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At present, enterprises need data for data analysis. They mostly use a kind of point-to-point data transmission form, which does not have regulation mechanism in the process. However, it has the problem of low data reliability, including two parts as follows: 1) Faulty sensor affects the collected data amount of the terminal server; 2) offensive data invade the data in transmission. In view of this, we propose the hierarchical private cloud architecture, including three aspects as follows. Firstly, we use distributed computing and virtualization capabilities of cloud computing to realize the hierarchical transmission of data. Secondly, through this mechanism of hierarchical transmission and classification algorithm of machine learning, we realize hierarchical filtering of offensive data. Finally, by combining hierarchical transmission mechanism with threshold value, classification algorithm, and limit tolerance mechanism, we regulate the data amount to monitor fault sensor in real time. Experiments are conducted to assess the proposed architecture’s performance. The results show that each layer acts as a protective screen to counterattack the offensive data, which shows good robustness, real-time, and adaptive ability. Moreover, to compare with OM mode, the identification efficiency of fault sensor of TM mode is improved by 2 times. Also, TM mode improves 33.33% identification acuity, which is suitable for the enterprises that are mainly based on streaming computing. In summary, the hierarchical private cloud architecture achieves the filtering of offensive data and the real-time identification of faulty sensor, which guarantees the security, accuracy, and integrity of the data transmission process.

Functional electrical stimulation (FES) provides an effective way for foot drop (FD) correction. To overcome the redundant and blind stimulation problems in the state-of-the-art methods, this study proposes a closed-loop scheme for an adaptive electromyography (EMG)-modulated stimulation profile. The developed method detects real-time angular velocity during walking. It provides feedbacks to a long short-term memory (LSTM) neural network for predicting synchronous tibialis anterior (TA) EMG. Based on the prediction, it modulates the stimulation intensity, taking into account of the subject-specific dead zone and saturation of the electrically evoked activation. The proposed method is tested on ten able-bodied participants and six FD subjects as proof of concept. The experimental results show that the proposed method can successfully induce the dorsiflexion of the ankle joint, and generate an activation pattern similar to a natural gait, with the mean Correlation Coefficient of 0.9021. Thus, the proposed method has the potential to help patients to retrieve normal gait. [ABSTRACT FROM AUTHOR]

In this brief, we propose a novel thresholding methodology using a combination an adaptive homomorphic filter (AHF) and a weighted sinc interpolated empirical mode decomposition (EMD) model for medical and nonuniformly illuminated image datasets. In this methodology, we use an AHF to correct the illumination of an image. Later, we obtain a smooth data component from the histogram of an illumination corrected image using weighted sinc interpolated EMD model. From this smooth data component, we obtain an optimal threshold value using global minimum criteria. By using this optimal threshold value, we obtain a thresholded image from the illumination corrected image. The proposed methodology is very simple and more robust to noise and easy for hardware implementation. The performance of the proposed thresholding methodology using weighted sinc interpolated EMD model is compared with other methods in the literature and the results clearly demonstrate the superiority of the proposed methodology for thresholding. [ABSTRACT FROM AUTHOR]

We propose a scheme for random bit generation with filtered amplified spontaneous emission (ASE). Using the filtered ASE, we can get larger signal fluctuations than when using directly detected ASE with a photodetector. Utilizing the delayed self-difference technique to improve the symmetry of filtered ASE, we experimentally achieve real-time 2.5-Gbit/s random sequence generation, which matches with the speeds of fiber communication for one-time pad encryption. Experimental results show that the generated random sequences can pass NIST tests as long as the delay time is beyond a threshold value, which is called the minimum delay time (MDT). Further simulation results indicate that there is a certain relationship between the MDT and bandwidth of the ASE signal. [ABSTRACT FROM PUBLISHER]

