User performance is crucial in interactive systems, capturing how effectively users engage with task execution. Prospectively predicting performance enables the timely identification of users struggling with task demands. While ocular and cardiac signals are widely used to characterise performance-relevant visual behaviour and physiological activation, their potential for early prediction and for revealing the physiological mechanisms underlying performance differences remains underexplored. We conducted a within-subject experiment in a game environment with naturally unfolding complexity, using early ocular and cardiac signals to predict later performance and to examine physiological and self-reported group differences. Results show that the ocular–cardiac fusion model achieves a balanced accuracy of 0.86, and the ocular-only model shows comparable predictive power. High performers exhibited targeted gaze and adjusted visual sampling, and sustained more stable cardiac activation as demands intensified, with a more positive affective experience. These findings demonstrate the feasibility of cross-session prediction from early physiology, providing interpretable insights into performance variation and facilitating future proactive intervention.

Reconfigurable intelligent surface (RIS) has gained much attention as a cost-effective solution to enhance connectivity and coverage in massive machine-type communication. However, the passive nature of RIS poses fundamental challenges to decoupling and estimating base station (BS)-RIS and RIS-device channels, as well as identifying active devices. To effectively tackle this issue, we cast the joint channel estimation and activity detection for RIS-assisted Internet-of-Things networks as a tensor-based two-layer problem by exploiting the channel sparsity and a multi-frame pilot training structure. The first layer involves the Canonical Polyadic (CP) decomposition of a third-order tensor observation, while the second layer addresses compressive sensing (CS)-based simple measurement vector (SMV) and multiple measurement vector (MMV) problems. Then, by leveraging the Bayesian inference framework, we propose a tensor-based approximate message passing (TAMP) algorithm to estimate one-hop BS-RIS channel, one-hop RIS-device channels, and active IoT devices simultaneously. Furthermore, we conduct the state evolution (SE) analysis of TAMP to theoretically characterize its MSE. Numerical results corroborate the superior estimation and detection performance of TAMP and demonstrate that our SE analysis perfectly predicts the actual MSE.

Salient object detection in optical remote sensing images (RSI-SOD) is critical in remote sensing, yet it faces challenges such as dependency on intensive pixel-level annotations and limited research on low-cost, weakly supervised methods. These challenges are compounded by difficulties in handling complex backgrounds and varying salient object features with existing CNN-based methods. We introduce the Global-Local Semantic Interaction Network (GLSIN), a high-performance, cost-effective RSI-SOD approach based on scribble supervision. GLSIN employs an encoder-decoder framework, blending a Transformer and CNN to create a Dual Branch Encoder that effectively captures both global and local features of images. The Global-Local Affinity Block (GLAB) and Feature Shrinkage Decoder with the Global-Local Fusion Block (GLFB) are integrated to enhance feature interaction and precision in saliency map generation. Experimental results on two public datasets show that our method achievesFmaxβ,Emaxξ,Sα, andMscores of 86.6%, 96.5%, 91.8%, and 0.7% on the EORSSD dataset, and 90.1%, 97.2%, 91.7%, and 1.1% on the ORSSD dataset, respectively. The performance surpasses existing weakly-supervised or unsupervised SOD methods and even some fully-supervised models.

Salient object detection in optical remote sensing images (RSI-SOD) has recently become a key area of research, driven by the unique challenges posed by the variability in remote sensing imagery. Traditional approaches, largely based on Convolutional Neural Networks (CNNs), are limited in handling the diverse scenarios of remote sensing due to their static network construction and reliance on local feature extraction. To tackle these limitations, we present the Adaptive Semantic Network (ASNet), a novel framework specifically designed for RSI-SOD. ASNet innovatively integrates Transformer and CNN technologies in a Dual Branch Encoder, which captures both global dependencies and local fine-grained image details. The network also features an Adaptive Semantic Matching Module (ASMM) for dynamically harmonizing filter responses to global and local contexts, an Adaptive Feature Enhancement Module (AFEM) that effectively enhances salient region features while restoring image resolution, and a Multi-scale Fine-grained Inference Module (MFIM) which refines high-level semantic features by integrating detailed low-level information, leading to the generation of precise, high-quality saliency maps. These components work in concert to adaptively respond to the complex nature of remote sensing images. Extensive experimental evaluations confirm that ASNet substantially outperforms existing models in the RSI-SOD task.

With the advent of the modern transportation era, road design needs to emphasize landscape and recreational functions more than traditional safety functions. However, quantifying drivers’ subjective perceptions and the objective visual landscape characteristics that influence them remains challenging due to the subjectivity of landscape perception, and the limitation of data size and the complexity of environmental information capture. This paper aims to overcome these limitations, based on deep learning techniques to achieve high-precision automatic identification and rapid processing of spatial visual elements in the highway landscape. Based on supervised fully connected neural networks, the ELO scoring algorithm, and ArcGIS, it realizes the quantitative evaluation of subjective perception emotions and visual mapping representation. Furthermore, the paper integrates optimization algorithms with deep learning, employing a Whale Optimization Algorithm (WOA) enhanced XGBoost regression model to analyze the interplay between spatial features and subjective experiences. The model’s effectiveness is confirmed through comparative experiments and SHAP analysis, offering insights into the coupling of subjective and objective data. The research results provide theoretical methods and quantitative analysis tools for the digitalization and refinement of highway landscape design, and also help researchers and urban planners to understand the interaction between subjective perception and objective semantics, providing new ways and insights for highway landscape planning and design.

