Dense visual prediction tasks, including object detection and segmentation, inherently require precise and discriminative positional information to delineate object boundaries and pixel regions. Recent DETR-based frameworks advance dense prediction tasks through iterative attention applied to content queries, with sampled proposals as position references. However, this paradigm suffers from the misaligned sampling distribution and insufficient interaction between the content and position features, thereby limiting the encoding effectiveness. To overcome these limitations, we investigate the encoding paradigm for content-position harmonization and propose an effective predictor for dense visual tasks, termed DAPE (DETR with hArmonized content-Position Encoding). DAPE introduces explicit position encoding to facilitate content enhancement while maintaining low memory overhead. To achieves this process, DAPE comprises a Shifted Query Sampler (SQS) that enforces strict alignment between the distributions of content and position queries, and a 2D Low-Rank Position Encoder (LRPE) that progressively modulates attention maps based on the aligned representations. DAPE provides a unified solution for various dense prediction tasks. Extensive experiments on object detection, instance segmentation, and few-shot detection benchmarks demonstrate that DAPE achieves state-of-the-art performance while reducing memory consumption.

In multi-robot coverage control, ground robots aim to cover and monitor a domain optimally. However, when covering an extensive domain like a densely forested potential fire site, the sensing capabilities of ground robots are limited, resulting in poor coverage. Leveraging the aerial robots’ ability to expand sensing ranges through high-altitude flight, this paper proposes a fully distributed, air-ground coverage control scheme to address this challenge. First, aerial robots provide a low-resolution coverage of the domain. Then, they use coarse but broad sensing information to guide ground robots, with short-range but high-resolution sensing, to achieve a high-resolution coverage. Simultaneously, each aerial robot dynamically adjusts its cell size to match its load, enhancing the coverage performance. The convergence of the control scheme is proved and its performance is evaluated through simulations and experiments.

Surface Defect Detection (SDD) aims to accurately localize defects based on predefined category labels in industrial manufacturing. Different from generic object detection, the industrial environment introduces significant challenges due to interference and unrelated background textures, leading to increased confusion between defect and non-defect features. In this work, we identify and analyze the structural characteristics and relations inherent in defect features. This analysis enables effectively distinguishing defects from non-defect areas, thereby enhancing the discriminative power for surface defect detection. Based on this insight, we propose a novel surface defect detection framework, named UnfoldDet. This framework focuses on separating defect and non-defect features and reasoning about the relations among defects. Specifically, we formulate the feature separation as an optimization problem with structural constraints. By expressing its iterations as network stages, we introduce an unfolding fusion module (UFM) to progressively separate and fuse multi-scale features. At the instance level, we propose a hierarchical relation encoder (HRE) to capture the inherent relations among defect instances. Through reasoning on positional and categorical relations, only highly related defect features are enhanced, while unrelated non-defect features are suppressed. Through extensive quantitative and qualitative experiments, as well as ablation studies on real-world datasets including ESD, CSD, and NEU-DET, we demonstrate the effectiveness of the proposed UnfoldDet in terms of both performance and computational efficiency. The code is available at https://github.com/xiuqhou/UnfoldDet.

As an effective feature extractor, Vision Transformer (ViT) has been widely applied to both image classification and object tracking tasks. In this paper, we revisit and enhance the classic Data-efficient image Transformer (DeiT) for these two tasks. The DeiT is optimized step-by-step across different modules, including its patch stem, position embedding, and the development of efficient linear attention mechanisms. To address the performance degradation of linear attention, we propose Shift Expansion Linear Attention (SELA) which generates new heads with rich feature diversity through a simple but efficient cyclic shift operation. Additionally, SELA similarity minimization is added to cross-entropy loss to further enhance feature diversity. Based on these improvements, we develop SELA-ViT for image classification and further build SELA-Track for object tracking. With comparable model size and speed, SELA-ViT-T achieves a +4.8% improvement in Top-1 accuracy over DeiT-T on ImageNet-1K and establishes a new state-of-the-art performance among linear attention methods. Furthermore, we validate SELA-ViT on five small datasets. On four benchmark object tracking datasets, SELA-Track exhibits improved tracking performance. The code and models are available at: https://github.com/saizhou777/SELA-ViT.

Traffic signal control (TSC) is still one of the most challenging and promising research issues in the field of transportation. Since traditional methods have difficulty in handling dynamically changing traffic flows, reinforcement learning (RL) methods have been introduced into TSC. However, the cost of practical application is critically high due to multiple sampling trials and long learning process. The Transformer architecture has recently attained remarkable results in natural language processing (NLP), but when applied to the field of RL, the standard Transformer architecture is difficult to optimize and faces the problem of hyperparameter sensitivity. In the paper, we transform TSC into a sequence modeling issue and propose a new evolution Transformer architecture to adjust the autoregressive model through reward, past states and actions in the traffic environment to directly generate the best predicted action. In addition, we use the feature evolution module (FEM) instead of residual connections to make the learning process more stable and efficient. Through experiments on public datasets, we demonstrate that our ETLight model achieves a state-of-the-art (SOTA): 1) It achieves the overall best performance on average travel time (ATT) metric, with improvements of up to 6.85%, 3.73% and 3.10% over the best conventional, RL and Transformer methods, respectively; 2) It has a more stable learning process, faster learning speed and better convergence compared to published TSC methods so far; and; 3) it has good robustness and is less sensitive to hyperparameter selection.

Anomaly detection has garnered significant attention for its extensive industrial application value. Most existing methods focus on single-view scenarios and fail to detect anomalies hidden in blind spots, leaving a gap in addressing the demands of multi-view detection in practical applications. Ensemble of multiple single-view models is a typical way to tackle the multi-view situation, but it overlooks the correlations between different views. In this paper, we propose a novel multi-view anomaly detection framework, Intra-view Decoupling and Inter-view Fusion (IDIF), to explore correlations among views. Our method contains three key components: 1) a proposed Consistency Bottleneck module extracting the common features of different views through information compression and mutual information maximization; 2) an Implicit Voxel Construction module fusing features of different views with prior knowledge represented in the form of voxels; and 3) a View-wise Dropout training strategy enabling the model to learn how to cope with missing views during test. The proposed IDIF achieves state-of-the-art performance on three datasets. Extensive ablation studies also demonstrate the superiority of our methods.

Despite deep learning-based methods perform remarkably well in surface defect detection recently, the unpredictable shapes and sizes of surface defects and complicated texture background still pose enormous challenges for existing methods. To address these problems, we propose a novel surface defect detection method based on YOLOv5 that combines multi-branch attention mechanism, multi-scale effective feature fusion and a lightweight neck structure, dubbed SABR-YOLO. Firstly, we embed Dual-Branch Convolution Module (DBCM) to the backbone network to enhance feature representations. Secondly, Multi-Scale Spatial Fusion (MSSF) is designed to fuse features with different scales effectively. Thirdly, we propose an Attention-Based Upsampling (ABUp) module to capture local features and focus on subtle defects. In addition, we design the Context-Aware Feature Pyramid (CAFP) by applying the ABUp and making improvements to neck structure. Finally, we evaluate our framework SABR-YOLO on the NEU-DET and HRIPCB datasets, and experimental results show that our method achieves higher accuracy compared to the state-of-the-art methods.

