Generating molecules with desired chemical properties is a crucial and promising area of research in drug discovery, as it has the potential to accelerate the identification of novel therapeutic compounds. Recent developments in diffusion models have showcased their remarkable generative capabilities, effectively handling continuous data modalities such as images and audio. However, when it comes to generating discrete data, particularly molecular representations like SMILES strings and molecular graphs, these models encounter significant challenges, especially in few-shot learning scenarios where only a limited number of samples are available. In this paper, we explore the potential of diffusion models for generating continuous representations of molecules-molecular images. Specifically, we propose ProtoDiff, a diffusion-based method that incorporates few-shot learning for molecular image generation. We frame molecular image generation as a few-shot controllable generation problem that extracts prototypes from a limited set of molecules to guide the generation process and introduces a novel sparsity regularization in the objective function of diffusion to emphasize the meaningful pixels of molecules, i.e., the limited pixels of the chemical bonds. We train and evaluate ProtoDiff on the ChEMBL dataset, achieving new state-of-the-art results on the majority of molecular generation tasks.

Osteoporosis is a common skeletal disease that seriously affects patients’ quality of life. Traditional osteoporosis diagnosis methods are expensive and complex. The semi-supervised model based on diffusion model and class threshold sinusoidal decay proposed in this paper can automatically diagnose osteoporosis based on patient’s imaging data, which has the advantages of convenience, accuracy, and low cost. Unlike previous semi-supervised models, all the unlabeled data used in this paper are generated by the diffusion model. Compared with real unlabeled data, synthetic data generated by the diffusion model show better performance. In addition, this paper proposes a novel pseudo-label threshold adjustment mechanism, Sinusoidal Threshold Decay, which can make the semi-supervised model converge more quickly and improve its performance. Specifically, the method is tested on a dataset including 749 dental panoramic images, and its achieved leading detect performance and produces a 80.10% accuracy.

The cooperative system of multiple Autonomous Underwater Vehicles (AUVs) is becoming increasingly popular in environment survey, target search and many other marine coverage tasks. Coverage path planning is essential prior to task implementation to avoid path redundancy or area omission. A significant issue is that most existing underwater coverage path planning studies assume that AUVs can always obtain accurate position estimates, without considering the problem of positioning error divergence in underwater navigation systems. This article proposes a coverage path planning method based on Deep Reinforcement Learning (DRL) for the leader-follower AUVs cooperative navigation mode. The method ensures that the AUV formation successfully completes the coverage task while utilizing distance measurements between the leader and follower AUVs to achieve cooperative position estimation with bounded errors. Grid division strategy is used to ensure a close distance between the leader and following AUVs. The Double Deep QNetwork (DDQN) learning algorithm is applied for global cooperative coverage path planning, with a prior positioning uncertainty dictionary built based on the relationship between positioning errors and cooperative paths. An online path replanning method is also designed to avoid unknown static and dynamic obstacles. The proposed method is validated through simulations and lake experiments using unmanned surface vehicles. Compared to existing full-coverage path planning strategies, it demonstrates significant improvements in positioning accuracy, enabling AUVs to maintain high-precision navigation and accurately track the planned path during task execution.

Sparse Bayesian Learning (SBL) is recognized for its efficacy in sparse signal recovery, the computational demand escalates significantly with increasing data dimensionality due to the matrix inversion at each iteration. An Inverse-Free sparse Bayesian Learning (IF-SBL) approach has been introduced to mitigate computational complexity. However, IF-SBL converges easily to a sub-optimal solution with false peaks due to the neglect of the correlation between atoms. In this paper, we analyze causes of false peaks in IF-SBL. Subsequently, a novel dynamically updated embedded Bayesian threshold is designed to mitigate the interference caused by false peaks. This innovative approach retrieves the stability and reliability without significantly increasing signal recovery complexity compared with IF-SBL. Simulation experiments validate the results.

