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Spatiotemporal fusion is a method of fusing high spatial resolution low temporal resolution remote sensing images and low spatial resolution high temporal resolution in order to obtain high spatiotemporal resolution remote sensing images, which can provide data support for temporal observation of fine objects, and plays an important role in the fields of Earth sciences, environmental monitoring, and so on. This article reveals an issue that is often overlooked in the field of deep learning-based spatiotemporal fusion: the discontinuity between image blocks and image blocks. This discontinuity may have an impact on the visualization of remote sensing images and subsequent applications. In this regard, this article proposes a spatially seamless stitching approach to optimize the spatiotemporal fusion model based on deep learning. By using this method, we successfully obtain high-quality fused remote sensing images with smoother transitions. The spatiotemporal fusion model used in the experiment is a generative adversarial network-based spatiotemporal fusion model (GAN-STFM) and the data are from the Beijing Gaofen-6 dataset (BJGF6). After our splicing method, the ratio of root-mean-square error (RMSE) at the splicing seam to the overall RMSE is reduced from 1.28 to 0.99, which effectively improves the continuity of the image. This new image splicing method has the potential to improve the utility of deep learning-based spatiotemporal fusion algorithms, which has application value for generating large-scale long time series remote sensing datasets with high temporal and high spatial resolution.

This article discusses the design and implementation strategy for highly reliable communication systems that support autonomous ambulances on an unmanned aerial vehicle platform. Statistics show that more accidents occur during heavy rainfall, but radio links that operate in excess of 10 GHz are particularly prone to rain-induced attenuation and depolarization. Maximizing quality of service to provide reliable wireless links for telemedicine systems is therefore an important issue to be thoroughly addressed in optimizing system reliability. A prognostics and network health management framework for automated adjustment of the link and system margins is proposed, based on statistical results of point rainfall attenuation obtained from long-term measurement and scattering with a case study of a 39 GHz signal propagating through rain. The results are applied to a self-cognizant prognostics algorithm for smart autonomous ambulances that support critical operations across difficult terrain. Near forward scattering of 58° and near backward scattering of 175°, as well as perpendicular scattering, were studied. This provides important insights into implementing self-cognizant prognostics system resource management such that 5G-based, as well as moving toward 6G, telemedicine systems can be optimized for reliability given the appropriate system fade margin derived from the measurement results. [ABSTRACT FROM AUTHOR]

This paper introduces a novel method for road target segmentation in the context of autonomous driving based on stereo disparity maps. The proposed method utilizes topological persistence threshold analysis to address the challenges of selecting appropriate thresholds. The approach involves converting stereo road images into uv-disparity maps, extracting road planes using v-disparity maps, and calculating occupancy grid maps using u-disparity maps. Persistence diagrams are then constructed by generating segmentation results under various threshold parameters. By establishing persistence boundaries in these diagrams, the most significant regions are identified, enabling the determination of robust segmentation thresholds. Experimental validation using KITTI stereo image datasets demonstrates the effectiveness of the proposed method, with low error rates and superior performance compared to other segmentation methods. The research holds potential for application in autonomous driving systems.

The compensation process for the state of polarization (SOP) from quantum signal plays an important role in the practical implementation of continuous-variable quantum key distribution (CV-QKD). In contrast to the previous scheme using an expensive digital dynamic polarization controller, we choose a cheaper manual polarization controller interfaced with a digital algorithm based on Kalman filter to achieve compensation of the polarization loss under the condition that the SOP of the fiber is relatively stable. This paper also enumerates the details of other digital signal processing method to achieve final secret key rate. And a pilot-sequential Gaussian modulated coherent state (GMCS) continuous-variable quantum key distribution system with a local local oscillator (LLO) is experimentally implemented based on the analysis of excess noise. Finally, the experimental results show that the method of polarization loss compensation takes advantages in maintaining the stability of the final secret key rate. And a secret key rate of 110 kbps is achieved within 20 km optical fiber (with 4 dB loss) under the finite-size block of $1 \times {10^{6}}$ during a more extended time in the laboratory.