Object extraction from either aerial or satellite images is a research focus in the field of image processing, where it is essential to determine whether pixels on an image should be classified as object or background. This is a difficult issue, particularly for those pixels which are uncertain for such a determination. This paper presents a newly developed object extraction method that is based on fuzzy topology. First, the idea that the concept object extraction is understood as a fuzzy set in fuzzy topological space is introduced, i.e., the fuzzy object in object extraction, which is composed of its interior and a portion of its boundary, in fuzzy topological space is introduced. Second, the segmentation of the fuzzy object into the interior and the boundary described. Two threshold values induced by the optimal threshold value are proposed for determining the interior, boundary, and exterior of the fuzzy object. Third, object extraction regarding the boundary is introduced, where the connectivity analysis is applied to determine whether the pixels within the boundary should classified as the object. Finally, as an example of application, the developed fuzzy-topology-based object extraction method is applied to boulder extraction from aerial images. The experimental results demonstrate that the newly proposed method provides better object extraction result, particularly to those boundary pixels which are difficult to determine if they should be classified as the object by the existing solutions. The main contribution this paper includes the following two points: 1) Theoretically, this paper introduces fuzzy topology into object extraction where the object and the image background are understood as fuzzy sets in fuzzy topological space, and 2) this paper newly proposes the connectivity analysis method for determining if the pixels within the object boundary should be classified as the object. The overall quality of object extraction is thus improved greatly. [ABSTRACT FROM AUTHOR]

Recently, the piecewise linear representation (PLR) method has been applied to the stock market for pattern matching. As such, similar patterns can be retrieved from historical data and future prices of the stock can be predicted according to the patterns retrieved. In this paper, a different approach is taken by applying PLR to decompose historical data into different segments. As a result, temporary turning points (trough or peak) of the historical stock data can be detected and inputted to the backpropagation neural network (BPN) for supervised training of the model. After this, a new set of test data can trigger the model when a buy or sell point is detected by BPN. An intelligent PLR (IPLR) model is further developed by integrating the genetic algorithm with the PLR to iteratively improve the threshold value of the PLR. Thus, it further increases the profitability of the model. The proposed system is tested on three different types of stocks, i.e., uptrend, steady, and downtrend. The experimental results show that the IPLR approach can make significant amounts of profit on stocks with different variations. In conclusion, the proposed system is very effective and encouraging in that it predicts the future trading points of a specific stock. [ABSTRACT FROM PUBLISHER]

We present an adaptive algebraic multigrid algorithm. The method is intended for large sparse matrix equations that arise from finite-element discretizations of the stray field in three-dimensional micromagnetism on nonuniform or unstructured grids. It uses a varying threshold value to control the grid ratio, trying to optimize the overall efficiency of the algebraic multigrid solver. We present numerical results and compare them with the preconditioned conjugate gradient method. [ABSTRACT FROM AUTHOR]

A treatment of the density of states of electromagnetic normal modes of slab photonic crystals is given to illustrate the thickness dependence of the Q values of the slab modes of arrayed dielectric spheres. The onset of the laser oscillation is then examined for a slab photonic crystal of arrayed cylinders by simulating active cylinders as having a complex dielectric constant of negative imaginary part γ. It is shown that the divergence of the transmittivity of light occurs as an indication of the onset of lasing, when the slab is thick enough to give high Q values to the slab modes. The threshold value of γ is discussed in comparison with a one dimensional (1-D) multilayer stack of thin film, [ABSTRACT FROM PUBLISHER]

Magnetic levitation force is a key factor that influences energy loss in flywheel energy storage applications. Experiments on the relationships between levitation force and YB/sub 2/Cu/sub 3/O/sub x/ sample geometry have been conducted. The levitation force has been conducted. The levitation force has been measured for single-domain YBa/sub 2/Cu/sub 3/O/sub x/ samples with different thicknesses and diameters. It has been found that the thickness dependence agrees well with a current model. However, the measured levitation force that was found only within the linear regime. The levitation force in fact increases as the diameter decreases until a threshold value is reached. As the diameter of HTS sample is reduced smaller than the testing magnet (d=12 mm) the force experiences a rather rapid fall. This behaviour can be qualitatively described by a magnetic dipole model based on Meissner currents. Other factors that dominate levitation force are also discussed. [ABSTRACT FROM PUBLISHER]