As a longstanding scientific challenge, accurate and timely ocean forecasting has always been a sought-after goal for ocean scientists. However, traditional theory-driven numerical ocean prediction (NOP) suffers from various challenges, such as the indistinct representation of physical processes, inadequate application of observation assimilation, and inaccurate parameterization of models, which lead to difficulties in obtaining effective knowledge from massive observations, and enormous computational challenges. With the successful evolution of data-driven deep learning in various domains, it has been demonstrated to mine patterns and deep insights from the ever-increasing stream of oceanographic spatiotemporal data, which provides novel possibilities for revolution in ocean forecasting. Deep-learning-based ocean forecasting (DLOF) is anticipated to be a powerful complement to NOP. Nowadays, researchers attempt to introduce deep learning into ocean forecasting and have achieved significant progress that provides novel motivations for ocean science. This article provides a comprehensive review of the state-of-the-art DLOF research regarding model architectures, spatiotemporal multiscales, and interpretability while specifically demonstrating the feasibility of developing hybrid architectures that incorporate theory-driven and data-driven models. Moreover, we comprehensively evaluate DLOF from datasets, benchmarks, and cloud computing. Finally, the limitations of current research and future trends of DLOF are also discussed and prospected.

Accurately predicting Arctic sea ice concentration (SIC), especially during the melting season at subseasonal scales, is essential for advancing our knowledge of global climate change. Currently, statistical models for SIC prediction face three major challenges: limited spatial resolution and forecast timeliness, inadequate representation of sea ice dynamic processes, and difficulties in predicting SIC during the melting season. To address these challenges, this study introduces super-resolution SIC Transformer (SR-SICFormer), a deep learning-based, data-driven model designed for fine-scale, long-term Arctic SIC prediction. We also propose a novel physical constraints loss function, ConIce loss, which integrates thermodynamic and dynamic sea ice processes into the training procedure, aiming to improve the model’s predictive accuracy. Satellite remote sensing data are used for training and validating the model. Experimental results demonstrate that SR-SICFormer outperforms traditional statistical and numerical models in extended-range SIC prediction tasks, achieving a$5\times $spatial super-resolution factor and a 15-day forecast period. The model achieves a root-mean-square error (RMSE) of 0.0504, a correlation coefficient (r) of 0.9420, a peak signal-to-noise ratio (PSNR) of 30.32 dB, and a structural similarity index measure (SSIM) of 0.9297. For the$2\times $super-resolution and 60-day forecast during the melting season, SR-SICFormer maintains strong performance, keeping SIC residuals within [−0.3, 0.3]. In addition, the ConIce loss function effectively preserves both sea ice extent (SIE) and internal concentration distributions, ensuring that extended-range forecast results closely match ground truth in both SIE and concentration.

In the field of qualitative decision making with hesitant fuzzy linguistic term sets (HFLTSs), distance measure (DM) is a significant concept that reflects the degree of difference between HFLTSs. Various DMs among HFLTSs have been proposed, which enhance the applicability of HFLTSs in multicriteria decision making (MCDM) under qualitative hesitation information. However, existing strategies not only have their own pros and cons but also fail to measure linguistic assessments from multiple linguistic term sets with different distributions (i.e., multigranular unbalanced linguistic information). In this article, we first propose a new strict DM between two hesitant fuzzy linguistic elements (HFLEs) based on the Wasserstein distance of their semantics intervals. Then, two novel kinds of DMs (Euclidean and Chebyshev forms) for HFLTSs are proposed and their strictness is proved. Weighted and ordered weighted versions of HFLTSs based on Euclidean form are derived. Afterward, illustrative examples, simulations, and related analyses are given to demonstrate the rationality of the proposals. Finally, two cases are applied to verify the efficiency and practicality of the novel measures in real life.