As industrial manufacturing quality standards rise, demand for advanced defect detection models has surged. Compared to generic objects, industrial defects exhibit more diverse and complex shapes and sizes. Traditional detection models typically process each instance in isolation, leading to incomplete detections (e.g. fragmented or redundant bounding boxes) when facing such complex defect patterns. To address these challenges, we propose Relational Enhancement Network for defect detection, which enhances defect features by exploring implicit spatial and semantic relations. Our model introduces a position embedding module to map geometric features into a high-dimensional space. A relational enhancement module is proposed to integrate geometric and semantic features, capturing complex interactions among defects to enhance the original features. This process is dynamically adjusted through a relational refining mechanism. The proposed position-sensitive loss further aligns classification task with localization task using spatial metrics. Experiments on three industrial defect benchmark datasets (metals, bearings, engines, and LEDs) show our method outperforms state-of-the-art approaches in detection precision and addresses incomplete defect detection. Additionally, our method exhibits strong transferability, theoretically offering clear improvements to any similar-structured methods. The code is available at https://github.com/lhht/Relational-Enhancement-Network

Camouflage image generation plays a vital role in various research fields. Current methods typically rely on manually selecting and blending objects with backgrounds, which often produce incongruous combinations where the object does not seamlessly integrate with the background, leading to unrealistic and unnatural visual outcomes. To address these challenges, we introduce a novel Attention Fusion Diffusion Model (AFDM) designed to generate realistic camouflage images from a single input image containing an object and its surrounding background. The AFDM framework is comprised of two essential components: an Attention Fusion Module, which adeptly integrates object features with surrounding background information to produce convincingly camouflaged objects, and a content guidance strategy designed to mitigate content drift during the fusion process, thereby ensuring that the camouflaged image remains faithfully aligned with the original content. In addition, we build the outdoor Solidier Dataset(OSD) for advancing camouflage target recognization and in-depth research on this topic. Extensive experiments and user studies demonstrate the performance of our method in camouflage image generation and its potential to enhance image segmentation-related fields. Our code and dataset will be available at https://github.com/xhxhzhz/AFDM.

Multi-agent adversarial tasks such as swarm robotics and autonomous vehicle coordination, demand efficient decentralized collaboration under partial observability. While model-free multi-agent RL (MF-MARL) methods suffer from necessitating extensive environment interactions, most existing multi-agent model-based RL (MA-MBRL) methods fail to align with the Centralized Training with Decentralized Execution (CTDE) paradigm, which limits system flexibility. This paper proposes a novel modular architecture with refined observations (MARO) to achieve the CTDE paradigm by decoupling agents from the world model. Key innovations include: 1) an enhanced world model with weighted loss and history-augmented rollout for high-quality data generation; 2) a dual-stream semantic decomposition network (DSDN) that performs fine-grained decomposition of observations to refine action mapping and mitigate performance degradation from information loss. Extensive experiments on the StarCraft Multi-Agent Challenge (SMAC) demonstrate superior performance over opponents, validating the effectiveness and advancement of MARO.

In recent years, visible-infrared object detection has achieved significant progress. However, most existing methods primarily emphasize the shared features between the two modalities while overlooking their feature differences. To address this limitation, we propose the Difference-Guided Modality Fusion Network, which can effectively improve the fusion and detection performance of modalities. Specifically, we propose a cross-modal data augmentation strategy to overcome the limitations of single-modality reliance by exchanging the partial modal information. To further capture and analyze feature differences between modalities, we introduce a differential attention fusion approach that models a difference matrix across modal channels, thereby quantifying and strengthening the salient features of the two modalities. Additionally, we develop a modality-aware dynamic learning mechanism that employs a loss function that can simultaneously focus on the differences and common parts of the modalities, guiding the model to adaptively learn features between the modalities. Experimental results on FLIR, LLVIP and M3FD datasets demonstrate the effectiveness of the proposed method, with mAP reaching 42.3%, 67.5% and 59.0% respectively.

Despite the significant advancements made in industrial defect detection, accurately and timely identifying complex and small-sized defects remains a challenge. Most current lightweight defect detectors are unable to fully extract both global and local contextual information due to their simplified network architectures. To address the above issues, this paper introduces a novel real-time detector DSK-YOLO, which efficiently enhances global and local contextual information with a lower number of parameters. Specifically, DSK-YOLO comprises two key components: DSKblock and DSKSR. The DSKblock employs dilated separable kernels to expand the effective receptive fields (ERFs) without deep layer stacking, thereby identifying complex defects. For small-sized defects detection, we develop a feature-level super resolution (SR) auxiliary branch to enhance local contextual information in the training phase. Moreover, the train-only SR branch brings no extra computational overhead for inference, making it an impressive choice for real-time tasks. Experimental results demonstrate that, on the industrial datasets NEU-DET and ESD, DSK-YOLO achieves mAP of 45.7% and 64.8%, which are 1.4% and 1.0% higher than those of the baseline model YOLOv8n. Our proposed DSK-YOLO offers a favorable tradeoff between precision and parameters compared to state-of-the-art models.

This letter is concerned with a three-dimensional target motion analysis issue using azimuth and elevation measurements. The nonlinear relationship between these measurements and target dynamics often poses challenges for conventional methods, especially in high-noise environments. To address this challenge, a novel multi-agent deep reinforcement learning (MADRL)-based estimator is proposed for target motion parameter estimation. Specifically, by modeling each component of the target motion parameter as an individual agent, the target motion parameter estimation process is framed as a cooperative Markov game. An MADRL framework is then introduced to solve this problem. Simulation results demonstrate that the proposed algorithm achieves higher estimation accuracy than existing estimators.

This paper investigates the cooperative fuzzy tracking control of nonlinear unmanned surface vehicles with input quantization and event-triggered mechanism. The proposed cooperative control scheme consists of two parts: (i) the distributed observer and (ii) the dynamic event-based fuzzy tracking controller. The distributed observer is designed to obtain the nonlinear leader’s trajectory information on a directed communication topology. Under this framework, uncertain nonlinearity within the vehicle model is approximated through fuzzy logic systems, and, according to the state of the distributed observer, the dynamic event-based adaptive fuzzy tracking control law is developed with an input switching quantizer. Furthermore, a prescribed performance method is introduced to ensure the transient performance of tracking errors and obtain zero-tracking errors ultimately, which is proved through Lyapunov stability theory. Finally, the effectiveness of the proposed control strategy is verified by simulation experiments.

Information-based autonomous robot exploration methods, aiming to maximize the exploration rewards, e.g., mutual information (MI), get more prevalent in field robotics applications. However, most MI-based exploration methods assume known poses or use inaccurate pose uncertainty approximation, which may lead to deviation or even failure when exploring prior unknown environments. In this paper, we explicitly consider full-state (pose & map) uncertainty for balancing exploration and localizability, i.e., avoiding the robot guiding itself to complex scenes with high exploration rewards but hard to localize. We first propose a Rao-Blackwellized particle filter-based localization and mapping framework (RBPF-CLAM) for a dense environmental map with continuous occupancy distribution. Then we develop a new closed-form particle weighting method to improve the localization accuracy and robustness. We further use these weighted particles to approximate the unknown pose uncertainty and combine it with our previous confidence-rich mutual information (CRMI) metric to evaluate the expected information utility of the robot’s new control actions. This new information metric is calleduncertainCRMI (UCRMI). Dataset experiments show our RBPF-CLAM improves about 44.7% average root mean square error than the state-of-the-art RBPF localization method, and real-world experimental results show that our UCRMI reduces the pose uncertainty about 32.85% more than CRMI and 25.36% time cost than UGPVR in the exploration of unknown and unstructured scenes given sparse measurements, which shows better performance than other state-of-the-art information metrics.Note to Practitioners—This work was motivated by the problem of ‘planning for state estimation’ for a range-sensing robot, i.e., the robot can choose a better future place to facilitate its localization more accurately and explore new areas rationally to gather more information. Existing methods mainly assume the robot’s poses during the exploration can be estimated by an independent localization approach or simply propagated via a predefined probabilistic distribution. However, localization failure would lead to higher planning deviation for the planner that does not consider the pose uncertainty, and manually set parametric distribution is more prone to overestimate the pose uncertainty. This paper proposes an RBPF-based localization and mapping scheme and an improved particle weight update method in a confidence-rich map, then uses the weighted particles to approximate trajectory entropy and combines it with CRMI to evaluate the expected information gain of a candidate action/node. Our newly defined information function ‘UCRMI’ can prevent the robot from exploring too aggressively without considering its localizability in prior unknown and unstructured environments. These scenes may lack robust features to conduct feature-based SLAM or lack accurate external localization information such as GPS. This method can be applied in underwater, planetary, and subterranean robot exploration tasks, even using low-resolution sensors. Future work mainly involves adapting UCRMI to applications in large-scale scenes using small autonomous platforms.