In this paper, the total ionizing dose (TID) effects of the Sense-Switch pFLASH cell consisting of sense (T1) and switch (T2) co-floating gate pFLASH transistors fabricated with a $0.13\mu \text{m}$ eFLASH technology are studied. Firstly, the impact of TID effects on the programming/erasing state’s characteristics of T1and T2 transistors are mainly analyzed though Current-Voltage (I-V) and Transconductance-Voltage (Gm-V) curves. Secondly, the re-programming/erasing state’s characteristics of the pFLASH device after TID irradiation, and its threshold voltage, sub-threshold swing, maximum transconductance and other parameters are thoroughly investigated. Lastly, it can be concluded that the threshold voltage of T1 and T2 transistors is mainly determined by the changes of floating gate charge (i.e., programming state: electron emission and hole injection; erasing state: electron injection and hole emission), trap charge concentration in oxide layers around floating gate (Not) and trap charge concentration in the interface trap of tunnel oxide (Nit). Meanwhile, the absolute values of the programming and erasing threshold voltage of T1 and T2 transistors decrease linearly with the cumulative dose, and the change rates are about 2.25 mV/rad (Si) and 1.45 mV/rad (Si), respectively. In addition, the charge change of floating gate under the ionizing radiation can be compensated by re-programming and re-erasing, but the charge of Nit and Not will increase with the increase of cumulative dose. [ABSTRACT FROM AUTHOR]

Based on the basic theories of Poole Frenkel (P-F) emission, Fowler Nordheim (F-N) tunnel and Ohmic transport models, the linear relationship between breakdown voltage (BV) and leakage, I-V and accelerated stress test (AST) characteristics of a-Si dielectric layers are analyzed by the manufactured metal-to-metal (MTM) anti-fuse cells in details. The effects of different ion implant conditions on the amorphization degree of a-Si films are characterized by X-Ray diffraction (XRD). Meanwhile, the influence of annealing and ion implant process on the electrical properties of a-Si thin films are studied. Lastly, it is found that the implantation of E15 Si, Ar and SiF3 ions can promote the further amorphization of a-Si films, effectively improve the BV uniformity, reduce the leakage current and enhance the reliability of MTM anti-fuse cells. Especially, SiF3 ion implantation can reduce the leakage current of MTM anti-fuse cells by two orders of magnitude, which is mainly attributed to a-Si conductive band being greatly elevated by strong negative F ions, and it results in the increase of the potential barrier of a-Si/metal interface. Therefore, it can be further proved that SiF3 ion implant process may promote modification of a-Si films. [ABSTRACT FROM AUTHOR]

In this study, we introduce an improved semisupervised deep learning approach, and demonstrate its suitability for modeling the relationship between forest structural parameters and satellite remote sensing imagery and producing forest maps. The improved approach is based on a popular UNet model, modified and fine-tuned to improve the forest parameter prediction performance. Within the improved model, squeeze-and-excitation blocks are embedded to recalibrate the multisource features via retrieved channel-wise self-attention and a novel cross-pseudo regression strategy is implemented to train the model in a semisupervised way. The improvement imposes consistency learning on two perturbed network branches: 1) generating regression pseudo-reference; 2) expanding the dataset size. For demonstration, we used satellite synthetic aperture radar (SAR) Sentinel-1 and multispectral optical Sentinel-2 images as remote sensing data, complemented with reference data represented by forest tree height as one of the key forest structural variables. The study area is located in a boreal forestland in Central Finland. Proposed approach showed larger accuracy compared to traditional machine learning methods such as random forests and boosting trees, and baseline UNet model. Best accuracy figures for forest tree height were achieved with combined SAR and optical imagery and were as small as 24.1% root-mean-square error (RMSE) on pixel-level and 15.4% RMSE on forest stand level.

