In this paper, we present a novel small-scale fading model, named the extended k-u model, which incorporates the imbalance of multipath clusters by adding a new parameter based on the original k-u model. The extended k-u model has more accurate modeling capability than the extended η-u model in scenarios with line-of-sight (LoS) paths. Additionally, it is mathematically more tractable than the a-k-η-u model. The extended k-u model provides an effective channel modeling tool for millimeter (mmWave) LoS scenarios. Through theoretical derivations, we obtain closed-form expressions for the key statistical characteristics of this model, including the probability density function, the cumulative distribution function, moments of arbitrary order, and the moment generating function. Based on these statistics, this study further derives and analyzes the expressions for some performance metrics of the communication system, including the amount of fading, the probability of outage, the average bit error rate, and the effective rate. Using the measured fading data extracted from literature, which cover communication scenarios at 28 GHz, 65 GHz, and 92.5645 GHz with LoS paths, we apply the proposed model in mmWave scenarios and compare it with the k-u model and the extended η-u model. The results show that the extended k-u model has better capability in characterizing such fading than the other two models, verifying that this extension enhances its ability to model LoS mmWave scenarios.

MOTIVATION: Multiomics data analysis is essential for scientific discovery in precision medicine. However, translating analysis results of omics data analysis into novel scientific hypotheses remains a significant challenge. Human experts must manually review analysis results and generate new hypotheses based on extensive and interconnected biomedical prior knowledge, which is subjective and not scalable. While large language models can accelerate the discovery, their reasoning improves when grounded in structured, auditable, and comprehensive biomedical prior knowledge. However, biomedical knowledge is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of artificial intelligence systems to fully leverage biomedical data for scientific discovery. RESULTS: We developed BioMedGraphica, a novel all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified textual prior knowledge graph containing 2 306 921 entities and 27 232 091 relations. In addition, we present a novel textual-numeric graph (TNG) data structure concept, where textual information captures prior biological knowledge (e.g. transcription start sites, functions, mechanisms), numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data structure for developing novel graph analysis models. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph database can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

Teachers use visual and verbal cues in instructional videos to guide attention and support learning. While previous studies have examined different cueing designs, the effects of combining visual and verbal cues (dual-modality cues) on learning are still unclear. This study investigated whether verbal cues characterized by prosodic elements, including pitch, pauses, and stress, could diminish the benefits of visual cues emphasized by color and bold text. Using a 2 (visual cues: without vs. with) × 2 (verbal cues: without vs. with) between-subjects experimental design, we collected eye-tracking data from 128 undergraduate participants. The results showed that the dual-modality cues resulted in shorter fixation durations and frequencies during text processing, increased extraneous load, and decreased germane load, leading to poorer learning performance. These findings partially confirm the hypothesis concerning the interference effect between cue modalities and highlight the advantages of a single-modal cue for video learning.

Statistical timing characterization for standard cell library poses significant challenges to accuracy and runtime cost. Prior analytical and learning-based methods neglect the profound influence induced by the layout-dependent parasitic resistor and capacitor (RC) network in cell netlist as well as the timing correlation between the topological structures of cells and process, voltage, and temperature (PVT) corners for model training, resulting in tremendous simulation effort and poor accuracy. In this work, a Statistical timing Characterization framework via Heterogeneous graph attention network and Active learning with parasitic RC Reduction (StatCHAR) is proposed, where the transistors and parasitic RC in cell are represented as heterogeneous nodes for graph learning and redundant RC nodes are removed to alleviate node imbalance issue and improve accuracy. The significant training data are selected from the full characterization set with active learning strategy to achieve the optimal balance between simulation overhead for the training set and prediction precision for the remaining test set. The proposed framework was validated with typical standard cells under multiple PVT corners with TSMC 22nm process, which achieves an excellent prediction with a relative Root Mean Square Error (rRMSE) of only 2.43% with only 11.9% of total characterization data for training, demonstrating an accuracy improvement of 2.7$\times \sim 12.1\times $for statical timing analysis on benchmark circuits compared to competitive learning based methods and a characterization runtime reduction by 7.5$\times $.

Predicting photovoltaic (PV) generation is essential for ensuring grid reliability, optimizing energy allocation, and promoting a green transition in the global energy structure. Previous PV power prediction models only focus on the temporal relationships within exogenous sequences (such as temperature, cloud cover, and humidity) or endogenous sequences (i.e., PV generation), neglecting the impact of exogenous factors on the vulnerable PV generation process. However, the complexity of the physical environment makes it challenging to accurately model the interaction between the two types of variables. To address this issue, we propose a PV generation forecasting network with dynamic-static correlation fusion between endogenous and exogenous variables to improve prediction accuracy. Specifically, we, respectively, encode the exogenous and endogenous variables into static and dynamic components, and accordingly design static and dynamic networks to learn the complex environment, fully capturing the impact of different exogenous components on PV generation. Subsequently, we integrate the dynamic-static enhanced representations through a dual network, achieving lightweight representation prediction. Our model significantly improves PV generation prediction accuracy, achieving state-of-the-art (SOTA) results across four real-world datasets, with average reductions of 12.87% in mean squared error (MSE) and 13.89% in mean absolute error (MAE) compared with competitive baselines. This advancement significantly enhances PV generation forecasting and fosters global sustainability.

In this paper, we propose a novel integrated sensing and communication (ISAC) scheme tailored for UAVs-enabled vehicular networks, which leverages the information coverage capabilities of multiple UAVs and addresses critical challenges posed by multiple moving users. Unlike many traditional scheme, our scheme efficiently leverages ISAC signal echoes and real-time data uploads to provide communication services while achieving accurate sensing, thereby overcoming issues of resource waste and low operational efficiency. In the scheme, we aim to optimize both communication and sensing indicators, taking into account practical issues such as energy saving and collision avoidance for UAVs. However, the inherent complexity of multi-objective stochastic optimization in dynamic environments and limited communication resources render centralized UAV control inconvenient. To address the above challenges, we propose a novel multi-agent reinforcement learning (MARL) algorithm based on local information to realize the distributed adaptive control of motion decision, power selection, and channel allocation for UAVs. The algorithm combines random network distillation (RND) and dynamic data augmentation with multi-agent deep deterministic policy gradient (MADDPG) to encourage agents to explore effectively under sparse rewards and improve MADDPG's policy learning ability in finite data, thus approaching the global optimal solution. Experimental results demonstrate that the proposed algorithm can improve communication and sensing performance by more than 16.71% and 68.26% compared with other baselines and satisfy the set constraints. Furthermore, by adjusting hyperparameters, we can optimize the ISAC performance while achieving different energy savings levels for UAVs, proving that the designed scheme can reduce the waste of resources and improve the ISAC operation efficiency.

