Simulation with realistic and interactive agents represents a key task for autonomous vehicle (AV) software development in order to test AV performance in prescribed, often long-tail scenarios. In this work, we propose SceneDiffuser, a scene-level diffusion prior for traffic simulation. We present a singular framework that unifies two key stages of simulation: scene initialization and scene rollout. Scene initialization refers to generating the initial layout for the traffic in a scene, and scene rollout refers to closed-loop simulation for the behaviors of the agents. While diffusion has been demonstrated to be effective in learning realistic, multimodal agent distributions, two open challenges remain: controllability and closed-loop inference efficiency and realism. To this end, to address controllability challenges, we propose generalized hard constraints, a generalized inference-time constraint mechanism that is simple yet effective. To improve closed-loop inference quality and efficiency, we propose amortized diffusion, a novel diffusion denoising paradigm that amortizes the physical cost of denoising over future simulation rollout steps, reducing the cost of per physical rollout step to a single denoising function evaluation, while dramatically reducing closed-loop errors. We demonstrate the effectiveness of our approach on the Waymo Open Dataset, where we are able to generate distributionally realistic scenes, while obtaining competitive performance in the Sim Agents Challenge, surpassing the state-of-the-art in many realism attributes.

Satellite constellation is envisioned as an important approach to enhancing the capability of existing networks. Technologies such as Network Function Virtualization (NFV) and Service Function Chain (SFC) have been proposed to make satellites much more tolerant to different kinds of service requests. Owing to Software-Defined Networks (SDN), satellite constellations can be managed in a more flexible manner. However, due to the high variability of constellation and multi-types of Virtual Network Functions (VNFs), it becomes a major challenge to select appropriate instances of VNFs to concatenate for routing when orchestrating resources to map SFC requests. In this paper, we investigate SFC Mapping Problem (SFC-MP) in a dynamically changing logical segmented network to provide stable communication service. We formulate the problem as a Binary Integer Programming (BIP) model. In addition, we propose a new Continuous Logical Segmented Network Resource Aware Routing (LSNRAR-Continuous) algorithm for SFC requests routing to achieve network load balancing and reduce the impact of VNF migration on services. Simulation results show that the algorithm can effectively solve the SFC-MP problem, and the proposed algorithm has higher performance in terms of average delay, mapping cost and service stability compared with existing literature algorithms.

Massive MIMO systems are vulnerable to pilot spoofing attacks (PSAs) since the estimated channel state information can be contaminated by the eavesdropping link, thus incurring severe information leakage in downlink transmission. To safeguard legitimate communications, this paper proposes a PSA detection method which relies on pilot manipulation. Specifically, users randomly partition pilot sequences into two parts, where the first part remains unchanged and the second one is multiplied with a diagonal matrix. Although a malicious node may follow the same way to send pilots, this makes it more likely to be detected. According to the principle of the likelihood-ratio test, the proposed detector is designed based on a decision metric that does not include the legitimate channel. This feature differentiates our scheme from existing ones and remarkably improves the detection accuracy. Besides, the possibility of performance enhancement by joint detection is discussed. Furthermore, based on pilot manipulation, a jamming-resistant receiver is designed. The key of this receiver is a new channel estimator that is robust to the PSA. Finally, extensive simulations are carried out to validate our proposed algorithms.

Half-wavelength AC transmission (HWACT) possessed economic advantages due to its characteristics. However, the problem of secondary arc current limits its development and application. The neutral reactor is not suitable for HWACT and high speed grounding switch (HSGS) has many drawbacks. In this paper, the characteristics of secondary arc current in HWACT are studied in detail. Then, active suppression method for HWACT secondary arc current is proposed, which is based on a converter. Simulation study in PSCAD/EMTDC is carried out to provide its theory feasibility. The simulation results show that the proposed method is able to limit secondary arc current, speed up the arc extinction and improve the system's fault recovery efficiency in HWACT.

Automatic speaker naming is the problem of localizing as well as identifying each speaking character in a TV/movie/live show video. This is a challenging problem mainly attributes to its multimodal nature, namely face cue alone is insufficient to achieve good performance. Previous multimodal approaches to this problem usually process the data of different modalities individually and merge them using handcrafted heuristics. Such approaches work well for simple scenes, but fail to achieve high performance for speakers with large appearance variations. In this paper, we propose a novel convolutional neural networks (CNN) based learning framework to automatically learn the fusion function of both face and audio cues. We show that without using face tracking, facial landmark localization or subtitle/transcript, our system with robust multimodal feature extraction is able to achieve state-of-the-art speaker naming performance evaluated on two diverse TV series. The dataset and implementation of our algorithm are publicly available online.

