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As the Internet of Things (IoT) becomes increasingly prevalent, there is a surge in wireless-connected devices. In this context, one technique that has garnered significant attention is visible light communication (VLC) due to its ultra-wide and license-free frequency resource. However, a critical issue in utilizing VLC in IoT lies in its lack of smooth cooperation with ubiquitous radio frequency (RF)-based wireless networks. RF signals can use duplex techniques to transmit and receive bidirectional signals with one antenna. In contrast, VLC's light-emitting diodes (LEDs) and PIN diodes cannot operate in duplex mode, posing challenges in establishing two unidirectional VLC links. To address the mismatch between VLC and RF, we propose a light-based RF transceiver design called LiRF, capable of smooth transmission of RF signals through VLC for IoT devices. To verify its feasibility, we first build a prototype using 802.11ax (WiFi-6) network interface cards (NICs) in the 5 GHz channel. Experimental results show that LiRF is compatible with the 802.11bb standard, supporting TCP/IP data streams at 750 Mbps with a 200 MHz Superluminescent Diode-PIN (SLD-PIN) transceiver and at 600 Mbps with a 180 MHz LED-PIN transceiver. To the best of our knowledge, this is the first real-time bidirectional VLC system utilizing WiFi NICs capable of achieving near-Gbps data rates for a single spatial stream without altering the RF designs. LiRF paves the way for seamlessly integrating VLC into upcoming IoT networks, supporting high-speed, low-latency applications like Virtual Reality and Augmented Reality.

A novel acquisition scheme of pseudo-noise (PN) codes is proposed for spreading satellite communication systems relying on the proposed multi-voter model of this paper. Based on the proposed model, the acquisition of PN codes can be attributed to a voting and selecting mechanism to pick out the erroneous chips. Although message passing algorithm (MPA) is a feasible algorithm for decoding PN codes, MPA does not get good detection performance due to the limited number of parity check equations in the acquisition scheme. To overcome the negative impact of the limited number of parity check equations, this paper proposes single-voter and multi-voter models, which combine chip and sequence estimation on the basis of the chip-flipping (CF). It is known that the bit-flipping (BF), weighted-bit-flipping (WBF) and other algorithms based on BF are credible for low density parity check (LDPC) codes. Because of the lack of research, these algorithms have not been extended to the detection of PN codes. As the same as BF, the inputs of the proposed CF algorithm are the hard-decision samples. For the proposed CF, there exists an optimal flipping-threshold which is similar to the random weight of the WBF. Owing to the low computation complexity of CF, the unlimited number of parity check equations can be enabled in the voting model. The experimental results show that the detection performance of the proposed method of $N > $ 15 is improved by 2 dB compared with MAP-based method and 4 dB compared with LEAP-based method at the detection probability 99%.

Visible light communications (VLC) utilizing LEDs for transmissions have been widely considered a revolutionary solution for next-generation networks such as 6G. Frequency response (FR) is of great importance for LEDs, but the inherent internal quantum efficiency (IQE) of LEDs is often disregarded, resulting in imprecise FR models. Although it is widely known that IQE varies with the injected direct current (DC), the impact of IQE on the frequency response has not been thoroughly analyzed. To address this issue, we have developed a precise electro-optical (E-O) FR model by incorporating the ABC model of IQE, named IQE-FR. To validate the accuracy of IQE-FR, we conducted the experiments using commercial off-the-shelf (COTS) LEDs including a tri-color LED and three monochromatic LEDs. The IQE-FR model aligns well with the experimental findings in terms of the maximum FR value, while state-of-the-art FR models that ignore the IQE effects do not yield consistent results. Specifically, we discovered that the red LEDs exhibit a stable DC gain when the biasing current rises to 300 mA, but the highest DC gain for green and blue LEDs can only be achieved under 100 mA. For green and blue LEDs, the biasing currents are much lower than their typical working currents (350 mA and 500 mA). To address the trade-off between luminance and DC gain, we propose cascading multiple LEDs operating at a low current corresponding to the maximum response. Experimental results demonstrate that four cascaded monochromatic LEDs exhibit similar bandwidth and response gain larger than 11 dB compared to a single LED at the same biasing current while delivering four times the emission power, which is very close to the theoretical value of 12 dB. Furthermore, we find that the DC gain improvement by cascading multiple LEDs is nonlinear and bounded by an upper limit as the number of LEDs increases, and as a cost-effective solution, a few tens of LEDs is recommended. To the best of our knowledge, this is the first attempt to apply the IQE model to study the LEDs’ precise FR performances for VLC. We believe that the precise IQE-FR model opens new avenues for optimizing VLC system performance by considering the effects of IQE.