This article addresses the cooperative tracking control problem for heterogeneous multiple marine vehicles with a nonautonomous leader. A fully distributed smooth observer is proposed to estimate the trajectory of the leader, mitigating the influence of its control input. Based on the observer, three decentralized adaptive fuzzy event-based controllers are designed with distinct triggering strategies, i.e., fixed, relative, and switching threshold triggering strategies, which utilize fuzzy-logic systems and event-triggering mechanisms to address the challenge of model uncertainties and communication constraints of marine vehicles. The proposed methods ensure the zero-error tracking without Zeno behavior, as demonstrated through Lyapunov analysis. Numerical simulations validate the effectiveness of the proposed approaches.

Self-supervised Learning (SSL), including mainstream contrastive learning, has achieved significant success in learning visual representations without the need for data annotations in 3D vision. While most contrastive learning methods focus on instance-level information through random affine transformations, they pay limited attention to the intrinsic structures within point clouds. In this work, we propose a novel SSL paradigm for point cloud representation learning, called CCPoint, which incorporates a novel form of data corruption as a negative augmentation strategy. Specifically, we degrade the input point cloud with various corruptions and conduct contrastive learning among the augmented, raw, and corrupted points to learn robust and discriminative representations. To preserve the semantic structure of the point cloud even under heavy degradation, an auxiliary reconstruction decoder is introduced into the corruption branch to provide an additional supervision signal. We explore four families of corruptions—affine, noise, masking, and combined transformations. Different from previous methods that rely on multi-modal data or complex network architectures, CCPoint achieves state-of-the-art performance on three widely used datasets (ModelNet40, ScanObjectNN, and ShapeNetPart) with a lightweight and efficient structure, reaching top linear accuracies of 92.4% and 86.2% on ModelNet40 and ScanObjectNN, respectively.

As an emerging decentralized machine learning technique, federated learning organizes collaborative training and preserves the privacy and security of participants. However, untrustworthy devices, typically Byzantine attackers, pose a significant challenge to federated learning since they can upload malicious parameters to corrupt the global model. To defend against such attacks, we propose a novel robust aggregation method-maximum correntropy aggregation (MCA), which applies the maximum correntropy criterion (MCC) to derive a central value from parameters. Different from the previous use of MCC for denoising, we utilize it as a similarity metric to measure parameter distribution and aggregate a robust center. Correntropy in MCC, with all even-order moments of the parameter, contains high-order statistical properties, which allows for a comprehensive capture of parameter characteristics, thus helping to prevent interference from attackers. Meanwhile, correntropy extracts information from the parameters themselves, without requiring the proportion of malicious attackers. Through the fixed-point iteration, we solve the optimization objective, demonstrating the linear convergence of the iteration formula. Theoretical analysis reveals the robustness aggregation property of MCA and the error bound between MCA and the global optimal solution, with linear convergence to the optimal solution neighborhood. By performing independent identically distribution (IID) and non-IID experiments on three different datasets, we show that MCA exhibits significant robustness under mainstream attacks, whereas other methods cannot withstand all of them.

Point cloud-based 3-D object detection is a significant and critical issue in numerous applications. While most existing methods attempt to capitalize on the geometric characteristics of point clouds, they neglect the internal semantic properties of point and the consistency between the semantic and geometric clues. We introduce a semantic consistency (SC) mechanism for 3-D object detection in this article, by reasoning about the semantic relations between 3-D object boxes and its internal points. This mechanism is based on a natural principle: the semantic category of a 3-D bounding box should be consistent with the categories of all points within the box. Driven by the SC mechanism, we propose a novel SC network (SCNet) to detect 3-D objects from point clouds. Specifically, the SCNet is composed of a feature extraction module, a detection decision module, and a semantic segmentation module. In inference, the feature extraction and the detection decision modules are used to detect 3-D objects. In training, the semantic segmentation module is jointly trained with the other two modules to produce more robust and applicable model parameters. The performance is greatly boosted through reasoning about the relations between the output 3-D object boxes and segmented points. The proposed SC mechanism is model-agnostic and can be integrated into other base 3-D object detection models. We test the proposed model on three challenging indoor and outdoor benchmark datasets: ScanNetV2, SUN RGB-D, and KITTI. Furthermore, to validate the universality of the SC mechanism, we implement it in three different 3-D object detectors. The experiments show that the performance is impressively improved and the extensive ablation studies also demonstrate the effectiveness of the proposed model.

DETR-like methods have significantly increased detection performance in an end-to-end manner. The main-stream two-stage frameworks of them perform dense self-attention and select a fraction of queries for sparse cross-attention, which is proven effective for improving performance but also introduces a heavy computational burden and high dependence on stable query selection. This paper demonstrates that suboptimal two-stage selection strategies result in scale bias and redundancy due to the mismatch between selected queries and objects in two-stage initial-ization. To address these issues, we propose hierarchical salience filtering refinement, which performs transformer encoding only on filtered discriminative queries, for a bet-ter trade-off between computational efficiency and precision. The filtering process overcomes scale bias through a novel scale-independent salience supervision. To com-pensate for the semantic misalignment among queries, we introduce elaborate query refinement modules for stable two-stage initialization. Based on above improvements, the proposed Salience DETR achieves significant improvements of +4.0% AP, +0.2% AP, +4.4% AP on three challenging task-specific detection datasets, as well as 49.2% AP on COCO 2017 with less FLOPs. The code is available at https://github.com/xiuqhou/Salience-DETR.

To handle the non-stationarity of the environment and the curse of dimensionality issues in multi-agent reinforcement learning, gathering information through communication is a critical part. Existing frameworks have proposed centralized or distributed structures to deal with the problem. However, they either have problems of robustness or problems of high communication costs. This paper adopts a hierarchical zeroth-order policy optimization (HZOPO) algorithm for cooperative multi-agent reinforcement learning (MARL) problems. The agents are divided into different groups with high-and low-level entities. A hierarchical communication structure is implemented to reach global consensus. It is shown that the HZOPO algorithm can balance both convergence and communication efficiency in cooperative MARL environments. The convergence of the algorithm is also proved.

This paper investigates the problem of distributed target tracking via underwater acoustic sensor networks (UASNs) with fading channels. The degradation of signal quality due to wireless channel fading can significantly impact network reliability and subsequently reduce the tracking accuracy. To address this issue, we propose a modified distributed unscented Kalman filter (DUKF) named DUKF-Fc, which takes into account the effects of measurement fluctuation and transmission failure induced by channel fading. The channel estimation error is also considered when designing the estimator and a sufficient condition is established to ensure the stochastic boundedness of the estimation error. The proposed filtering scheme is versatile and possesses wide applicability to numerous scenarios, e.g., tracking a maneuvering underwater target with underwater sensor nodes (USNs) equipped with acoustic sensors. Considering the constraints of network energy resources, the issue of investigating the energy cost of DUKF-Fc is discussed in the simulation and accordingly, the results demonstrate the robustness and energy-efficiency of the proposed filtering procedure.

The localization of underwater acoustic sensor networks (UASNs) has emerged as a critical research area in the marine information fusion field. Generally, the convex optimization method is adopted to solve the localization problem. However, this method has limitations in complex underwater environments, since it is difficult to transform the nonconvex optimization problem into a convex optimization problem under such conditions. Recently, deep reinforcement learning (DRL) has shown great potential and promise in solving intricate optimization tasks. Motivated by this, we propose to adopt DRL for UASNs localization to improve accuracy and robustness. The key challenge is that existing DRL-based methods require discretization of the environment, which leads to a compromise between search time and localization precision. To address this challenge, we first model the localization problem as a Markov decision process (MDP) with continuous state and action spaces and subsequently introduce a continuous control DRL framework to solve the localization problem. Within this framework, we develop three continuous control DRL-based localization estimators to address the localization problem in unsupervised, supervised, and semisupervised scenarios. Comprehensive simulations demonstrate the effectiveness of our approach, as the proposed solutions exhibit several advantageous features compared to traditional methods, such as: 1) compared with the convex optimization-based method, the convex relaxation is not required; 2) compared with the least squares method, the proposed estimators are capable of converging to a global optimal state; and 3) compared with the discrete control DRL method, the proposed estimators reduce localization time and enhance localization accuracy significantly.

