Electroencephalogram (EEG)-driven innovations have gained prominence as pivotal research frontiers in various domains, including neurological disorder diagnostics, sensorimotor rehabilitation, and brain–computer interfaces (BCIs), and so on. Traditional EEG decoding methods encounter difficulties in effectively extracting global features from EEG signals while demonstrating constrained adaptability to high-dimensional EEG patterns. Furthermore, the scarcity of labeled EEG significantly restricts the applicability of supervised learning paradigms. To address these challenges, we propose IGEEGc , a novel unsupervised decoding method specifically designed for unlabeled EEG signal analysis. First, we extend the trend-based information granule (TIG) model by incorporating frequency-domain features, thereby developing a frequency-trend-based information granule (FTIG) model that simultaneously captures time-frequency features of EEG signals. Subsequently, an adaptive granule feature weighting mechanism is developed to dynamically optimize the contributions of heterogeneous granule features toward enhancing clustering accuracy. Finally, constraints are integrated into the unified one-step spectral clustering framework to optimize the objective function and enhance clustering performance. Comparative experiments across 30 real-world datasets in multiple scenarios demonstrate IGEEGc ’s consistent superiority over state-of-the-art methods, with its robustness and efficacy being validated through comprehensive sensitivity analyses and ablation studies. The proposed method not only addresses the inherent high-dimensional complexity of EEG signals but also establishes an effective paradigm for processing unlabeled EEG data. The source code can be found at https://github.com/Ljyyds/IGEEGc .

Nowadays, various graph convolutional networks (GCNs) to process graph-structured data have been proposed for hyperspectral image (HSI) classification. Nevertheless, most GCN-based HSI classification methods emphasize graph node feature aggregation instead of graph pooling, resulting in them being shallow networks and unable to extract deep discriminative features. Besides, to obtain the new graph after the pooling layer, current graph pooling methods used for HSI classification just consider node feature information to select important nodes and directly discard unselected nodes, which could be a subjective process and may cause information loss. To solve this issue, we propose a novel graph U-Net with topology-feature awareness pooling (the so-called TFAP graph U-Net) for HSI classification considering a deep network to extract compelling features and automatically selecting nodes beneficial to classification. More specifically, to establish a more precise pooled graph, the graph’s topology structure and node feature information are taken into account, making the node selection process more convincing and objective. Furthermore, to allow that graph nodes preserve more useful graph information, our method aggregates node features from neighboring nodes that may not be selected, which can alleviate the loss of information during the pooling process. Moreover, a cross-attention module (CAM) is used to filter out irrelevant or noisy features. Finally, we evaluate the proposed method on three public HSI datasets, i.e., Indian Pines, University of Pavia (PaviaU), and University of Houston. The experimental results demonstrate the superiority of the proposed approach compared with other state-of-the-art methods.

Deep graph convolutional networks (GCNs) have shown promising performance in traffic prediction tasks, but their practical deployment on resource-constrained devices faces challenges. First, few models consider the potential influence of historical and future auxiliary information, such as weather and holidays, on complex traffic patterns. Second, the computational complexity of dynamic graph convolution operations grows quadratically with the number of traffic nodes, limiting model scalability. To address these challenges, this study proposes a deep encoder-decoder model named AIMSAN, which comprises an auxiliary information-aware module (AIM) and a sparse cross-attention-based graph convolutional network (SAN). From historical or future perspectives, AIM prunes multi-attribute auxiliary data into diverse time frames, and embeds them into one tensor. SAN employs a cross-attention mechanism to merge traffic data with historical embedded data in each encoder layer, forming dynamic adjacency matrices. Subsequently, it applies diffusion GCN to capture rich spatial-temporal dynamics from the traffic data. Additionally, AIMSAN utilizes the spatial sparsity of traffic nodes as a mask to mitigate the quadratic computational complexity of SAN, thereby improving overall computational efficiency. In the decoder layer, future embedded data are fused with feed-forward traffic data to generate prediction results. Experimental evaluations on three public traffic datasets demonstrate that AIMSAN achieves competitive performance compared to state-of-the-art algorithms, while reducing GPU memory consumption by 41.24%, training time by 62.09%, and validation time by 65.17% on average.

The rapid development of 5G and the widespread use of smart terminal devices bring explosive video traffic growth. Edge caching technology in mobile edge computing systems stores popular content of most interest to users in advance in edge servers closer to mobile users to alleviate the pressure of traffic congestion and excessive access latency caused by centralized storage. However, how to obtain popular videos while protect user data privacy with only implicit user feedback data, such as liking, viewing, favoriting, etc., is the key challenge. To tackle these challenges, we proposed a proactive video caching scheme based on federated learning and implicit feedback (FIPC). First, a mobile edge-cloud system model contained a three-tier network architecture is developed. Then, we introduced the federated process for training the denoised auto-encoder model and video caching in detail. Finally, experimental results in Movielens dataset show that, without user data leakage, the proposed FIPC scheme outperforms the baseline caching algorithm using user feedback data.

