics and then a utility function is designed to introduce it into the relay selection procedure so as to offer a relay more opportunity to be selected when its required transmission power is lower than usual. Simulation results show that the proposed method achieves improved performance in extending the lifetime by both reducing the average transmission power and lowering the residual battery energy.

Inter-cell interference is a serious problem in multicell OFDMA systems. In this paper, an iterative interference price based power allocation algorithm with inter-cell interference coordination is proposed for downlink multi-cell OFDMA systems. In the algorithm, each base station announces a price for interference and performs power allocation based on the price information received from other base stations with the objective of maximizing the net utility of the local cell. Through price information updating and transmit power reallocation in every iteration, the algorithm can intelligently coordinate the inter-cell interference in the system. Besides, the algorithm can be implemented distributedly with price information shared among neighboring cells. Considering the heavy feedback overhead from the users to the base stations, a simplified algorithm which considers only the major interference is also proposed. Simulation results show that when the inter-cell interference is serious, the proposed algorithms can effectively increase the transmission rate compared with iterative water-filling.

In this paper, distributed power allocation schemes are studied for amplify-and-forward (AF) cooperative relay networks, where only partial channel state information (CSI) is available at the source and relays. Aiming at minimizing the total transmit power while providing a target outage probability, a scheme is first investigated in which the source decides transmit power and a relay-forwarding threshold, and each relay makes individually the transmit decision based on the threshold and its own CSI. Then a single relay power allocation scheme is proposed to simplify the implementation complexity, in which only one relay is selected to forward messages. Simulation results illustrate the performance improvement of the proposed schemes.