In this paper, we focus on the proximate orbital pursuit-evasion game of two spacecraft with magnitude-bounded continuous controls. Two scenarios are considered depending on whether the pursuer can access the control magnitude of the evader initially. When the pursuer accesses such information, we propose a fast numerical method for computing a sub-optimal control of the pursuer that guarantees the capture of the evader. The key to accelerating the solving is using a polynomial to approximate an important integration in the control computation, whose direct computing involves repeated calculations of matrices’ singular values. When the control magnitude of the evader is unavailable, we first propose a simple estimator for the evader’s control magnitude, by which the pursuer can estimate the maximal control effort of the evader disclosed over the history based on measured states. Based on the estimate, another fast method for computing the sub-optimal pursuing control is proposed based again on polynomial approximation. Then, considering practical measurements, we analyze how measurement noises influence the estimation of the control magnitude and the pursuing control computation. Finally, we present numerical examples to test the proposed computing methods and discuss the influence of noises.

Navigation through crowded human environments is challenging for social robots. While reinforcement learning has been adopted for its capacity to capture complex interactions, the training process often relies on simulators to replicate realistic crowd behaviors, ensuring cost-efficiency. Existing crowd simulation methods typically rely on either handcrafted rules, which may lead to overly aggressive navigation, or learning from human trajectory demonstrations, which can be challenging to generalize effectively. In this paper, we introduce a data-driven crowd simulation method called SocialGAIL, which leverages Generative Adversarial Imitation Learning (GAIL) to emulate real pedestrian navigation in crowded environments. SocialGAIL utilizes an attention-based graph neural network to encode observations and employs a generator-discriminator architecture to closely mimic pedestrian behavior. We propose a set of metrics to evaluate the faithfulness of crowd simulation. Experimental results demonstrate that SocialGAIL outperforms baseline methods in terms of goal-reaching, intermediate state faithfulness, trajectory faithfulness, and adherence to global trajectory patterns. The code of our approach is available at https://github.com/William-island/SocialGAIL.

Versatile video coding (VVC) aims to achieve high compression but also issues like varying content/network conditions. Existing rate control (RC) methods struggle to achieve optimal quality under these complex scenarios. This paper proposes a novel RC scheme for VVC based on a linear model. The Lagrange minimization multiplier is introduced under bit budget constraints, allowing optimized bit allocation. RC optimization is formulated as a convex solution, and is derived into the optimal quantization parameter (QP) for RC. Experimental analysis demonstrates the proposed linear model-based RC algorithm performances are better compared to other state-of-the-art methods due to their use of a linear model and optimal QP determination.

With the widespread adoption of virtual reality and 360-degree video, there is a pressing need for objective metrics to assess quality in this immersive panoramic format reliably. However, existing image quality assessment models developed for traditional fixed-viewpoint content do not fully consider the specific perceptual issues involved in 360-degree viewing. This paper proposes a 360-degree image full-reference quality assessment (FR-IQA) methodology based on a multi-channel architecture. The proposed 360-degree FR-IQA method further optimizes and identifies the distorted image quality using two easily obtained useful saliency and depth-aware image features. The convolutional neural network (CNN) is designed for training. Furthermore, the proposed method accounts for predicting user viewing behaviors within 360-degree images, which will further benefit the multi-channel CNN architecture and enable the weighted average pooling of the predicted FR-IQA scores. The performance is evaluated on publicly available databases to demonstrate the advantages brought by the proposed multi-channel model in performance evaluation and cross-database evaluation experiments, where it outperforms other state-of-the-art ones. Moreover, an ablation study exhibits good generalization ability and robustness.