Lightweight and layered structure containers in serverless edge computing (SEC) provide flexible service configurations and computing for vehicles with diverse service requests in the Vehicle-Infrastructure Collaboration (VIC) environment. Despite progress in service request scheduling for the VIC system, the effect of layer sharing between different service images on request scheduling has not been fully explored. Additionally, the cold-start latency of service containers in SEC can significantly degrade the responsiveness of vehicle services, and container retention is proposed to minimize its impact and improve overall system performance. However, the existing research neglects the complex coupling relationship between request scheduling and container retention decisions, while focusing on the single decision optimization problem. Consequently, minimizing system costs by single decision optimization may not achieve the effect of joint decision optimization. To bridge this gap, we study the joint service request scheduling and container retention problem based on layer sharing and container caching. First, we model the joint decision problem with specific constraints and aim to minimize the long-term system cost while considering vehicle mobility. Second, an online co-decision scheme called Onco is proposed to solve the problem, which incorporates request scheduling and container retention for multiple vehicle services. Finally, both synthetic and real trace-driven simulation experiments have been conducted to evaluate the performance of Onco. The experimental results show that Onco outperforms state-of-the-art baselines in terms of system cost reduction and response time improvement.

Mobile edge computing allows for high-performance and low-latency applications by delegating computation and data processing tasks to edge servers. However, ensuring the integrity of cached data on these servers can be challenging due to their limited resources. Current designs often use a per-edge multi-round approach, which necessitates multiple communication rounds between each edge server and the application vendor (AppVend). This approach results in high communication and computational costs, as well as the stragglers effect during batch verification. To address these inefficiencies, we propose a Hierarchical Signature Aggregation for Edge Data Integrity (HSA-EDI) verification design. Our design adopts a novel per-edge one-round approach, which significantly reduce the number of communication rounds to one for each edge server, while mitigating the impact of stragglers. Furthermore, it remarkably reduces computational costs through a hierarchical aggregation mechanism. This mechanism supports intra-edge signature aggregation at the edge server level, followed by inter-edge aggregation at the AppVend, which enhances overall efficiency. We then conduct a theoretical analysis of HSA-EDI’s correctness, security, and communication, computation, and storage efficiency. Experimental results validate its superior performance over state-of-the-art designs.

Graph convolutional network (GCN) has recently received increasing attention in hyperspectral image (HSI) classification, benefiting from its superiority in conducting shape adaptive convolutions on arbitrary non-Euclidean structure data. However, the performance of GCN heavily depends on the quality of the initial graph. Conventional GCN-based methods only adopt spectral-spatial similarity to build the initial graph without extracting other contextual information from neighboring nodes. In addition, most GCN-based methods use shallow layers, which cannot extract deep discriminative features from HSIs under the limited number of training samples. To solve these issues, we propose a superpixel feature learning via offset graph U-Net for HSI classification, which can learn deep discriminative features from HSIs. Multiple strategies of measuring similarity among superpixels are utilized to build the initial graph, including spectral information, spatial information and context-aware information among nodes, making the initial graph more accurate. Furthermore, the graph U-Net structure, containing the graph pooling layer and the graph unpooling layer, is helpful in constructing deep GCN layers and learning multi-scale features, which can alleviate the oversmoothing problem. Moreover, an offset module is introduced to emphasize the local spectral-spatial information. Finally, we comprehensively evaluate the proposed method on three public data sets. The experimental results demonstrate the superiority of the proposed approach compared with other state-of-the-art methods.

Electroencephalogram (EEG)-based applications in Brain-Computer Interfaces (BCIs, or Human-Machine Interfaces, HMIs), diagnosis of neurological disease, rehabilitation,etc, rely on supervised techniques such as EEG classification that requires given class labels or markers. Incomplete or incorrectly labeled or unlabeled EEG data are increasing with the ever-expanding amount of EEG data generated by such applications and the ambiguities these generate degrade the performance of supervised techniques. To address the challenging task of clustering EEG data with limitedprioriknowledge, we introduce a semi-supervised graph embedding EEG clustering approach termedConsEEGcwith multiple constraints,i.e., label-transformed connectivity constraints that constrains the connection or disconnection among EEG data, compactness-and-scatter constraint that constrains the intra-cluster compactness and inter-cluster scatter of EEG clusters, and fairness constraint that constrains the fair ratio of elements between EEG clusters, to make best use of limitedprioriknowledge of EEG data and to achieve better EEG clustering results.ConsEEGcis conducted with an optimization objective function that integrates pseudo label learning, least-square error minimization and multiple constraints, and it can quickly converge to local optima. The experiments demonstrate thatConsEEGccan efficiently yield good clustering results on various types of real-world EEG datasets, compared to state-of-the-art standard unsupervised and semi-supervised EEG/time series clustering algorithms.

