A dual-band phase shifter with simultaneous and independent phase control across two operating bands, designed for emerging low Earth orbit (LEO) satellite communications systems in the K/Ka-band, is presented. The proposed structure utilizes a dual-stub-loaded 3-dB hybrid to separate signals by their frequency, allowing for independent phase tuning in each band. To achieve a wide phase range, low insertion loss, and minimal phase range deviation, both the 3-dB hybrid and reflective loads are optimized for their respective bands. Additionally, the circuit can seamlessly switch between phase shifter and diplexer modes without redesign, enhancing its versatility. To validate the proposed concept, phase shifter and diplexer prototypes operating at$\mathrm {19.3~GHz}~\text {to}~\mathrm {20.3~GHz}$and$\mathrm {29.5~GHz}~\text {to}~\mathrm {30.5~GHz}$have been fabricated and tested. Both prototypes demonstrate a phase tuning range exceeding$\mathrm {180~\!^{\circ }}$with a maximum phase range deviation of$\mathrm {\pm 1.25~\!^{\circ }}$and an insertion loss below 2.5 dB. For the diplexer mode, port-to-port isolation exceeds 25 dB across all phase states. The proposed phase shifter design offers simplicity, robustness, and flexibility, with flat phase range and amplitude, while allowing independent tunable phase states across transmit and receive bands, making it highly suitable for LEO satellite communications systems.

ReLU is a widely used activation function in deep neural networks. This paper explores the stability properties of the ReLU map. For any weight matrixA∈ Rm×nand bias vectorb∈ Rmat a given layer, we define the condition number κA,bas κA,b=UA,b/LA,b, whereUA,bandLA,bare the upper and lower Lipschitz constants, respectively. We first demonstrate that for any given A and b, the condition number satisfies κA,b≥ √ 2. Moreover, when the weights of the network at a given layer are initialized as random i.i.d. Gaussian variables and the bias term is set to zero, the condition number asymptotically approaches this lower bound. Our findings offer valuable insights into the characteristics of randomly initialized neural networks, contributing to a better understanding of their initial behavior and potential performance.

This article adopts the event-triggered control strategy (E-TCS) to achieve the practical predefined-time synchronization (PPTS) for dynamic complex networks (DCNs) with Markov switching topologies under stochastic denial-of-service (SDoS) attacks. We consider the Markov switching topologies, and the coupling weight of the complex networks between nodes is dynamic. For the proposed E-TCS, the minimum inter-event interval can be directly obtained, thereby eliminating the Zeno phenomenon. By employing the time-varying function, all states of the DCNs can achieve PPTS within the predefined time. Concretely, in contrast to finite/fixed-time synchronization, utilizing PPTS enables the arbitrary setting of convergence time, independent of initial values and controller parameters. Notably, the SDoS attacks occur with a certain probability within the attack intervals. Moreover, the intermittent attacks and the average non-attack rate are considered, and this approach leads to less conservative results. Additionally, we prove that a higher average non-attack rate makes it easier for all states of the DCNs to achieve PPTS. Finally, the validity of the proposed E-TCS is verified by the examples of Chua’s circuit and the Kuramoto oscillator network.

Chaotic signals are spread-spectrum nonlinear signals with initial value sensitivity that can potentially provide safe and anti-jamming digital communication systems. However, to reduce receiver complexity and achieve smooth demodulation, traditional chaos-based wireless communication systems require the transmission of additional chaotic reference signals, which reduces the spectral efficiency and degrades the security characteristics of the chaotic signals. Recent work has explored deep learning (DL)-aided transceivers to address these issues. However, these techniques have not yet fully exploited the inherent characteristics of chaotic signals, e.g., spectral properties. To maximize the potential of chaotic signals in wireless communication, this paper proposes a power spectral density-based deep learning chaos shift keying (PSD-DLCSK) receiver. The proposed PSD-DLCSK scheme effectively utilizes the spectral properties of chaotic signals, where the PSD of received signals serves as input to a deep neural network (DNN) for symbol detection. The proposed DNN architecture combines long short-term memory networks with self-attention mechanisms to effectively capture the temporal dependencies and spectral properties of chaotic signals, enabling reliable intelligent demodulation without chaotic reference signals. Extensive simulations demonstrate that PSD-DLCSK achieves superior bit error rate (BER) performance compared to traditional receivers as well as other DL-aided schemes over additive white Gaussian noise and multipath Rayleigh fading channels.