Visible light communications (VLC) has experienced rapid development in recent years as a strong competitor for next-generation wireless applications due to its wider bandwidth, higher security, and better electromagnetic immunity compared with conventional radio frequency (RF) microwaves. Although state-of- the-art VLC systems can achieve Gbps data rates by employing equalization schemes, designing a general low-complexity VLC transmitter with hundreds of MHz 3-dB bandwidth is still challenging due to the narrow modulation bandwidth nature of light emitting diodes (LEDs). In this paper, we first present a second-order equivalent circuit model for the LED, based on which we propose a general second-order equalizer (GSE) with low complexities, consisting of less than 5 passive capacitors, inductors, and resistors. We show that the GSE can enlarge the LED transmitter's bandwidth to a few hundred MHz. To validate our GSE, we build a broadband VLC transmitter using commercial-off-the-shelf (COTS) red, green and blue (RGB) LEDs, whose bandwidth is 14 MHz, by summing up three colours. Experimental results show that our proposed GSE can extend the transmitter's 3-dB bandwidth from 14 MHz to 1.5 GHz. Furthermore, we demonstrate that a VLC system utilizing the proposed GSE transmitter can achieve 1.15 Gbps data rates at a distance of 250 cm with a bit error ratios (BERs) below the forward error correction (FEC) limit $3.8 \times 10^{-3}$.

Visible light communications (VLC) is a good candidate technology for the 6th generation (6G) wireless communications. Red, green, and blue (RGB) light-emitting diodes (LEDs) based VLC has become an important research branch due to its low price and high reliability. However, the saturation of photodiode (PD) caused by the ambient background light may seriously degrade the bit error rate (BER) performance of an RGB-VLC system's three spatially uncoupled information streams (i.e., red, green, and blue LEDs can transmit different data packets simultaneously) in practical applications. To mitigate the ambient light interference in point-to-point RGB-VLC systems, we propose, PNC-VLC, a network-coded scheme that uses two LEDs with the same color at the transmitter to transmit two different data streams and we make use of the naturally overlapped signals at the receiver to formulate physical-layer network coding (PNC). The adaptivity of PNC-VLC could effectively improve the BER degradation problem caused by the saturation of PD under the influence of ambient light. We conducted simulations based on the parameters of commercial off-the-shelf (COTS) products to prove the superiority of the PNC-VLC under the influence of four typical illuminants. Simulation results show that the PNC-VLC system can maintain a better and more stable system BER performance under different ambient background light conditions. Remarkably, with 2/3 throughput efficiency, PNC-VLC can bring 133.3% gain to the BER performance when compared with RGB-VLC under the Illuminant A interference model, making it a good option for VLC applications with unpredictable ambient background interferences.

