Impulse breakdown voltages of triggered multistage vacuum gap

Several sealed-off triggered vacuum gaps are connected in series to improve hold-off voltage. The characteristics of impulse breakdown voltage of these series-connected gaps are investigated experimentally. The sum hold-off voltage of series-connected gaps decreases to a unit hold-off voltage when the maximum value of voltage division ratio across the gaps increases to unity. Self-breakdown probability of the series-connected gaps is always higher than that of a single gap under the same conditions. Hence, stage efficiency of the multistage gap decreases with increasing number of stages. Its value is 90 percent with 2-stage gap and 75 percent with 5-stage gap, respectively, under the same voltage division ratio and the same gap length (2.0 mm) in each stage. Triggered breakdown voltage of 2- or 3- stage gap is several hundred volts when all gaps are triggered simultaneously at the peak of the main impulse wave and a working voltage range is nearly 100 percent in this case. The working voltage range decreases with number of stages. Its value is 45 percent with 3-stage gap and 15 percent with 5-stage gap, respectively, when one triggered gap is fired for switching.     

Summarizing consumer reviews

Journal of Intelligent Information Systems - E-commerce giants like Amazon rely on consumer reviews to allow buyers to inform other potential buyers about a product’s pros and cons. While...     

Numerical method using cubic B-spline for the heat and wave equation

In the present paper, the cubic B-splines method is considered for solving one-dimensional heat and wave equations. A typical finite difference approach had been used to discretize the time derivative while the cubic B-spline is applied as an interpolation function in the space dimension. The accuracy of the method for both equations is discussed. The efficiency of the method is illustrated by some test problems. The numerical results are found to be in good agreement with the exact solution.     

Free and forced convection boundary layer flow through a porous medium with large suction

An analytical study is made of the free and forced convection boundary layer flow past a porous medium bounded by a semi-infinite vertical porous plate. Locally similar solutions are then obtained by a perturbation method for large suction. Solutions for the velocity and temperature distributions are shown graphically for various suction velocities and values of the driving parameter G_r/R, where G_r is the Grashof number and R_e is the Reynolds number. The corresponding values of the skin friction coefficient and the Nusselt number are finally shown in tabular form.     

Ensemble learning of diffractive optical networks

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.Specifically,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation speed.Diffractive deep neural networks(D²NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D²NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and imaging.Here,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble learning.After independently training 1252 D²NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D²NNs that collectively improved the image classification accuracy.Through this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D²NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D²NNs within each ensemble.These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.     

Tooling device design for vibration-assisted high speed shaping of PMMA

PMMA optical components that are used as one of the most important parts of high precision equipments and machines are increasingly replacing the glass due to the various advantages of PMMA. Especially in Light Guide Panels, the PMMA sheet that is used in Liquid Crystal Displays plays an important role in scattering the incident light and requires very fine machining as the sheet is directly related to the optical characteristics of the panels. The High Speed End milling and High Speed Shaping processes that are widely adopted and applied to the precise machining of Light Incident Plane still have quality problems, such as cracks, breakages, poor waviness, and straightness. This paper presents the tooling device design for machining a Light Incident Plane through vibration-assisted High Speed Shaping for increasing the optical quality by minimizing the above-mentioned problems. The cutting tool and the tool post presented in this paper are designed by the authors to increase the magnitude of the cutting stroke by adopting the resonant frequency without weakening the stiffness and to reduce vibrations during even high speed feeding. The dynamic characteristics of the cutting tool and the tool post are evaluated through simulation and experiment as well. The results reveal very appropriate dynamic characteristics for vibration-assisted High Speed Shaping.     

New look on relay selection strategies for full-duplex multiple-relay NOMA over Nakagami-m fading channels

Wireless Networks - By removing the orthogonal use of radio-resources, non-orthogonal multiple access (NOMA) has been introduced to improve the spectral efficiency of fifth generation (5G) and...     

VLM Precoded SLM Technique for PAPR Reduction in OFDM Systems

Orthogonal frequency division multiplexing (OFDM) is perhaps the most spectrally efficient, robust transmission technique discovered so far for communication systems, and it also mitigates the problem of multipath environment. High peak-to-average power ratio (PAPR) has always been a major drawback of the OFDM systems. In this article, a new precoding technique has been proposed based on Vandermonde-like matrix (VLM) and selective mapping (SLM) to reduce PAPR in OFDM systems. VLM precoding reduces the autocorrelation of the input sequences while SLM takes an advantage of the fact that the PAPR is very sensitive to phase shifts of the signal. The main advantage of this proposed scheme is to achieve a significant reduction in PAPR without increasing the system complexity. Computer simulations show that, the proposed method outperforms the existing precoding techniques without degrading the error performance of the system.     

CCH: a clique based asymmetric rendezvous scheme for cognitive radio ad-hoc networks

Wireless Networks - Rendezvous (RDV) in cognitive radio ad-hoc networks (CRAHNs) is one of the key ways for a pair of unknown cognitive radio users to initiate communications. CRAHN is a...     

A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback.

A content-based image retrieval (CBIR) framework for diverse collection of medical images of different imaging modalities, anatomic regions with different orientations and biological systems is proposed. Organization of images in such a database (DB) is well defined with predefined semantic categories; hence, it can be useful for category-specific searching. The proposed framework consists of machine learning methods for image prefiltering, similarity matching using statistical distance measures, and a relevance feedback (RF) scheme. To narrow down the semantic gap and increase the retrieval efficiency, we investigate both supervised and unsupervised learning techniques to associate low-level global image features (e.g., color, texture, and edge) in the projected PCA-based eigenspace with their high-level semantic and visual categories. Specially, we explore the use of a probabilistic multiclass support vector machine (SVM) and fuzzy c-mean (FCM) clustering for categorization and prefiltering of images to reduce the search space. A category-specific statistical similarity matching is proposed in a finer level on the prefiltered images. To incorporate a better perception subjectivity, an RF mechanism is also added to update the query parameters dynamically and adjust the proposed matching functions. Experiments are based on a ground-truth DB consisting of 5000 diverse medical images of 20 predefined categories. Analysis of results based on cross-validation (CV) accuracy and precision-recall for image categorization and retrieval is reported. It demonstrates the improvement, effectiveness, and efficiency achieved by the proposed framework.