The Gamma distribution based generalized linear model ( $Ga$ GLM) is a kind of statistical model feasible for the positive value of a non-stationary stochastic system, in which the location and the scale are regressed by the corresponding explanatory variables. This paper theoretically investigates the asymptotic properties of maximum likelihood estimates (MLE) of $Ga$ GLM, which can benefit the further interval estimates, hypothesis tests and stochastic control design. First, the score function and the Fisher information matrix for $Ga$ GLM are derived. Then, the Lyapunov condition is derived to ensure the asymptotic normality of the score function normalized by the Fisher information matrix. Based on this condition, the asymptotic normality of the MLE of $Ga$ GLM is proven. Finally, a numerical example is given to testify the asymptotic properties obtained in the research. The numerical results indicate that the MLE of $Ga$ GLM converged to a normal distribution as the number of sample measurements increased.

In this paper, novel Dual-Functional Radar-Communication (DFRC) waveforms with peak average power ratio (PAPR) constraint are designed, which are under the multiple-input multiple-output (MIMO) radar-communication system. The DFRC waveforms transmitted by multiple antennas can send communication information to many downlink cellular users and detect radar targets simultaneously. An optimization model is established to minimize the downlink multi-user interference (MUI) under PAPR constraint. The model is non-convex quadratically constrained quadratic programs (QCQP) and can be derived into a convex problem and solved by using the semi-definite relaxation (SDR) technique with rank-one approximation. Numerical simulations demonstrate that our proposed waveforms can achieve a better radar performance in practical scenarios without sacrificing the communication performance.

High spatial resolution and broad spatial coverage data on fine particulate matter (PM2.5) are of great significance to estimating the exposure to PM2.5. However, this type of data is currently very limited worldwide. In addition, the COVID-19 pandemic in China, starting in January 2020, may have led to significant variations in the PM2.5 concentrations. To identify the variations and causes of PM2.5 concentrations before and after the COVID-19 pandemic from January 23 to March 24 during 2018–2020, in this article, a geographically weighted regression model with a 1 km spatial resolution covering all of mainland China was developed. The overall R and RMSE values of the model cross validation were 0.91 and 17.19 μg/m3, respectively, indicating that the model performed satisfactorily in estimating the PM2.5 values. Then, based on the satellite-based PM2.5 values, the results show that the PM2.5 values fluctuated significantly across mainland China before and after the COVID-19 outbreak. Additionally, the mean PM2.5 values decreased by 5.41 μg/m3 in 2020 compared to 2019. In Hubei Province, the mean PM2.5 values increased by 1.85 μg/m3 in 2019 compared to 2018, whereas they dramatically decreased by 23.18 μg/m3 in 2020 compared to 2019. Finally, the results show that anthropogenic factors were primarily responsible for the variations in the PM2.5 concentrations in Heilongjiang, Jilin, and Liaoning provinces; whereas, both meteorological and anthropogenic factors were responsible for the variations in Hubei, Henan, Anhui, Shandong, and Jiangsu provinces during the study period. These results provide an important reference for the future development of air pollution control policies in China.

The Faraday laser, with Faraday anomalous dispersion optical filter (FADOF) as frequency-selection element, is a natural narrow-bandwidth light source for laser physic experiments. In this work, a dual-frequency (DF) Faraday laser is demonstrated for the first time on Cs D2 line at 852 nm. The frequencies of the two modes of DF Faraday laser hinge on the peak transition frequencies of 852 nm FADOF transmittance spectrum corresponding to the ground state F=4 and F=3. The frequency difference between the two modes is tunable over a range of 1.4 GHz with the temperature of Cs vapor cell inside the FADOF. The linewidth of each laser mode is less than 33 kHz. Furthermore, the most probable linewidth of the beat-note spectra between the two modes is 902.95 Hz at different vapor-cell temperatures. Such a DF Faraday laser has many potential applications in atomic physics, such as sub-Doppler spectroscopy and coherent population trapping atomic clocks.