We propose a novel high performance computing (HPC) network architecture$\mathrm {HFOS}_{L}$based on$L$parallel levels distributed low radix fast optical switches (FOS). We provide a detailed description of the blade, FOS and the operation of the$\mathrm {HFOS}_{L}$network. The$\mathrm {HFOS}_{L}$HPC network is highly scalable, and HFOS4 architecture can support an extremely large HPC network of 65,536 blades under distributed FOS with the same radix of 16 in each level. In principle, the$\mathrm {HFOS}_{L}$HPC network can be built by FOSes with different radices at each level. To find out the best configuration of FOS at each level and solve the energy and cost optimization problem in$\mathrm {HFOS}_{L}$network, we break down all the components in the FOS and develop the energy and cost models for the FOS. We verify that the energy and cost per radix functions of FOS are convex functions. Given this foundation, the theoretical investigation of the energy and cost optimization problem shows that the$\mathrm {HFOS}_{L}$network could achieve the minimum energy and cost only when the FOS radices of all levels in$\mathrm {HFOS}_{L}$network are the same. Besides, the cost and power consumption of$\mathrm {HFOS}_{L}$networks are compared with a widely used Leaf-Spine network.

Random noise attenuation is essential in seismic data processing. In this paper, we propose an unsupervised method called “shot2shot with re-de-noising regularization” to remove random noise. Shot2Shot (S2S) is a new way to train a denoising neural network. S2S takes a shot-gather and its multiple neighboring shot-gathers as input and labels of the neural network, respectively. The principle that S2S can eliminate noise is the correlation of seismic waves and the independence of random noise between neighboring shot-gathers. Because neural networks are more likely to learn correlated information between inputs and labels rather than independent information. Although S2S is effective in denoising, this mode of training may lead to relatively coarse results. Therefore, we propose re-de-noising regularization to make the results of S2S more refined. The re-de-noising regularization consists of two penalty terms that balance each other, the re-de-noising term and the stability term. The stability term is responsible for introducing more fine content from the observations, such as weak waves, but this can introduce new noise. Thus the re-de-noising term is used to avoid the interference of this new noise. Experimentally, our method outperforms other state-of-the-art methods in terms of quantitative results. Visually, our method not only removes the noise but also reconstructs the noisy data more completely. In addition, we explain the role of S2S and re-de-noising regularization more intuitively through ablation experiments. Finally, the robustness of the key hyperparameters is discussed.

Seismic data interpolation is an indispensable part of seismic data processing. In recent years, deep-learning-based interpolation algorithms for seismic data have become popular due to their high accuracy. However, a considerable amount of work has focused on the migration of concepts and algorithms in deep-learning-based methods while ignoring the implicit properties of seismic data itself. In this article, we propose the regeneration prior, which is an implicit property of seismic data with respect to the interpolation function, and are used for self-supervised seismic data interpolation tasks. In mathematical form, the regeneration prior can be considered as a regular term describing the structure of the seismic data. Theoretically, the regeneration prior is a necessary condition to obtain an optimal interpolation function. Experimentally, the proposed method achieves significant improvement in accuracy and intuitive visualization in comparison with advanced unsupervised or self-supervised methods. In addition, we provide an intuitive interpretation of the regeneration prior, and our study shows that the regeneration prior plays an anti-overfitting structuring role in the parameter learning process of the interpolation function. Finally, we analyze the robustness of the regeneration prior. The experimental results show that the performance of the regeneration prior is stable despite the fact that the hyperparameters associated with the regeneration prior are perturbed in a considerable range.

This paper addresses the question of whether an optical wireless communication (OWC) LiFi system preferably uses LEDs near their most efficient operating point, which is preferred for environmentally-friendly highly efficient illumination, or that one should preferably drive the LED into its droop regime to speed up its response. In fact, additional non-radiative recombination speeds up the LED response to modulation but spoils efficiency. We introduce the differential (Internal and External) Quantum Efficiency (dIQE; dEQE), as a particular derivative of quantum efficiencies. It quantifies the efficiency at which a current modulation translates into an optical signal. We optimize the current density, thus how deeply the LED is to be driven into droop, using DC-offset (DCO) OFDM throughput expressions for the first–order low–pass LED response. Short– range small–coverage systems prefer high current densities to achieve very high throughputs. LiFi with wide opening angles, e.g. covering tens of square meters are better off with a high dIQE, thus lower current densities.

