Visible light communication (VLC) employs light-emitting diodes (LEDs) for simultaneous illumination and wireless data transmission, offering advantages such as unlicensed spectrum, immunity to electromagnetic interference, and intrinsic security. Conventional PAM-VLC transmitters generally rely on a single high-power LED driven by analog front-end components, such as digital-to-analog converters and power amplifiers, which increase hardware complexity, power consumption, and thermal burden. To address these limitations, this paper proposes an energy-efficient spatial-combining VLC transmitter in which multiple LEDs are directly driven by FPGA GPIO ports, without using DACs or power amplifiers. Multilevel PAM is digitally realized by controlling the number of activated LEDs, and the emitted optical signals are spatially combined through an optical lens. Experimental results demonstrate reliable 1 m free-space transmission. At a bit-error rate (BER) of 3.8 & times; 10(-3), the proposed scheme achieves SNR gains of 0.75 dB for PAM-4 and 0.8 dB for PAM-8 over the conventional pulse amplitude modulation (PAM)-VLC architecture. Moreover, the proposed transmitter reduces power consumption by 38.7%. These results confirm that digitally driven multi-LED spatial combining is a promising solution for low-cost and energy-efficient VLC systems.

Indoor positioning is essential in applications such as intelligent warehousing and innovative healthcare. Conventional solutions, such as the Global Navigation Satellite System (GNSS) and Wi-Fi, suffer from low accuracy or high deployment costs. In contrast, visible light positioning (VLP) offers immunity to electromagnetic interference and low-cost deployment. However, VLP systems still face challenges due to multipath-induced noise and insufficient use of temporal correlations in fingerprint models. To address these issues, an attention-mechanism-based bidirectional long short-term memory (BiLSTM) and a convolutional neural network indoor VLP (ABC-iVLP) are proposed in the paper. The proposed ABC model comprises a three-stage framework for dynamic weight allocation that incorporates a signal-to-noise attention mechanism to dynamically suppress noise, a BiLSTM to capture temporal dependencies in received signal strength, and a Convolutional Neural Network (CNN) to extract spatial features. Additionally, the proposed ABC-iVLP model is validated in a 2 m × 2 m × 3 m room, yielding a maximum positioning error of 6.47 cm and an average positioning error of 2.33 cm. Compared with the ABC iVLP model without attention, BiLSTM, and CNN, the average positioning error is reduced by 32.5%, 43.4%, and 18.7%, respectively. Furthermore, robustness is verified across varying LEDcounts and ground heights, demonstrating the superiority of ABC-iVLP over existing schemes.

When discussing the spin-dependent optical perfect absorption (PA) phenomenon in a micro-nano structure, the designed structure should contain symmetry-breaking compositions that generally require an applied magnetic field. Fortunately, the multi-Weyl semimetal (mWSM) with natural time-reversal symmetry breaking property provides feasible schemes to investigate the spin-dependent PA without an external magnetic field. Recently, most existing schemes are primarily restricted to single- or dual-band spin-dependent PA in mWSM-based systems. Here, we present a heterostructure comprising five one-dimensional photonic crystals (PCs) and four identical mWSM layers to discuss the tetra-band spin-dependent PA. Results show that, due to the topological edge state-coupled mode and the nonzero off-diagonal term of mWSM, the PA of left-hand circularly polarized and right-hand circularly polarized light is attained at four distinct frequencies, respectively. More importantly, the tetra-band spin-dependent PA phenomenon can be regulated effectively via the tilt degree of Weyl cones, Fermi energy, topological charge, Weyl nodes separation, mWSM thickness, and the periods of the middle three PCs. This study provides an efficient scheme to achieve tetra-band adjustable spin-dependent PA without an external magnetic field, which may have potential applications in spin-dependent photonic devices.