To overcome the performance limitations and computational complexity induced by the unimodular constraint in conventional hybrid precoding designs, this paper proposes a novel optoelectronic base station (OE-BS) architecture, replacing the traditional phase shifter network with an optical network. The proposed OE-BS architecture facilitates simultaneous amplitude and phase control in the analog domain, effectively eliminating the unimodular constraint and significantly enhancing the design flexibility. Based on the proposed OE-BS architecture, a closed-form hybrid precoding solution is first derived for narrowband systems, thus avoiding iterative optimization procedures. For wideband systems, a user-selective hybrid precoding algorithm is developed, where the digital precoder is designed according to the zero-forcing criterion, and the analog precoder and power allocation are jointly optimized using an alternating optimization method. Simulation results demonstrate that the proposed OE-BS schemes outperform conventional methods in both narrowband and wideband scenarios, enhancing overall system performance while reducing computational complexity.

In this paper, we propose an innovative relay-aided physical layer security scheme leveraging cooperative transmission and jamming that operates effectively in time-division-multiple-access-based multi-user visible light communication (MuVLC) systems with obstructed communication links. First, to circumvent obstacles between the light source (LS) and legal receivers, we utilize a transmission relay (TR) to forward information signals from the LS to the receiver plane. Subsequently, to ensure communication security, we select a jamming relay to enlarge the channel capacity gap between eavesdroppers and legal receivers through cooperative jamming. Using stochastic geometry, we derive closed-form expressions for key performance metrics, including the cumulative distribution function of secrecy capacity, security outage probability, and their lower and upper bounds, both with and without relay cooperation. To further enhance system security, we introduce a disk-shaped security-protected zone around the TR. All analytical expressions are numerically verified through Monte Carlo simulations, serving as benchmarks to evaluate security performance. Numerical results confirm that our proposed scheme can ensure continuous signal reception and high-level security. Furthermore, while relay cooperation modestly enhances system security, the introduction of a security-protected zone around the TR yields substantial security improvement, which offers practical insights for designing secure time-division-multiple-access-based MuVLC where communication links are obstructed.

Unmanned Aerial Vehicles (UAVs) assisted Visible Light Communication (VLC) systems offer a promising solution for high-speed data transmission and illumination in applications such as traffic offloading and post-disaster rescue. This work presents a framework for dynamic trajectory planning in a UAV-assisted VLC system. We cast this dynamic trajectory planning as a multi-objective Partially Observable Markov Decision Process (POMDP), in which each UAV must (i) maximize its cumulative VLC capacity at the service points, (ii) minimize the risk of collisions while navigating a 3D environment with stochastically placed obstacles and serving ground terminals, and (iii) minimize the energy consumption. To solve this POMDP, we introduce a Deep Recurrent Q-Network (DRQN) to aggregate temporal dependencies. The resulting policy enables UAVs to navigate complex 3-D environments more safely and with higher communication capacity than memory-less Deep Q-Network (DQN) or simulated annealing (SA) baselines in both single- and multi-agent settings.

We propose an alternating optimization (AO) approach to enhance the performance of energy-efficient multi-user visible light communication (MU-VLC) systems, which integrates both lighting and communication functions using multi-input multi-output (MIMO) technology and sub-array architecture. The rate-splitting multiple access (RSMA) technique is used to fully exploit the VLC channel as well as flexibly handle the interference and noise. Numerical results exhibit the superiority of the proposed scheme over the minimum mean squared error (MMSE) and successive interference cancellation (SIC) methods under various user quality of service (QoS) requirements. Moreover, the energy efficiency (EE) achieved by the proposed scheme surpasses that of existing VLC MIMO systems, highlighting its potential for green communication applications.

In underwater wireless optical communications (UWOC), channel model is crucial for system design and performance evaluation. The absorption and scattering of seawater may lead to energy loss and direction change of photons, introducing dispersion in both time and space domains, which complicates channel modeling. There have been many studies on single domain channel modeling, but few consider the joint modeling of time and space domains, especially in multiple-input multiple-output (MIMO) scenarios. In this paper, we consider UWOC MIMO links and propose a weighted delay exponential polynomial (WDEP) to model the joint spatial-temporal response of general UWOC MIMO links with arbitrary numbers of light sources and receivers. Numerical results show that the proposed WDEP model fits well with the Monte Carlo simulation results of UWOC MIMO links in turbid water environments including harbor water and coastal water.

The rapid proliferation of Internet of Things (IoT) is generating an unprecedented volume of distributed data, necessitating efficient decentralized learning paradigms. Federated learning (FL) has emerged as a compelling distributed collaborative intelligence framework, renowned for its privacy protection benefits. However, the communication overhead associated with intermediate model exchanges remains a critical bottleneck in FL. Aiming at reducing the communication cost of FL equipped with promising over-the-air computation (AirComp) technique, this work designs specialized model compression schemes for both uplink and downlink communications. For uplink transmission with AirComp, we analyze its unique constraints and propose a hybrid global sparsification scheme that combines the benefits of conventional Top-k and Rand-k algorithms. We further develop an algorithm to strategically allocate transmission budgets between the two concatenated sparsification operations, accounting for both model temporal correlation and the cost of index synchronization. For downlink transmission, we introduce a group-based mixed-precision quantization (MPQ) scheme and integrates the broadcast of grouping information with uplink sparsification pattern to further mitigate communication burden. Moreover, we conduct theoretical analysis under realistic channel conditions and typical FL settings to validate the advantages and establish convergence guarantees of our approaches. Experimental results demonstrate that, compared to existing schemes, the proposed methods significantly improve communication efficiency and ensure client scalability, and concurrently verify the benefits of the uplink-downlink synergistic design.

Graph neural networks (GNNs) have attracted significant interest recently since they can effectively process and analyze graph-structured data commonly found in real-world applications. However, the predicament that GNNs are difficult to train becomes worse as the layers increase. The essence of this problem is that stacking layers will reduce the stability of forward propagation and gradient back-propagation. And as the increasing scale of models (measured by the number of parameters), how to efficiently and effectively adapt it to particular downstream tasks becomes an intriguing research issue. In this work, motivated by the effect of orthogonality constraints, we propose a simple orthogonal training framework to impose the orthogonality constraints on GNNs, which can help models find a solution vector in a specific low dimensional subspace and stabilize the signaling processes at both the forward and backward directions. Specifically, we propose a novel polar decomposition-based orthogonal initialization (PDOI-R) algorithm, which can identify the low intrinsic dimension within the Stiefel Manifold and stabilize the training process. Extensive experiments demonstrate the effectiveness of the proposed method in multiple downstream tasks, showcasing its generality. The simple method can help existing state-of-the-art models achieve better performance.

Image/video denoising in low-light scenes is an extremely challenging problem due to limited photon count and high noise. In this paper, we propose a novel approach with contrastive learning to address this issue. Inspired by the success of contrastive learning used in some high-level computer vision tasks, we bring in this idea to the low-level denoising task. In order to achieve this goal, we introduce a new denoising contrastive regularization (DCR) to exploit the information of noisy images and clean images. In the feature space, DCR makes the denoised image closer to the clean image and far away from the noisy image. In addition, we build a new feature embedding network called Wnet, which is more effective to extract high-frequency information. We conduct the experiments on a real low-light dataset that captures still images taken on a moonless clear night in 0.6 millilux and videos under starlight (no moon present). The results show that our method can achieve a higher PSNR and better visual quality compared with existing methods.