Recently, the coherent optic wireless communication (OWC) has received extensive attention due to its superiority over traditional intensity modulated direct detection (IMDD) systems. Yet, the Doppler effect could be a showstopper for coherent OWC which is sensitive to the frequency offset and spread. Thus we introduce a new modulation scheme, which called orthogonal time frequency space (OTFS) modulation, into the coherent OWC to solve the Doppler problem. OTFS transforms traditional time-varying channel into delay-Doppler (DD) domain, which ensures all transmit symbols experience an almost identical and slowly varying sparse channel. Thus full channel diversity in time and frequency can be obtained when a suitable receiver is used. In addition, most of the research on Doppler effect only consider the random Doppler spread and average frequency shift to the signal, while ignoring the spectral spread caused by the frequency-dependent Doppler shift of a wideband signal such as radar signals and terahertz signals. To accurately model the Doppler effect, it is necessary to calculate the Doppler frequency shift according to the frequency bin of each sub-carrier, thereby the overall frequency offset and spectral spread are included. This manuscript proposes a Doppler channel model based on frequency-domain subband partition (FDSP), and for the first time, this wideband Doppler channel model is applied into wideband OTFS and orthogonal frequency division multiplexing (OFDM) systems. We carried out simulation for OTFS and OFDM under different velocities. Simulation results show that the OTFS can resist high Doppler frequency shift in high mobility scenarios, and in the case of large subcarrier spacing, OTFS is less affected by the Doppler frequency shift of each subcarrier than OFDM.

Abstract Multiple‐input multiple‐output with frequency diverse array (FDA‐MIMO) radar has drawn great attention due to providing the range‐angle beampattern via designing the transmit beamforming matrix. In this work, the authors investigate the transmit beamforming matrix optimization for Low‐Probability‐of‐Intercept (LPI) of FDA‐MIMO radar, which can accurately control the transmit beam energy to meet the LPI requirements and the clutter suppression. The idea of the transmit beamforming matrix design is to minimise the transmit power in a specific direction and simultaneously maximise the signal‐to‐interference‐plus‐noise ratio under the power constraint on each array element. To this end, a constrained multiple‐ratio fractional programming model with concerning the transmit beam matrix and the receive filter is first constructed, and then, it is transformed into two suboptimisation problems using a circular iterative approach. Moreover, the specific solution of the transmit beamforming matrix is obtained using the quadratic transformation method and the alternating direction method of multipliers algorithm. In addition, the computational complexity is also analysed in this paper. The simulation results demonstrate the correctness and effectiveness of the proposed method.

Seismic data reconstruction is an important technology in seismic data processing. Existing reconstruction methods have achieved promising performance for regularly/randomly missing cases. However, recovering consecutive missing data remains challenging due to the loss of large amounts of information in local regions. In this paper, we devise a novel network called RRMFI-Net, which is mainly constructed by a Recurrent Residual Multiscale Feature Inference (RRMFI) module and a Recurrence Adjustment Attention (RAA) module. The RRMFI module infers and fills the missing regions multiple times, and uses the result as a clue for the next inference, which makes the result more elegant. To ensure that there is no ambiguity between the results of multiple inferences, we devise an RRA module, which is fused into the RRMFI module to obtain padding information from a long distance. Experimentally, we compare RRMFI-Net with supervised state-of-the-art methods, demonstrating that RRMFI-Net is more effective on multiple indicators. Furthermore, we conduct ablation studies discussing the impact of key network hyperparameters.

Seismic data are generally sampled irregularly and sparsely along spatial coordinates because economic costs and obstacles hinder the regular arrangement of geophones in the field. Thus, the sampled seismic data often contain missing traces which result in difficulties for later processing steps. To alleviate this issue, versatile interpolation methods have been developed to interpolate the missing traces. However, the existing models for recovering seismic data with consecutively missing traces in a large amplitude range tend to produce artifacts and blurred signal details. We propose in this paper a hybrid loss guided coarse-to-fine model which consists of a coarse network and a refinement network to allow different regions of seismic data to be recovered in different stages. The coarse network is designed to reconstruct the strong signals and the refinement network is implemented subsequently to recover the weak signals. In addition, the refinement network focuses its attention on the areas which are not well recovered by the coarse network via a weight-masked mechanism. By resorting to the hybrid loss function L1+SSIM+Relativistic Average Least-Square Generative Adversarial Network (RaLSGAN), our model enables more accurate and realistic signal details to be reconstructed. Experiments with synthetic and field data demonstrate that our model is superior to the existing mainstream approaches and the role of the key components is also investigated through ablation studies.