(2N+1) selective harmonic elimination pulse-width modulation (SHE-PWM) is an effective switching strategy of the modular multilevel converter in medium-voltage cases. In these cases, the number of sub-modules (SMs) is not high. Compared to the traditional (N+1) SHE-PWM, (2N+1) SHE-PWM has N more voltage levels, thus it can yield much better harmonic performance. However, the task of selective harmonic elimination also becomes much more complicated since there are more switching angles to be determined. This paper proposes a differential harmony search algorithm (DHS) with a novel harmony improvisation procedure for solving this problem. The Bayesian optimization method is applied to find the optimal parameter configuration for DHS. The performance of DHS is compared with 6 other metaheuristic algorithms including differential evolution (DE), harmony search (HS), genetic algorithm (GA), particle swarm optimization (PSO), teaching and learning-based optimization (TLBO), and ant colony optimization (ACO). The comparison is conducted on a set of 100 (2N +1) SHE-PWM instances by varying the modulation index from 0.01 to 1.0 with a step of 0.01. The numerical results show that the proposed DHS outperforms other compared methods in terms of objective function values, algorithm robustness, the magnitude of fundamental harmonic, and the calculated total harmonic distortion values. The switching angles obtained by DHS are further validated by both Matlab/Simulink simulation and hardware experiment.

A variation of the One-to-one Pickup and Delivery Problem (OPDP) in connected graphs, the Split Demand One-to-one Pickup and Delivery Problem with the Shortest-path Transport along Real-life Paths (SDOPDPSTRP) is abstracted from passenger train operation plans based on networks. Unlike the classical OPDP, in the SDOPDPSTRP: the demands can be split and must be transported along the shortest path according to passengers requirements and vehicles should travel along a real-life path. A new kind of integer programming model is formulated for the SDOPDPSTRP based on the connection relationship between pickup-delivery demands (pd-pairs). Two different categories of splitting strategies are proposed to solve the SDOPDPSTRP: split the demands before the calculation and split the demands during the calculation. Two Multi-Start Variable Neighborhood Descent (a MS_VND originating from the other literature and a new MS_VND’ IN developed in this article) and seven neighborhood operators are proposed for these two splitting strategies to solve the SDOPDPSTRP. The results show that Approach III outperforms Approach I and Approach II in terms of average solutions with the same algorithm termination conditions and in terms of time efficiency, which has great practical significance for real-life transport organizations.

Being a promising candidate technology for the fifth-generation cellular systems, the ultra-dense network (UDN) is capable of improving the system throughput by deploying more small cells (SCs) in the existing macro-cells (MCs). In most cases, base stations (BSs) from both the MCs tier and SCs tier are modeled as two independent homogeneous Poisson point processes (PPPs) or other point processes derived from a PPP. In the previous works, the area spectral efficiency (ASE) and energy efficiency (EE) are evaluated from a typical user's perspective, and this method works well in single-user scenarios. However, there are multiple users sharing spectral resources in practical cellular systems, and the above-mentioned method cannot work in these scenarios. Therefore, we investigate the ASE and EE from the perspective of a typical BS. Analytical results show that the ASE increases with the increasing SCs density, which means that deploying more SCs in the MCs coverage area is a feasible way to enhance the system throughput. However, the energy consumption of the entire network is increasing as the SCs density increases. Therefore, we employ the firefly algorithm to jointly optimize the ASE and EE for the two-tier heterogeneous UDN. Finally, the optimal system parameters can be found under two fixed weights for the ASE and EE, respectively.

This paper investigates deep learning for risk detection and trajectory tracking at construction sites. Typically, safety officers are responsible for inspecting and verifying site safety due to many potential risks. Traditional target detection algorithms depend heavily on hand-crafted features. However, these features are difficult to design, and detection accuracy is poor. To solve these problems, this paper proposes a deep-learning-based detection algorithm that uses pedestrian wearable devices (e.g., helmets and colored vests) to identify pedestrians. We train a special dataset by labeling helmets and colored vests to detect the two features among construction workers. Specifically, Kalman filter and Hungarian matching algorithms are employed to track pedestrian trajectories. The testing experiment is run on an NVIDIA GeForce GTX 1080Ti with a detection speed of 18 frames/s. The mean average precision can reach 0.89 when the intersection over union is set at 0.5.

