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Elastic optical networks (EONs) are considered as the most promising technology for interconnecting data centers. With the rapid growth of inter- datacenter traffic, power consumption of EONs becomes a significant challenge. In this work, we present a lightpath management algorithm that uses traffic prediction techniques to eliminate unnecessary lightpath termination and re- establishment so as to...
CNNs have made an undeniable impact on computer vision through the ability to learn high-capacity models with large annotated training sets. One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically smaller) target dataset. This is usually accomplished through fine-tuning a fixed-size network on new target data. Indeed, virtually every contemporary...
Automatic analysis of the mood of a piece of music is of great value in music searching, understanding, recommendation and some other music-related applications. Different from most of previous methods that adopted a discriminative mood classification scheme, in this paper, we propose a generative multimodal method for automatically classifying the mood of a piece of music based on effective learning...
In order to find out the tradeoff between recovery time and resource overhead when a link failure occurs, we propose a software-defined based fast restoration scheme for elastic optical datacenter networks, called precomputation based restoration path (P-RP). By extending the controller functionality and OpenFlow protocol in software defined networking (SDN) technology, we establish a novel elastic...
This paper considers the problem of moving source localization by introducing an additional Doppler frequency shift (DFS) measurement into the previous location system using time differences of arrival (TDOAs) and frequency differences of arrival (FDOAs). Because of the separation of associated data channels and the difference in the estimation techniques, the DFS is independent of TDOA and FDOA measurements...
The finer spectrum granularity unit in elastic optical networks makes wider spectrum services more likely to be restricted by the constraints of spectrum continuity and spectrum contiguity (2SC), leading to higher blocking probability (BP). Obviously, it would deteriorate the fairness among multi-granularity services. Therefore, a fair dynamic routing and spectrum allocation algorithm is proposed...
In this paper, we explore an approach to generating detectors that is radically different from the conventional way of learning a detector from a large corpus of annotated positive and negative data samples. Instead, we assume that we have evaluated “off-line” a large library of detectors against a large set of detection tasks. Given a new target task, we evaluate a subset of the models on few samples...
Collaborative representation based classification (CRC) has been successfully used for visual recognition and showed impressive performance recently. However, it directly uses the training samples from each class as the subspaces to calculate the minimum residual error for a given testing sample. This leads to high residual error and instability, which is critical especially for a small number of...
Recently, sparse representation based classification (SRC) has been successfully used for visual recognition and showed impressive performance. Given a testing sample, SRC computes its sparse linear representation with respect to all the training samples and calculates the residual error for each class of training samples. However, SRC considers the training samples in each class contributing equally...
Energy security, greenhouse gas emissions, and debate on climate change caused increased interest of penetrating of renewable power generation into power systems. Among the renewable power generation technologies, wind power generation takes a key share of load supply. However, the wind is intermittent and its output needs to rely on auxiliary supports to uniformly serve the energy customers. The...
The current sparse representation framework is to decouple it as two subproblems, i.e., alternate sparse coding and dictionary learning using different optimizers, treating elements in bases and codes separately. In this paper, we treat elements both in bases and codes ho-mogenously. The original optimization is directly decoupled as several blockwise alternate subproblems rather than above two. Hence,...
Sparse representation technique has been widely used in various areas of computer vision over the last decades. Unfortunately, in the current formulations, there are no explicit relationship between the learned dictionary and the original data. By tracing back and connecting sparse representation with the K-means algorithm, a novel variation scheme termed as self-explanatory convex sparse representation...
A W-band two-stage amplifier MMIC has been developed using a InP-based high electron mobility transistor (HEMT) technology. The two-stage amplifier has been realized in combination with coplanar waveguide topology and cascode transistors, thus leading to a compact chip-size of 1.85 mm×0.932 mm and an excellent small-signal gain of 25.7 dB at 106 GHz. The successful design of the two-stage amplifier...
This article describes a novel signal separation algorithm for the purpose of separating the signals overlapped both in time and frequency domain in HF Band by using direction-finding (DF) data. According to the HF wide band DF data, the time-frequency-power spectrum and its corresponding azimuth information are first transformed into azimuth-frequency-energy spectrum of DF data along the time axis;...
The e-carboline compound and evodiamine were synthesized with improved Bischler-Napieralski-type and Diels-Alder-type reactions by taking tryptamine and N-methyl anthranilic acid as starting materials, enjoying the characteristics of few steps, simple ness, mild reaction conditions, high productivity and less pollution etc. Conduct elementary analyses on the resulting compounds and characterize them...
Ultra-Wide Band radio-over-fiber (UWBoF) techno-logy involves two kinds of communications. One is the UWB wireless communication, and the other is optical fiber communi-cation. In this paper, the UWBoF receiving system based on the external modulation such as Mach-Zehnder (M-Z) modulator is analyzed. The UWBoF components and the UWBoF receiver structure are introduced. The received high frequency...
Recently, dictionary learned by sparse coding has been widely adopted in image classification and has achieved competitive performance. Sparse coding is capable of reducing the reconstruction error in transforming low-level descriptors into compact mid-level features. Nevertheless, dictionary learned by sparse coding does not have the ability to distinguish different classes. That is to say, it is...
Non-negative Matrix Factorization (NMF) has become a powerful tool for image representation due to its enhanced semantic interpretability under non-negativity. Unfortunately, two types of neighborhood information essential to representation are lost in NMF. For individual image, the local structure information is missing in the vectorization, which can then be avoided by Non-negative Tensor Factorization...
A five-stage W-band low-noise amplifier (LNA) based on the authors' InP/InGaAs double heterojunction bipolar transistors (DHBTs) process is reported. The LNA achieves a peak gain of 33.1 dB and 7.8 dB noise figure at 81GHz. Its output-related 1 dB compression point (P1dB) lies at 5.8 dBm. The high gain and linearity of the LNA is mainly attributed to the performance of the DHBTs exhibiting a high...
Currently the most popular HBTs model are unscalable and cannot be used for device structure optimization which must primarily be calibrated with already fabricated and measured devices. To overcome the problem, a physical model for scaling and optimizing layers structure of InGaAs/InP double heterojunction bipolar transistors (DHBTs) based on hydrodynamic simulation is developed in this paper.
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