Container has been widely used in application development and management systems. However, there are two major challenges faced in the real deployment at edge servers. The varying workload of service requests and the startup delay of containers force a flexible resource provisioning scheme in containerized edge computing. To this end, we proposeCEC, a containerized edge computing framework for dynamic resource provisioning, and especially for the smart connected community where exists multiple intelligent applications.CECintegrates workload prediction and resource pre-provisioning to enable low latency of user service requests and high utilization of edge resources. First, we present an online periodic request prediction algorithm. Then, we designed a control-based resource pre-provisioning algorithm based on the predicted request distribution, which is a self-adaptive controller to tune the resource for containers. We evaluate the performance ofCECby simulation and system experiments. The simulation experiment shows that the prediction accuracy of the proposed algorithm is higher than other two prediction algorithms. The testbed experiments demonstrate that the control-based resource pre-provisioning algorithm has low service latency and high resource utilization compared with baselines.

With the development of Internet of Things and mobile computing, the explosive proliferation of latency-sensitive applications raises high computation demands for mobile devices. To this end, offloading computation of applications to edge-enabled Internet of Vehicles (IoV) has emerged as an effective solution. However, most of the existing studies on this issue assume that IoV can be easily formed in the practical environment, and neglect the dependency relationship between tasks of the offloading application. In this article, we first give several observations based on the analysis results of the real traffic dataset to verify the feasibility of aggregating vehicular resources in the real world. Then, we design a Latency-aware Real-time Scheduling Framework for the edge-enabled IoV, named LARS, in which mobile users can offload applications to LARS, and the offloading tasks can be scheduled to the appropriate vehicular resources in real-time. First, we propose a clustering-based algorithm to generateHerds, which treats connected vehicles as edge computation resources to provide cooperative computing services. Second, considering the dependency relationship between tasks in the job, we present a greedy-based task scheduling algorithm for offloading jobs, the objective of which is to minimize the total latency of the job as well as maximize the resource utilization ofHerds. The simulation experiment based on the real traffic dataset shows thatHerdsgenerated by the proposed clustering-based algorithm can maintain a stable period to provide computing service, and the experiments on testbed include two case studies demonstrate that the superiority of the proposed scheme compared to baselines, in terms of latency and resource utilization.

Spatial–temporal graph modeling plays an important role in the fields of transportation, meteorology, and social networks. Traffic flow prediction is a classic spatial–temporal modeling task. Existing methods usually do not take into account the asynchronous spatial–temporal correlation in traffic data. In addition, due to the complexity and variability of traffic data, long-term traffic forecasting is highly challenging. In order to solve the above problems, this article proposes a new deep learning-based asynchronous dilation graph convolution network (ADGCN) to model the spatial–temporal graphs. We mine the asynchronous spatial–temporal correlation in the traffic network, and propose the asynchronous spatial–temporal graph convolution (ASTGC) operation to extract this special relationship. Furthermore, we extend the dilated 1-D causal convolution to a graph convolution. The receptive field of the model increases exponentially with the increase of the network depth. Experiments are conducted on three public traffic data sets, and the results show that the prediction performance of ADGCN is better than the existing counterpart methods, especially in long-term prediction tasks.

Recently, graph convolutional network (GCN) has drawn increasing attention in hyperspectral image (HSI) classification, as it can process arbitrary non-Euclidean data. However, dynamic GCN that refines the graph heavily relies on the graph embedding in the previous layer, which will result in performance degradation when the embedding contains noise. In this letter, we propose a novel dual residual graph convolutional network (DRGCN) for HSI classification that integrates two adjacency matrices of dual GCN. In detail, one GCN applies a soft adjacency matrix to extract spatial features, whereas the other utilizes the dynamic adjacency matrix to extract global context-aware features. Subsequently, the features extracted by dual GCN are fused to make full use of the complementary and correlated information among two graph representations. Moreover, we introduce residual learning to optimize graph convolutional layers during the training process, to alleviate the over-smoothing problem. The advantage of dual GCN is that it can extract robust and discriminative features from HSIs. Extensive experiments on four HSI datasets, including Indian Pines, Pavia University, Salinas, and Houston University, demonstrate the effectiveness and superiority of our proposed DRGCN, even with small-sized training data.

