The high-speed Electric Multiple Units(EMUs) is widely used in railway transportation. The secondary voltage ripple on the DC-Link of the traction converter endangers the safety of the rolling stock. The existing passive secondary voltage ripple suppression devices are larger and heavier. In this paper, an active filter and its control strategy for the CRH3 EMUs traction converter are proposed, whick can absorb secondary pulsating power with smaller size and less weight. The proposed active filter meets the design requirements for pulsating power and key parameters are calculated. A simulation model based on Matlab/Simulink verifies the active secondary voltage ripple suppression strategy for single-phase rectifier. An experimental platform based on the FPGA controller is built to verify the correctness and effectiveness of the proposed strategy. It is shown that the size and weight reduction of the secondary voltage ripple suppression devices can be achieved without affecting the performance.

The Industrial Internet of Things (IIoT) has attracted considerable attention because of its capability in turning common objects into connective devices. In IIoT, Deterministic Networking (DetNet) is an important scenario that can provide the network layer ultra-low latency support. In this paper, we focus our attention on the asymptotic cross-layer analysis of delay-violation-probability and power tradeoff in DetNet. More specifically, we find that zero delay-violation-probability transmission cannot be achieved under causal channel status with finite average power consumption. To support the requirement of DetNet under casual channel status, we prove that zero delay-violation-probability transmission can be achieved through frequency diversity, the use of multiple antennas, and cooperative diversity. Under non-causal channel status, DetNet can be achieved when the hard delay constraint is more than one time slot. Moreover, we derive the optimal tradeoff between the delay-violation-probability and average power consumption under causal channel status, which is further verified through numerical simulations.

Ultra-Reliable and Low-Latency Communications (URLLC) has attracted considerable attention because of its potential applications in factory automation, automated driving, and telesurgery anticipated for the era of the sixth-Generation (6G) networks. In URLLC with random channel gains and a hard delay constraint, the scheduling of backlogged queues and finite blocklength coding in the physical layer will make it very challenging to specify its performance limits. In this paper, we focus our attention on the asymptotic cross-layer analysis of URLLC when the Signal-to-Noise Ratio (SNR) is sufficiently high. More specifically, we find that a fundamental tradeoff exists among the service capability, latency, and error probability in the high SNR regime, which is characterized by a gain conservation equation. The main result of this work reveals that the sum of our defined service rate gain, real-time gain, and reliability gain is equal to one under the optimal policy. Numerical simulations are also exploited to validate that the derived gain conservation equation holds even with bounded random arrival.

Ultra-Reliable and Low-Latency Communications (URLLC) has attracted considerable attention because of its potential applications in factory automation, automated driving, and telesurgery anticipated for the era of the sixth-Generation (6G) networks. In URLLC with random channel gains and a hard delay constraint, the scheduling of backlogged queues and finite blocklength coding in the physical layer make it rather challenging to specify its performance limit. In this paper, we focus our attention on the asymptotic cross-layer analysis of URLLC when the Signal-to-Noise Ratio (SNR) is sufficiently high. More specifically, we find that a fundamental tradeoff exists between the latency and error probability in the high SNR regime, which is characterized by a gain conservation equation. The main result of this work reveals that the sum of our defined real-time gain and reliability gain is equal to one under the optimal scheduling policy. Numerical simulations are also exploited to validate that the derived gain conservation equation holds even with bounded random arrival.

Age of Information (AoI) has attracted much attention recently due to its capability of characterizing the freshness of information in Industrial Internet of Things (IIoT). To improve the information freshness over a fading channel shared by multiple users, efficient scheduling methods are highly desired for IIoT. In this paper, we are interested in a cross-layer scheduling policy, in which power and rate-adaptive transmissions are enabled in the physical layer. More specifically, we adopt a probabilistic scheduling method to minimize AoI while satisfying an average power constraint. The optimization of the probabilistic scheduling policy is formulated as a Constrained Markov Decision Process (CMDP), the dimension of which grows exponentially with the number of users and the buffer size. To overcome the prohibitive computational cost, we reveal the threshold-based structure of the AoI-optimal scheduling. Then we conceive a low complexity iterative algorithm that gives us the optimal freshness-aware scheduling policy.