Optical character recognition (OCR) has become one of the most important techniques in computer vision, given that it can easily obtain digital information from various images on the Internet of Things (IoT). However, existing OCR techniques pose a big challenge in the recognition of the Chinese uppercase characters due to their poor performance. In order to solve the problem, this paper proposes a deep learning-aided OCR technique for improving recognition accuracy. First, we generate a database of the Chinese uppercase characters to train four neural networks: a convolution neural network (CNN), a visual geometry group, a capsule network, and a residual network. Second, the four networks are tested on the generated dataset in terms of accuracy, network weight, and test time. Finally, in order to reduce test time and save computational resources, we also develop a lightweight CNN method to prune the network weight by 96.5% while reducing accuracy by no more than 1.26%.

Human emotions recognition (HERO) is considered as one of the important techniques for realizing the intelligent Internet of Things. The demand for a robust and precise facial expression recognition algorithm is urgent for the HERO. In this paper, we propose a deep recognition algorithm based on the ensemble deep learning model. The proposed algorithm consists of three sub-networks with different depths. Each sub-network is comprised of convolutional neutral networks and trained independently. The sub-network with more convolutional layers recognizes emotions by extracting local details such as the features of eyes and mouth, while the sub-network with less convolutional layers focuses on the macrostructure of the input image. The three sub-networks are assembled together to constitute the whole model. The experiment is based on the Kaggle facial expression recognition challenge database (FER2013), the Japanese female facial expression database, and the AffectNet database. The experimental results show that the proposed algorithm achieves a test accuracy of 71.91%, 96.44%, and 62.11% better than other competitors, and increases the test accuracy by approximately 2-3% than unique sub-networks.

The coding tools of the High Efficiency Video Coding (HEVC) standard were essentially designed for ordinary video contents. They have not fully exploited the long-term temporal dependence of surveillance videos and conference videos, in which the backgrounds are generally stationary. In this paper, a periodic-enhanced frame-based long-short-term reference scheme is presented for coding the videos with stationary backgrounds. First, periodic-enhanced frames are proposed based on global rate-distortion optimization, in which the background error propagation characteristic is introduced to reduce the long-term temporal redundancy. Second, two limitations, such as temporary occlusion and quantization error, are analyzed as applying the enhanced frame on a default HEVC hierarchical prediction structure. In order to reduce the limitations, a long-short-term reference scheme is presented based on the enhanced frames. In this scheme, the periodic-enhanced frames are employed as a long-term reference picture, and the adjacent frames are set as short-term reference pictures. The proposed method is verified by testing on the conference videos and surveillance videos.

In the downlink transmission scenario, power allocation and beamforming design at the transmitter are essential when using multiple antenna arrays. This paper considers a multiple input–multiple output broadcast channel to maximize the weighted sum-rate under the total power constraint. The classical weighted minimum mean-square error (WMMSE) algorithm can obtain suboptimal solutions but involves high computational complexity. To reduce this complexity, we propose a fast beamforming design method using unsupervised learning, which trains the deep neural network (DNN) offline and provides real-time service online only with simple neural network operations. The training process is based on an end-to-end method without labeled samples avoiding the complicated process of obtaining labels. Moreover, we use the “APoZ”-based pruning algorithm to compress the network volume, which further reduces the computational complexity and volume of the DNN, making it more suitable for low computation-capacity devices. Finally, the experimental results demonstrate that the proposed method improves computational speed significantly with performance close to the WMMSE algorithm.

This paper is concerned with the spectrum sensing problem for cognitive radio networks with correlated receiving multiple antennas in the time-varying fading channel. We first consider the scenario that all the antennas have the same noise variance and present a generalized real-valued weighted-covariance-based detection (GRWCD) method. In particular, we derive the distribution of the GRWCD statistic under the null hypothesis, which allows us to develop the theoretical decision threshold for a given false alarm probability. Besides, we derive the distribution of the GRWCD statistic under the alternative hypothesis, which enables us to provide a mathematical expression for the detection probability as well as the theoretical receiver operating characteristic. Meanwhile, we consider a more general scenario of unequal per-antenna noise variances and present a modified GRWCD method as well as the theoretical expressions of the decision threshold. The simulation results are provided to verify the accuracy of the derived results and show that the proposed two methods are capable of providing performance improvement over several advanced methods in the literature.