Currently, decarbonization has become an emerging trend in the power system arena. However, the increasing number of photovoltaic units distributed into a distribution network may result in voltage issues, providing challenges for voltage regulation across a large-scale power grid network. Reinforcement learning based intelligent control of smart inverters and other smart building energy management (EM) systems can be leveraged to alleviate these issues. To achieve the best EM strategy for building microgrids in a power system, this paper presents two large-scale multi-agent strategy evaluation methods to preserve building occupants’ comfort while pursuing system-level objectives. The EM problem is formulated as a general-sum game to optimize the benefits at both the system and building levels. The α -rank algorithm can solve the general-sum game and guarantee the ranking theoretically, but it is limited by the interaction complexity and hardly applies to the practical power system. A new evaluation algorithm (TcEval) is proposed by practically scaling the α -rank algorithm through a tensor complement to reduce the interaction complexity. Then, considering the noise prevalent in practice, a noise processing model with domain knowledge is built to calculate the strategy payoffs, and thus the TcEval-AS algorithm is proposed when noise exists. Both evaluation algorithms developed in this paper greatly reduce the interaction complexity compared with existing approaches, including ResponseGraphUCB (RG-UCB) and α InformationGain ( α -IG). Finally, the effectiveness of the proposed algorithms is verified in the EM case with realistic data.

This article addresses the cooperative time-varying formation fuzzy tracking control problem for a cluster of heterogeneous multiple marine surface vehicles subject to unknown nonlinearity and actuator failures. The proposed cooperative control scheme consists of two parts: 1) a distributed time-varying formation observer and 2) a decentralized adaptive fuzzy tracking controller. The distributed observer is designed to obtain a predefined time-varying formation pattern under a directed communication topology. Subsequently, based on the states of the distributed observer, a decentralized fuzzy tracking control law is developed using fuzzy-logic systems and the adaptive approach. Lyapunov functions are constructed to guarantee that the controlled marine vehicles attain the desired time-varying formation with asymptotical stability of tracking errors. Finally, simulation results are presented to validate the efficacy of the proposed control methodology.

The article investigates the decentralized periodic dynamic event-based load frequency control problem for a class of multiarea nonlinear power systems with uncertain parameters. For overcoming the limitations on the knowledge of studied power systems, the interval type-2 (IT2) fuzzy model is synthesized by using local linear models relevant to some operation points. Under the IT2 fuzzy framework, the decentralized periodic dynamic event-based fuzzy control law is proposed to reduce the bandwidth burden of communication networks. Based on the Lyapunov stability theory, a sufficient condition is presented such that closed-loop systems are exponentially stable with a given$H_{\infty }$performance. The existence condition of the controller gains and the triggering scheme's parameters is expressed in terms of matrix inequalities. The obtained results are extended to two situations, i.e., the decentralized periodic static event-based fuzzy control and the decentralized periodic sampling fuzzy control. Compared with the latter two control approaches, the developed decentralized periodic dynamic triggering strategy can provide the lowest communication frequency. Finally, the validity and superiority of the developed method are demonstrated by simulation results.

Marine target searching is a complex task due to large search areas, unique signal propagation characteristics, and limited visibility, posing significant challenges for single-agent or homogeneous multi-agent systems. In response, we propose a novel hierarchical heterogeneous multi-agent (HHMA) framework designed for underwater search scenarios. This framework integrates three types of vehicles moving in different domains—unmanned aerial, surface, and underwater vehicles, effectively overcoming the limitations of single or double-agent configurations. We begin by elucidating the advantages of the HHMA system in target searching, providing the kinematic modeling, while also transforming sonar detecting data and defining the search problem. The mission is decomposed to three human-comprehensible subtasks that are adaptive to both environmental conditions and equipment capabilities: moving, target estimating and trajectory planning. The target estimating subtask is effectively modeled as a Markov Decision Process, retaining its memory capability. Additionally, we extend multi-agent reinforcement learning to multi-policy reinforcement learning, facilitating the training of interdependent policies. The efficacy of our approach is demonstrated through simulations, comparing it with rule-based methods. Simulation results underscore the significance of the HHMA system and validate the proposed training methodology.

Scene graph generation is a significant and challenging task for scene understanding. Most existing methods are confined to the 2D space (i.e. images) or additional use of segmentation information, while neglecting the richer spatial and geometric information of 3D space. In this paper, we propose a novel method to generate scene graphs from 3D point clouds. Specifically, our model consists of three parts: a point feature extraction backbone, a box head, and a relation head. The feature extraction backbone extracts base features directly from raw point clouds, and the box head produces detected 3D bounding boxes. Final 3D scene graphs are obtained from the relation head which takes the extracted features and 3D boxes as inputs. We also design a point RoI module which sequentially processes points inside 3D boxes with a bidirectional LSTM. To further leverage the geometric characteristics of point clouds, we propose a location attention module which learns the influence of relative locations between objects. We introduce the RelationScanNet dataset with densely annotated semantic and geometric relationships, which extends one of the most widely used dataset ScanNetV2 in 3D indoor scene understanding. We test the proposed method on the RelationScanNet dataset and 3DSSG dataset. The results prove the strength of our method.

Under the purpose of achieving the optimal voltage control strategy in power grid system, multi-agent evaluation algorithms like a-rank are widely used. However, in large-scale systems with massive agents and strategies, these methods are not time feasible. Therefore, a two-stage population-based multi-agent evaluation algorithm is proposed to solve voltage control problem in large-scale power grid systems. For stage one, a population is first established for each agent. And then, individuals in the populations randomly combined to form joint strategies. Base on the max and mean reward from the interaction between joint strategies and the environment, populations evolve to a near-optimal joint strategy. Stage two takes the above near-optimal joint strategy as the starting point, and uses a strategy search algorithm with maximum transfer possibility to find the Markov-Conley chain in the system. Finally, the above two-stage method is simulated in 10 and 32-agent power grid systems to verify the effectiveness.

Passive detection can work for a long time with low energy consumption in underwater surveillance. However, tracking unknown noncooperative targets with only direction angles is challenging, and the tracking performance of multiple targets is poor. Based on several passive sensors in the underwater sensor network (UWSN), a feature-aided state estimation method is used to start tracking unknown targets. The feature-aided joint probabilistic data association combined with the particle filter method is also proposed to improve the passive tracking performance of multiple targets. The track management and the fusion strategy are given to remove fake tracks and obtain correct trajectories of unknown targets. The simulation results show that the feature-aided method can quickly start and effectively track multiple noncooperative targets with passive sensors. Compared with other methods, the proposed method can track targets more accurately with the advantages of low energy consumption and less exposure in various environments.

Defect inspection, also known as defect detection, is significant in mobile screen quality control. There are some challenging issues brought by the characteristics of screen defects, including the following: (1) the problem of interclass similarity and intraclass variation, (2) the difficulty in distinguishing low contrast, tiny-sized, or incomplete defects, and (3) the modeling of category dependencies for multi-label images. To solve these problems, a graph reasoning module, stacked on a classification module, is proposed to expand the feature dimension and improve low-quality image features by exploiting category-wise dependency, image-wise relations, and interactions between them. To further improve the classification performance, the classifier of the classification module is redesigned as a cosine similarity function. With the help of contrastive learning, the classification module can better initialize the category-wise graph of the reasoning module. Experiments on the mobile screen defect dataset show that our two-stage network achieves the following best performances: 97.7% accuracy and 97.3% F -measure. This proves that the proposed approach is effective in industrial applications.

This article investigates the adaptive fuzzy asynchronous control problem for discrete-time nonhomogeneous Markov jump power systems under hybrid attacks. A nonhomogeneous Markov process is used to describe the phenomenon of transient failures occurring in power lines and subsequent switching of associated circuit breakers. The corresponding nonhomogeneous hidden Markov model is utilized to detect the jump modes of power systems. Both deception attack and denial-of-service attack are analyzed simultaneously owing to the vulnerability of power systems. With detected modes and fuzzy logic systems, an adaptive fuzzy asynchronous control strategy is proposed. Using the mode-dependent Lyapunov function, the existence conditions of the desired controller law are obtained such that the closed-loop power systems are bounded stable in the mean-square sense. Finally, the usefulness of the developed control strategy is demonstrated by a numerical example.