Brain-computer interface (BCI) is a promising and very helpful technology. BCI studies have attempted to predict arm movements to control robotic arm depending on movement intentions. However, the low accuracy of movement prediction is a critical challenge in predicting arm movements. The aim of this study was to predict arm movement with high accuracy from non-invasive neural signals using the deep learning algorithm. We compared the prediction accuracies of the conventional method and long short-term memory (LSTM) using non-invasive MEG signals. This is the first study that applied LSTM to predict arm movements from non-invasive neural signals. The coefficients of correlation between real signals and signals predicted by the MLR on the x-, y-, and z-axes were 0.677 ± 0.139 (mean ± SD), 0.689 ± 0.140, and 0.785 ± 0.103, respectively. The coefficients of correlation between real signals and signals predicted by the LSTM on the x-, y-, and z-axes were 0.978 ± 0.004, 0.980 ± 0.005, and 0.980 ± 0.008, respectively. The prediction accuracy was highly improved using the LSTM algorithm. Our results suggest that highly accurate prediction of arm movement is possible without surgery using the deep learning algorithm. We expect that the deep learning algorithm will facilitate the control of a robot arm using non-invasive signals in real life.

The low-latency advantages of fog computing can be applied to solve high transmission latency problems of many network architectures in Internet of Vehicles. Therefore, this paper studies the application of fog computing in Internet of Vehicles. Considering that the fog network equipment deployed in Internet of Vehicles is relatively scattered, a new network architecture is proposed, which integrates cloud computing, fog computing and software defined network and other technologies. The proposed framework uses software defined network to centrally control fog network and obtains equipment performance of fog network. Furthermore, the optimal load balancing strategy is developed by communication overhead and other information. Based on time delay modeling of fog network, we study the time delay modeling of cloud-fog network and the energy consumption modeling of fog network. In addition, this paper models the selection process of data transmission network and data calculation execution server of delay-tolerant data as a partially observable Markov decision process optimization strategy in software defined Internet of Vehicles. By observing the state of system, current storage makes optimal decisions on data transmission and selection of computing nodes, thereby minimizing system overhead. Simulation results show that the proposed scheme can effectively reduce transmission delay and system overhead, improve data calculation efficiency.

This paper proposes a robust Poisson multi-Bernoulli mixture (PMBM) filter with inaccurate process and measurement noise covariances. A derivation of the robust PMBM filter is provided for jointly estimating the kinematic state, the predicted state covariance, and the measurement noise covariance. By modeling the augmented state as a Gaussian inverse Wishart inverse Wishart (GIWIW) distribution, a computationally feasible implementation of the robust PMBM (GIWIW-PMBM) filter is given for linear Gaussian systems. To guarantee the conjugacy of the GIWIW distribution, the variational Bayesian (VB) approach is employed to approximate the posterior density. Finally, simulation results show that the GIWIW-PMBM filter has the best overall performance compared to existing state-of-the-art filters regarding computational cost and filtering performance.

The fast and diversified development of Internet services leads to numerous services with differentiated delay tolerance of optical connections set-up are converging in the optical network. Consequently, the average set-up delay of optical connection is increased, which will inevitably bring pressures to optical networks. To solve this problem, a delay aware routing spectrum assignment (RSA) algorithm based on services scheduling in priority queues with dynamic virtual topology construction (QVT-RSA) is proposed. First, the QVT-RSA algorithm reasonably identifies various different services and arranges them in order according to their priority, waiting time and delay tolerance requirements. Then, software defined networks (SDN) controller with multi-threads technology is adopted to innovatively parallel serve the ordered service requests to reduce the average set-up delay of optical connections, which also offset the induced delay caused by services scheduling. The network resource is accurately shared among multi-threads using the proposed mutual exclusive resource utilization model realized by dynamic virtual topology construction. The continuously coloring and usable virtual topology linkage reconfiguration can provide topology resources for service requests and adapt the dynamic transformation among serving, waiting and new arriving service requests. The shortest-path routing and first fit spectrum assignment (SPFF-RSA) algorithm is used as the baseline algorithm. Furthermore, by combining the SPFF-RSA algorithm and partial strategy of our strategy, the QSPFF-RSA and V-RSA algorithms are proposed as baseline algorithms as described in V-B. The simulation experiment results demonstrate that the average optical connections set-up delay of QVT-RSA algorithm is reduced compared with SPFF-RSA and QSPFF-RSA algorithms, and the blocking probability of QVT-RSA algorithm is reduced compared with SPFF-RSA, QSPFF-RSA and VRSA algorithms. The optical connections set-up success probability within delay tolerance about high, medium and low priority services of QVT-RSA algorithm is higher than that of the SPFF-RSA, QSPFF-RSA and VRSA algorithms.

