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As a class of randomized learner model, stochastic configuration networks (SCNs) have been successfully applied in a few data analytics tasks. Given the industrial big data modeling tasks, however, the original SCNs potentially lead to excessive training time. To this end, this paper extends SCNs to a hybrid parallel version, termed HPSCNs. In the hybrid parallel learning algorithm, two SCNs are synchronously constructed. The difference between the two of them is that one employs a point-incremental algorithm, and another one adopts a block-incremental algorithm. Moreover, a data parallel method is established based on a dynamic block strategy to accelerate the establishment of candidate node pool for each SCN. Additionally, this paper proposes an adaptive hyperparameter adjustment method, which allows the hyperparameters in the supervisory mechanism to be automatically adjusted along with the learning process. Comparative experiments are first conducted through four large-scale benchmark datasets, followed by the fully discussion on the algorithm parameters. Finally, a practical industrial application case is made, where a grinding particle size soft sensor is developed based on HPSCN, showing the effectiveness of the proposed algorithm.

Cloud contamination is a common phenomenon in the optical remote sensing field, which limits their application in land surface studies and causes the waste of satellite images. This letter presents a new framework for removing thin clouds from visible images based on multiscale dark channel prior (MDCP). The cloud removal of cloudy images (target images) is carried out with the assistance of a different temporal cloudless image (reference image) from the perspective view of multiscale transform (MST). In order to make it more suitable for the application of thin cloud removal, two improvements are made to this traditional fusion method. For one thing, a dark channel prior module is integrated into the low-frequency component of the target image in the framework of MST. For another, we choose the weighted average for high-frequency components and sparse representation (SR) for low-frequency components as fusion rules. After the fusion process, the modified Laplacian sharpening whose model is optimized is carried out. The performance of MDCP was evaluated with both simulated and real cloudy images. Experimental results show that the proposed MDCP has a good performance.

Radio-frequency fingerprinting (RFF) exploiting hardware characteristics has been employed for device recognition to enhance the overall security. However, the performance unreliability in long-term experiments, channel fading interference, and unauthorized devices verification are three open problems that restrict the development of RFF recognition. To address these issues, a robust RFF extraction scheme based on three corresponding algorithms is studied. For the first problem, a long-term stacking of repetitive symbols (LSRSs) algorithm is proposed to reduce the acquired signal variance, which contributes to the identification accuracy and long-term stability. For the second issue, we propose an artificial noise adding (ANA) algorithm to enhance the recognition robustness through regularization and channel adaptation. For the third issue, a verification algorithm based on the generative Gaussian probabilistic linear discriminant analysis (GPLDA) model is developed to handle unauthorized devices. Our robust RFF extraction scheme is verified in the experiments with 54 CC2530 ZigBee devices. It enables reliable node identification with the accuracy of 99.50% in the short rang line-of-sight (SLOS) scenarios for signals collected over 18 months, and 95.52% in the extensive multipath fading experiments. The equal error rate (EER) of the verification experiments with six authorized devices versus six unseen unauthorized devices is as low as 0.63%.

The disadvantage of low resolution and low signal-to-noise ratio (SNR) of thermal infrared (TIR) data makes it difficult to be applied in practice, although it has been studied for decades. In contrast, visible and near infrared (VNIR) and shortwave infrared (SWIR) data have higher resolution and SNR. In this letter, an ensemble extreme learning machine (EELM) is proposed, which is based on the extreme learning machine (ELM), to fuse together VNIR, SWIR, and TIR to improve TIR resolution and SNR. The associated ELM nets are trained with correlated training data which are selected from the random forest method. The temperature estimation cube can be obtained after putting SWIR data and VNIR data into these networks. Finally, the cube structure is adjusted by an ensemble rule to obtain the final temperature estimation results. We test the EELM algorithm on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) L1B data set and compare it with other scholars’ algorithms. The experimental results show that the EELM algorithm can not only improve the resolution of TIR data, but also estimate the subpixel temperature of TIR more accurately.