This paper mainly studies the localization and mapping of range sensing robots in the confidence-rich map (CRM) and then extends it to provide a full state estimate for information-theoretic exploration. Most previous works about active simultaneous localization and mapping and exploration always assumed the known robot poses or utilized inaccurate information metrics to approximate pose uncertainty, resulting in imbalanced exploration performance and efficiency in the unknown environment. This inspires us to extend the confidence-rich mutual information (CRMI) with measurable pose uncertainty. Specifically, we propose a Rao- Blackwellized particle filter-based localization and mapping scheme (RBPF -CLAM) for CRM, then we develop a new closed-form weighting method to improve the localization accuracy without scan matching. We further derive the uncertain CRMI (UCRMI) with the weighted particles by a more accurate approximation. Simulations and experimental evaluations show the localization accuracy and exploration performance of the proposed methods.

This article investigates the underwater node dynamic localization and prediction problems in a dynamic sensor network. Node localization in the Internet of underwater things is the basis of target tracking and ocean monitoring. At present, most of the node location algorithms assume calm sea and fixed node location. However, the current velocity is uncertain in space and time. The nodes are drifted with the current motion. Therefore, most of the localization algorithms lose efficacy in the actual marine environment. In order to solve the above problems, a node dynamic prediction algorithm is proposed. First, the node mobility model is improved, which is more suitable for the actual marine environment. Second, a frequency-based anchor node prediction algorithm is designed to improve anchor node location accuracy. Third, when the ordinary node receives the signals sent by anchor nodes of different depths, a deep information-based weighted fusion method is designed for the ordinary node localization in order to mine more information in each direction. Finally, location and prediction simulation in sensor networks is carried out. The results show that the proposed node localization and prediction algorithm is more accurate than SMLP and high-precision localization with mobility prediction algorithms and prove the enhanced effect of our method in dynamic marine.

Robots need more intelligence to complete cognitive tasks in home environments. In this paper, we present a new cloud-assisted cognition adaptation mechanism for home service robots, which learns new knowledge from other robots. In this mechanism, a change detection approach is implemented in the robot to detect changes in the user’s home environment and trigger the adaptation procedure that adapts the robot’s local customized model to the environmental changes, while the adaptation is achieved by transferring knowledge from the global cloud model to the local model through model fusion. First, three different model fusion methods are proposed to carry out the adaptation procedure, and two key factors of the fusion methods are emphasized. Second, the most suitable model fusion method and its settings for the cloud-robot knowledge transfer are determined. Third, we carry out a case study of learning in a changing home environment, and the experimental results verify the efficiency and effectiveness of our solutions. The experimental results lead us to propose an empirical guideline of model fusion for the cloud-robot knowledge transfer.

This paper describes a route planner that enables an autonomous underwater vehicle to selectively complete part of the predetermined tasks in the operating ocean area when the local path cost is stochastic. The problem is formulated as a variant of the orienteering problem. Based on the genetic algorithm (GA), we propose the greedy strategy based GA (GGA) which includes a novel rebirth operator that maps infeasible individuals into the feasible solution space during evolution to improve the efficiency of the optimization, and use a differential evolution planner for providing the deterministic local path cost. The uncertainty of the local path cost comes from unpredictable obstacles, measurement error, and trajectory tracking error. To improve the robustness of the planner in an uncertain environment, a sampling strategy for path evaluation is designed, and the cost of a certain route is obtained by multiple sampling from the probability density functions of local paths. Monte Carlo simulations are used to verify the superiority and effectiveness of the planner. The promising simulation results show that the proposed GGA outperforms its counterparts by 4.7%–24.6% in terms of total profit, and the sampling-based GGA route planner (S-GGARP) improves the average profit by 5.5% compared to the GGA route planner (GGARP).

This article investigates the cooperative output regulation problem for heterogeneous nonlinear multiagent systems subject to disturbances and quantization. The agent dynamics are modeled by the well-known Takagi-Sugeno fuzzy systems. Distributed reference generators are first devised to estimate the state of the exosystem under directed fixed and switching communication graphs, respectively. Then, distributed fuzzy cooperative controllers are designed for individual agents. Via the Lyapunov technique, sufficient conditions are obtained to guarantee the output synchronization of the resulting closed-loop multiagent system. Finally, the viability of proposed design approaches is demonstrated by an example of multiple single-link robot arms.

This article investigates the cooperative output regulation problem for discrete-time heterogeneous multiagent Markov jump systems. Two cases are studied: 1) output regulation quadratic control in the case where the exosystem is accessible to all agents and 2) cooperative output regulation quadratic control in the case where only a part of agents can directly communicate with the exosystem. The hidden Markov models are employed to describe the asynchronous modes of the agents and their corresponding controllers. Via the jumping regulator equation, asynchronous control laws are constructed and the algorithms to obtain control parameters are presented in terms of linear matrix inequalities. For the first case, the optimal synchronous/mode-dependent control law, which is a special case of the asynchronous control protocol, is also given via the stochastic dynamic programming approach. Finally, an example is given to illustrate the effectiveness of the proposed approaches.

This article analyzes the problem of the sliding-mode control (SMC) design for discrete-time piecewise nonhomogeneous Markov jump nonlinear systems (MJNSs) subject to an external disturbance with time-varying transition probabilities (TPs). A discrete-time asynchronous integral sliding surface is constructed, which yields matched-nonlinearity-free sliding-mode dynamics (SMDs). Then, by using the mode-dependent Lyapunov function technique, a sufficient condition is established for ensuring the stochastic stability of SMD with extended dissipation. The solution to designing controller gains is obtained. Moreover, an SMC law and an adaptive law are, respectively, derived for driving the system trajectories to move into a predetermined sliding-mode region with specified precision. Finally, the feasibility and effectiveness of the new design are verified and demonstrated by a simulation example.

The multivariate autoregressive (MVAR) model is widely used in describing the dynamics of nonlinear systems, in which the estimates of model parameters and underlying states can be achieved by dual extended Kalman filter (DEKF). However, when the measurements are corrupted by complicated non-Gaussian noises, the DEKF based on the minimum mean-square error (MMSE) criterion may provide biased estimates. In the present article, we develop a novel dual Kalman-type filter, referred to as DEKF under minimum error entropy (MEE) with fiducial points (MEEFs-DEKF) to deal with the non-Gaussian noises. First, the equivalent state-space model and parameter-space model are presented based on the MVAR model. Then, an optimality criterion based on MEE with fiducial points (MEEFs) is applied in the batch-mode regression equation to improve the robustness. Finally, a fixed-point iteration algorithm gives the posterior estimates of state and parameter. Simulation results confirm that the proposed MEEF-DEKF can achieve excellent performance in various noises with different distributions, especially in heavy-tailed and multimodal noises.

This article is concerned with the cluster consensus control problem for multiagent linear systems with a directed communication topology, where only relative output measurements of neighboring agents are available to each agent. Motivated by the pinning control technique, both static and dynamic intermittent output control strategies are proposed. Using Lyapunov functions, sufficient conditions are developed to ensure cluster consensus with existence-guaranteed control parameters. Both periodic and nonperiodic operations of intermittent controllers are investigated. Finally, the effectiveness of the theoretical results is demonstrated by a simulation example.

This paper investigates the task coordination of multi-robot where each robot has a private individual temporal logic task specification; and also has to jointly satisfy a globally given collaborative temporal logic task specification. To efficiently generate feasible and optimized task execution plans for the robots, we propose a hierarchical multi-robot temporal task planning framework, in which a central server allocates the collaborative tasks to the robots, and then individual robots can independently synthesize their task execution plans in a decentralized manner. Furthermore, we propose an execution plan adjusting mechanism that allows the robots to iteratively modify their execution plans via privacy-preserved inter-agent communication, to improve the expected actual execution performance by reducing waiting time in collaborations for the robots. The correctness and efficiency of the proposed method are analyzed and also verified by extensive simulation experiments.