Paving the way toward next-generation communications, reconfigurable intelligent surface (RIS) based free-space optical (FSO) links have emerged as a strong candidate in the unlicensed spectrum for enhanced capacity and signal quality. However, existing modulation schemes constrain the FSO links’ performance and are unsuitable for RIS-induced multipath fading. To address the issue, we firstly introduce the optical orthogonal time frequency space (O-OTFS) modulation scheme into such links, with orbital angular momentum (OAM) optical beams combined to enhance the performance. Moreover, we propose two combination systems with separate and integrated OAM-OTFS respectively, and further propose a novel quad-mode (QM)-OAM-OTFS scheme within the integrated system, demonstrating significant performance enhancement over counterparts. Numerical results indicate that the separate OAM-OTFS system enhances performance in terms of power and spectral efficiencies but reduces system robustness to turbulence with increasing OAM modes, the proposed QM-OAM-OTFS scheme effectively mitigates atmospheric turbulence effects and achieves efficient power and spectral utilization.

Integrated sensing and communication (ISAC) is a key technology of next generation wireless communication. Backscatter communication (BackCom) plays an important role for internet of things (IoT). Then the integration of ISAC with BackCom technology enables low-power data transmission while enhancing the system sensing ability, which is expected to provide a potentially revolutionary solution for IoT applications. In this paper, we propose a novel backscatter-ISAC (B-ISAC) system and focus on the joint beamforming design for the system. We formulate the communication and sensing model of the B-ISAC system and derive the metrics of communication and sensing performance respectively, i.e., communication rate and detection probability. We propose a joint beamforming scheme aiming to optimize the communication rate under sensing constraint and power budget. A successive convex approximation (SCA) based algorithm and an iterative algorithm are developed for solving the complicated non-convex optimization problem. Numerical results validate the effectiveness of the proposed scheme and associated algorithms. The proposed B-ISAC system has broad application prospect in IoT scenarios.

In data driven deep learning, distributed sensing and joint computing bring heavy load for computing and communication. To face the challenge, over-the-air computation (OAC) has been proposed for multi-sensor data aggregation, which enables the server to receive a desired function of massive sensing data during communication. However, the strict synchronization and accurate channel estimation constraints in OAC are hard to be satisfied in practice, leading to time and channel-gain misalignment. The paper formulates the misalignment problem as a non-blind image deblurring problem. At the receiver side, we first use the Wiener filter to deblur, followed by a U-Net network designed for further denoising. Our method is capable to exploit the inherent correlations in the signal data via learning, thus outperforms traditional methods in term of accuracy. Our code is available at https://github.com/auto-Dog/MOAC_deep.

More recently, unmanned aerial vehicle (UAV)-assisted full-duplex (FD) communication has been considered to enhance system capacity and reliability. Nevertheless, traditional UAV-assisted systems based on radio frequency (RF) face certain challenges, including limited bandwidth and inability to operate in RF-denied areas. In fact, the optical wireless communication (OWC) approach can effectively resolve the aforementioned issues, which is rarely employed in UAV-assisted systems. In this paper, we develop a UAV-assisted FD OWC system utilizing light-emitting diodes (LEDs) and vertical-cavity surface-emitting laser (VCSEL) arrays for downlink (DL) and uplink (UL) light sources to facilitate communications of ground users (GUs). To effectively allocate resources for GUs with different priorities, we introduce a priority-focused UAV trajectory planning approach for this system and apply deep reinforcement learning (DRL) techniques to determine UAV flight trajectories, thereby enhancing the DL communication capacity for each GU. Numerical results demonstrate that the proposed system improves the DL communication capacity of each GU, and successfully achieves the intended tilting of communication resources with the introduced priority sets.

Mobile edge computing (MEC) is an efficient method to tackle computationally intensive tasks for mobile devices (MDs). However, current studies about MEC do not consider that different MDs have different preferences for delay and energy consumption. Thus, we propose a MD preference-based MEC computing model addressing three optimization goals: delay preference, energy preference, and their balance, tailored to diverse MD preference. This optimization problem is formulated as a mixed integer non-linear programming (MINLP) task with four optimization variables: offloading decisions, channel allocation, power allocation, and resource allocation. Additionally, we propose a novel genetic artificial fish swarm cooperative optimization algorithm (GAFSCOA) to solve this problem, which integrates genetic algorithm (GA) and artificial fish swarm algorithm (AFSA), respectively. Numerical results show our proposed model can achieve different optimization goals according to MDs’ preferences. Compared with GA and AFSA, our proposed GAFSCOA demonstrates 30.71% faster convergence speed and 46.09% better optimization results. Furthermore, in comparison with other baseline algorithms, our algorithm yields superior optimization results.

In this paper, we apply non-orthogonal multiple access (NOMA) to unmanned aerial vehicle (UAV)-assisted visible light communication (VLC) systems, to accommodate the demand of multiple ground users. Specifically, we jointly optimize the trajectory planning of UAV, the grouping of users, and the allocation of power, to maximize the total rate of users. However, it is hard to solve this non-convexity and NP-hard problem by traditional optimization algorithms. Moreover, it is also difficult for deep reinforcement learning (DRL) algorithm to train due to the limitation of single-type action space. Therefore, we adopt a hybrid action space DRL (HA-DRL) algorithm to solve the problem, designing the grouping of users as discrete action, and the displacement and power allocation of UAV as continuous action. Numerical results show that the proposed algorithm can significantly improve the system performance by exploiting the user grouping operation in these UAV-assisted NOMA-VLC systems, and can achieve faster convergence and better performance than traditional single-type action DRL algorithms.

The up-to-date project authorization request (PAR) for IEEE 802.11 bn envisions achieved Ultra High Reliability capability on the basis of the Extremely High Throughput Wi-Fi (IEEE 802.11 be). Specifically, it calls for optimizing the 95th percentile of the latency distribution and MAC Protocol Data Unit (MPDU) loss while ensuring high throughput. The vision is challenging due to the competing nature of Wi-Fi channel access, especially in the case of overlapping basic service set (OBSS). This challenge gives rise to an emerging research topic of Wi-Fi, i.e., low-latency channel access. In this paper, we materialize low-latency channel access via multi-agent reinforcement learning (MARL). To meet the Wi-Fi legacy requirement, we do not drop the carrier sense multiple access with collision avoidance (CSMA/CA) protocol but resolve to tune contention window (CW) being intelligent, a critical control parameter of the protocol. The objective of intelligent adapting CW is to minimize tail latency constrained by throughput and MPDU loss, which is consistent with the PAR. The control optimization problem is then solved by MARL. The adopted method promises effectiveness in distributed learning, which is validated in many simulations covering different topologies. Extensive simulation results show that this method has an average substantive gain of more than 25%.