Unmanned Aerial Vehicle (UAV) swarm offers extended coverage and is a vital solution for many applications. A key issue in UAV swarm control is to cover all targets while maintaining connectivity among UAVs, referred to as a multi-target coverage problem. With existing dynamic routing protocols, the flying ad hoc network suffers outdated and incorrect route information due to frequent topology changes. This might lead to failures of time-critical tasks. One mitigation solution is to keep the physical topology unchanged, thus maintaining a fixed communication topology and enabling static routing. However, keeping physical topology unchanged may sacrifice the coverage. In this article, we propose to maintain a fixed communication topology among UAVs, which allows certain changes in physical topology, so that to maximize the coverage. We develop a distributed motion planning algorithm for the online multi-target coverage problem with the constraint of keeping communication topology intact. As the communication topology needs to be timely updated when UAVs leave or arrive at the swarm, we further design a topology-management protocol. Experimental results from the ns-3 simulator show that under our algorithms, UAV swarms of different sizes achieve significantly improved delay and loss ratio, efficient coverage, and rapid topology update.

We propose a full-reference image quality assessment (FR-IQA) method by incorporating low-level and high-level image features. First, in contrast to the preexisting deep IQA methods, which only use the features extracted by the deep network, we not only use the image gradient to replace the low-level features in the first two stages of the deep network, but also combine them with the middle-stage features of the deep network to construct the new low-level features. The deep features of shallow layers contain some unwanted noise and further result in a decline in IQA performance. Second, we combine the global features extracted by the self-attention-based model with the semantic features extracted by the convolutional neural network to form the high-level features. Instead of directly using the self-attention-based model trained on the classification task, we first train a no-reference (NR) IQA regression model on a larger dataset and then use the global features of this NR-IQA model. The self-attention-based model can capture the internal connections of the image and is more effective in extracting global information due to its larger perceptual field. In the final pooling stage, we combine the average pooling and the standard deviation pooling to obtain the dispersion and concentration of the similarity maps for a more comprehensive description of quality. Experiments show that our FR-IQA method is able to obtain competitive results on three standard IQA datasets.

Deep Neural Network (DNN) models have notably improved the efficiency of machine learning tasks. However, their high storage and computational costs restrict their deployment on resource-limited embedded devices. Knowledge distillation (KD) has emerged as a promising approach for compressing DNN models. However, two challenges in KD, namely the capacity gap problem and the time-consuming redundancy problem, have hindered its performance and efficiency in compression. To alleviate these challenges, this paper proposes a novel framework, called Ability-Aware Knowledge Distillation (AAKD). AAKD introduces a knowledge sample selection strategy and an adaptive teacher switching strategy based on the dynamic awareness of the student’s ability. This enables the framework to automatically select suitable knowledge samples and teacher networks according to the increasing representation ability of students. Extensive experiments on different datasets and models have demonstrated that AAKD can enhance the performance of compact student models, significantly improve the efficiency of distillation, and lead to higher compression rates.

In energy harvesting communication systems, it is possible for a transmitter to schedule the transmission by jointly scaling the rate and turning the transmitter ON/OFF adaptively. Such a joint rate and sleep schedule can greatly increase the throughput achieved by the transmitter with battery constraints. However, most existing works on joint rate and sleep scheduling assume the transition between different states does not have any cost, i.e., energy or time consumption. This is not realistic while the energy and time needed for booting a transmitter, i.e., turning a transmitter from OFF to ON, are not small enough to be ignored in most cases. In this paper, we investigate the joint rate and sleep scheduling on system throughput with more general booting consumption considered in energy harvesting communication systems. We first identify the structural properties of the optimal solution for the model with booting consumption considered. Inspired by these observations, we develop an optimal offline algorithm and an online heuristic algorithm to solve the problem. Experimental results from simulations and real tests show that the proposed algorithms can achieve much higher throughput on average in a realistic energy harvesting communication system, compared to those algorithms that only consider rate scheduling or ignore the booting consumption.