In high efficiency video coding (HEVC), transformation and quantization are separately performed to eliminate the perceptual redundancy of visual signals. However, a uniform quantizer can inevitably degrade the compression efficiency of fixed transform matrices due to varying space-frequency characteristics of video content. This paper introduces a joint optimization of transform and quantization approach for video coding. First, we compute a content dependent transform from the reconstructed reference by a fast Karhunen-Loéve transform (KLT). Second, using a template-based rate regularization, we jointly optimize transform and quantization (JOTQ) as a rate constrained optimization problem and obtain a feasible solution to improve coding performance. Finally, we design fast algorithms and early terminations to reduce the computational complexity of JOTQ. The experimental results show that JOTQ outperforms several previous methods by providing Bjøntegaard Delta rate reductions of 4.11% and 3.38% on average under the low-delay and random-access configuration, respectively.

The five-level hybrid clamped inverter (5L-HC) is a promising new topology for medium-voltage applications. However, to minimize the capacitors and ensure good performance of the converter, capacitor voltage ripples, as well as voltage balancing control, need further study. Based on carrier-phase-shift PWM (CPS-PWM), this paper gives a rigorous analysis of the ripple characteristics of all the capacitors (which consist of three groups: the flying capacitors, the central dc capacitor, and the upper and lower dc capacitors) under both normal and unbalanced operating conditions. The results can serve as a guide for the optimal design of the capacitors. First, as for the balancing issue, a self-balancing mechanism of the central dc capacitor, which is similar to the better-known self-balancing feature of the flying capacitors, is revealed. Then, a new voltage balancing strategy is proposed for these two groups of capacitors to cope with disturbances arising from various nonideal factors. For the upper and lower dc capacitors, which suffer from intrinsic imbalance, this paper proposes a better method to calculate the optimal zero-sequence voltage (ZSV), which is to be injected to achieve the balancing objective. In this paper, the ripple analysis and balancing strategy proposed are verified with simulations and experiments.

The emerging computer vision and deep learning technologies are being applied to the intelligent analysis of sports training videos. In this paper, a deep learning based fine-grained action recognition (FGAR) method is proposed to analyze soccer training videos. The proposed method was applied to indoor training equipment for evaluating whether a player has stopped a soccer ball successfully or not. First, the problem of FGAR is modeled as human-object (player-ball) interactions. The object-level trajectories are proposed as a new descriptor to identify fine-grained sports videos. The proposed descriptor can take advantage of high-level semantic and human-object interaction motions. Second, a cascaded scheme of deep networks based on the object-level trajectories is proposed to realize FGAR. The cascaded network is constructed by concatenating a detector network with a classifier network (a long-short-term-memory (LSTM)-based network). The cascaded scheme takes the advantage of the high efficiency of the detector on object detection and the outstanding performance of the LSTM-based network on processing time series. The experimental results show that the proposed method can achieve an accuracy of 93.24%.

The emerging high efficiency video coding (HEVC) Standard has significantly improved the compression performance in comparison with its predecessor H.264/AVC. However, it was originally designed for generic video contents. The backgrounds are generally static in the surveillance and conference videos. The background coding errors will propagate to the subsequent frames in coding the videos. In this paper, a background error propagation (BEP) model-based rate distortion optimization (RDO) scheme in HEVC is proposed for the surveillance and conference videos. First, the R-D performance of the long-term frames is optimized globally. The global RDO scheme can efficiently exploit the background error propagation. Second, a BEP model is studied to express the linear relationship between the distortion of the first frame and that of its subsequent frames. Based on the BEP model, enhanced frames are proposed to be coded with a small quantization parameter so as to improve the global performance. Third, a decay model is proposed to investigate the variation of the error propagation ratio as the frame order increased. Based on the decay model, a periodical optimization scheme is presented by deploying the enhanced frames periodically. Experiments are tested on the surveillance and conference videos. The results show that the proposed algorithm achieves significant performance improvement.