Age of Information (AoI) has attracted much attention recently due to its capability of characterizing the freshness of information. To improve information freshness over fading channels, efficient scheduling methods are highly desired for wireless transmissions. However, due to the channel instability and arrival randomness, optimizing AoI is very challenging. In this paper, we are interested in the AoI-optimal transmissions with rate-adaptive transmission schemes in a buffer-aware system. More specifically, we utilize a probabilistic scheduling method to minimize the AoI while satisfying an average power constraint. By characterizing the probabilistic scheduling policy with a Constrained Markov Decision Process (CMDP), we formulate a Linear Programming (LP) problem. Further, a low complexity algorithm is presented to obtain the optimal scheduling policy, which is proved to belong to a set of semi-threshold-based policies. Numerical results verify the reduction in computational complexity and the optimality of semi-threshold-based policy, which indicates that we can achieve well real-time service with a low computational complexity.

Endoscopy is one of the most commonly used tools for gastrointestinal examination. However, due to its close dependence on the experience of operators, the missed diagnosis rate of early gastric cancer (EGC) is still high. Therefore, the auxiliary detection of EGC based on endoscopic images is of great significance. In order to better fuse low-level features with rich details and high-level features with sufficient semantics, in this paper, we propose a deeper-shallower feature enhancement (DSF) module, in which the feature maps from both deeper layer and shallower layer are combined to enhance the features of current layer. Then the enhanced features are used as the next shallower input in the bottom-up path feature fusion (BUF) module, which transmits the enhanced features in a bottom-up manner. In order to capture features that focus more on accurate information, the channel-wise attention (CA) module is applied before prediction. Moreover, an endoscopic image dataset that contains four categories of lesions is established for implementing the task of EGC detection. Experimentally, the proposed method achieves the fl-scores of 90.65%, 63.34% and 89.95% on Kvasir dataset, ETIS-Larib dataset and our endoscopic dataset respectively, outperforming the baseline by 0.62%, 3.14% and 2.03% respectively.

Ultra-Reliable and Low-Latency Communication (URLLC), aiming at meeting the hard delay constraint, plays a central role in 5G or even 6G mobile communications, thereby attracting much recent attention. However, satisfying such constraint over fading channels means a critical or even impossible mission for the physical layer because channel inversion exhibits a large power penalty in extreme fading environments. In this paper, we present a cross-layer approach to minimize the delay violation probability, the probability that hard delay constraint can not be assured, in URLLC over fading channels. More specifically, a probabilistic scheduling scheme that jointly exploits the queue state and the channel state is conceived. We formulate a Constrained Markov Decision Process (CDMP) to minimize the delay violation probability with a given power constraint. Variable substitution and linear programming are adopted to obtain the optimal solution. The optimal policy is shown to be threshold-based, which also induces the piecewise linear delay violation probability-power tradeoff. The tradeoff curve characterizes the theoretical lower bound of the delay violation probability with a given power constraint.

Being capable of characterizing the freshness of information, Age of Information (AoI) has attracted much attention recently. To provide better real-time service over fading channels, efficient scheduling methods are highly desired for wireless transmissions with freshness requirements. However, due to the channel instability and arrival randomness, it is challenging to achieve the optimal AoI. In this paper, we are interested in the AoI-optimal transmissions with truncated channel inversion, which has a low complexity transceiver architecture exploiting fixed coding and modulation. More specifically, we utilize a probabilistic scheduling method to minimize the AoI while satisfying an average power constraint. By characterizing the probabilistic scheduling policy with a Constrained Markov Decision Process (CMDP), we formulate a Linear Programming (LP) problem. Further, we present a low complexity algorithm to obtain the optimal scheduling policy, which is proved to belong to a set of semi-threshold-based policies. Numerical results verify the reduction in computational complexity and the optimality of semithreshold-based policy, which indicates that we can achieve well real-time service with a low calculating complexity.