Ozone synthesis in air dielectric barrier discharge (DBD) was studied with an emphasis on the effects of power sources and humidity. Discharge characteristics were investigated to understand the physical properties of plasma and corresponding system performance. It was found that 10-ns pulsed DBD produced a homogeneous discharge mode, while ac DBD yielded an inhomogeneous pattern with many microdischarge channels. At a similar level of the energy density (ED), decreasing the flowrate is more effective in the production of ozone for the cases of the ac DBD, while increased voltage is more effective for the pulsed DBD. Note that the maximum ozone production efficiency (110 g/kWh) was achieved with the pulsed DBD. At the ED of $\sim 85$ J/L, the ozone concentrations with dry air were over three times higher than those with the relative humidity of 100% for both the ac DBD and pulsed DBD cases. A numerical simulation was conducted using a global model to understand a detailed chemical role of water vapor to ozone production. It was found HO2 and OH radicals from water vapor significantly consumed O atoms, resulting in a reduction in ozone production. The global model qualitatively captured the experimental trends, providing further evidence that the primary effect of humidity on ozone production is chemical in nature. [ABSTRACT FROM AUTHOR]

Privacy amplification (PA) is a vital procedure in quantum key distribution (QKD) to shrink the eavesdropper's information about the final key almost to zero. With the increase of repeat frequency of discrete variable QKD (DV-QKD) system, PA processing speed has become the bottleneck in many high-speed DV-QKD systems. In this paper, a high-speed adaptive field-programmable gate array (FPGA)-based PA scheme using a fast Fourier transform (FFT) is presented. To decrease the computation complexity, a modified 2-D FFT-based Toeplitz PA scheme is designed. To increase the processing speed of the scheme on the constraint of limited resources, a real-value oriented FFT acceleration method and a fast read/write balanced matrix transposition method are designed and implemented in our scheme. The experimental results on a Xilinx Virtex-6 FPGA demonstrate that the throughput is nearly double of the latest FPGA based Toeplitz PA scheme according to the literature. Besides, this scheme owns not only the good adaptivity to compression ratio but also the compression ratio independent resource consumption. Therefore, this scheme can fit many high-speed QKD applications.

Data in the real world is often dirty. Inconsistency is an important kind of dirty data; before repairing inconsistency, we need to detect them first. The time complexities of the current inconsistency detection algorithms are super-linear to the size of data and not suitable for the big data. For the inconsistency detection of big data, we develop an algorithm that detects inconsistency within the one-pass scan of the data according to both the functional dependency (FD) and the conditional functional dependency (CFD) in our previous work. In this paper, we propose inconsistency detection algorithms in terms of FD, CFD, and Denial Constraint (DC). DCs are more expressive than FDs and CFDs. Developing the algorithm to detect the violation of DCs increases the applicability of our inconsistency detection algorithms. We compare the performance of our algorithm with the performance of implementing SQL queries in MySQL and BigQuery. The experimental results indicate the high efficiency of our algorithms.

Frequent itemset mining is an important operation to return all itemsets in the transaction table, which occur as a subset of at least a specified fraction of the transactions. The existing algorithms cannot compute frequent itemsets on massive data efficiently, since they either require multiple-pass scans on the table or construct complex data structures which normally exceed the available memory on massive data. This paper proposes a novel precomputation-based frequent itemset mining (PFIM) algorithm to compute the frequent itemsets quickly on massive data. PFIM treats the transaction table as two parts: the large old table storing historical data and the relatively small new table storing newly generated data. PFIM first pre-constructs the quasi-frequent itemsets on the old table whose supports are above the lower-bound of the practical support level. Given the specified support threshold, PFIM can quickly return the required frequent itemsets on the table by utilizing the quasi-frequent itemsets. Three pruning rules are presented to reduce the size of the involved candidates. An incremental update strategy is devised to efficiently re-construct the quasi-frequent itemsets when the tables are merged. The extensive experimental results, conducted on synthetic and real-life data sets, show that PFIM has a significant advantage over the existing algorithms and runs two orders of magnitude faster than the latest algorithm.

