Avoiding runway incursions is a significant challenge and a top priority in aviation. Due to all causes of runway incursions belong to human factors, runway incursion prevention systems should remove human from the system operation loop as much as possible. Although current runway incursion prevention systems have made big progress on how to obtain accurate and sufficient information of aircraft/vehicles, they cannot predict and detect runway incursions as early as experienced air traffic controllers by using the same surveillance information, and cannot give explicit instructions and/or suggestions to prevent runway incursions like real air traffic controllers either. In one word, human still plays an important position in current runway incursion prevention systems. In order to remove human factors from the system operation loop as much as possible, this paper proposes a new type of runway incursion prevention system based on logic-based reasoning. The system predicts and detects runway incursions, then gives explicit instructions and/or suggestions to pilots/drivers to avoid runway incursions/collisions. The features of the system include long-range prediction of incidents, explicit instructions and/or suggestions, and flexible model for different policies and airports. To evaluate our system, we built a simulation system, and evaluated our system using both real historical scenarios and conventional fictional scenarios. The evaluation showed that our system is effective at providing earlier prediction of incidents than current systems, giving explicit instructions and/or suggestions for handling the incidents effectively, and customizing for specific policies and airports using flexible model.

Paragraph segmentation is a text segmentation task. Iikura et al. achieved excellent results on paragraph segmentation by introducing focal loss to Bidirectional Encoder Representations from Transformers. In this study, we investigated paragraph segmentation on Daily News and Novel datasets. Based on the approach proposed by Iikura et al., we used auxiliary loss to train the model to improve paragraph segmentation performance. Consequently, the average F1-score obtained by the approach of Iikura et al. was 0.6704 on the Daily News dataset, whereas that of our approach was 0.6801. Our approach thus improved the performance by approximately 1%. The performance improvement was also confirmed on the Novel dataset. Furthermore, the results of two-tailed paired t-tests indicated that there was a statistical significance between the performance of the two approaches.

LDCs system with finite-rate error-free feedback is proposed in this letter. The optimal transmission codeword is selected at the receiver and the codeword index is sent to the transmitter. A simple random search algorithm is introduced for codebook generation. Moreover, the max-min singular value criterion is adopted for codeword selection. Simulation results showed that, with only 3-4 feedback bits, the low-complexity Zero-Forcing receiver can approach the Maximum-Likelihood (ML) performance.

Quasi cyclic LDPC (QC-LDPC) codes consisting of circulant permutation matrices (CPM-QC-LDPC) are one of the most attractive types of LDPC codes due to their many advantages. In this paper, we mainly do some research on CPM-QC-LDPC codes. We first propose a two-stage decoding scheme mainly based on parity check matrix transform (MT), which can efficiently improve the bit error rate performance. To optimize the tradeoff between hardware implementation complexity and decoding performance, an improved method that combines our proposed MT scheme with the existing CPM-RID decoding scheme is presented. An experiment shows that both schemes can improve the bit error rate (BER) performance. Finally, we show that the MT decoding mechanism can be applied to other types of LDPC codes. We apply the MT scheme to random LDPC codes and show that it can efficiently lower the error floor.

We address the problem of multiuser co-channel interference scheduling in multicell interference-limited networks. Our target is to optimize the network capacity under the SIR-balanced power control policy. Since it's difficult to optimize the original problem, we derive a new problem which maximizes the lower bound of the network capacity. Based on the analysis of this new problem, we propose an interference matched scheduling algorithm. This algorithm considers the caused co-channel interference and the channel conditions to schedule the "matched" users at the same time. We prove that this interference matched scheduling algorithm optimizes the lower bound of the network capacity for any arbitrary numbers of cells and users. Moreover, this scheduling method is low-complexity and can be implemented in a fully distributed fashion. Simulation results reveal that the performance of the proposed algorithm achieves near optimal capacity, even though it does not optimize the network capacity directly. Finally, the proposed algorithm holds a great gain over formerly proposed round robin and power matched scheduling method, especially when the scale of the network is large.

With the gradually increase of the application of new energy in microgrids, Electric Spring (ES), as a new type of distributed compensation power electronic device has been widely studied. The Generalized Electric Spring (G-ES) is an improved topology, and the space limitation problem in the traditional topology is solved. Because of the mode of G-ES use in the power grid, a reasonable solution to the voltage loss of the critical section feeder is needed. In this paper, the voltage balance equation based on the feedforward compensation coefficient is established, and a two cascade control strategy based on the equation is studied. The first stage of the two cascade control strategy is to use communication means to realize the allocation of feedforward compensation coefficients, and the second stage is to use the coefficients to realize feedforward fixed angle control. Simulation analysis shows that the proposed control strategy does not affect the control accuracy of the critical load (CL), and effectively improves the operational range of the G-ES.