Online social media today has a large number of users and has become a huge platform to collect and share the social data generated by the end users. In addition, based on the development of social applications, the social sensing system has been greatly developed to generate, transmit, and store, which is helping the prosperous of the social computing systems. However, the violation of the security of end users' social data stored and shared through the social computing system becomes a serious and urgent issue. The data protection on social media platforms is very different compared with the scenario of the traditional encryption algorithms, and most of the existing schemes are not suitable for data protection in the current social sensing and data-sharing system. In this article, we present a novel design based on the agnostic selective encryption concept to efficiently protect the social data based on the embedded block coding with optimized truncation system. By selectively encrypting only a small portion of the bitstreams in the middle layer of this coding system, a high level of protection and efficiency can both be achieved. We also experiment with our method on four common social data formats, and the security analysis tests are performed to verify the high protection level of our method.

The recent spades of cyber attacks have compromised end-users' data security and privacy in Medical Cyber-Physical Systems (MCPS) in the era of Health 4.0. Traditional standard encryption algorithms for data protection are designed based on a viewpoint of system architecture rather than a viewpoint of end-users. As such encryption algorithms are transferring the protection on the data to the protection on the keys, data safety, and privacy will be compromised once the key is exposed. In this paper, we propose a secure data storage and sharing method consisted of a selective encryption algorithm combined with fragmentation and dispersion to protect the data safety and privacy even when both transmission media (e.g. cloud servers) and keys are compromised. This method is based on a user-centric design that protects the data on a trusted device such as the end-users' smartphone and lets the end-user control the access for data sharing. We also evaluate the performance of the algorithm on a smartphone platform to prove efficiency.

Smart homes can provide complementary information to assist home service robots. We present a robotic misplaced item finding (MIF) system, which uses human historical trajectory data obtained in a smart home environment. First, a multi-sensor fusion method is developed to localize and track a resident. Second, a path-planning method is developed to generate the robot movement plan, which considers the knowledge of the human historical trajectory. Third, a real-time object detector based on a convolutional neural network is applied to detect the misplaced item. We present MIF experiments in a smart home testbed and the experimental results verify the accuracy and efficiency of our solution.

Summary The recent booming development of cloud computing has enabled a dramatical revolution for current enterprises to create values. Numerous benefits of utilizing cloud services are enhancing the efficiency of improving or creating new businesses. This trend is also associated with the rapid growth of the wireless networks, such as 5G. However, the great increase of deploying clouds also leads to new concerns in data security. One of the major concerns is private leakage that derives from insiders' malicious operations or attacks. This problem has raised a great restriction for financial firms to execute cloud applications. Focusing on this issue, we propose a new solution, entitled Privacy‐Preserving Smart Storage (PS2) model that targets at solving the privacy leakage problems within the existing threat models. The proposed approach uses a novel distributed data storage method to prevent financial enterprises from insiders' massive data mining‐based attacks.

Underwater mobile sensor networks (UMSNs) with free-floating sensors are more suitable for understanding the immense underwater environment. Target tracking, whose performance depends on sensor localization accuracy, is one of the broad applications of UMSNs. However, in UMSNs, sensors move with environmental forces, so their positions change continuously, which poses a challenge on the accuracy of sensor localization and target tracking. We propose a high-accuracy localization with mobility prediction (HLMP) algorithm to acquire relatively accurate sensor location estimates. The HLMP algorithm exploits sensor mobility characteristics and the multi-step Levinson-Durbin algorithm to predict future positions. Furthermore, we present a simultaneous localization and target tracking (SLAT) algorithm to update sensor locations based on measurements during the process of target tracking. Simulation results demonstrate that the HLMP algorithm can improve localization accuracy significantly with low energy consumption and that the SLAT algorithm can further decrease the sensor localization error. In addition, results prove that a better localization accuracy will synchronously improve the target tracking performance.

Underwater wireless sensor networks (UWSNs) can provide a promising solution to underwater target tracking. Due to limited energy and bandwidth resources, only a small number of nodes are selected to track a target at each interval. Because all measurements are fused together to provide information in a fusion center, fusion weights of all selected nodes may affect the performance of target tracking. As far as we know, almost all existing tracking schemes neglect this problem. We study a weighted fusion scheme for target tracking in UWSNs. First, because the mutual information (MI) between a node’s measurement and the target state can quantify target information provided by the node, it is calculated to determine proper fusion weights. Second, we design a novel multi-sensor weighted particle filter (MSWPF) using fusion weights determined by MI. Third, we present a local node selection scheme based on posterior Cramer-Rao lower bound (PCRLB) to improve tracking efficiency. Finally, simulation results are presented to verify the performance improvement of our scheme with proper fusion weights.

Virtual Reality (VR) research is accelerating the development of inexpensive real-time Brain Computer Interface (BCI). Hardware improvements that increase the capability of Virtual Reality displays and Brain Computer wearable sensors have made possible several new software frameworks for developers to use and create applications combining BCI and VR. It also enables multiple sensory pathways for communications with a larger sized data to users' brains. The intersections of these two research paths are accelerating both fields and will drive the needs for an energy-aware infrastructure to support the wider local bandwidth demands in the mobile cloud. In this paper, we complete a survey on BCI in VR from various perspectives, including Electroencephalogram (EEG)-based BCI models, machine learning, and current active platforms. Based on our investigations, the main findings of this survey highlights three major development trends of BCI, which are entertainment, VR, and cloud computing.

Summary Currently, the rapid increment of human detections has been changing people's daily life, However, the implementation is still facing the challenges caused by the restrictions of power and hardware. The computation process results in a heavy workload even though prior researches had made efforts to reduce the complexity. We focus on this issue and propose a new approach that employs the binarization‐based optimization algorithm to simplify the computation processes. Our approach is designed to minimize the necessary calculations and memory space. Moreover, we have developed the field‐programmable gate array‐based parallel architecture to apply partial dynamic reconfiguration, which enables the remote configurations for the number of processing units. An experimental evaluation is completed in order to examine our proposed approach. According to our experimental results, the detection precision can reach a miss rate less than 1.97% and a false positive rate of 1%. The energy cost is also reduced up to 36% comparing with the prior methods. Copyright © 2016 John Wiley & Sons, Ltd.

Recent quick expansions of mobile heterogeneous embedded systems have led to a remarkable hardware upgrade that support multiple core processors. The energy consumption is becoming greater along with the computation capacity grows. Cloud computing is considered one of the solutions to mitigating energy costs. However, the simply offloading the computations to the remote side cannot efficiently reduce the energy consumptions when the energy costs caused by wireless communications are greater than it is on mobile devices. In this paper, we focus on the problem of energy wastes when tasks are assigned to remote cloud servers or heterogeneous core processors. Our solution aims to minimize the total energy cost of the mobile heterogeneous embedded systems by using an optimal task assignment to heterogeneous cores and mobile clouds. The propose model is named as Energy-Aware Heterogeneous Resource Management Model (EA-HRM2), which is supported by a main algorithm Optimal Heterogeneous Task Assignment (OHTA) algorithm. Our experimental evaluations have proved our approach is effective to save energy when deploying heterogenous embedded systems in mobile cloud systems.

Map building is a fundamental task in many robotic applications. In this paper, we propose a novel approach for 3D mapping of indoor environments which allows a robot avatar to collaborate with a human seamlessly through a virtual reality (VR) device. The 3D map is created using the 3D data from an RGB-D camera mounted on the robot and simultaneously transmitted to a remote server, and then rendered to the VR device. On the other hand, the intentions of the user are inferred using the motion of the head movement based on hidden Markov models (HMMs), and then interpreted into commands to control the robot. We implement the proposed approach based on a modified Pioneer robot platform. The experimental results show the feasibility of the proposed system.