This paper proposes a high-enhanced high-dimensional superposed constellation orthogonal frequency division multiplexing (HD-SC-OFDM) encryption scheme in visible light communication (VLC) systems. This scheme innovatively adopts a cascaded architecture of the 3D Chua chaotic model and the 6D Lorenz chaotic model. It utilizes dynamic multi-cube entanglement architecture (MCEA) for bit-stream encryption and incorporates plane-coupled encryption for the HD constellation. HD chaos and HD-OFDM modulation are inherently compatible within a multi-dimensional mapping and multi-domain dynamic encryption framework, achieving a dual breakthrough in both communication security and transmission performance. Real-valued discrete cosine transform (DCT) and discrete Hartley transform (DHT) are further employed to balance signal-to-noise ratio (SNR) and reduce peak-to-average power ratio (PAPR) of HD-SC-OFDM. Experimental results show that the proposed 6D-SC-OFDM(3+3) achieves Q-factor gains of 3.09/0.64/0.22 dB, alongside data rate improvements of 9.79/0.35/0.14 Gbit/s, compared to 3D-OFDM ,six-dimensional geometrically shaped OFDM (6D-GS-OFDM) and 6D-SC-OFDM(3+2+1), respectively. Moreover, the encryption scheme provides a key space of 10313, ensuring strong resistance to brute-force attacks while maintaining relatively low computation complexity.

In the paper, dual chaotic encryption is proposed and experimentally demonstrated to enhance the transmission performance and physical layer security for an index modulation orthogonal time frequency space (IM-OTFS). Besides, a peak-to-average power ratio optimized encryption scheme using random interleaved partitioning time segmentation (RIPTS) is also proposed to enhance the auto-correlation. To verify the PAPR and encryption performance of the proposed scheme, a back-to-back and 80 km standard single-mode fiber transmission passive optical network (PON) system is conducted. The experimental results show that compared to the IM-OTFS without PAPR optimization, the proposed RIPTS scheme can improve 4.3dB PAPR performance. Moreover, compared with PTS, IPTS and RPTS schemes, it can also improve 0.8dB, 0.6dB and 0.5dB PAPR performance. In addition, the key space size of index and subcarrier encryption was approximately 2⁶⁰⁰⁰ and 4⁴⁰ , respectively.

Trains play a vital role in the life of residents. Fault detection of trains is essential to ensuring their safe operation. Aiming at the problems of many parameters, slow detection speed, and low detection accuracy of the current train image fault detection model, a fast and lightweight train image fault detection model using convolutional neural network (FL-TINet) is proposed in this study. First, the joint depthwise separable convolution and divided-channel convolution strategy are applied to the feature extraction network in FL-TINet to reduce the number of parameters and computation amount in the backbone network, thereby increasing the detection speed. Second, a mixed attention mechanism is designed to make FL-TINet focus on key features. Finally, an improved discrete K-means clustering algorithm is designed to set the anchor boxes so that the anchor box can cover the object better, thereby improving the detection accuracy. Experimental results on PASCAL 2012 and train datasets show that FL-TINet can detect faults at 119 frames per second. Compared with the state-of-the-art CenterNet, RetinaNet, SSD, Faster R-CNN, MobileNet, YOLOv3, YOLOv4, YOLOv7-Tiny, YOLOv8_n and YOLOX-Tiny models, FL-TINet’s detection speed is increased by 96.37% on average, and it has higher detection accuracy and fewer parameters. The robustness test shows that FL-TINet can resist noise and illumination changes well.

Face to DDoS attacks, an effective defense scheme based on SDN for DDoS attacks is proposed in the paper. The proposed scheme includes an attack module, a monitoring module, and a defense module. In the attack module, DDoS attack scenarios are simulated in Mininet by producing the attacking traffic. Then, in the monitoring module, sFlow technology is used to monitor and extract common attack traffic characteristics. Besides, in the defense module, DDoS attack defense is implemented with API in the ONOS controller. Furthermore, when the traffic exceeds the threshold, the speed is limited to prevent the network from being paralyzed, and the traffic type is categorized into attack, ordinary, or business traffic. Then, after DDoS defense is performed, the speed limit is restored, and the QoS rules are used to ensure the forwarding of business traffic. The simulation results demonstrated the excellent performance of the proposed SDN-based solution for defending against DDoS attacks.

This paper introduces a novel monitoring and scheduling strategy based on software-defined networking (SDN) to address the challenges of elephant flow scheduling and localization in conventional networks. The plan involves collecting and analyzing switch data, effectively monitoring elephant flows, and enhancing the traditional distributed solution. Meanwhile, elephant flow scenarios are simulated by the iperf tool, and Fat-Tree and Leaf-Spine topologies are simulated in Mininet. Experimental results demonstrate significant network stability and resource utilization improvements with the proposed strategy. Specifically, in the Leaf-Spine topology, the network throughput stabilized around 8 Mbps with minimal fluctuation and no congestion over a 120-s test, compared to multiple throughput drops to 0 Mbps under the Fat-Tree topology. In addition, the proposed scheduling approach takes advantage of monitoring and scheduling for elephant flow, a promising scheme to enhance traffic management efficiency in large-scale network environments.

