The evolution of communication network architectures is steering towards more sustainable, flexible, and lightweight designs, particularly with the advent of sixth-generation (6G) mobile communications. Spectrum-rich optical integrated systems are anticipated to play a crucial role in this transformation, offering significant advantages such as high data rates, reduced interference, and improved energy efficiency. This paper introduces and experimentally demonstrates a novel optical integrated communication, sensing, and power transfer (O-ICSPT) system. The proposed system integrates optical wireless communication, sensing, and wireless power transfer into a multifunctional framework, addressing the limitations of existing systems in terms of flexibility and resource utilization. The experimental setup investigates the effects of bias current, peak-to-peak voltage, and light source wavelength on the performance of each functional module. Experimental results indicate that the O-ICSPT system achieves a maximum data rate of approximately 632.58 Mbps, a best ranging root mean square error approaching 0 m, and a peak energy harvesting capability of about 10.02 mW. These findings underscore the potential of the O-ICSPT system in future 6G integrated communication networks, marking the first experimental validation of such a system.

Integrated satellite–terrestrial networks (ISTNs) are envisioned as a key component of 6G to support global connectivity for massive Internet of Things (IoT) devices. However, while terminals operate under strict energy and computational constraints and face additional challenges from satellite dynamics, current designs rarely integrate orbital information with device energy states in a unified system design, leaving significant room for improvement. Motivated by this, we propose a joint trajectory- and energy-aware framework for ISTNs operating in dynamic environments, supported by a low-complexity algorithm tailored for energy-constrained IoT devices. Specifically, we first develop a system model centered on a day–night separated hybrid energy-harvesting (DNEH) mechanism, where each device harvests light energy during the daytime and radio frequency (RF) energy from the gateway (GW) at night. Meanwhile, the GW broadcasts simplified orbital information to assist device operations, thereby forming an integrated communication–energy transfer framework that enhances system sustainability. Analytical characterizations are subsequently derived for the joint coverage probability, and simulations capture orbital dynamics such as the real-time distance variations. Building on the semi-analytical model, the proposed drift-regularized per-frame (DRPF) algorithm determines transmission opportunities that optimize the average Age of Information (AoI) by capturing the coupling between trajectory dynamics and energy availability. Numerical evaluations confirm the theoretical advantages of the proposed framework, showing substantial performance gains over the Immediate and Minimum Distance transmission baselines. Moreover, the optimized TD-SWIPT configuration and transmission scheduling translate these findings into actionable design guidelines for energy-constrained ISTNs.

This paper proposes CLIPGaussian, an improved training and rendering method for 3D Gaussian Splatting tailored to address the degradation of reconstruction quality under low-quality image conditions. By integrating a multi-view semantic evaluation module based on CLIP and a 3D spatial consistency loss, CLIPGaussian enhances the model’s sensitivity to object-level semantics, enabling better reconstruction even with limited or blurred training images. Furthermore, we introduce an unsupervised evaluation method leveraging semantic features and explicit 3D Gaussian properties to assess reconstruction quality, providing practical guidance in scenarios where ground truth is unavailable. Experiments on the Mip-NeRF360 dataset demonstrate CLIPGaussian’s superiority, particularly in low-quality image scenarios, validating both the training and evaluation approaches. This work paves the way for more robust and reliable 3D scene reconstruction from imperfect data. Our source code and pre-trained models are available at https://github.com/ZhChessOvO/CLIPGaussian .

Salient object detection (SOD) aims to identify the most prominent regions in images. However, the large model sizes, high computational costs, and slow inference speeds of existing RGB-D SOD models have hindered their deployment on real-world embedded devices. To address this issue, we propose a novel method named AirSOD, which is committed to lightweight RGB-D SOD. Specifically, we first design a hybrid feature extraction network, which includes the first three stages of MobileNetV2 and our Parallel Attention-Shift convolution (PAS) module. Using the novel PAS module enables capturing both long-range dependencies and local information to enhance the representation learning while significantly reducing the number of parameters and computational complexity. Secondly, we propose a Multi-level and Multi-modal feature Fusion (MMF) module to facilitate feature fusion, and a Multi-path enhancement for Feature Refinement (MFR) decoder for feature integration. The proposed method significantly reduces the model size by 63%, decreases the computational complexity by 43%, and improves the inference speed by 43% compared with the cutting-edge model (MobileSal). We test our AirSOD on six widely-used RGB-D SOD datasets. Extensive experimental results demonstrate that our method obtains satisfactory performance. The source codes will be made available.