The bound of the information transmission rate of direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) for visible light communication (VLC) with finite-alphabet inputs is yet unknown, where the corresponding spectral efficiency (SE) and energy efficiency (EE) stems out as the open research problems. In this paper, we derive the exact achievable rate of the DCO-OFDM system with finite-alphabet inputs for the first time. Furthermore, we investigate SE maximization problems of the DCO-OFDM system subject to both electrical and optical power constraints. By exploiting the relationship between the mutual information and the minimum mean-squared error, we propose a multi-level mercury-water-filling power allocation scheme to achieve the maximum SE. Moreover, the EE maximization problems of the DCO-OFDM system are studied, and the Dinkelbach-type power allocation scheme is developed for the maximum EE. Numerical results verify the effectiveness of the proposed theories and power allocation schemes.

Optical spatial modulation (OSM) and orthogonal frequency division multiplexing (OFDM) are two promising techniques for bandlimited intensity modulation/direct detection (IM/DD) optical wireless communication (OWC) systems. In this paper, we for the first time propose a novel enhanced OFDM-based OSM scheme for spectral efficiency improvement of ban-dlimited IM/DD OWC systems. The proposed enhanced OFDM-based OSM scheme can be considered as the combination of time-domain OSM (TD-OSM) and non-Hermitian symmetry OFDM (NHS-OFDM). In an OWC system adopting enhanced OFDM-based OSM, a pair of light-emitting diode (LED) transmitters are selected from the LED array, which are used to separately transmit the real and imaginary parts of a complex-valued NHS-OFDM signal. A modified maximum-likelihood (ML) detector is further developed to efficiently estimate the indexes of the LED pair and the real and imaginary parts of the transmitted complex-valued NHS-OFDM signal. We show that the proposed enhanced OFDM-based OSM scheme can achieve substantially improved spectral efficiency with moderate inter-channel interference and low transceiver complexity. Simulation results clearly verify the superiority of the proposed enhanced OFDM-based OSM scheme over the existing OFDM-based OSM schemes.

Optical Wireless Communication (OWC) is gaining popularity as it potentially offers interference–free communication in dense environments. By using LEDs, cells of very small size can be created with sharp boundaries thus bandwidth can be reused very densely. Building an effective communication systems starts from understanding the channel. In scientific literature, various models for the LED response are in use. This overview paper starts from the non-linear recombination of electrons and holes in the LED semiconductor junction, which then appears to lead to a very tractable and simple signal processing model. In fact, the LED channel substantially differs from a radio channel. The paper summarizes recent progress in developing an LED channel model, based on the physics of photon generation in the active region. The LED Quantum Well (QW) creates a first–order low–pass effect and the hole-electron re-combinations are inherently non-linear. Thereby the LED exhibits dynamic distortion, that is, non-linearities that are are intertwined with memory effects. The model can be used to reduce power consumption and to increase throughput of LED OWC systems.

Intensity Modulated (IM) optical communication over an LED channel requires the use of a non-negative signal that can also cope with the low-pass nature of LEDs. For this purpose, dedicated schemes such as Flip-OFDM and Assymetrically Clipped Optical (ACO)-OFDM have been proposed. We derive a common mathematical description on which both schemes rely. Exploiting this insight, we propose Continuous Phase Flip-OFDM (CP-Flip-OFDM) as an enhancement to Flip-OFDM. It ensures phase continuity at the transition between the two successive copies of the OFDM blocks, thereby it obviates the Cyclic Midfix between the first OFDM block and its flipped copy. Simultaneously, we derive a less compute-intensive way to generate and detect ACO-OFDM. Instead of creating an Hermitian-symmetry at the transmit Inverse FFT input, which is common in optical IM OFDM, we use zero padding. After the transmit IFFT, a phase ramp-up, i.e., a multiplication with a complex-valued exponential, is applied before truncating to a real and non-negative signal.

