On the Impact of Scale-Free Structure on End-to-End TCP Performance

2015 ◽  
Author(s):  
Yusuke Sakumoto ◽  
Hiroyuki Ohsaki
Keyword(s):  
Tcp Performance ◽  
Scale Free ◽  
End To End ◽  
The Impact

scholarly journals Fluid-Based Analysis for Understanding TCP Performance on Scale-Free Structure

2016 ◽  
Vol 24 (4) ◽  
pp. 660-668 ◽  
Author(s):  
Yusuke Sakumoto ◽  
Hiroyuki Ohsaki
Keyword(s):  
Tcp Performance ◽  
Scale Free

scholarly journals A Pronunciation Prior Assisted Vowel Reduction Detection Framework with Multi-Stream Attention Method

2021 ◽  
Vol 11 (18) ◽  
pp. 8321
Author(s):  
Zongming Liu ◽  
Zhihua Huang ◽  
Li Wang ◽  
Pengyuan Zhang
Keyword(s):  
Prior Knowledge ◽  
Language Learners ◽  
Native Speakers ◽  
Recognition Rate ◽  
Auxiliary Information ◽  
Vowel Reduction ◽  
Phoneme Recognition ◽  
End To End ◽  
The Impact ◽  
Auxiliary Input

Vowel reduction is a common pronunciation phenomenon in stress-timed languages like English. Native speakers tend to weaken unstressed vowels into a schwa-like sound. It is an essential factor that makes the accent of language learners sound unnatural. To improve vowel reduction detection in a phoneme recognition framework, we propose an end-to-end vowel reduction detection method that introduces pronunciation prior knowledge as auxiliary information. In particular, we have designed two methods for automatically generating pronunciation prior sequences from reference texts and have implemented a main and auxiliary encoder structure that uses hierarchical attention mechanisms to utilize the pronunciation prior information and acoustic information dynamically. In addition, we also propose a method to realize the feature enhancement after encoding by using the attention mechanism between different streams to obtain expanded multi-streams. Compared with the HMM-DNN hybrid method and the general end-to-end method, the average F1 score of our approach for the two types of vowel reduction detection increased by 8.8% and 6.9%, respectively. The overall phoneme recognition rate increased by 5.8% and 5.0%, respectively. The experimental part further analyzes why the pronunciation prior knowledge auxiliary input is effective and the impact of different pronunciation prior knowledge types on performance.

scholarly journals End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2085 ◽  
Author(s):  
Rami M. Jomaa ◽  
Hassan Mathkour ◽  
Yakoub Bazi ◽  
Md Saiful Islam
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Attack Detection ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Biometric Systems ◽  
Fingerprint Biometrics ◽  
End To End ◽  
The Impact

Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). Therefore, several approaches based on a fingerprint biometrics have been developed to increase the robustness against a PA. We propose an alternative approach based on the combination of fingerprint and electrocardiogram (ECG) signals. An ECG signal has advantageous characteristics that prevent the replication. Combining a fingerprint with an ECG signal is a potentially interesting solution to reduce the impact of PAs in biometric systems. We also propose a novel end-to-end deep learning-based fusion neural architecture between a fingerprint and an ECG signal to improve PA detection in fingerprint biometrics. Our model uses state-of-the-art EfficientNets for generating a fingerprint feature representation. For the ECG, we investigate three different architectures based on fully-connected layers (FC), a 1D-convolutional neural network (1D-CNN), and a 2D-convolutional neural network (2D-CNN). The 2D-CNN converts the ECG signals into an image and uses inverted Mobilenet-v2 layers for feature generation. We evaluated the method on a multimodal dataset, that is, a customized fusion of the LivDet 2015 fingerprint dataset and ECG data from real subjects. Experimental results reveal that this architecture yields a better average classification accuracy compared to a single fingerprint modality.

scholarly journals Expected performances of the Characterising Exoplanet Satellite (CHEOPS)

2020 ◽  
Vol 635 ◽  
pp. A22 ◽  
Author(s):  
A. Deline ◽  
D. Queloz ◽  
B. Chazelas ◽  
M. Sordet ◽  
F. Wildi ◽  
...  
Keyword(s):  
Light Curve ◽  
High Precision ◽  
Background Level ◽  
Monte Carlo Algorithm ◽  
Complete Analysis ◽  
Transiting Planets ◽  
End To End ◽  
Set Up ◽  
The Impact