Optical wireless communication (OWC) is considered to be a promising technology which will alleviate traffic burden caused by the increasing number of mobile devices. In this study, a novel vertical-cavity surface-emitting laser (VCSEL) array is proposed for indoor OWC systems. To activate the best beam for a mobile user, two beam activation methods are proposed for the system. The method based on a corner-cube retroreflector (CCR) provides very low latency and allows real-time activation for high-speed users. The other method uses the omnidirectional transmitter (ODTx). The ODTx can serve the purpose of uplink transmission and beam activation simultaneously. Moreover, systems with ODTx are very robust to the random orientation of a user equipment (UE). System level analyses are carried out for the proposed VCSEL array system. For a single user scenario, the probability density function (PDF) of the signal-to-noise ratio (SNR) for the central beam of the VCSEL array system can be approximated as a uniform distribution. In addition, the average data rate of the central beam and its upper bound are given analytically and verified by Monte-Carlo simulations. For a multi-user scenario, an analytical upper bound for the average data rate is given. The effects of the cell size and the full width at half maximum (FWHM) angle on the system performance are studied. The results show that the system with a FWHM angle of 4° outperforms the others.

The increasing number of mobile devices challenges the current radio frequency (RF) networks, e.g. wireless fidelity (WiFi) networks. Light Fidelity (LiFi) is considered as a promising complementary technology, which operates within the visible light spectrum and infrared spectrum. In an indoor scenario, a hybrid LiFi/WiFi network (HLWN) provides a potential solution to future wireless communications where LiFi augments WiFi in providing ultra-high speed and low latency wireless connectivity. In this paper, dynamic load balancing (LB) with handover in HLWNs is studied. The orientation-based random waypoint (ORWP) mobility model is considered to provide a more realistic framework to evaluate the performance of HLWNs. Based on the low-pass filtering effect of the LiFi channel, we firstly propose an orthogonal frequency division multiplexing access (OFDMA)-based resource allocation (RA) method in LiFi systems. Also, an enhanced evolutionary game theory (EGT)-based LB scheme with handover in HLWNs is proposed. In the EGT scheme, each user adapts their strategy to improve the payoff until LB is achieved across LiFi and WiFi. Then, the LiFi system uses the proposed OFDMA-based RA method while the WiFi system applies the carrier sense multiple access with collision detection (CSMA/CA). Simulation results show that in the LiFi system the OFDMA-based RA scheme outperforms the time division multiple access (TDMA) scheme in terms of both user data rate and fairness. Regarding LB in HLWNs, the proposed EGT scheme can achieve a remarkable enhancement in throughput compared to benchmark schemes, such as hard threshold (HT) scheme and random access point assignment (RAA) scheme.

As an emerging technology for wireless communications based on the visible light communication (VLC), Light-fidelity (LiFi) is considered as a complementary building block for fifth generation (5G) mobile networks. In this paper, we investigate the optimum field of view (FOV) for the angle diversity receiver (ADR) in LiFi cellular networks. The probability of visibility and the visible area of the ADR is defined. There is a trade off between the channel gain and visibility. An optimisation problem is formulated to maximise the channel gain, and the lower bound of the FOV of photodiodes (PDs) mounted on an ADR is derived. The simulation results show that the data rate is maximised when the FOV is minimised and the structure for the pyramid receiver (PR) achieving highest data rate is the one consisting of 6 PDs each with a FOV of 30 degree.

Sojourn time is an important parameter in the analysis and design of mobile cellular networks. It shows the expected time that a user equipment (UE) remains connected to the serving access point (AP) while moving in the network, i.e. stays within one cell. Therefore, an accurate estimation of the sojourn time is an essential element for mobility management. In this study, an analysis of the sojourn time for indoor light-fidelity (LiFi) cellular networks is presented based on the random waypoint (RWP) mobility model. It is initially assumed that the UE is oriented vertically upward and closed-form analytical expressions are derived. Monte-Carlo simulations are also provided to validate the analytical derivations and to gain more insight into the performance with different orientations. It is shown that the sojourn time mainly varies depending on the speed of the user. However, other factors such as the device orientation and the coverage area of cells also play a role in determining the sojourn time. The results offer several insights and design guidelines in consideration of this parameter for indoor LiFi networks.