Compressive sensing (CS) is an effective approach for compressive recovery, such as the imaging problems. It aims at recovering sparse signal or image from a small number of under-sampled data by taking advantage of the sparse signal structure. $L_{1/2}$ -norm regularization in CS framework has been considered as a typical nonconvex relaxation approach to approximate the optimal sparse solution, and can obtain stronger sparse solution than $L_{1}$ -norm regularization. However, it is very difficult to solve the nonconvex optimization problem efficiently resulted by $L_{1/2}$ -norm. In order to improve the performance of $L_{1/2}$ -norm regularization and extend the application, we propose a multiple sub-wavelet dictionaries-based adaptively-weighted iterative half thresholding algorithm (MUSAI- $L_{1/2}$ ) for sparse signal recovery. In particular, we propose an adaptive-weighting scheme for the regularization parameter to control the tradeoff between the fidelity term and the multiple sub-regularization terms. Numerical experiments are conducted on some typical compressive imaging problems to demonstrate that the proposed MUSAI- $L_{1/2}$ algorithm can yield significantly improved the recovery performance compared with the prior work.

The control accuracies of the injection time and fuel quantity are the most important factors affecting the fuel economy and emissions of an engine equipped with a high-pressure common-rail fuel injection system. This paper presents an intelligent dual-voltage driving control that can simultaneously reduce the response time of the injector and improve the accuracy of the injected fuel quantity. The designed method employs a novel boost dc-dc circuit and does not require an additional independent dc-dc module that includes an inductor switch MOSFET and a controller. During the time interval between two continuous injections, the low-side MOSFET serves as a dc-dc switch, and the software in the controller controls the dual-voltage dc-dc, which uses the injector as a charging inductor of the dc-dc module. The operation principle of this intelligent driving circuit is described. Experimental results show that this method can not only significantly decrease the response time of the injector but also improve the stability of the fuel injection process.

Traditional surveillance systems for observing water levels are often complex, costly, and time-consuming. In this paper, we developed a low-cost unmanned surveillance system consisting of remote measuring stations and a monitoring center. The system uses a map-based Web service, as well as video cameras, water level analyzers, and wireless communication routers necessary to display real-time water level measurements of rivers and reservoirs on a Web platform. With the aid of a wireless communication router, the water level information is transmitted to a server connected to the Internet via a cellular network. By combining complex water level information of different river basins, the proposed system can be used to forecast and prevent flood disasters. In order to evaluate the proposed system, we conduct experiments using three feasible methods, including the difference method, dictionary learning, and deep learning. The experimental results show that the deep learning-based method performs best in terms of accuracy and stability.

Nonorthogonal multiple access (NOMA) with successive interference cancellation is considered as one of the most promising schemes in multi-user access wireless networks. Based on the principles of NOMA and full-duplex (FD) communications, this paper proposes a novel FD-aided cooperative NOMA (FD-NOMA) scheme to optimize the maximum achievable rate region. Since selfinterference often exists in FD communications, a self-interference canceller is employed in this system. Specifically, three schemes that aim to maximize the achievable rate region are provided. The first one is investigated under the assumption that the transmitted power is fixed, while the other two schemes are achieved with the aid of two developed algorithms. For the purpose of studying the rate region in the nonideality condition, the error vector magnitudelevel is introduced in the analysis of the third scheme. Finally, analytical results demonstrate that the proposed FD-NOMA scheme outperforms the conventional schemes based on NOMA in terms of the rate region, and the maximum rate region of the FD-NOMA scheme is compared with different coefficients.