Most conventional optical OFDM (O-OFDM) schemes suffer from low spectral efficiency due to the interference introduced by time-domain signal processing. In this paper, we propose a novel design principle for layered O-OFDM schemes for intensity modulation with direct detection (IM/DD) optical wireless communication (OWC) to fully exploit the spectral resources. All layered O-OFDM schemes are designed to meet the constraint that the interference introduced by time-domain signal processing at each layer is designed mutually orthogonal to the frequency-domain signals at the current and lower layers. To satisfy this constraint, we discuss two approaches for time-domain signal processing and corresponding spectrum resource allocation, and present the subsequent transceiver design for the proposed layered O-OFDM schemes. The proposed layered O-OFDM schemes exhibit high spectral efficiencies, and numerical results demonstrate their low bit error rates (BERs) and high power efficiencies. Furthermore, the high flexibility of these schemes guided by the proposed design principle enables them to meet a wide range of diverse communication requirements.

Deep learning (DL) based methods have significantly pushed forward the state-of-the-art for image restoration (IR) task. Nevertheless, DL-based IR models are highly computation- and memory-intensive. The surging demands for processing higher-resolution images and multi-task paralleling in practical mobile usage further add to their computation and memory burdens. In this paper, we reveal the overlooked memory redundancy of the IR models and propose a Memory-Oriented Structural Pruning (MOSP) method. To properly compress the long-range skip connections (a major source of the memory burden), we introduce a compactor module onto each skip connection to decouple the pruning of the skip connections and the main branch. MOSP progressively prunes the original model layers and the compactors to cut down the peak memory while maintaining high IR quality. Experiments on real image denoising, image super-resolution and low-light image enhancement show that MOSP can yield models with higher memory efficiency while better preserving performance compared with baseline pruning methods.

The orthogonality constraints, including the hard and soft ones, have been used to normalize the weight matrices of Deep Neural Network (DNN) models, especially the Convolutional Neural Network (CNN) and Vision Transformer (ViT), to reduce model parameter redundancy and improve training stability. However, the robustness to noisy data of these models with constraints is not always satisfactory. In this work, we propose a novel two-stage approximately orthogonal training framework (TAOTF) to find a trade-off between the orthogonal solution space and the main task solution space to solve this problem in noisy data scenarios. In the first stage, we propose a novel algorithm called polar decomposition-based orthogonal initialization (PDOI) to find a good initialization for the orthogonal optimization. In the second stage, unlike other existing methods, we apply soft orthogonal constraints for all layers of DNN model. We evaluate the proposed model-agnostic framework both on the natural image and medical image datasets, which show that our method achieves stable and superior performances to existing methods. Supplementary materials can be found in https://github.com/nonameinformation/anonymous/tree/main.

In cognitive radio (CR), compressive spectrum sensing (CSS) has drawn much attention since it enjoys decent performance and facilitates fast implementation. Due to the spectrum's inherent block sparsity and the joint spectrum sampling, CSS is further modeled as a block multiple measurement vector (BMMV) problem, which can be solved by joint block greedy-iterative algorithms such as block simultaneous orthogonal matching pursuit (BSOMP). However, the feasibility of such methods are shadowed by their inflexible sampling rates and dependence on accurate sparsity information. To address this issue, this paper proposes an adaptive-blind block simultaneous orthogonal matching pursuit (AB-BSOMP) algorithm based on the BMMV model. The blind halting criterion for BSOMP is first derived, allowing spectrum recovery to be independent of a priori sparsity information. Furthermore, to guarantee the reliable and adaptive spectrum recovery, a sampling-controlled algorithm (SCA) is developed to calculate an optimal number of measurements dynamically. Finally, AB-BSOMP is proposed by combining the developed blind halting criterion and the SCA. Simulation results demonstrate that the elaborating algorithm performs reliable recovery under various signal-to-noise ratios (SNRs), and reduces computational complexity in high SNR conditions while maintaining exact detection.

We consider DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) low-pass visible light communication (VLC) systems in which water-filling and uniform power loading strategies have almost the same rate performance. However, conventional bit loading based on either water-filling or uniform power loading cannot reach the maximum achievable rate because the number of bits loaded per subcarrier can only be integers. In this paper, we apply pairwise coding (PWC) to solve this problem by performing joint coding on pairs of subcarriers to balance their symbol error rate (SER) performance. We present a closed-form expression for the optimal parameter of PWC and an approximation of its theoretical performance, and propose an enhanced bit loading scheme with PWC to improve the system rate. Numerical results suggest that the enhanced scheme has a significant rate improvement and approaches the maximum achievable rate for moderate to high signal-to-noise ratio (SNR).

Automatic segmentation of retinal vessels is a critical task in fundoscopic image analysis. The emergence of deep learning has shown promising abilities of feature representation, particularly with Convolutional Neural Networks (CNNs). However, the fixed receptive field in CNNs limits their ability to adapt to the scale variation of natural vascular networks and capture nonlocal context dependencies across feature maps. To address these limitations, we propose a novel model called ARA-GAN that can adaptively extract nonlocal feature contexts and aggregate multi-scale information for retinal vessel segmentation. The proposed model comprises a novel Generative Adversarial Network (GAN) as the overall framework to obtain global information and strong robustness. Additionally, we integrate Residual Nonlocal Attention (RNA) Module into the framework to adaptively capture nonlocal context dependencies across the input features. Finally, we add a Pyramid Pooling Module (PPM) to extract the morphological characteristics of natural retinal vessels at multiple scales. Our experimental results demonstrate that our method outperforms state-of-the-art approaches on both the DRIVE and STARE datasets.

Federated learning (FL) is a promising solution to harness the advances of machine learning under the premise of privacy security, whereas the communication overhead of model exchange remains an obstacle to deploying FL in wireless networks. To tackle this challenge, we consider the non-uniform quantization of the global model in this work. By formulating the optimization of quantization intervals as a Markov decision process (MDP), we propose a deep reinforcement learning (DRL)- based approach to improve the performance of the quantizer for FL. Through crafting a compound reward function, the DRL agent is guided to reduce the quantization error and training loss simultaneously. Furthermore, a dual time-scale mechanism between FL and DRL is adopted to ensure that the actor and critic models of DRL converge more steadily. Simulations on various real-world datasets reveal that the proposed method can provide higher accuracy and faster convergence than the existing uniform quantizers, and can retain these benefits when applying the learned policy to a similar learning task.

In this paper, we present Point Cloud Color Constancy, in short PCCC, an illumination chromaticity estimation algorithm exploiting a point cloud. We leverage the depth information captured by the time-of-flight (ToF) sensor mounted rigidly with the RGB sensor, and form a 6D cloud where each point contains the coordinates and RGB intensities, noted as (x,y,z, r,g, b). PCCC applies the PointNet architecture to the color constancy problem, deriving the illumination vector point-wise and then making a global decision about the global illumination chromaticity. On two popular RGB-D datasets, which we extend with illumination information, as well as on a novel benchmark, PCCC obtains lower error than the state-of-the-art algorithms. Our method is simple andfast, requiring merely 16 x 16-size input and reaching speed over 140 fps (CPU time), including the cost of building the point cloud and net inference.