Mental fatigue is a gradual and cumulative phenomenon that manifests in the weakening of human physiological activities for ubiquitous edge computing in the Internet of Things. In this paper, two groups of Stroop tasks with different difficulty levels are proposed to induce fatigue, which is evaluated via electroencephalogram (EEG). Wavelet packet decomposition and sample entropy algorithm are utilized to analyze the EEG signals in both sober and fatigue state. The experiment results show that compared with the sober state, the fatigue state has a higher α wavelet relative energy and 8 wavelet relative energy and significantly lower β wave relative energy (P

Corresponding to the continual development of human-computer interaction technology, the use of emotional computing (EC) is gradually emerging in the Internet of Things (IoT). Emotion recognition is considered a highly valuable aspect of EC. Numerous studies have examined emotion recognition based on electroencephalogram (EEG) signals, but the recognition rate is unreliable. In this paper, a feature extraction method is proposed that is based on double tree complex wavelet transform (DTCWT) and machine learning. The emotions of 16 subjects are induced under video stimulation, and the original signal is acquired using a Neuroscan device. Both EEG and electromyography (EMG) signal are then eliminated by band-pass filtering, and the reconstructed signal of each frequency band is obtained by DTCWT. Finally, support vector machine (SVM) is utilized to classify three kinds of emotions: calm, happy, and sad, obtaining a classification accuracy of 90.61%. Results show that the proposed algorithm can effectively extract the feature vector and improve the problem of low accuracy in multiple class recognition.

For graph $G=(V,E)$ , the open neighborhood of a vertex $v$ is $N(v)=\{u\in V|uv\in E\}$ . A function $f: V(G)\rightarrow \{0, 1, 2\}$ is called an Italian dominating function of $G$ if $\sum _{u\in N(v)}f(u)\geq 2$ , for each vertex $f(v)=0$ . The weight of $f$ is $w(f)=\sum _{v\in V(G)}f(v)$ . The minimum weight of an Italian dominating function of $G$ is called the Italian domination number of $G$ , denoted by $\gamma _{I}(G)$ . In this paper, we present a bagging approach and a partitioning approach to investigate the Italian domination number of Cartesian product of circles and paths, $C_{n}\Box \,P_{m}$ . We determine the exact values of the Italian domination numbers of $C_{n}\Box \,P_{3}$ , $C_{3}\Box \,P_{m}$ . We also present some bounds on the Italian domination number of $C_{n}\Box \,P_{m}$ for $n, m\geq 4$ .

Privacy amplification is an indispensable step in postprocessing of continuous-variable quantum key distribution (CV-QKD), which is used to distill unconditional secure keys from identical corrected keys between two distant legal parties. The processing speed of privacy amplification has a significant effect on the secret key rate of a CV-QKD system. We report the high-speed parallel implementation of the length-compatible privacy amplification algorithm based on a graphic processing unit. The length-compatible algorithm is used to satisfy the security requirements of privacy amplification at different transmission distances when considering finite-size effect. We achieve the speed of privacy amplification over 1 Gb/s at arbitrary input length and the speed is one to two orders of magnitude faster than previous demonstrations, which supports the high-speed real-time CV-QKD system and ensures the security of privacy amplification.

Balanced homodyne detector (BHD) that can measure the field quadratures of coherent states has been widely used in a range of quantum information technologies. Generally, the BHD tends to suffer from narrow bands and an expanding bandwidth behavior usually traps into a compromise with the gain, electronic noise, and quantum to classical noise ratio, etc. In this paper, we design and construct a wideband BHD based on radio frequency and integrated circuit technology. Our BHD shows bandwidth behavior up to 1.2 GHz and its quantum to classical noise ratio is around 18 dB. Simultaneously, the BHD has a linear performance with a gain of 4.86 k and its common mode rejection ratio has also been tested as 57.9 dB. With this BHD, the secret key rate of continuous-variable quantum key distribution system has a potential to achieve 66.55 Mb/s and 2.87 Mb/s, respectively, at the transmission distance of 10 and 45 km. Besides, with this BHD, the generation rate of quantum random number generator could reach up to 6.53 Gb/s.