Light-Fidelity (LiFi) has been considered as a complementary technology to radio frequency (RF) communications. The reliability of a LiFi channel highly depends on the availability and alignment of line-of-sight (LOS) links. In this study, we investigate the effect of receiver orientation including both polar and azimuth angles on the LOS channel gain in a LiFi system. The optimum tilt angle is calculated, which depends on both the user's location and direction. The probability density function (PDF) of signal-to-noise ratio (SNR) is derived for on-off keying (OOK) modulation. Using the derived PDF of SNR, the bit-error ratio (BER) of OOK in an additive-white Gaussian noise (AWGN) channel with random orientation of the receiver is evaluated. It is shown that the effect of random orientation is negligible if the optimum tilt angle is chosen. Finally, we assess the effect of random orientation on the Shannon-Hartley upper bound capacity.

Light Fidelity (LiFi) is an emerging technology for future high-speed indoor wireless communications. Co-channel interference (CCI) caused by the dense deployment of LiFi access points (APs) can be effectively mitigated by using angle diversity receivers (ADRs). ADRs require signal combining where the combining weights depend on the selection of serving APs. In this paper, the AP selection (APS) strategy considering handover is studied. A novel APS scheme based on evolutionary game theory (EGT) is proposed for the LiFi network using ADRs. The performance of the proposed scheme is comprehensively analysed and compared with the APS scheme based on signal strength strategy (SSS). The result shows that, in terms of ADRs with SBC/MRC, the EGT-based APS scheme achieves more than 5% improvement in quality of service (QoS) compared with the SSS-based APS scheme. With the sub-optimum weights of maximum ratio combining (MRC) for ADRs, the EGT-MRC scheme can achieve more than 20 Mbps data rate improvement compared with LiFi systems using single photodiode (PD) receiver.

Light-fidelity (LiFi) is a networked optical wireless communication (OWC) solution for high-speed indoor connectivity for fixed and mobile optical communications. Unlike conventional radio frequency wireless systems, the OWC channel is not isotropic, meaning that the device orientation affects the channel gain significantly, particularly for mobile users. However, due to the lack of a proper model for device orientation, many studies have assumed that the receiver is vertically upward and fixed. In this paper, a novel model for device orientation based on experimental measurements of 40 participants has been proposed. It is shown that the probability density function (PDF) of the polar angle can be modeled either based on a Laplace (for static users) or a Gaussian (for mobile users) distribution. In addition, a closed-form expression is obtained for the PDF of the cosine of the incidence angle based on which the line-of-sight (LOS) channel gain is described in OWC channels. An approximation of this PDF based on the truncated Laplace is proposed and the accuracy of this approximation is confirmed by the Kolmogorov-Smirnov distance. Moreover, the statistics of the LOS channel gain are calculated and the random orientation of a user equipment (UE) is modeled as a random process. The influence of the random orientation on signal-to-noise-ratio performance of OWC systems has been evaluated. Finally, an orientation-based random waypoint (ORWP) mobility model is proposed by considering the random orientation of the UE during the user's movement. The performance of ORWP is assessed on the handover rate and it is shown that it is important to take the random orientation into account.

Visible light communications (VLC) and Millimetre wave (mmWave) systems are two emerging technologies for short-range mobile communications. One of the limiting factors of both VLC and mmWave systems is the random orientation of mobile devices that can significantly affect the channel gain in both systems. Since there is no proper model for device orientation, many studies have assumed that device orientation is fixed or modelled as a uniform distribution. To address this issue, an experimental study of mobile user behaviour is conducted and a statistical orientation model is proposed in this paper. The results show that the probability density function (PDF) of the elevation angle follows a Laplace distribution. Based on the statistical orientation model, Monte-Carlo simulations are carried out to analyse the system performance of VLC and mmWave systems. The statistics of signal-to-noise-ratio (SNR) are compared with the experiment-based simulation results in both VLC and mmWave systems.

Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions despite the fact that deliberate behaviour differs in visual appearance, audio profile, and timing from spontaneously occurring behaviour. To address this problem, efforts to develop algorithms that can process naturally occurring human affective behaviour have recently emerged. Moreover, an increasing number of efforts are reported toward multimodal fusion for human affect analysis including audiovisual fusion, linguistic and paralinguistic fusion, and multi-cue visual fusion based on facial expressions, head movements, and body gestures. This paper introduces and surveys these recent advances. We first discuss human emotion perception from a psychological perspective. Next we examine available approaches to solving the problem of machine understanding of human affective behavior, and discuss important issues like the collection and availability of training and test data. We finally outline some of the scientific and engineering challenges to advancing human affect sensing technology.