Soft frequency reuse (SFR) is a widely adopted frequency reuse mechanism to mitigate inter-cell interference (ICI) in cellular networks, which splits resource blocks (RBs) into two groups to serve cell-edge and cell-center users, respectively. In this paper, the joint design of RB grouping, frequency and power allocation is investigated for downlink transmission in the SFR-based multi-cell networks, where the imperfect knowledge of the statistical channel state information (S-CSI) is considered. Firstly, we derive a closed-form ergodic rate and propose a centralized iterative scheme for maximizing the minimum user ergodic rate to achieve user fairness using successive convex approximation (SCA). Furthermore, to facilitate practical implementation, a decentralized scheme is proposed, where the problem is refor-mulated as a global consensus problem and solved by exploiting alternating direction method of multipliers (ADMM). Simulation results demonstrate that our proposed schemes outperform the state-of-the-art counterpart.

The recently proposed dual-functional radar-communication (DFRC) system aims to save more resources by sharing the hardware platform and spectrum while providing users with both communication and sensing services. However, in previous works of DFRC systems, the issue of adaptively designing waveforms without prior information on targets in a dynamic environment has not been studied yet. In this paper, we propose a cognitive dual-functional radar-communication (CDFRC) system with closed-loop feedback to sense the targets and design waveform with a dynamic beampattern to address this issue. We construct a generalized likelihood ratio test (GLRT) target detection scheme to obtain information on targets and propose a cognitive waveform design method utilizing feedback information. Our proposed system can jointly optimize the multi-user interference (MUI) and detection probabilities of potential targets while ensuring the detection performance of detected targets. Numerical results indicate that, compared with previous work, our proposed system can effectively detect the target and adaptively design the waveform to ensure the ability of target detection and communication.

Illumination estimation refers to estimating the chromaticity vector of illumination, and can be used to recover the surface color under white light. Dual-illuminant is a common scenario in computational illumination estimation tasks. A straightforward way to correct the dual-illuminant image can be estimating a spatially-varying illumination map. However, it is hindered by the lack of large-scale annotated datasets for data-driven methods. In this paper, we propose a novel approach to obtain the dominant dual-illuminant and the pixel-wise illuminant map on real dual-illuminant raw images. Our method consists of 1) dual-illuminant image generator (DIG) to synthesize dual-illuminant images from unique-illuminant datasets assuming the Lambertian model; 2) dual-illuminant estimation network (DE-Net) to estimate illuminant both globally and locally. Quantitative experiments show that with DIG synthesized dual-illuminant images, DE-Net obtains the best accuracy in dual-illumination detection and estimation on the Gehalr-shi dataset and Mutlti-Illuminant Multi-Object dataset.

Federated learning (FL) is a paradigm where many clients collaboratively train a model under the coordination of a central server, while keeping the training data locally stored. However, heterogeneous data distributions over different clients remain a challenge to mainstream FL algorithms, which may cause slow convergence, overall performance degradation and unfairness of performance across clients. To address these problems, in this study we propose a reinforcement learning framework, called PG-FFL, which automatically learns a policy to assign aggregation weights to clients. Additionally, we propose to utilize Gini coefficient as the measure of fairness for FL. More importantly, we apply the Gini coefficient and validation accuracy of clients in each communication round to construct a reward function for the reinforcement learning. Our PG-FFL is also compatible to many existing FL algorithms. We conduct extensive experiments over diverse datasets to verify the effectiveness of our framework. The experimental results show that our framework can outperform baseline methods in terms of overall performance, fairness and convergence speed.

In this paper, we consider the physical-layer security for single-input single-output (SISO) indoor visible light communication (VLC) wiretap systems in the presence of multipath reflections. We derive both the lower and upper bounds on the secrecy capacity in the context of both the symbol- and block-based transmission policies. To enhance the secrecy performance relying on block transmission policy, we propose a low-complexity amplitude scaling (AS) scheme by scaling the amplitudes of different symbols in each block to maximize the achievable secrecy rate. We further provide the upper bound on the optimal secrecy rate achieved by precoding for the sake of validating the effectiveness of the proposed AS scheme. Numerical results suggest that the secrecy performance is severely degraded by inter-symbol interference (ISI) imposed by multipath reflections, which yet can be alleviated by long-block based transmission. Moreover, the proposed AS scheme can enhance the secrecy performance significantly and achieve a secrecy rate very close to the optimal solution.

In support of effectively discovering the market value of resources and dynamic resource provisioning, auction design has recently been studied in the cloud. However, there are limitations due to the inability to accept time-varying user demands or offline settings. These limitations create a large gap between the real needs of users and the services available from cloud providers. In addition, existing auction mechanisms do not consider service interruption due to server failures caused by software or hardware problems. To address the limitations of existing auction mechanisms and to avoid service interruption, this paper targets a more general scenario of online cloud resource auction design where: 1) users can request multiple types of time-varying resources; and 2) at least one server is available for each accepted bid even when one or more servers fail; and 3) profit is maximized over the system execution span. Specifically, we model the profit maximization problem using an Integral Linear Programming (ILP) optimization framework, which offers an elastic model for time-varying user demands. In addition, we design an online, truthful, and time efficient auction mechanism consisting of a price-based allocation strategy and a pricing function. The online allocation strategy allocates multiple types of resource to each user while satisfying the time-varying demands and ensuring at least one server is available for each user in each allocated time slot. Lastly, the efficacy of online auctions is validated through careful theoretical analysis and trace-driven simulation studies.

We study server consolidation problem in clouds under simultaneous failures of multiple servers, where consolidation means that cloud providers put tenants on shared servers to improve resource utilization and thus reduce operation and maintenance costs. With replicas of each tenant put on multiple servers, our objective is to minimize the total number of opened servers and ensure that a particular failure will not result in overload on any remaining server. In this paper, we propose Rotation algorithm. It packs comparable sizes replicas into the same type of servers and adopts a cyclic shift method to quickly reuse those already-opened servers without the need of new ones for new tenants. Through experimental evaluations, we show that the proposed algorithms can achieve a better performance than existing works and produce near-optimal replications allocation.

Distributed multi-controller deployment is a key issue in the innovative Software Defined Network (SDN) to scale network while improving performance and reliability. It is interesting to know how many controllers should be deployed and where to locate under a wide range of performance sensitive and completive constraints, including latency, fair load distribution as well as cost. We solve this problem by minimizing propagation latency and controller cost. The required number of controllers is determined based on requests and controller capacity. Due to the uneven distribution of network load, it is more likely to deploy controllers on nodes with high request density. A clustering algorithm NWDP (Node Weight Deployment Policy) is thus proposed based on node weight to choose location of multi-controller. To achieve effectively, autonomous and dynamic deployment in large-scale networks, we further propose a supervised graph convolution network model with fusion features(FF-GCN). The open network database Internet Topology Zoo is adopted to evaluate the effectiveness of our algorithms. Simulation results show that NWDP efficiently outperforms traditional algorithms in medium-sized topology, and the trained FF-GCN can figure out the deployment in a 702 nodes large-scale topology with an average prediction accuracy of 90%.