The special characteristics of slowly moving infrared targets, such as containing only a few pixels, shapeless edge, low signal-to-clutter ratio, and low speed, make their detection rather difficult, especially when immersed in complex backgrounds. To cope with this problem, we propose an effective infrared target detection algorithm based on temporal target detection and association strategy. First, a temporal target detection model is developed to segment the interested targets. This model contains mainly three stages, i.e., temporal filtering, temporal target fusion, and cross-product filtering. Then a graph matching model is presented to associate the targets obtained at different times. The association relies on the motion characteristics and appearance of targets, and the association operation is performed many times to form continuous trajectories which can be used to help disambiguate targets from false alarms caused by random noise or clutter. Experimental results show that the proposed method can detect slowly moving infrared targets in complex backgrounds accurately and robustly, and has superior detection performance in comparison with several recent methods.

Noise statistics are essential for estimation performance. In practical situations, however, a priori information of noise statistics is often imperfect. Previous work on noise statistics identification in linear systems still requires initial prior knowledge of the noise. A novel approach is presented in this paper to solve this paradox. First, we apply the H ∞ filter to obtain the system state estimates without the common assumptions about the noise in conventional adaptive filters. Then by applying state estimates obtained from the H ∞ filter, better estimates of the noise mean and covariance can be achieved, which can improve the performance of estimation. The proposed approach makes the best use of the system knowledge without a priori information with modest computation cost, which makes it possible to be applied online. Finally, numerical examples are presented to show the efficiency of this approach.

This paper proposes and implements an auditory perception platform for a home service robot that serves the elderly living alone at home. A layered architecture for the proposed platform was developed to realize the various auditory perception capabilities while enabling a remote caregiver to involve in the sound event recognition process. We successfully implemented the software for this architecture that realizes robot services and auditory services for developing high level auditory applications. The robot is able to estimate the sound source position and recognize human speech in the room with multiple sound sources, as well as to collaborate with the caregiver on sound event recognition. Our experimental results validated the proposed platform.

Teaching robots manipulative skills through human demonstration is an important research problem and can be used to quickly program robots in future manufacturing industries. To understand human demonstration, manipulative actions need to be recognized. To improve the recognition performance, we use three kinds of sensors to capture the motion and force involved in the fine manipulative actions. In addition, by taking advantage of the action/object correlation, the recognition accuracy can be further improved. In the proposed approach, important features for individual actions are selected first. Hidden Markov Models (HMMs) are employed to characterize the temporal changes. Then, a Bayesian model is adopted to model the object/action dependency. Our approach was evaluated through experiments on assembly tasks. The experimental results show that the proposed approach can recognize manipulative actions effectively.

This paper investigates the H ∞ trajectory tracking control for a class of nonlinear systems with time-varying delays by virtue of Lyapunov-Krasovskii stability theory and the linear matrix inequality (LMI) technique. A unified model consisting of a linear delayed dynamic system and a bounded static nonlinear operator is introduced, which covers most of the nonlinear systems with bounded nonlinear terms, such as the one-link robotic manipulator, chaotic systems, complex networks, the continuous stirred tank reactor (CSTR), and the standard genetic regulatory network (SGRN). First, the definition of the tracking control is given. Second, the H ∞ performance analysis of the closed-loop system including this unified model, reference model, and state feedback controller is presented. Then criteria on the tracking controller design are derived in terms of LMIs such that the output of the closed-loop system tracks the given reference signal in the H ∞ sense. The reference model adopted here is modified to be more flexible. A scaling factor is introduced to deal with the disturbance such that the control precision is improved. Finally, a CSTR system is provided to demonstrate the effectiveness of the established control laws.

Long propagation delay, limited bandwidth, and high bit error rate pose great challenges in media access control (MAC) protocol design for underwater acoustic networks. A MAC protocol called slotted floor acquisition multiple access (slotted-FAMA) suitable for underwater acoustic networks is proposed and analyzed. This FAMA based protocol adds a time slot mechanism to avoid DATA packet collisions. However, slotted-FAMA is not suitable for dense networks since the multiple request-to-send (RTS) attempts problem in dense networks is serious and greatly limits the network throughput. To overcome this drawback, this paper proposes a slotted-FAMA based MAC protocol for underwater acoustic networks, called RC-SFAMA. RC-SFAMA introduces an RTS competition mechanism to keep the network from high frequency of backoff caused by the multiple RTS attempts problem. Via the RTS competition mechanism, useful data transmission can be completed successfully when the situation of multiple RTS attempts occurs. Simulation results show that RC-SFAMA increases the network throughput efficiency as compared with slotted-FAMA, and minimizes the energy consumption.

The papers in this special section present the most recent research work that showcases the state-of-the-art of human-centered computing and its potential applications in developing truly smart home automation systems.

Detecting behavioral anomalies in human daily life is important to developing smart assisted-living systems for elderly care. Based on data collected from wearable motion sensors and the associated locational context, this paper presents a coherent anomaly detection framework to effectively detect different behavioral anomalies in human daily life. Four types of anomalies, including spatial anomaly, timing anomaly, duration anomaly, and sequence anomaly, are detected using a probabilistic theoretical framework. This framework is based on complex activity recognition using dynamic Bayesian network modeling. The maximum-likelihood estimation algorithm and Laplace smoothing are used in learning the parameters in the anomaly detection model. We conducted experimental evaluation in a mock apartment environment, and the results verified the effectiveness of the proposed framework. We expect that this behavioral anomaly detection system can be integrated into future smart homes for elderly care.

On one hand, due to the energy and bandwidth constraint of underwater wireless sensor networks (UWSNs), local data quantization/compression is not only a necessity, but also an integral part of the design of UWSNs; on the other hand, since underwater nodes provide measurements for target tracking based on UWSNs, node topology, which is made up of the underwater nodes, may affect the performance of target tracking. This paper studies the effect of node topology on the target tracking in UWSNs using quantized measurements. Firstly, by using the knowledge of geometry, the effects of four typical topologies on target tracking using quantized measurements are analyzed qualitatively. The four typical topologies include two nodes are close to each other, three nodes are close to each other, three nodes are co-linear, and three nodes form a regular triangle. Secondly, under the condition of quantized measurements, the relationship between the posterior Cramer-Rao lower bound (PCRLB) and node's position is derived to evaluate the arbitrary topology. Thirdly, our target tracking scheme consisting of the optimal topology selection scheme by minimizing PCRLB, the optimal fusion center selection scheme by minimizing energy consumption, and the multisensor particle filter with quantized measurements is designed. Last, simulation results show the effectiveness of the proposed scheme.

This paper investigates the H∞ state estimation problem for a class of discrete-time nonlinear systems of the neural network type with random time-varying delays and multiple missing measurements. These nonlinear systems include recurrent neural networks, complex network systems, Lur'e systems, and so on which can be described by a unified model consisting of a linear dynamic system and a static nonlinear operator. The missing phenomenon commonly existing in measurements is assumed to occur randomly by introducing mutually individual random variables satisfying certain kind of probability distribution. Throughout this paper, first a Luenberger-like estimator based on the imperfect output data is constructed to obtain the immeasurable system states. Then, by virtue of Lyapunov stability theory and stochastic method, the H∞ performance of the estimation error dynamical system (augmented system) is analyzed. Based on the analysis, the H∞ estimator gains are deduced such that the augmented system is globally mean square stable. In this paper, both the variation range and distribution probability of the time delay are incorporated into the control laws, which allows us to not only have more accurate models of the real physical systems, but also obtain less conservative results. Finally, three illustrative examples are provided to validate the proposed control laws.

This paper proposes and implements a framework for human-robot collaboration in a Mobile Visual Sensor Network (MVSN). A collaborative architecture for the proposed human-integrated MVSN was developed to allow the human operator and robots to collaborate to perform surveillance tasks. We successfully implemented the MVSN so the user can control the deployment of the mobile sensors through his head movement. We also explored using computer vision techniques and navigation techniques on the robot nodes to conduct active human target detection. The robot nodes, therefore, are able to detect human faces while exploring the unknown environment, and then relay the face images to the operator for target recognition. In this way, humans and robots can complement each other to accomplish surveillance tasks. Our experimental results validated the proposed framework.

