Efficiently counting Markov equivalent directed acyclic graphs (DAGs) is crucial in graphical causal analysis. Wienöbst et al. (2023) introduced a polynomial-time algorithm, known as the Clique-Picking algorithm, to count the number of Markov equivalent DAGs for a given completed partially directed acyclic graph (CPDAG). This algorithm iteratively selects a root clique, determines fixed orientations with outgoing edges from the clique, and generates the unresolved undirected connected components (UCCGs). In this work, we propose a more efficient approach to UCCG generation by utilizing previously computed results for different root cliques. Our method introduces the concept of super cliques within rooted clique trees, enabling their efficient transfer between trees with different root cliques. The proposed algorithm effectively reduces the computational complexity of the Clique-Picking method, particularly when the number of cliques is substantially smaller than the number of vertices and edges.

Rigid robots can achieve precise motions but expose shortcomings in system complexity, fabrication cost, and humanrobot interaction, which motivates researchers to develop various soft robots to fill these gaps. Electro-hydraulic actuators (EHAs) have received widespread attention and been used in many soft robots due to impressive high-strain, fast-speed and rapidresponse characteristics. However, existing EHAs face challenges in achieving large-deformation, high-robustness, and low-weight simultaneously. This limits the application of EHAs in robotic systems that are weight-sensitive or require fail-safe and faulttolerant behavior. Here, we present a lightweight (0.98 g) electropneumatic actuator (EPA) filled with air and only 0.1-mL liquid dielectric, which achieves high-speed bending from 11° to 93.5° in 60 ms, large-angle bending from 11° to 104° in 2 s (the largest in current EHAs), and high-frequency swing at 20 Hz. The EPA is ultrarobust and can operate properly after being punctured by four needles or crushed twice by a 1500-kg vehicle. Furthermore, to validate the above features of EPAs, three applications are demonstrated at a voltage of 6 kV, including four-finger grippers, fast-crawling robots, and water-walking robots. This work pushes the boundaries of robustness and lightweight for EHAs, providing a foundation for the application of electro-pneumatic actuation in soft robotics.

Millimeter wave (mmWave) systems rely on predefined codebooks for both initial access and data transmission. To compensate the high pathloss of mmWave signal, base station(BS) and user equipment(UE) to be equipped with large antenna arrays which make those codebooks consist of a large number of candidate narrow beams. Both the BS and UE needs to search for a optimal beam from their codebooks that provides maximum received power, such procedure may cause huge beam training overhead. Besides, codebook based beam management limits the maximum beamforming gain as it is bounded by the spatial granularity of the codewords. To overcome these limitations, in the paper, we design a deep learning (DL) based beam training method with partial codebook sweeping. Unlike the existing works using machine learning (ML) or DL to predict the best beam ID from the codebook, the DL model directly outputs the beamforming weights of the analog phase shifters which maximize certain metric, e.g. received signal to noise ratio (SNR). The neural network (NN) is trained offline using simulated environments according to the 3GPP channel models and is then deployed online to predict the optimal beamforming vector with partial beams sensing. Simulation results show that our proposed model outperforms the standard DFT-based codebook with significantly reduced beam training overhead, and enhance the beamforming gain which reflects on the achievable rates.

Hypervelocity impact (HVI) vibration source identification and localization have found wide applications in many fields, such as manned spacecraft protection and machine tool collision damage detection and localization. In this paper, we study the synchrosqueezed transform (SST) algorithm and the texture color distribution (TCD) based HVI source identification and localization using impact images. The extracted SST and TCD image features are fused for HVI image representation. To achieve more accurate detection and localization, the optimal selective stitching features OS SST+TCD are obtained by correlating and evaluating the similarity between the sample label and each dimension of the features. Popular conventional classification and regression models are merged by voting and stacking to achieve the final detection and localization. To demonstrate the effectiveness of the proposed algorithm, the HVI data recorded from three kinds of high-speed bullet striking on an aluminum alloy plate is used for experimentation. The experimental results show that the proposed HVI identification and localization algorithm is more accurate than other algorithms. Finally, based on sensor distribution, an accurate four-circle centroid localization algorithm is developed for HVI source coordinate localization.

Quantization is the characterization of analogue-to-digital converters (ADC) in massive MIMO systems. The design of quantization function or quantization thresholds is found to relate to quantization step, which is the factor that adapts with the changing of transmit power and noise variance. With the objective of utilizing low-resolution ADC is reducing the cost of massive MIMO, we propose an idea as if it is necessary to have adaptive-threshold quantization function. It is found that when maximum-likelihood (ML) is employed as the detection method, having quantization thresholds fixed for low-resolution ADCs will not cause significant performance loss. Moreover, such fixed-threshold quantization function does not require any information of signal power which can reduce the hardware cost of ADCs. Simulations have been carried out in this paper to make comparisons between fixed-threshold and adaptive-threshold quantization regarding various factors.

Considering a densely populated area where a mobile device, with a single RF chain, shares its message with a set of mobile devices through narrowband mmWave channel, an analogue-beam splitting approach is proposed to achieve a good capacity and coverage trade-off. The proposed approach aims at maximizing the capacity of the mmWave multicast channel through antenna-element grouping and adaptive phase shifting, which takes into account of the inter-beam interference. When receivers are randomly distributed on a circle centered at the transmitter, according to the uniform distribution, it is found that the impact of inter-beam interference on the channel capacity can be negligibly small, and thus the analogue-beam splitting approach can be largely simplified in practice. Computer simulations are carried out to elaborate our theoretical study and demonstrate considerable advantages of the proposed analogue-beam splitting approach.