Recently, graph convolutional network (GCN) has been applied for hyperspectral image (HSI) classification and obtained better performance. The main issue in HSI classification is that the high-resolution HSI contains more complex spectral-spatial structure information. However, the previous GCN-based methods applied in HSI classification only adopted a shallow GCN layer and they can not extract the deeper discriminative features. In addition, these methods ignored the complementary and correlated information among multi-order neighboring information extracted by multiple GCN layers. In this letter, a novel feature fusion via deep residual graph convolutional network is proposed to explore the internal relationship among HSI data. On the one hand, benefiting from residual learning to alleviate the over-smoothing problem, we can construct deep GCN layers to excavate deeper abstract features of HSI. On the other hand, we fuse the outputs of different GCN layers, and thus, the local structural information within multi-order neighborhood nodes can be fully utilized. Extensive experiments on four real HSI data sets, including Indian Pines, Pavia University, Salinas, and Houston University, demonstrate the superiority of the proposed method compared with other state-of-the-art methods in various evaluation criteria.

With the rapid development of Internet social media platforms and the popularization of smart terminal devices, video services such as short videos have surged. However, the massive amount of smart devices connected to the core network causes the increase of load on the backhaul link, which makes traditional cloud computing unable to meet the low-latency requirement of user for video services fully. To this end, we propose to implement proactive video caching at the edge to improve the quality of user experience. Thus, a novel collaborative video caching framework at the edge, CVC, is proposed. In this framework, we present a video request prediction model based on federated learning, aiming to reduce communication costs. Also, we design two methods to improve the hit rate and reduce latency, including a collaborative caching decision method (CCD) and a collaborative service response method (CSR). Under the video service scenario, the experimental results show that CVC can improve the cache hit rate and reduce the waiting delay of users compared with the traditional caching algorithms. Simultaneously, CVC can also reduce the overall system’s communication cost and caching cost.

In the era of 5G, with the increasing demands on computation and massive data traffic of the Internet of Things (IoT), mobile edge computing (MEC) and ultradense network (UDN) are considered to be two enabling and promising technologies, which result in the so-called ultradense edge computing (UDEC). Task offloading as an effective solution offers low latency and flexible computation for mobile users in the UDEC network. However, the limited computing resources at the edge clouds and the dynamic demands of mobile users make it challenging to schedule computing requests to appropriate edge clouds. To this end, we first formulate the transmitting power allocation (PA) problem for mobile users to minimize energy consumption. Using the quasiconvex technique, we address the PA problem and present a noncooperative game model based on subgradient (NCGG). Then, we model the problem of joint request offloading and resource scheduling (JRORS) as a mixed-integer nonlinear program to minimize the response delay of requests. The JRORS problem can be divided into two problems, namely, the request offloading (RO) problem and the computing resource scheduling (RS) problem. Therefore, we analyze the JRORS problem as a double decision-making problem and propose a multiple-objective optimization algorithm based on i-NSGA-II, referred to as MO-NSGA. The simulation results show that NCGG can save the transmitting energy consumption and has a good convergence property, and MO-NSGA outperforms the existing approaches in terms of response rate and can maintain a good performance in a dynamic UDEC network.

Binary decision diagram (BDD) and Boolean satisfiability (SAT) are two common techniques of logic circuit-based Boolean reasoning. Since circuit-width is a good measure of circuit complexity, in this paper, a circuit-width based heuristic for Boolean reasoning is presented, it can be used for integrating the BDD-based engine and SAT-based engine, and takes advantages of both engines. Thus this heuristic can avoid the potential memory explosion during constructing the BDDs, and can prevent the time-out phenomenon of SAT techniques. Compared with the previous heuristics, the proposed heuristic can save more computational resources, and can improve the performance of Boolean reasoning algorithms. This heuristic has been applied in combinational circuit test generation successfully. Experimental results show that, the proposed heuristic can be used for the Boolean reasoning with multiple engines efficiently.

Error diagnosis is becoming more difficult in VLSI circuit designs due to the increasing complexity. In this paper, we present an algorithm based on verification for improving the accuracy of design error diagnosis. This algorithm integrates three-valued logic simulation and Boolean satisfiability (SAT). It uses test patterns generated by a gate level stuck-at fault ATPG tool for parallel pattern simulation, and uses SAT-based Boolean comparison to enhance the three-valued simulation, in which universally quantified conjunction normal formulas (CNF) represent the unknown constraints in the implementation with black boxes, and does not need circuit structural transformation. Our approach can quickly and efficiently eliminate many false candidates, experimental results on ISCAS'85 circuits show the accuracy and the speed of this approach.

In verification of embedded core-based design, the port order fault (POF) model focuses on the errors in connections between the ports of the cores and the surrounding circuits, thus considerably reduces the verification complexity and time. This paper investigated the automatic verification pattern generation for POF and developed an effective algorithm of verification for POF using SAT instead of BDD. The problem of detecting POF was transformed into SAT, which was efficiently solved by a state-of-the-art efficient SAT solver.