Drowsiness is one of the main causes of severe traffic accidents occurring in our daily life. In order to reduce the number of drowsiness-induced accidents, various researches have been conducted with the aim of finding practical and non-invasive drowsiness detection systems by using behavioral measuring techniques. Many of the previous works on behavioral measuring techniques have mainly focused on the analysis of eye closure and blinking of the driver. It is recently that more attention started to shift to inclusion of other facial expressions and only few, among those researches, have been done on the analysis of temporal dynamics of facial expressions for drowsiness detection. In this paper we propose a new method of analyzing the facial expression of the driver through Hidden Markov Model (HMM) based dynamic modeling to detect drowsiness. We have implemented the algorithm using a simulated driving setup. Experimental results verified the effectiveness of the proposed method.

We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli (SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets. Since most clutter measurements do not participate in the update step, the computing time is reduced significantly. Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.

This brief proposes an output tracking control for a class of discrete-time nonlinear systems with disturbances. A standard neural network model is used to represent discrete-time nonlinear systems whose nonlinearity satisfies the sector conditions. H∞ control performance for the closed-loop system including the standard neural network model, the reference model, and state feedback controller is analyzed using Lyapunov-Krasovskii stability theorem and linear matrix inequality (LMI) approach. The H∞ controller, of which the parameters are obtained by solving LMIs, guarantees that the output of the closed-loop system closely tracks the output of a given reference model well, and reduces the influence of disturbances on the tracking error. Three numerical examples are provided to show the effectiveness of the proposed H∞ output tracking design approach.

In this paper, we propose and develop a framework for automatic switching of manual driving and autonomous driving based on driver drowsiness detection. We first present the scale-down intelligent transportation system (ITS) testbed. This testbed has four main parts: an arena; an indoor localization system; automated radio controlled (RC) cars; and roadside monitoring facilities. Second, we present the drowsiness detection algorithm which integrates facial expression and racing wheel motion to recognize driver drowsiness. Third, a manual and autonomous driving switching mechanism is developed, which is triggered by the detection of drowsiness. Finally, experiments were performed on the ITS testbed to demonstrate the effectiveness of the proposed framework.

In this paper, H∞ synchronization and state estimation problems are considered for different types of chaotic systems. A unified model consisting of a linear dynamic system and a bounded static nonlinear operator is employed to describe these chaotic systems, such as Hopfield neural networks, cellular neural networks, Chua's circuits, unified chaotic systems, Qi systems, chaotic recurrent multilayer perceptrons, etc. Based on the H∞ performance analysis of this unified model using the linear matrix inequality approach, novel state feedback controllers are established not only to guarantee exponentially stable synchronization between two unified models with different initial conditions but also to reduce the effect of external disturbance on the synchronization error to a minimal H∞ norm constraint. The state estimation problem is then studied for the same unified model, where the purpose is to design a state estimator to estimate its states through available output measurements so that the exponential stability of the estimation error dynamic systems is guaranteed and the influence of noise on the estimation error is limited to the lowest level. The parameters of these controllers and filters are obtained by solving the eigenvalue problem. Most chaotic systems can be transformed into this unified model, and H∞ synchronization controllers and state estimators for these systems are designed in a unified way. Three numerical examples are provided to show the usefulness of the proposed H∞ synchronization and state estimation conditions.

This paper is concerned with the problem of state estimation for a class of discrete-time chaotic systems with or without time delays. A unified model consisting of a linear dynamic system and a bounded static nonlinear operator is employed to describe these systems, such as chaotic neural networks, Chua's circuits, Hénon map, etc. Based on the H∞ performance analysis of this unified model using the linear matrix inequality approach, H∞ state estimator are designed for this model with sensors to guarantee the asymptotic stability of the estimation error dynamic systems and to reduce the influence of noise on the estimation error. The parameters of these filters are obtained by solving the eigenvalue problem. As most discrete-time chaotic systems with or without time delays can be described with this unified model, H∞ state estimator design for these systems can be done in a unified way. Three numerical examples are exploited to illustrate the effectiveness of the proposed estimator design schemes.

This brief studies exponential H(infinity) synchronization of a class of general discrete-time chaotic neural networks with external disturbance. On the basis of the drive-response concept and H(infinity) control theory, and using Lyapunov-Krasovskii (or Lyapunov) functional, state feedback controllers are established to not only guarantee exponential stable synchronization between two general chaotic neural networks with or without time delays, but also reduce the effect of external disturbance on the synchronization error to a minimal H(infinity) norm constraint. The proposed controllers can be obtained by solving the convex optimization problems represented by linear matrix inequalities. Most discrete-time chaotic systems with or without time delays, such as Hopfield neural networks, cellular neural networks, bidirectional associative memory networks, recurrent multilayer perceptrons, Cohen-Grossberg neural networks, Chua's circuits, etc., can be transformed into this general chaotic neural network to be H(infinity) synchronization controller designed in a unified way. Finally, some illustrated examples with their simulations have been utilized to demonstrate the effectiveness of the proposed methods.

The speed gap between processor and memory is the major bottleneck for modern computing systems. Many modern processors, such as the CELL processor, employ multi-core, multimodule architecture to hide memory access latency. However, making effective use of multiple memory modules remains difficult, considering the combined effect of performance and energy requirements. This paper studies the scheduling and assignment problem that optimize both energy and performance. An efficient algorithm, EALSPP (Energy Aware Loop Scheduling with Prefetching and Partition), is proposed. The algorithm attempts to maximize energy saving while hiding memory latency with the combination of loop scheduling, data prefetching, memory partition, and heterogeneous memory module type assignment. Experimental results demonstrate the effectiveness of our approach.

A unified neural network model termed standard neural network model (SNNM) is advanced. Based on the robust L(2) gain (i.e. robust H(infinity) performance) analysis of the SNNM with external disturbances, a state-feedback control law is designed for the SNNM to stabilize the closed-loop system and eliminate the effect of external disturbances. The control design constraints are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms (e.g. interior-point algorithms) to determine the control law. Most discrete-time recurrent neural network (RNNs) and discrete-time nonlinear systems modelled by neural networks or Takagi and Sugeno (T-S) fuzzy models can be transformed into the SNNMs to be robust H(infinity) performance analyzed or robust H(infinity) controller synthesized in a unified SNNM's framework. Finally, some examples are presented to illustrate the wide application of the SNNMs to the nonlinear systems, and the proposed approach is compared with related methods reported in the literature.

In order to conveniently analyze the stability of recurrent neural networks (RNNs) and successfully synthesize the controllers for nonlinear systems, similar to the nominal model in linear robust control theory, the novel neural network model, named delayed standard neural network model (DSNNM) is presented, which is the interconnection of a linear dynamic system and a bounded static delayed (or nondelayed) nonlinear operator. By combining a number of different Lyapunov functionals with S-procedure, some useful criteria of global asymptotic stability and global exponential stability for the continuous-time DSNNMs (CDSNNMs) and discrete-time DSNNMs (DDSNNMs) are derived, whose conditions are formulated as linear matrix inequalities (LMIs). Based on the stability analysis, some state-feedback control laws for the DSNNM with input and output are designed to stabilize the closed-loop systems. Most RNNs and neurocontrol nonlinear systems with (or without) time delays can be transformed into the DSNNMs to be stability-analyzed or stabilization-synthesized in a unified way. In this paper, the DSNNMs are applied to analyzing the stability of the continuous-time and discrete-time RNNs with or without time delays, and synthesizing the state-feedback controllers for the chaotic neural-network-system and discrete-time nonlinear system. It turns out that the DSNNM makes the stability conditions of the RNNs easily verified, and provides a new idea for the synthesis of the controllers for the nonlinear systems.

A neural-model-based control design for some nonlinear systems is addressed. The design approach is to approximate the nonlinear systems with neural networks of which the activation functions satisfy the sector conditions. A novel neural network model termed standard neural network model (SNNM) is advanced for describing this class of approximating neural networks. Full-order dynamic output feedback control laws are then designed for the SNNMs with inputs and outputs to stabilize the closed-loop systems. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. It is shown that most neural-network-based nonlinear systems can be transformed into input-output SNNMs to be stabilization synthesized in a unified way. Finally, some application examples are presented to illustrate the control design procedures.