In this paper, we consider the artificial noise (AN) aided multiple-input single-output (MISO) secure systems in which a multi-antenna transmitter simultaneously transmits the combination of information-bearing signal and artificial noise to a single-antenna legitimate receiver and eavesdropper. At the transmitter front-ends, the signal and/or AN will be amplified by high-power amplifiers (HPAs) which however may work in a nonlinear region and introduce nonlinear distortion. We calculate the received signal-to-noise ratio (SNR) at legitimate receiver and eavesdropper under HPA nonlinearity, and derive the approximated closed-form expression of secrecy rate. Numerical results suggest that the approximated theoretical secrecy performance is very close to Monte Carlo simulations for relatively lower SNRs, and is severely degraded by the nonlinear distortion caused by HPAs as the transmit power increases. Moreover, we further provide a power allocation strategy between information-bearing signal and AN to alleviate the nonlinear distortion.

Hypertension is a prevalent disease that is known to affect the vascular system especially to the people with poor living habits and lifestyles. Virtual reality (VR) is effective to interact with people to release their pressure and cheer them up, which however is less conducted towards manipulating blood pressure and hypertension. In this paper, we consider how hypertension can be treated with VR devices and design virtual reality river bathing therapy (VRBT) with respect to a combination of traditional methods through sensory stimulation, audio interventions, and motor training.

Driving behavior plays a key role in the interaction between vehicle and driver in transportation systems. Some applications about driving behavior in Advanced Driver Assistance Systems (ADAS) improve driving safety significantly. This paper introduces the driving context and models driving behavior in a combination of cognitive perspective and data-driven perspective. First, we use a cognitive fusion method by adding a delay time module to fuse the environmental information and inside information. To better capture the driving context relationship between outside and inside features, we transfer the behavior prediction task to the sequence labeling task by introducing the visual inertia hypothesis. We propose the Predictive-Bi-LSTM-CRF algorithm which used the Bidirectional Long-Short Term Memory Networks (Bi-LSTM) and Conditional Random Field (CRF) as the loss layer to model the driving behavior. Besides, we define a new comprehensive evaluation metric for the prediction task considering F1-score and the prediction time before maneuver together. Our experiment results achieve the state of art performance on the Brain4Cars dataset and demonstrate the applicability of our theory.

Underwater wireless optical communication (UWOC) has huge potential for its high speed, low latency and reliable security. However, besides the channel impairments such as absorption and scattering, dynamic ocean environments can also degrade the system performance and even incur link interruptions. In this paper, we propose a 4 × 4 fixed-scale multiple-input multiple-output UWOC system to remove the impediment of link misalignment. The system employs two collimated lens to perform a mapping from each source to associated photodetector. The bit-error rate expression is also derived to evaluate the system performance. Numerical simulations illustrate that the absorption and scattering induced path loss can be alleviated by this system when the scattering angles are relatively small and the communication range is moderate. Furthermore, the system diminishes the sensitivity of underwater vehicles to dynamic ocean environments. By taking the window truncation effect into consideration, we quantify the enhancement of system robustness to link misalignment.

In this paper, we consider the impact of turbulence on performance of UWOC systems and investigate capacity and bit-error rate (BER) of underwater wireless optical links under weak and strong turbulence by deriving the expressions of average capacity and BER. Numerical results suggest that turbulence degrades both capacity and BER performance as expected. This work provides a theoretical analysis tool for system design and performance evaluation of UWOC systems.

In underwater wireless optical communications(UWOC), absorption and scattering cause attenuation and dispersion in both time and space domains. There have been many studies on temporal channel investigation and modeling such as impulse response but fewer on spatial behavior. In this paper, we consider the characteristics of actual light sources, i.e., laser diodes (LDs) and light-emitting diodes (LEDs), and propose a uniform spatial channel (USC) model for UWOC system employing single or multiple light sources. We first simulate the irradiance distributions of single-source UWOC systems based on Monte Carlo method and fit them with a closed-form expression for both types of light sources. Then we generalize this single-source model to multiple-source link geometry considering uniform linear array and circular array of light sources. Numerical results suggest that the proposed model fits well with the irradiance distributions of UWOC links regardless of the type, number and array geometry of light sources for various water types.

Automatic segmentation of retinal vessels is an important step in fundoscopic image analysis. Recently, convolutional-neural-network-based methods have been widely explored in this vision task. However, the local fixed receptive field makes network unable to collect global information and adapt to scale variation of retinal vessels. In this paper, we propose a novel RNA-Net which can capture nonlocal context dependencies across the inputs and extract multi-scale features for segmentation task. Firstly, we build a Residual Nonlocal Attention (RNA) Module, which can guide the network to pay more attention to task-related regions of the whole feature map. Secondly, to better capture the morphological characteristics of natural blood vessels, Pyramid Pooling Module (PPM) is added to capture features at multiple scales. Experimental results on two public datasets DRIVE and STARE clearly demonstrate that our method outperforms the current state-of-the-art approaches.

Vehicular ad hoc network (VANET) is a promising technology for intelligent transportation systems, where dedicated short range communication (DSRC) is usually used for inter-vehicle communications. When the vehicle density is high, the randomly access feature of the carrier sense multiple access with collision avoidance (CSMA/CA) mechanism in DSRC will lead to the increased channel contention delay. Therefore, visible light communication (VLC) can be introduced to form a heterogeneous VLC-DSRC network for delay reduction. Although VLC has no contention delay, one VLC link can only be established between two adjacent vehicles, which might increase the delay caused by multi-hop communications. In this paper, a delay-minimization link selection scheme is proposed to select appropriate links for vehicles according to actual situations and minimize average delay. Simulation results demonstrate that the proposed scheme outperforms the considered benchmarks in terms of average transmission delay.

Monte-Carlo models are analyzed for multiple scattering channels in optical wireless communications. It is demonstrated that the system impulse response function can be obtained by Monte-Carlo integration model. The convergence performance for the Monte-Carlo integration model is analyzed and improved by introducing different sampling methods. The simulation results show that the gamma function model for channel impulse response function can only be applied to the cases where the common volume between the transmitted light beam and the receiving field-of-view is open. Numerical simulation suggests that for a three-order scattering case, the computation efficiency of the Monte-Carlo integration model based on partial importance sampling is about 12 times of the original Monte-Carlo integration model based on uniform sampling, and 5.6 times of the widely used Monte-Carlo simulation model. The numerical results also show that the Monte-Carlo integration model based on partial importance sampling has higher computation efficiency than the Monte-Carlo simulation model in a higher-order scattering communication scenario.

Resource scheduling is one of the main problems for multi-satellite Tracking, Telemetry and Command (TT&C) networks. Traditional multi-resource joint scheduling algorithms are with long solution time, low efficiency, high computational cost, and simple description on the system. Deep Neural Network (DNN) provides a possible new way to solve those problems, but it is difficult to handle correlations among the input data. This motivates our work to solve the strong correlation problem based on the accumulated historical data, and thus enables DNN for TT&C resource scheduling. By discretizing the data, multiple constraints and related attributes are transformed into different flags, and some binary bits of the data are used to reflect the constraint relationship. Then, we can use DNN model and construct an intelligent TT&C resource scheduling system to handle multiple constraints and data attributes (such as priorities among tasks and others). This improves the efficiency of TT&C resources utilization and automation. Effectiveness of the proposed model is verified by simulations.