This paper describes a novel approach for creating virtual 3D see-through experiences on large-size 2D displays. The approach aims at simulating the real-life experience of observing an outside world through one or multiple windows. Here, the display screen serves as a virtual window such that viewers moving in front of the display can observe different part of the scene which is also moving in the opposite direction. In order to generate this see-through experience, a virtual 3D scene is first created and placed behind the display. Then, the viewers' 3D positions and motion are tracked by a 3D viewer tracking method based on multiple infra-red cameras. The virtual scene is rendered by 3D graphics engines in real-time speed, based on the tracked viewer positions. A prototype system has been implemented on a large-size tiled display and was able to bring viewers a realistic and natural 3D see-through experience.

We present a novel approach to inferring 3D volumetric shape of both moving objects and static background from video sequences shot by a moving camera, with the assumption that the objects move rigidly on a ground plane. The 3D scene is divided into a set of volume elements, termed as voxels, organized in an adaptive octree structure. Each voxel is assigned a label at each time instant, either as empty, or belonging to background structure, or a moving object. The task of shape inference is then formulated as assigning each voxel a dynamic label which minimizes photo and motion variance between voxels and the original sequence. We propose a three-step voxel labeling method based on a robust photo-motion variance measure. First, a sparse set of surface points are utilized to initialize a subset of voxels. Then, a deterministic voxel coloring scheme carves away the voxels with large variance. Finally, the labeling results are refined by a graph cuts based optimization method to enforce global smoothness. Experimental results on both indoor and outdoor sequences demonstrate the effectiveness and robustness of our method.

We present a method for detecting motion regions in video sequences observed by a moving camera, in the presence of strong parallax due to static 3D structures. The proposed method classifies each image pixel into planar background, parallax or motion regions by sequentially applying 2D planar homographies, the epipolar constraint and a novel geometric constraint, called "structure consistency constraint". The structure consistency constraint, as the main contribution of this paper, is derived from the relative camera poses among three frames and implemented within the "Plane+Parallax" framework. Unlike previous planar-parallax constraints, the proposed constraint does not require the reference plane to be constant across multiple views. It directly measures the inconsistency between the projective structures from the same point under camera motion and reference plane change. The structure consistency constraint is capable of detecting moving objects followed by a moving camera in the same direction, a so called degenerate configuration where the epipolar constraint fails. We demonstrate the effectiveness and robustness of our method with experimental results on real-world video sequences.

We present a novel method to obtain a 3D Euclidean reconstruction of both the background and moving objects in a video sequence. We assume that, multiple objects are moving rigidly on a ground plane observed by a moving camera. The video sequence is first segmented into static background and motion blobs by a homography-based motion segmentation method. Then classical "Structure from Motion" (SfM) techniques are applied to obtain a Euclidean reconstruction of the static background. The motion blob corresponding to each moving object is treated as if there were a static object observed by a hypothetical moving camera, called a "virtual camera". This virtual camera shares the same intrinsic parameters with the real camera but moves differently due to object motion. The same SfM techniques are applied to estimate the 3D shape of each moving object and the pose of the virtual camera. We show that the unknown scale of moving objects can be approximately determined by the ground plane, which is a key contribution of this paper. Another key contribution is that we prove that the 3D motion of moving objects can be solved from the virtual camera motion with a linear constraint imposed on the object translation. In our approach, a planartranslation constraint is formulated: "the 3D instantaneous translation of moving objects must be parallel to the ground plane". Results on real-world video sequences demonstrate the effectiveness and robustness of our approach.

We present a novel approach to detect and track independently moving regions in a 3D scene observed by a moving camera in the presence of strong parallax. Detected moving pixels are classified into independently moving regions or parallax regions by analyzing two geometric constraints: the commonly used epipolar constraint, and the structure consistency constraint. The second constraint is implemented within a "plane+parallax" framework and represented by a bilinear relationship which relates the image points to their relative depths. This newly derived relationship is related to trilinear tensor, but can be enforced into more than three frames. It does not assume a constant reference plane in the scene and therefore eliminates the need for manual selection of reference plane. Then, a robust parallax filtering scheme is proposed to accumulate the geometric constraint errors within a sliding window and estimate a likelihood map for pixel classification. The likelihood map is integrated into our tracking framework based on the spatio-temporal joint probability data association filter (JPDAF). This tracking approach infers the trajectory and bounding box of the moving objects by searching the optimal path with maximum joint probability within a fixed size of buffer. We demonstrate the performance of the proposed approach on real video sequences where parallax effects are significant.