In this paper, we consider a light fidelity (LiFi)-enabled bidirectional Internet of Things (IoT) communication system, where visible light and infrared light are used in the downlink and uplink, respectively. In order to efficiently improve the energy efficiency (EE) of the bidirectional LiFi-IoT system, non-orthogonal multiple access (NOMA) with a quality-of-service (QoS)-guaranteed optimal power allocation (OPA) strategy is applied to maximize the EE of both downlink and uplink channels. We derive closed-form OPA sets based on the identification of the optimal decoding orders in both downlink and uplink channels, which can enable low-complexity power allocation. Moreover, we propose an adaptive channel and QoS-based user pairing approach by jointly considering users' channel gains and QoS requirements. We further analyze the EE and the user outage probability (UOP) performance of both downlink and uplink channels in the bidirectional LiFi-IoT system. Extensive analytical and simulation results demonstrate the superiority of NOMA with OPA in comparison to orthogonal multiple access (OMA) and NOMA with typical channel-based power allocation strategies. It is also shown that the proposed adaptive channel and QoS-based user pairing approach greatly outperforms individual channel/QoS-based approaches, especially when users have diverse QoS requirements.

LEDs, particularly those used for Visible Light Communications (VLC), have a limited bandwidth, while above their 3 dB bandwidth, the roll-off is relatively gentle. If the modulation bandwidth would be limited to the 3 dB LED bandwidth, the achievable rate would be unacceptably constrained. Hence, effective communication systems need to optimize the use of bandwidth significantly above this 3 dB point. Orthogonal Frequency Division Multiplexing (OFDM) is a popular method to fine-tune the amount of power and constellation as a function of the channel response over different frequencies. Various power and bit loading strategies have been proposed and simulated in literature, but their performance was not captured in expressions. This manuscript derives these for optimal waterfilling, uniform and pre-emphasized power loading for the LED channel, that severely attenuates high frequencies. We also investigate the influence of practical discrete constellations and verify our new results experimentally. Interestingly, simple uniform loading only falls less than 1~2% short of the throughput achieved by waterfilling, but when we restrict OFDM to discrete QAM constellation sizes, the penalty for uniform loading is 1.5 dB. Inspired by the good performance of uniform power loading, we propose an algorithm to find the best discrete bit loading for uniform power within an optimized band. As pre-emphasis is nonetheless attractive because a flattened channel does not need adaptive sub-carrier loading, we quantify its penalty. This can be modest provided that the system can adapt its transmit bandwidth, thereby adaptively switching upper sub-carriers to zero power.

This work investigates handover in hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets). In such a network, the handover process becomes challenging due to two main factors: i) the relatively short coverage range of a single access point (AP), and ii) the largely overlapping coverage areas of different networks. As a result, HLWNets are susceptible to frequent handovers. To reduce the handover rate, the concept of handover skipping (HS) was introduced, which enables handovers between non-adjacent APs. However, conventional HS methods rely on knowledge about the user's trajectory, which is not readily available at the AP. In this paper, a novel HS scheme is proposed on the basis of reference signal received power (RSRP) and its rate of change, with an adaptive network preference adopted. Since RSRP is commonly used in the existing handover schemes, the proposed method requires no additional signalling between the user and the AP. Simulation results show that the new method can effectively reduce unnecessary handovers, especially those between LiFi and WiFi. Compared to the standard and trajectory-based handover schemes, the proposed method improves network throughput by up to about 120% and 30%, respectively.