In indoor environment, a hybrid network consisting of light fidelity (LiFi) and wireless fidelity (WiFi) is capable of retaining both the high-speed data transmission and the ubiquitous coverage, where the network access type of users can be optimized to improve the performance. Since visible light communication mainly depends on the line-of-sight (LoS) transmission, it is susceptible to channel blockage, which should be considered by users to select the appropriate type of network access. In the existing literature, LiFi channel blockage parameters are regarded as known for users to determine the access type. However, in practical scenarios, the estimation of blockage parameters lags behind their variations, and users can not get the real-time blockage information. In this paper, a support-vector-machine- based (SVM-based) network access type decision scheme is proposed in hybrid LiFi and WiFi networks. By taking the correlation of blockage parameters between adjacent periods into account, SVM is adopted to achieve high equivalent data rate when accurate blockage parameters are unknown. Simulation results demonstrate that the proposed scheme outperforms the considered benchmarks under different scenarios in terms of the equivalent data rate performance.

Absorption and scattering are traditionally considered as the most important factors to affect the performance of underwater optical wireless communications (UOWC). Recently, the theoretical models from free space optical (FSO) communications are applied to model the underwater turbulence, and the turbulence-induced fading may introduce fluctuations to the light intensity. However, the effect of turbulence on UOWC channels might be different from FSO channels due to the interference from absorption and scattering. In this work, we first introduce the log-normal distribution to represent the weak turbulence. After that, we deduce the average capacity of turbulent buoy- based multiple-input multiple-output (MIMO) systems. Numerical results demonstrate that turbulence will boost the average capacity under low transmitted signal-to-noise ratio (SNR) and reduce the average capacity when the transmitted SNR which is defined as the transmitted power divided by the noise at the receiver is sufficiently high enough. Besides, stronger turbulence exerts more influence on the capacity, and the increasing attenuation length will eliminate the effect of turbulence. Moreover, MIMO could offset the impact of turbulence-induced fading, which indicates that it cannot improve the capacity performance under low SNR but could bring positive effects when SNR becomes high.

Advanced Driver Assistance Systems (ADAS) improve driving safety significantly. They alert drivers from unsafe traffic conditions when a dangerous maneuver appears. Traditional methods to predict driving maneuvers are mostly based on data-driven models alone. However, existing methods to understand the driver's intention remain an ongoing challenge due to a lack of intersection of human cognition and data analysis. To overcome this challenge, we propose a novel method that combines both the cognition-driven model and the data-driven model. We introduce a model named Cognitive Fusion-RNN (CF-RNN) which fuses the data inside the vehicle and the data outside the vehicle in a cognitive way. The CF-RNN model consists of two Long Short-Term Memory (LSTM) branches regulated by human reaction time. Experiments on the Brain4Cars benchmark dataset demonstrate that the proposed method outperforms previous methods and achieves state-of-the-art performance.

Massive multi-input multi-output (MIMO) technology is promising by employing a large number of antennas at the base station to support a large amount of users. However, due to the limitation of analog front-ends at the base station, the antenna selection and user scheduling strategies are essential to achieve spatial diversity and reduce hardware cost at the same time. In this work, we consider the strategy of joint antenna selection and user scheduling (JASUS) for uplink massive MIMO systems and propose a greedy two-step JASUS algorithm referred to as largest minimum singular value based JASUS (LMSVJASUS). In its first step, a simplified downward branch and bound based JASUS is used to find a near-optimal antenna and user sets whose channel matrix has the near-largest MSV. In its second step, a swapping-based algorithm is proposed to find a better solution by swapping antennas and users between the selected and the discarded. The numerical results suggest that the proposed algorithm outperforms traditional approaches in terms of system sum-rate and computational complexity.

PM2.5 is one of the major indicators of ambient air quality which has become a focus of public attention. Urban PM2.5 can be measured by air quality monitoring stations which are costly and not sufficiently installed in a city. In this paper, we aim to infer the PM2.5 information at the place where there is no air quality monitoring station. As PM2.5 concentration varies over time and space domains, we propose a joint topic model to jointly model the spatial and temporal patterns of PM2.5. Numerical results suggest that the proposed model achieves better inference based on five related datasets compared with traditional methods.

Understanding human drivers' behavior is critical for the self-driving cars, and has been intensively studied in the past decade. We exploit the widely available camera and motion sensor data from car recorders, and propose a hybrid method of recognizing driving events based on the random forest approach. The classification results are analyzed by comparing different features, classifiers and filters. A high accuracy of 98.1% on driving behavior classification is obtained and the robustness is verified on a dataset including 2400 driving events.

In underwater wireless sensor networks (USWNs), localizing unknown nodes is essential for most applications while is more complex than that of terrestrial WSNs. In this paper, we propose a range-based localization scheme using deep neural network (DNN). Numerical results suggest that the proposed DNN localization algorithm outperforms traditional schemes using least squares support vector machines (LS-SVM) or generalized least squares (GLS) in terms of localization accuracy and efficiency. Moreover, the proposed algorithm requires a small number of anchor nodes, which is plausible for practical applications.

Localization of sensor nodes is important for wireless sensor networks (WSNs) especially for underwater WSNs (UWSNs). Among the existing UWSN localization approaches, reverse localization scheme (RLS) is an event-driven method suitable for underwater surveillance. RLS adopts the strongest arrival or the first arrival as the direct path, which is however not accurate enough due to the severe multipath effect in underwater acoustic channels. We propose median RLS (MRLS) to select the median path and weighted RLS (WRLS) by assigning each path with a possibility to be the direct path for UWSNs. Numerical results have validated the proposed schemes and further suggested that WRLS outperforms MRLS and traditional approaches and is capable to diminish the multipath effect.

Passenger hotspots searching is essential to increase profits for taxis drivers in urban area. In this paper, we propose a two-step approach for pick-up hotspots searching. In the first step, a traveling similarity model is built to quantify the similarity of traveling behaviors. In the second step, we utilize affinity propagation and simulated annealing to identify the daily passenger hotspots in a selected period. Numerical results based on GPS data of Manhattan taxis suggest that the proposed approach outperforms the traditional spatio-temporal clustering regardless of buffer radius.

The deployment of charging infrastructures is a key factor for the operation of electric taxis in urban area. This paper focuses on the operational efficiency and the charging convenience of electric taxis, and introduces a two-step optimization process of the charging station location for electric taxis. We propose a modified k-means clustering method to divide the urban area into multiple service regions according to the market demand distribution. Then we utilize the optimal location model in the service region to calculate the optimal sites of the charging stations to maximize the operational efficiency and charging convenience. We collect GPS data of electric taxis in Shenzhen and examine the behavior of our proposed approach. Numerical results suggest that the proposed approach outperforms the traditional particle swarm optimization (PSO) method and has improved the operational efficiency by 14.03% and reduced the average distance for the charging service by 12.30% compared with the actual physical sites.