To substantially enhance robot intelligence, there is a pressing need to develop a large model that enables general-purpose robots to proficiently undertake a broad spectrum of manipulation tasks, akin to the versatile task-planning ability exhibited by LLMs. The vast diversity in objects, robots, and manipulation tasks presents huge challenges. Our work introduces a comprehensive framework to develop a foundation model for general robotic manipulation that formalizes a manipulation task as contact synthesis. Specifically, our model takes as input object and robot manipulator point clouds, object physical attributes, target motions, and manipulation region masks. It outputs contact points on the object and associated contact forces or post-contact motions for robots to achieve the desired manipulation task. We perform extensive experiments both in the simulation and real-world settings, manipulating articulated rigid objects, rigid objects, and deformable objects that vary in dimensionality, ranging from one-dimensional objects like ropes to two-dimensional objects like cloth and extending to three-dimensional objects such as plasticine. Our model achieves average success rates of around 90%. Supplementary materials and videos are available on our project website at https://manifoundationmodel.github.io/.

We present ClothesNet: a large-scale dataset of 3D clothes objects with information-rich annotations. Our dataset consists of around 4400 models covering 11 categories annotated with clothes features, boundary lines, and keypoints. ClothesNet can be used to facilitate a variety of computer vision and robot interaction tasks. Using our dataset, we establish benchmark tasks for clothes perception, including classification, boundary line segmentation, and keypoint detection, and develop simulated clothes environments for robotic interaction tasks, including rearranging, folding, hanging, and dressing. We also demonstrate the efficacy of our ClothesNet in real-world experiments. Supplemental materials and dataset are available on our project webpage at https://sites.google.com/view/clothesnet.

Homography estimation is an important image alignment method that has been widely used in computer vision applications. Traditional methods heavily rely on the distribution of features and usually fail in low-texture and large-baseline scenes. Most learning-based methods use convolutional neural networks(CNNs) to extract features. However, the dense features extracted in this way have a limited receptive field, leading to poor accuracy of results. In this paper, we propose a novel method for homography estimation. We first estimate the projective transformation between the reference image and the target image at a coarse level and then refine the estimated homography at the fine level. Unlike approaches that use simple CNNs or global correlations to search correspondences, we add self- and cross-attention layers in the transformer to enhance the feature correlations. The experiments show that our method significantly outperforms the existing solutions in challenging large-baseline scenes.

Abstract Homography estimation serves an important role in many computer vision tasks. Depending heavily on hand‐craft feature quality, traditional methods degenerate sharply in scenes with low texture. Existing deep homography methods can handle the low‐texture problem but are not robust for scenes with low overlap rates and/or illumination changes. This paper proposes a novel unsupervised homography estimation method that can simultaneously handle such low overlap and illumination change. Specifically, a powerful module, named global transformer contextual encoder (GTCE) is first designed, together with a correlation encoder to effectively aggregate global contextual information and reduce matching ambiguity between feature maps. Moreover, a hybrid photo‐perceptual loss for unsupervised homography estimation is proposed. The proposed loss function considers alignment information on both pixel level and perceptual level thus helping this network to be more adaptive to various scenes, including normal cases and illumination change cases. The results of extensive experiments on synthetic and real‐world datasets demonstrate the superiority of this proposed method over current state‐of‐the‐art solutions especially on challenging scenes with low overlap rates, repetitive patterns and illumination changes.

This paper presents Botzone, a competitive platform for game AI education and research. It aims to simplify the teaching process of game AI courses, inspire learners to self-study, and acting as a dataset for game AI research. This platform is a universal online multi-agent game AI platform, designed to evaluate different implementations of game AI by applying them to agents in a variety of games and compete with each other, featuring an ELO ranking system and a contest system for users to evaluate their AI programs. It has been successfully used in various AI competitions and courses in practice, and has the extensibility to support more games and languages, as well as further usages such as studying machine learning on game AI. In this paper, we firstly describe the structure and features of Botzone, then focus on our experience in utilizing Botzone for a programming course.