This paper proposes an adaptive orthogonal frequency division multiplexing with Reed-Solomon coding (Adaptive OFDM-RS) scheme for mobile visible light communication (VLC) scenarios. This scheme supports real-time code rate selection based on channel state information, choosing k from the set 163, 183, 203, 223, 243. By continuously monitoring the channel signal-to-noise ratio (SNR), the system dynamically adjusts the RS coding strength to maintain error-free transmission while maximizing spectral efficiency. Simulation results demonstrate that the proposed real-time code rate selection mechanism achieves: error-free transmission at SNR ≥ 13 dB for a 16-QAM modulated VLC-OFDM system; and error-free transmission at SNR ≥ 20 dB for a 64-QAM modulated system. These results validate the high robustness and effectiveness of the system under dynamic channel conditions.

To improve the spectral efficiency of a visible light communication (VLC) system, an enhanced Long-Short Term Memory Neural Network (LSTM-NN)-based time-domain equalizer is proposed and demonstrated for an orthogonal time frequency space (OTFS) modulation. Besides, an OTFS-VLC system with different cyclic prefix (CP) lengths is established in a simulation and experiment setup to verify the effectiveness of the proposed LSTM-NN-based equalizer. The simulation and experiment results indicate that the proposed method shows a significant bit error rate (BER) improvement compared to the traditional least squares (LS) equalization algorithm in the OTFS-VLC system without CP, achieving a BER below the hard decision forward error correction (HD-FEC) limit of 3.8 x 10-3 at a bit rate of 747.73 Mbit/s. In addition, the proposed LSTM-NN-based equalizer improves error vector magnitude (EVM) performance from-12.62 dB to-14.73 dB at the receiver.

In the OTFS-IM system, physical layer security (PLS) and peak-to-average power ratio (PAPR) are two critical issues that will affect the safe transmission and efficiency of the system. This paper uses dual chaotic encryption on the index bits and data bits of the index modulation orthogonal time frequency space (OTFS-IM) system. Besides, a peak-to-average power ratio optimized encryption scheme using random interleaved partitioning time segmentation (RIPTS) is also proposed to reduce autocorrelation. To verify the PAPR and encryption performance of the proposed scheme, a Back-to-back (B2B) and 80 km standard single-mode fiber (SSMF) transmission passive optical network (PON) system is conducted. The simulation and experimental results show that compared to the OTFS-IM without PAPR optimization, the proposed RIPTS scheme can improve 4.3 dB PAPR performance. Moreover, compared with partial transmit sequence (PTS), interleaved PTS (IPTS), and random PTS (RPTS) schemes, it can also improve 0.8 dB, 0.6 dB, and 0.5 dB PAPR performance and effectively reduce the inter-carrier interference(ICI). In addition, the dual chaotic encryption system is controlled by six parameters, so the total key space can be up to 1090, respectively. The research in this paper provides a reliable solution to the PLS problem of the OTFS-IM PON system and optimizes the system transmission performance.

In this letter, an enhanced transfer learning-based automatic modulation recognition (AMR) scheme is proposed and experimentally demonstrated in orthogonal frequency division multiplexing visible light communication (OFDM-VLC) systems. Within the proposed Gaussian transfer learning (GTL) framework, a ResNet deep convolutional neural network (DCNN) is constructed using Gaussian constellations as the training set. The performance of the proposed method is evaluated by using GoogLeNet, AlexNet and ImageNet transfer learning (ITL) as benchmarks. Experimental results show that, using only 120 training samples with a limited number of constellation points per sample, ResNet with ITL achieves accuracy improvements of 13% and 7% over GoogLeNet and AlexNet with ITL, respectively. Furthermore, ResNet with the proposed GTL achieves an additional 4% accuracy enhancement compared to ResNet utilising conventional ITL.