PM2.5 is one of the major ambient air pollutants to threaten our health in urban area. However, there are only a few air monitoring stations in a city, which make it difficult to precisely measure PM2.5 concentration at the place without installation of air monitor. In this paper, we consider the PM2.5 inference problem and propose a model taking the PM2.5 nonlinear characteristic into account based on support vector machine (SVM). We collect the features of meteorology, geographical locations and PM2.5 indexes observed by air monitoring stations. Unlike the previous work, we add points of interest (POIs) feature and reduce its dimension by latent Dirichlet allocation (LDA) model due to its sparsity and adopt wavelet decomposition to improve the inference accuracy. Numerical results show that the proposed approach outperforms other related methods in terms of RMSE, correlation coefficient and mean absolute error (MAE) with the real value.

In underwater wireless optical communications (UWOC), absorption and scattering characterize the link properties since photons may suffer these two processes with energy loss and direction change, respectively, when interacting with water molecules or suspended particles. In this work, we consider the effects of absorption and scattering on the probability distribution, i.e., normalized intensity distribution, of photons in space and time domains. Our prior work proposed a stochastic channel model to represent the spatial-temporal probability distribution of propagated photons only for nonscattering and single scattering components of UWOC links. However, multiple scattering will dominate the scattering behavior of the underwater environment with long communication distance and/or more turbid water type. In this work, we take into account all three types of components including nonscattering, single and multiple scattering, and present a more general stochastic channel model which fits well with Monte Carlo simulations in turbid water environment such as coastal and harbor water. Based on the proposed channel model, we also evaluate the performance of path loss, scattering richness, and attenuation of UWOC links. Numerical results suggest that multiple scattering can compensate the path loss overestimated by traditional approaches. Furthermore, scattering richness and attenuation tend to increase but have opposite effects to raise and reduce the received probabilities of higher order scattered photons, respectively, as link range increases.

Inspired by the migration and division in cells' life-time, we introduce an asynchronous cluster head rotation(ACR) scheme to address unbalanced load in large-scale wireless sensor network and prolong its lifetime. ACR provides an on-demand and low-overhead scheme to rotate CHs asynchronously. Clusters migrate or reproduce using a closed-loop and heuristic mechanism, according to their energy condition individually. Simulation experiments comparing ACR with several state-of-art clustering algorithms demonstrate that ACR obtains an extended lifetime while maintaining a harmonious topology. We probed further into the simulation results and reveal the distinctions between ACR and other clustering schemes.

Background subtraction is a popular technique used in accurate foreground extraction with a stationary background. Since most outdoor surveillance videos are taken in complex environments, their "stationary" backgrounds change in some unknown patterns, which make the perfect foreground extraction very difficult. Based on visual background extractor (ViBe) scheme, in this paper we propose a new background subtraction algorithm which includes two innovative mechanisms and several other improved technique tricks. The paper inherits and develops background modeling based on pixel sample values, and use dynamic noise sampling and complementary learning to overcome the pixel-wise background model's intrinsic shortcomings. Besides, the algorithm works on the quantitative analysis without any estimation of the probability density function (pdf). Hence, it takes relatively low computational cost. Extensive experiments on a popular public dataset show that the proposed method has much better precision than ViBe, and could get the best precision and the highest average ranking compared with 27 state-of-the-art algorithms presented on the change detection website.

In underwater wireless optical communication (UWOC) links, multiple scattering may cause temporal spread of beam pulse characterized by the impulse response, which therefore results in inter-symbol interference (ISI) and degrades system error performance. The impulse response of UWOC links has been investigated both theoretically and experimentally by researchers but has not been derived in simple closed-form to the best of our knowledge. In this paper, we analyze the optical characteristics of seawater and present a closed-form expression of double Gamma functions to model the channel impulse response. The double Gamma functions model fits well with Monte Carlo simulation results in turbid seawater such as coastal and harbor water. The bit-error-rate (BER) and channel bandwidth are further evaluated based on this model for various link ranges. Numerical results suggest that the temporal pulse spread strongly degrades the BER performance for high data rate UWOC systems with on-off keying (OOK) modulation and limits the channel bandwidth in turbid underwater environments. The zero-forcing (ZF) equalization designed based on our channel model has been adopted to overcome ISI and improve the system performance. It is plausible and convenient to utilize this impulse response model for performance analysis and system design of UWOC systems.

We consider the target coverage problem and lifetime performance of wireless sensor networks (WSNs). Traditional target coverage schemes only utilize the total energy and fixed/adjustable sensing range and have lower energy efficiency. In this paper, we propose an energy-efficient target coverage algorithm by taking into account the remaining energy and number of covered targets. Numerical results suggest that, regardless of the numbers of targets and sensors, the proposed algorithm achieves longer lifetime than traditional schemes.

We consider a clustered wireless sensor network where sensors are randomly distributed within a circle area with the only cluster head in the center. After the cluster head broadcasts source message to nearby sensor nodes, those neighboring nodes which have successfully decoded the message may help relay data for the head to the destination using space-time block coding (STBC) scheme to form a virtual multi-input-multi-output (vMIMO) system. We first analyze the average outage performance and overall energy consumption including transmission energy and the circuit energy. We then present a transmission scheme to maximize system throughput as well as its performance dependence on data rate and power allocation decisions. Numerical results have validated the proposed scheme and shown the significant throughput increment with appropriate data rate and power allocation decisions.

In traditional wireless sensor networks (WSNs), the power supply is a limiting factor on the lifetime of sensor nodes. Recently, energy harvesting technology has made it possible to develop autonomous WSNs with theoretical unlimited lifetimes. However, the change of power supply calls for a different version of network protocol. In this paper, we introduce Energy Potential Function which is utilized to measure the node's capability of enery harvesting and extend the traditional protocol LEACH to Energy Potential LEACH which is suitable for energy harvesting WSNs. Energy Potential LEACH can not only extend network lifetimes, but also improve the network throughput in energy harvesting WSNs. We evaluate the proposed protocol analytically and numerically, and find that it exhibits a better performance than previous work in terms of lifetimes and throughput.

We consider the impact of transmitter correlation, mutual coupling and matching networks on the design and performance of MIMO MRC systems with limited feedback. We present codebook design techniques for three measures of transmitted power and we investigate the impact of antenna matching on the performance of these codebooks. Numerical results suggest that, regardless of the power measure and matching networks, the benefits of MIMO MRC with limited feedback can be achieved with antennas spaced as close as 0.2 - 0.3 wavelengths apart.

We consider the impact of transmitter correlation and mutual coupling on MIMO MRC systems. We present optimal transmission strategies for three input power metrics as well as formulas for the resulting outage probabilities. Numerical results suggest that, regardless of the power metric, most of the performance benefits of MIMO MRC can be obtained with transmit antennas spaced as close as 0.2 - 0.3 wavelengths.