The ability to handle multi-view facial expressions is important for computers to understand affective behavior under less constrained environment. However, most of existing methods for facial expression recognition are based on the near-frontal view face data, which are likely to fail in the non-frontal facial expression analysis. In this paper, we conduct an investigation on analyzing multi-view facial expressions. Three local patch descriptors (HoG, LBP, and SIFT) are used to extract facial features, which are the inputs to a nearest-neighbor indexing method that identifies facial expressions. We also investigate the influence of feature dimension reductions (PCA, LDA, and LPP) and classifier fusion on the recognition performance. We test our approaches on multi-view data generated from BU-3DFE 3D facial expression database that includes 100 subjects with 6 emotions and 4 intensity levels. Our extensive person-independent experiments suggest that the SIFT descriptor outperforms HoG and LBP, and LPP outperforms PCA and LDA in this application. But the classifier fusion does not show a significant advantage over SIFT-only classifier.

The existing methods of facial expression recognition are typically based on the near-frontal face data. The analysis of non-frontal-view facial expression is a largely unexplored research. The accessibility to a recent 3D facial expression database (BU-3DFE database) motivates us to explore an interesting question: whether non-frontal-view facial expression analysis can achieve the same as or better performance than the existing frontal-view facial expression method. Our extensive recognition experiments on data of 100 subjects with 5 yaw rotation view angles suggests that the non-frontal-view facial expression classification can outperform frontal-view facial expression recognition, given the manually labeled facial key points.

Advances in computer processing power and emerging algorithms are allowing new ways of envisioning human-computer interaction. Although the benefit of audio-visual fusion is expected for affect recognition from the psychological and engineering perspectives, most of existing approaches to automatic human affect analysis are unimodal: information processed by computer system is limited to either face images or the speech signals. This paper focuses on the development of a computing algorithm that uses both audio and visual sensors to detect and track a user's affective state to aid computer decision making. Using our multistream fused hidden Markov model (MFHMM), we analyzed coupled audio and visual streams to detect four cognitive states (interest, boredom, frustration and puzzlement) and seven prototypical emotions (neural, happiness, sadness, anger, disgust, fear and surprise). The MFHMM allows the building of an optimal connection among multiple streams according to the maximum entropy principle and the maximum mutual information criterion. Person-independent experimental results from 20 subjects in 660 sequences show that the MFHMM approach outperforms face-only HMM, pitch-only HMM, energy-only HMM, and independent HMM fusion, under clean and varying audio channel noise condition.

Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. Promising approaches have been reported, including automatic methods for facial and vocal affect recognition. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions-despite the fact that deliberate behavior differs in visual and audio expressions from spontaneously occurring behavior. Recently efforts to develop algorithms that can process naturally occurring human affective behavior have emerged. This paper surveys these efforts. We first discuss human emotion perception from a psychological perspective. Next, we examine the available approaches to solving the problem of machine understanding of human affective behavior occurring in real-world settings. We finally outline some scientific and engineering challenges for advancing human affect sensing technology.

The ability of a computer to detect and appropriately respond to changes in a user's affective state has significant implications to human-computer interaction (HCI). In this paper, we present our efforts toward audio-visual affect recognition on 11 affective states customized for HCI application (four cognitive/motivational and seven basic affective states) of 20 nonactor subjects. A smoothing method is proposed to reduce the detrimental influence of speech on facial expression recognition. The feature selection analysis shows that subjects are prone to use brow movement in face, pitch and energy in prosody to express their affects while speaking. For person-dependent recognition, we apply the voting method to combine the frame-based classification results from both audio and visual channels. The result shows 7.5% improvement over the best unimodal performance. For person-independent test, we apply multistream HMM to combine the information from multiple component streams. This test shows 6.1% improvement over the best component performance

Automatic multimodal recognition of spontaneous affective expressions is a largely unexplored and challenging problem. In this paper, we explore audio-visual emotion recognition in a realistic human conversation setting - Adult Attachment Interview (AAI). Based on the assumption that facial expression and vocal expression be at the same coarse affective states, positive and negative emotion sequences are labeled according to Facial Action Coding System Emotion Codes. Facial texture in visual channel and prosody in audio channel are integrated in the framework of Adaboost multi-stream hidden Markov model (AMHMM) in which Adaboost learning scheme is used to build component HMM fusion. Our approach is evaluated in the preliminary AAI spontaneous emotion recognition experiments.