This paper investigates the error rate performance of parallel combinatorial spread spectrum (PC/SS) communicaion systems that use coherent and differential multiphase modulation: multiphase parallel combinatorial spread spectrum (MPC/SS) communication systems. The PC/SS systems are multicode SS systems based on orthogonal pseudo-noise (PN) sequences. Data is transmitted by delivering a combination of multiple PN sequences among a set of pre-assigned PN sequences. In the MPC/SS systems, every PN sequence on transmission is modulated by q-ary coherent or differential phase shift keying (PSK). Symbol error rate (SER) and average bit error rate (BER) in coherent and differential MPC/SS systems are investigated. The BER comparison between the MPC/SS systems and simple multicode SS systems with q-ary coherent and differential PSK is also presented. Numerical results show that the MPC/SS systems are superior to the conventional q-ary PSK systems, if they have equal spectral efficiency.

Post-Silicon Tunable (PST) buffers are widely adopted in high-performance integrated circuits to fix timing violations introduced by process variations. In typical optimization procedures, the statistical timing analysis of the circuits with PST clock buffers will be executed more than 2000 times for large scale circuits. Therefore, the efficiency of the statistical timing analysis is crucial to the PST clock buffer optimization algorithms. In this paper, we propose a stochastic collocation based efficient statistical timing analysis method for circuits with PST buffers. In the proposed method, we employ the Howard algorithm to calculate the clock periods of the circuits on less than 100 deterministic sparse-grid collocation points. Afterwards, we use these obtained clock periods to derive the yield of the circuits according to the stochastic collocation theory. Compared with the state-of-the-art statistical timing analysis method for the circuits with PST clock buffers, the proposed method achieves up to 22X speedup with comparable accuracy.

A low-complexity time-invariant angle-range dependent directional modulation (DM) based on time-modulated frequency diverse array (TM-FDA-DM) is proposed to achieve point-to-point physical layer security communications. The principle of TM-FDA is elaborated and the vector synthesis method is utilized to realize the proposal, TM-FDA-DM, where normalization and orthogonal matrices are designed to modulate the useful baseband symbols and inserted artificial noise, respectively. Since the two designed matrices are time-invariant fixed values, which avoid real-time calculation, the proposed TM-FDA-DM is much easier to implement than time-invariant DMs based on conventional linear FDA or logarithmical FDA, and it also outperforms the time-invariant angle-range dependent DM that utilizes genetic algorithm (GA) to optimize phase shifters on radio frequency (RF) frontend. Additionally, a robust synthesis method for TM-FDA-DM with imperfect angle and range estimations is proposed by optimizing normalization matrix. Simulations demonstrate that the proposed TM-FDA-DM exhibits time-invariant and angle-range dependent characteristics, and the proposed robust TM-FDA-DM can achieve better BER performance than the non-robust method when the maximum range error is larger than 7km and the maximum angle error is larger than 4°.

As advances in networking technology help to connect industrial control networks with the Internet, the threat from spammers, attackers and criminal enterprises has also grown accordingly. However, traditional Network Intrusion Detection System makes significant use of pattern matching to identify malicious behaviors and have bad performance on detecting zero-day exploits in which a new attack is employed. In this paper, a novel method of anomaly detection in industrial control network is proposed based on RNN-GBRBM feature decoder. The method employ network packets and extract high-quality features from raw features which is selected manually. A modified RNN-RBM is trained using the normal traffic in order to learn feature patterns of the normal network behaviors. Then the test traffic is analyzed against the learned normal feature pattern by using osPCA to measure the extent to which the test traffic resembles the learned feature pattern. Moreover, we design a semi-supervised incremental updating algorithm in order to improve the performance of the model continuously. Experiments show that our method is more efficient in anomaly detection than other traditional approaches for industrial control network.

Self-dithered digital delta-sigma modulators (DDSMs) are commonly used in fractional-N frequency synthesizers due to their ability to eliminate unwanted spurs from the synthesizer’s spectra without requiring additional hardware. However, when operating with a low-bit input, self-dithered DDSMs can still suffer from spurious tones at certain inputs. In this paper, we propose a self-dithered MASH 1-1-1-1 structure to mitigate the spur issue in the self-dithered MASH DDSMs. The proposed self-dithered MASH 1-1-1-1 suppresses the spurs with shaped dithering and achieves 4th order noise shaping.

Chinese-Vietnamese cross-lingual event retrieval aims to retrieve the Vietnamese sentence describing the same event as a given Chinese query sentence from a set of Vietnamese sentences. Existing mainstream cross-lingual event retrieval methods rely on extracting textual representations from query texts and calculating their similarity with textual representations in other language candidate sets. However, these methods ignore the difference in event elements present during Chinese-Vietnamese cross-language retrieval. Consequently, sentences with similar meanings but different event elements may be incorrectly considered to describe the same event. To address this problem, we propose a cross-lingual retrieval method that integrates event elements. We introduce event elements as an additional supervisory signal, where we calculate the semantic similarity of event elements in two sentences using an attention mechanism to determine the attention score of the event elements. This allows us to establish a one-to-one correspondence between event elements in the text. Additionally, we leverage the multilingual pre-trained language model fine-tuned based on contrastive learning to obtain cross-language sentence representation to calculate the semantic similarity of the sentence texts. By combining these two approaches, we obtain the final text similarity score. Experimental results demonstrate that our proposed method achieves higher retrieval accuracy than the baseline model.