The retrieval of land surface temperature (LST) using thermal infrared (TIR) data is important in many applications. However, TIR data usually suffer from low spatial resolution. We introduce a novel subpixel LST estimation model using the information-transfer-based adaptive ensemble extreme learning machine (IT-AEELM). The proposed method constructs a reliable relationship between subpixel LST and the input high-resolution visible and near-infrared (VNIR) data, short-wave infrared (SWIR) data, and low-resolution TIR data. Based on a detailed analysis of different ground objects, we divide the input data into multiple subsets. Instead of using consistent land surface parameters (LSPs), we utilize different LSPs to characterize the land surface properties in each subset. The VNIR-SWIR-LSPs data and the low-resolution LST are used to train a novel IT-AEELM network, where a feedback ensemble learning scheme is introduced to effectively remove inaccurate estimates. The main difference of the model against existing methods is that it builds a robust architecture at different spatial scales, which provides benefits including lower demand for training data, more rapid and accurate acquisition of subpixel LST, and better adaption to heterogeneous land surface. Numerical experiments demonstrate that the proposed method significantly improves the accuracy of subpixel LST compared with the state-of-the-art algorithms.

Most image fusion algorithms based on hyperspectral unmixing perform worse with the lower spatial resolution of hyperspectral image (HSI) for the reason that the estimated endmembers and abundance deviate from the truth value. Therefore, it is more meaningful to unmix the low spatial resolution hyperspectral image (LRHSI) accurately, which is also helpful to improve the image fusion performance. In order to enhance the spatial resolution of LRHSI, this article proposes an alternating direction iterative nonnegative matrix factorization (ADINMF) based on linear hyperspectral unmixing algorithm. It takes multispectral image as a constraint to improve the spatial resolution of LRHSI. First, we use blind source separation to initialize the endmember and abundance of hyperspectral and multispectral images, respectively. Then, we alternately update the endmembers and abundance in the framework of nonnegative matrix factorization by multiplication iterative algorithm. The updated endmembers and abundance are constrained to each other. We compare the experimental results of simulated dataset and three groups of real datasets. Experimental results show that the proposed method not only accurately extracts the endmembers of LRHSI, but also obtains a significant fusion performance improvement.

A Gaussian multiple-input single-output (MISO) fading channel is considered. We assume that the transmitter, in addition to the statistics of all channel gains, is aware instantaneously of a noisy version of the channel to the legitimate receiver. On the other hand, the legitimate receiver is aware instantaneously of its channel to the transmitter, whereas the eavesdropper instantaneously knows all channel gains. We evaluate an achievable rate using a Gaussian input without indexing an auxiliary random variable. A sufficient condition for beamforming to be optimal is provided. When the number of transmit antennas is large, beamforming also turns out to be optimal. In this case, the maximum achievable rate can be expressed in a simple closed form and scales with the logarithm of the number of transmit antennas. Furthermore, in the case when a noisy estimate of the eavesdropper's channel is also available at the transmitter, we introduce the SNR difference and the SNR ratio criterions and derive the related optimal transmission strategies and the corresponding achievable rates.

The minimization of polarization multiplexing angle in a polarization-division-multiplexed (PDM) transmission system is explored. An effective polarization demultiplexing algorithm based on Stokes space is proposed to realize the demultiplexing of two nonorthogonal polarization states. Both simulation and experimental results verify the effectiveness of the proposed algorithm for the transmission of a 56-Gb/s PDM-dual phase-shift keying (DPSK) signal over different polarization multiplexing angles (i.e., θ 1/4 23°, 45°, 67°, 90°) and different lengths of transmission links (i.e., back-to-back, 320 km, 400 km).

As service-oriented architecture matures and more Web services become available, developers must test an ever-increasing volume of services. A framework that defines and evaluates test-case potency based on coverage relationships can reduce testing effort while maintaining testing's effectiveness.