Energy harvesting techniques have been increasingly employed in both consumer and industrial applications to provide clean energy supply. Among the many available energy harvesting techniques, ambient energy harvesting (AEH) is a promising one as it harvests free energy from the environment and, thus, is economically efficient. AEH techniques, however, heavily depend on the dynamic environment and are thus uncontrollable and unstable. More recently, the wireless power transfer (WPT) technique has attracted significant attentions due to its highly controllable feature when powering low-cost devices. Unfortunately, WPT faces strict regulatory limitations to provide high power density and requires charging infrastructures installed to perform effective wireless energy transfer. The pros and cons of the two techniques motivate this work to design a hybrid energy harvesting method by charging a device using a combination of AEH and WPT to maximize the throughput of a wireless system. Specifically, this work first proposes an optimal offline charging scheme to maximize the point-to-point data throughput of a wireless system by fully utilizing the ambient energy and providing extra power supply through WPT to determine the transmission rates. An online heuristic algorithm is further proposed to improve the computational efficiency for practical scenarios when the system has the estimation of future AEH patterns. Our experimental results show that the proposed approaches are effective in maximizing the data throughput when compared to the state of the art.

Recent years, researchers have proposed numerous positioning algorithms for wireless sensor networks, in which range-free schemes are more attractive because of their low-cost advantage. However, in practical circumstance, the raw range-free information (neighboring set and hop-counts et al.) is usually unreliable due to combined factors such as irregular signal patterns, environment noise and so on. To make matters worse, the unreliable localization information will lead to the reduction of localization accuracy. In order to tackle this problem, we propose a Reputation-based Revising Scheme (RRS) to preprocess the raw localization information before applying any positioning algorithm. In RRS, we reevaluate the reliability of raw information by utilizing the neighboring relationship. After being processed through RRS, the raw information is close to the information collected in ideal environment. According to our detailed simulations, we demonstrate that the revised localization information has 27% lower average error rate than the raw one. Based on the revised information, the traditional range-free localization algorithms are capable to achieve higher accuracy.

Because of rapid channel attenuation in lossy environment, there is a severe packet drop loss problem in wireless ad hoc networks. Though MIMO (Multi-Input Multi-Output) technology can alleviate this effect greatly, it will result in higher transmission energy depletion as using more transmit antennas. Lots of schemes have been proposed to reduce such energy cost by transmit antenna selection. However, most of those only consider QoS requirements between neighbor nodes while ignoring QoS requirements for entire network. In this paper, we investigate an energy-efficient transmit antenna selection problem for many-to-one communications, while guaranteeing a certain packet drop loss ratio from any node to the sink. We prove that this problem (Transmit Antenna Selection Problem, TASP) is NP-hard. Thus, a greedy algorithm TASA (Transmit Antenna Selection Algorithm) is proposed to solve this problem. This paper also prove the correctness of the proposed algorithm and the time complexity of TASA algorithm is O(|E|+|V|-MT), where |V| represents the number of nodes, |E| indicates the number of links and MTis the number of transmit antennas on each node, respectively. Our numerical results show that in a 100 node network, TASA can reduce transmission energy cost by 44.3% on average.

In recent years, many localization algorithms have been proposed for wireless sensor networks, in which the hop-count based localization schemes are attractive due to the advantage of low cost. However, these approaches usually utilize discrete integers to calculate the hop-counts between nodes. Such coarse-grained hop-counts make no distinction among one-hop nodes. More seriously, as the hop-counts between nodes increase, the cumulative deviation of hop-counts would become unacceptable. In order to solve this problem, we propose the concept of fine-grained hop-count. It is a kind of float-type hop-count, which refines the coarse-grained one close to the actual distance between nodes. Based on this idea, we propose a fine-grained localization algorithm (AFLA). In AFLA, we first refine the hop-count information to obtain fine-grained hop-counts, then use the Apollonius circle method to achieve initial position estimations, and finally further improve the localization precision through confidence spring model (CSM). We conduct the comprehensive simulations to demonstrate that AFLA can achieve 30% higher average accuracy than the existing hop-count based algorithm in most scenarios and converge much faster than the traditional mass-spring model based scheme. Furthermore, AFLA is robust to achieve an approximate 35% accuracy even in noisy environment with a DOI of 0.4.