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.

As a power- and bandwidth-efficient modulation scheme, the optical spatial modulation (SM) technique has recently drawn increased attention in the field of visible light communications (VLC). To guarantee the number of bits mapped by the transmitter's index at each timeslot is an integer, the number of transmitters (i.e., light-emitting diodes) in the SM-based VLC system is often set be a power of two. To break the limitation on the required number of transmitters and provide more design flexibility, this paper investigates the SM-based VLC with an arbitrary number of transmitters. Initially, a channel-adaptive bit mapping (CABM) scheme is proposed, which includes three steps: bit mapping in the space domain, bit mapping in the signal domain, and channel adaptive mapping. The proposed CABM scheme allows operation with an arbitrary number of transmitters, and is verified to be an efficient scheme through numerical results. Based on the CABM scheme, the information-theoretical aspects of the SM-based VLC are analyzed. The theoretical expression of the mutual information is first analyzed. However, it is very hard to evaluate system performance. To obtain more insights, a lower bound of the mutual information is derived, which is in closed form. Both theoretical analysis and numerical results show that the gap between the mutual information and its lower bound is small. Finally, to further improve the system performance, the precoding scheme is proposed for the SM-based VLC. Numerical results show that the system performance improves dramatically when using the proposed precoding scheme.

Skyline query is a typical preference query method. Due to its capacity of extracting interesting information from multi-dimensional datasets abided by multiple criterions, the skyline query has been extensively studied. All of the existing studies assume that the data are complete and available, which may not hold in many real applications because of device exception, privacy protection and other reasons. Datasets with missing attribute values or missing tuples are called incomplete datasets. In this paper, we mainly discussed the case of incomplete attribute values in a dynamic dataset. First, considering the dynamic dataset, we propose the kISkyline algorithm based on the traditional sliding window model with a split bucket strategy. We then propose the sISkyline algorithm based on a real point, virtual point, and shadow point with a split bucket strategy along with the traditional sliding window model. Finally, simulation results are provided to demonstrate the feasibility and effectiveness of these two new algorithms.

Skyline query retrieves a set of skyline points which are not dominated by any other point and has attracted wide attention in database community. Recently, an important variant G-Skyline is developed. It aims to return optimal groups of points. However, when data dimensionality is high, G-Skyline result has too many groups, which makes that users cannot determine which groups are satisfactory. To find less but more representative groups of points, in this paper, we propose a novel concept of $k$ -dominant G-Skyline, which first adopts $k$ -dominance to retrieve more representative points and then computes the groups not $k$ -dominated by others. In addition, we present a two-phase algorithm to efficiently compute $k$ -dominant G-Skyline groups. In the first phase, we construct a $lk$ DG structure while pruning the points never included in any $k$ -dominant G-Skyline group as much as possible. In the second phase, using $lk$ DG, we propose two efficient $k$ -dominant G-Skyline searching methods SM-P and SM-G, which generate new candidate groups from single points and ancestor groups, respectively. Our experimental results indicate that our proposed algorithms are more efficient than the baseline methods on real and synthetic data sets.

The constraint following stabilization problem of aerospace mechanical manipulators containing uncertainty is investigated. Due to the inevitable modeling error and external disturbance, there always exists uncertainty. To guarantee that the mechanical system follows prescribed constraints (holonomic or non-holonomic), two classes of adaptive robust controls are proposed. The system performance is represented based on a $\varphi $ -measure. The control scheme consists of two parts: the nominal control part and the adaptive robust control part. By the Udwadia–Kalaba theory, the nominal control is proposed to force the nominal mechanical system to meet the constraints. Furthermore, the adaptive robust control is proposed to address the uncertainty issue, while the adaptation law is proposed to estimate the bounding information of the uncertainty. Under the control, the system is guaranteed to follow the constraint regardless of the uncertainty.