In this paper, we propose a novel non-orthogonal multiple access (NOMA) technique from a spatial domain (SD) perspective for indoor multiple-input multiple-output visible light communication (MIMO- VLC) systems. By fully exploiting the spatial distributions of light-emitting diode (LED) transmitters in the ceiling and users over the receiving plane, SD-NOMA is achieved by assigning all the users to different LEDs in the MIMO-VLC system. Hence, each user only receives data from a specific LED and users assigned to the same LED can use the overall modulation bandwidth of the system. Moreover, a signal-to-noise ratio (SNR) based LED selection scheme is further proposed for each user to efficiently select its desired LED. The achievable rates of a general indoor MIMO-VLC system using conventional MIMO orthogonal frequency division multiple access (MIMO-OFDMA) and the proposed SD-NOMA are analytically derived. The superiority of SD-NOMA over conventional MIMO-OFDMA for multi-user MIMO-VLC systems is successfully verified by detailed analytical results.

Staircase codes (SCCs) are typically decoded using iterative bounded-distance decoding (BDD) and hard decisions. In this paper, a novel decoding algorithm is proposed, which partially uses soft information from the channel. The proposed algorithm is based on marking certain number of highly reliable and highly unreliable bits. These marked bits are used to improve the miscorrection-detection capability of the SCC decoder and the error-correcting capability of BDD. For SCCs with 2-error-correcting Bose-Chaudhuri-Hocquenghem component codes, our algorithm improves upon standard SCC decoding by up to 0.30 dB at a bit-error rate (BER) of 10-7. The proposed algorithm is shown to achieve almost half of the gain achievable by a genie decoder with this structure. The increased complexity caused by bit marking and additional calls to the component BDD decoder is discussed as well. Our algorithm is also extended (with minor modifications) to product codes. The simulation results show that in this case, the algorithm offers gains of up to 0.5 dB at a BER of 10-7.

The visible light communication (VLC) can provide data transmission via the illumination light emitting diodes (LED). This paper introduces a new model to analyze the bit error rate (BER) of binary phase modulation in VLC for an arbitrary modulation depth and data duty cycle while taking into account both the Gaussian and signal-dependent shot noise. The impact of the driver design on the BER and its impact on ripple have not been considered in detail before. We compare two different LED driver schemes, namely directly adapting the driver control loop and binary shunting. We address data rate, BER, and power efficiency, for which we propose to use the extra energy per symbol above unmodulated light. We further introduce an analysis of the effect that (truncated) ripple interference has in the (matched or other) filter of the receiver. Ripple causes a harmful interference in VLC, and thus a BER expression is derived to include its effect. Two approximations are proposed to model the ripple interference, and their accuracies are compared by simulations. A low-pass filtering is proposed to alleviate the impact of ripple interference in VLC system.

This paper addresses the nonlinear memory effects in the response of typical illumination light emitting diodes (LEDs), in order to enhance the performance of visible light communication (VLC) systems. These LEDs have a limited bandwidth of only several MHz. To reflect the physical mechanisms in the quantum well, we describe the LED transient response by a nonlinear dynamic differential equation. Three different mechanisms of the nonlinearity are relevant in the double hetero-structure LEDs, which result in dynamic nonlinearities, that is, a mixture of nonlinearities and memory effects. Hitherto, generic pre-distorter and non-linear equalizers have been studied for the LEDs. Yet this paper shows that recombination rates of photon generation can be translated into an equivalent discrete-time circuit that can be inverted. This allows us to develop a new pre-distorter with a simpler and more efficient structure than previously studied and overly generic approaches. The novel pre-distorter along with a parameter estimation can effectively overcome LED nonlinearity for high-speed VLC with amplitude-based single carrier modulations, including ON-OFF keying and pulse amplitude modulation-4 systems, and with the multi-carrier orthogonal frequency-division multiplexing. We report experimentally obtained eye-diagrams, first to justify our choice for the LED model on which our nonlinear pre-distorter have been based, and second to verify the effectiveness in enhancing the VLC link performance to the extent predicted by our model.

The need for real-time data mining has long been recognized in various application domains. However existing methodologies are still limited to the optimization of single classical data mining algorithms. In this paper, we investigate the development of a general purpose methodology for real-time data mining and propose a novel supporting framework. In the methodology, definition, characteristics and principles of real-time data mining are finely studied. The framework is proposed based on the novel dynamic data mining process model. The model offers the ability to incrementally update data mining knowledge and synchronously execute data mining tasks; an implementation of the framework and a case study are also presented.