Reconfigurable intelligent surface (RIS) has been recently regarded as a disruptive candidate technology for enabling next generation wireless communication. It can establish favorable propagation environment to facilitate low-power and spectrally efficient data transmission, possessing attractive potential to support massive access. However, the required activity detection and channel estimation for RIS-assisted massive access is quite challenging due to the passive nature of the conventional reflecting elements. To this end, this paper considers massive access for RIS-assisted communication systems with semi-passive elements, which can operate in sensing mode for receiving signals. Then, by exploiting the sparsity of the RIS-BS channel in the virtual angular domain as well as the sporadic transmission of massive connectivity, we formulate the joint activity detection and channel estimation as a special bilinear recovery problem, which is a combination of sparse matrix factorization, compressed sensing (CS)-based generalized multiple measurement vector (GMMV) problem and matrix completion. Furthermore, we propose a novel hierarchical message passing-based algorithm to address the problem, in which approximate message passing (AMP)-based approximations are adopted to reduce the computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm and its superior performance compared with state-of-the-art baseline schemes.

Developing mobile multimedia cloud services over heterogeneous wireless networks poses a challenge for service continuity. The degraded link quality and connection losses are likely to happen and these may affect service execution times and service availability in mobile cloud computing scenarios. To improve handoff quality and minimize utilized bandwidth, we propose a self-adaption handoff scheme for multimedia services in mobile cloud computing. The proposed scheme uses multipath transmission for media flows, and consists of the duplicate mode and the effective mode, which are changed according to the network condition. Analytic model and simulation are developed to investigate our new scheme. The results demonstrate that the new mechanism can realize seamless handoff for multimedia services in cloud, reduce the packet loss rate, as well as obtain a more efficient use of the scarce wireless bandwidth and the power of mobile devices.

Mobile devices are becoming the primary platforms for many users who always roam around when accessing the cloud computing services. From this, the cloud computing is integrated into the mobile environment by introducing a new paradigm, mobile cloud computing. In the context of mobile computing, the battery life of mobile device is limited, and it is important to balance the mobility performance and energy consumption. Fortunately, cloud services provide both opportunities and challenges for mobility management. Taking the activities of cloud services accessing into consideration, the authors propose a service‐aware location update mechanism, which can detect the presence and location of the mobile device without traditional periodic registration update. Analytic model and simulation are developed to investigate the new mechanism. The results demonstrate that the service‐aware location update management can reduce the location update times and handoff signalling, which can efficiently save power consumption for mobile devices.

IP multimedia subsystem (IMS) is over IP network architecture, but mobile IP cannot directly support session mobility controlled by session initiation protocol-based signaling. The long signaling delay for session reestablishment in application layer always results in session interruptions during the handoff. Therefore, handoff poses a challenge for quality of service (QoS) maintenance in IMS that targets to offer real-time multimedia applications over wireless mobile networks. The existing approaches to solve this problem depend on the advance resource reservation and the optimization of handoff control. Unfortunately, big cost of the advance resource reservation in neighboring domains is a major problem that leads to a serious signaling load and a waste of wireless bandwidth. To solve this issue, we present an enhanced IMS handoff mechanism (EHM) based on user mobility prediction to save network resources by avoiding multiple useless advance reservations. In addition, to support the heterogeneous access networks in IMS domain, EHM evolves a network selective scheme to utilize the network resources more efficiently. The architecture of EHM and the advance QoS negotiation signaling are also presented. We model the cost, the handoff delay and the session blocking probability for EHM and the previous work. Analytical and simulation results show that EHM can enhance the handoff performance, such as reducing resource reservation cost greatly, decreasing session reestablishment delay and making good use of multiple access network resources.

To determine the potential signaling traffic reductions, the session establishment procedures are investigated. The investigation shows that, the S-CSCF (Serving Call Session Control Function) is the major bottleneck in IMS (IP Multimedia Subsystem) and the existing 3GPP (the 3rd Generation Partnership Project) Service Triggering Algorithm (3GPP STA) increases largely the end to end session setup delay. To reduce the session setup delay and improve the system performance, a new Group based Service Triggering Algorithm (GSTA) is proposed. And then the modeling of 3GPP STA and GSTA are presented. Theoretical analysis and simulation results show that, GSTA can efficiently reduce the signaling traffic load of the S-CSCF, increase the throughput of the system and considerably reduce the session setup delay, improve IMS network quality of service.

In the service invocation mechanism of current IMS (IP multimedia system) network, the serving-call session control function (S-CSCF) invokes each application server (AS) sequentially to perform user's service profile. This mechanism makes SIP request be forwarded excessively among the S-CSCF and application servers (ASs), so it is easy to result in the heavy load of the S-CSCF entity and long call set-up delay. Service capability interaction manager (SCIM) is an entity providing service invoking capability and service interaction management, which is out of standards at present. This paper proposes a novel distributed SCIM (DSCIM) service invocation mechanism within IMS service provision architecture. It aims at reducing the call set-up delay in IMS service layer along with decreasing the load of the S-CSCF entity by invoking each AS consecutively without signaling being forwarded back to the S-CSCF. We model the service invocation mechanisms through Jackson network and the simulations of different scenarios verify that DSCIM service invocation mechanism can effectively reduce call set-up delay of the network and decrease the load level of the S-CSCF.