To simulate the human ability to assess affects, an automatic affect recognition system should make use of multi-sensor information. In the framework of multi-stream fused hidden Markov model (MFHMM), we present a training combination strategy towards audio-visual affect recognition. Different from the weighting combination scheme, our approach is able to use a variety of learning methods to obtain a robust multi-stream fusion result. We evaluate our approach in personal-independent recognition of 11 affective states from 20 subjects. The experimental results suggest that MFHMM outperforms IHMM which assumes the independence among streams, and the training combination strategy has the superiority over the weighting combination under clean and varying audio channel noise condition.

Advances in computer processing power and emerging algorithms are allowing new ways of envisioning human computer interaction. This paper focuses on the development of a computing algorithm that uses audio and visual sensors to detect and track a user's affective state to aid computer decision making. Using our multi-stream fused hidden Markov model (MFHMM), we analyzed coupled audio and visual streams to detect 11 cognitive/emotive states. The MFHMM allows the building of an optimal connection among multiple streams according to the maximum entropy principle and the maximum mutual information criterion. Person-independent experimental results from 20 subjects in 660 sequences show that the MFHMM approach performs with an accuracy of 80.61% which outperforms face-only HMM, pitch-only HMM, energy-only HMM, and independent HMM fusion.

The ability of a computer to detect and appropriately respond to changes in a user's affective state has significant implications to human-computer interaction (HCI). To more accurately simulate the human ability to assess affects through multi-sensory data, automatic affect recognition should also make use of multimodal data. In this paper, we present our efforts toward audio-visual affect recognition. Based on psychological research, we have chosen affect categories based on an activation-evaluation space which is robust in capturing significant aspects of emotion. We apply the Fisher boosting learning algorithm which can build a strong classifier by combining a small set of weak classification functions. Our experimental results show with 30 Fisher features, the testing error rates of our bimodal affect recognition is about 16% on the evaluation axis and 13% on the activation axis.

Perhaps the most fundamental application of affective computing will be Human-Computer Interaction (HCI) in which the computer should have the ability to detect and track the user's affective states, and make corresponding feedback. The human multi-sensor affect system defines the expectation of multimodal affect analyzer. In this paper, we present our efforts toward audio-visual HCI-related affect recognition. With HCI applications in mind, we take into account some special affective states which indicate users' cognitive/motivational states. Facing the fact that a facial expression is influenced by both an affective state and speech content, we apply a smoothing method to extract the information of the affective state from facial features. In our fusion stage, a voting method is applied to combine audio and visual modalities so that the final affect recognition accuracy is greatly improved. We test our bimodal affect recognition approach on 38 subjects with 11 HCI-related affect states. The extensive experimental results show that the average person-dependent affect recognition accuracy is almost 90% for our bimodal fusion.

This paper presents a new, compact, canonical graph-based representation, called Taylor expansion diagrams (TEDs). It is based on a general non-binary decomposition principle using Taylor series expansion. It can be exploited to facilitate the verification of high-level (RTL) design descriptions. We present the theory behind TEDs, comment upon its canonicity property and demonstrate that the representation has linear space complexity. Its application to equivalence checking of high-level design descriptions is discussed.

LPSAT is an LP-based comprehensive infrastructure designed to solve the satisfiability (SAT) problem for complex RTL designs containing both word-level arithmetic operators and bit-level Boolean logic. The presented technique uses a mixed integer linear program to model the constraints corresponding to both domains of the design. Our technique renders the constraint propagation between the two domains implicit to the MILP solver thus enhancing the overall efficiency of the SAT framework. The experimental results are quite promising when compared with generic CNF-based and BDD-based SAT algorithms.

Binary Decision Diagrams have been widely used to solve the Boolean satisfiability (SAT) problem. The individual constraints can be represented using BDDs and the conjunction of all constraints provides all satisfying solutions. However, BDD-related SAT techniques suffer from size explosion problems. This paper presents two BDD-based algorithms to solve the SAT problem that attempt to contain the growth of BDD-size while identifying solutions quickly. The first algorithm, called BSAT, is a recursive, backtracking algorithm that uses an exhaustive search to find a SAT solution. The well known unate recursive paradigm is exploited to solve the SAT problem. The second algorithm is exploited to solve the SAT problem. The second algorithm, called INCOMPLETE-SEARCH-USAT (abbreviated IS-USAT), incorporates an incomplete search to find a solution. The search is incomplete inasmuch as it is restricted to only those regions that have a high likelihood of containing the solution, discarding the rest. Using our techniques we were able to find SAT solutions not only for all MCNC&ISCAS benchmarks, but also for a variety of industry standard designs.