In this paper, we propose and implement a digital chaos-based encryption scheme that enhances the security of free space communication in a photonics-assisted terahertz wave (THz-wave) wireless data transmission system operating at 340 GHz. We experimentally demonstrate that the delivery of traditional and encrypted 16 Gbaud 16QAM-OFDM and PS-64QAM-OFDM (5.5 bit/symbol) signals over 20 km of standard single mode fiber (SSMF) and a wireless distance 25/54 m. The bit-error ratio (BER) performance of each signal meets the soft-decision forward-error-correction (SD-FEC) threshold of 4.2 x 10_2. The experimental results show that the encryption scheme does not degrade the performance of the OFDM signals. Furthermore, we analyze the encryption algorithm's security based on Kerckhoff's principle, which states that a tiny deviation from the correct keys would make the eavesdropper unable to obtain useful information, and the BER would remain around 0.5. The proposed encryption scheme based on a digital three-dimensional multi-scroll chaotic system, has a huge key space of size 2 145 bits, which can effectively prevent exhaustive key-search attacks.

Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at .

In this paper, we introduce the application of multiple-mode index modulation (MMIM) to filter bank multi-carrier (FBMC) for the first time in visible light communication (VLC) systems. Additionally, we propose a group-interleaved precoding (GIP) technique to enhance the performance of MM-FBMC-IM-based VLC systems. The GIP technique reduces complexity in precoding by grouping and achieves equalization of the signal-to-noise ratio (SNR) through subcarrier interleaving. Furthermore, we develop a robust low-complexity maximum likelihood (LCML) detector, which can maintain the same computational complexity as a conventional LCML detector and achieve similar performance as an ML detector. The effectiveness and superiority of the proposed MM-FBMC-IM-based VLC system with GIP are demonstrated through comprehensive validation by both simulation and experimental results.

We experimentally demonstrate 8-channel wavelength division multiplexing (WDM) signal transmission in 4 modes over 20-m multimode fiber (MMF) in an intensity modulation and direct-detection (IM/DD) system. By combining the probabilistic shaping (PS) technique with high order pulse amplitude modulation (PAM) format, 40-GBaud PS-PAM8 signal transmission in the WDM+MDM IM/DD system achieves a total net bit rate of 3-Tbit/s. With the aid of a multiple input single output (MISO) neural network (NN) equalizer, we obtain a 1.6-dB receiver sensitivity gain compared with traditional digital signal processing (DSP), including multiple input multiple outputs (MIMO) time domain equalizer (TDE).

In this paper, a symbol cyclic shift-based discrete Fourier transform (SCS-DFT) precoding for orthogonal time frequency space (OTFS) is proposed and experimentally demonstrated in the visible light communication (VLC) system. The proposed scheme effectively mitigates the channel fading effect of edge subcarriers caused by inter-symbol interference (ISI) when there is no cyclic prefix (CP) in OTFS. In addition, only a training sequence (TS) symbol can realize channel estimation and equalization, and it can reduce the implementation complexity. The experimental results demonstrated that, compared with the conventional OTFS modulation, the proposed SCS-DFT OTFS can enhance the transmission performance while maintaining the high spectral efficiency of the VLC system.

We experimentally demonstrate 8-channel wavelength division multiplexing (WDM) signal transmission in 4 modes over 20-m multimode fiber (MMF) in an intensity modulation and direct-detection (IM/DD) system. By combining the probabilistic shaping (PS) technique with high order pulse amplitude modulation (PAM) format, 40-GBaud PS-PAM8 signal transmission in the WDM + MDM IM/DD system achieves a total net bit rate of 3-Tbit/s. With the aid of a multiple input single output (MISO) neural network (NN) equalizer, we obtain a 1.6-dB receiver sensitivity gain compared with traditional digital signal processing (DSP), including multiple input multiple outputs (MIMO) time domain equalizer (TDE).

We propose a modified multi-symbol output (MSO) neural network (NN)-based equalization scheme. In this scheme, the multi-label realization is optimized to support high-order 64-QAM MSO, and the performance is enhanced by adding residual connections, which split linear and nonlinear equalization in one model. The proposed scheme is utilized in a 70-m D-band photonics-aided millimeter wave (MMW) transmission system. We successfully realized 12-Gbaud 64-QAM transmission with Q-factor satisfying SD-FEC.