Context. The characterisation of Earth-size exoplanets through transit photometry has stimulated new generations of high-precision instruments. In that respect, the Characterising Exoplanet Satellite (CHEOPS) is designed to perform photometric observations of bright stars to obtain precise radii measurements of transiting planets. The CHEOPS instrument will have the capability to follow up bright hosts provided by radial-velocity facilities. With the recent launch of the Transiting Exoplanet Survey Satellite (TESS), CHEOPS may also be able to confirm some of the long-period TESS candidates and to improve the radii precision of confirmed exoplanets. Aims. The high-precision photometry of CHEOPS relies on careful on-ground calibration of its payload. For that purpose, intensive pre-launch campaigns of measurements were carried out to calibrate the instrument and characterise its photometric performances. This work reports on the main results of these campaigns. It provides a complete analysis of data sets and estimates in-flight photometric performance by means of an end-to-end simulation. Instrumental systematics were measured by carrying out long-term calibration sequences. Using an end-to end model, we simulated transit observations to evaluate the impact of in-orbit behaviour of the satellite and to determine the achievable precision on the planetary radii measurement. Methods. After introducing key results from the payload calibration, we focussed on the data analysis of a series of long-term measurements of uniformly illuminated images. The recorded frames were corrected for instrumental effects and a mean photometric signal was computed on each image. The resulting light curve was corrected for systematics related to laboratory temperature fluctuations. Transit observations were simulated, considering the payload performance parameters. The data were corrected using calibration results and estimates of the background level and position of the stellar image. The light curve was extracted using aperture photometry and analysed with a transit model using a Markov chain Monte Carlo algorithm. Results. In our analysis, we show that the calibration test set-up induces thermally correlated features in the data that can be corrected in post-processing to improve the quality of the light curves. We find that on-ground photometric performances of the instrument measured after this correction is of the order of 15 parts per million over five hours. Using our end-to-end simulation, we determine that measurements of planet-to-star radii ratio with a precision of 2% for a Neptune-size planet transiting a K-dwarf star and 5% for an Earth-size planet orbiting a Sun-like star are possible with CHEOPS. These values correspond to transit depths obtained with signal-to-noise ratios of 25 and 10, respectively, allowing the characterisation and detection of these planets. The pre-launch CHEOPS performances are shown to be compliant with the mission requirements.

SE2IR Invest Market Rumor Spreading Model Considering Hesitating Mechanism

2019 ◽  
Vol 7 (1) ◽  
pp. 54-69 ◽  
Author(s):  
Hongxing Yao ◽  
Xiangyang Gao
Keyword(s):  
Mean Field ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Field Equations ◽  
Rumor Spreading ◽  
Immunization Strategy ◽  
Scale Free ◽  
Spreading Model ◽  
Mean Field Equations ◽  
The Impact

Abstract According to the actual situation of investor network, a SE2IR rumor spreading model with hesitating mechanism is proposed, and the corresponding mean-field equations is obtained on scale-free network. In this paper, we first combine the theory of spreading dynamics and find out the basic reproductive number R0. And then analyzes the stability of the rumor-free equilibrium and the final rumor size. Finally, we discuss random immune strategies and target immune strategies for the rumor spreading, respectively. Through numerical simulation, we can draw the following conclusions: Reducing the fuzziness and attractiveness of invest market rumor can effectively reduce the impact of rumor. And the target immunization strategy is more effective than the random immunization strategy for the communicators in the invest investor network.

Simulated Performance of SCTP and TFRC Over MANETs

2020 ◽  
Vol 16 (2) ◽  
pp. 69-83
Author(s):  
Ali H. Wheeb ◽  
Dimitris N. Kanellopoulos
Keyword(s):  
Multimedia Streaming ◽  
Traffic Load ◽  
Delivery Ratio ◽  
Transmission Protocol ◽  
Streaming Applications ◽  
Simulated Performance ◽  
End To End Delay ◽  
End To End ◽  
The Impact ◽  
Better Than

Mobile ad-hoc networks (MANETs) are composed of mobile nodes communicating through wireless medium, without any fixed centralized infrastructure. Providing quality of service (QoS) support to multimedia streaming applications over MANETs is vital. This paper focuses on QoS support, provided by the stream control transmission protocol (SCTP) and the TCP-friendly rate control (TFRC) protocol to multimedia streaming applications over MANETs. In this study, three QoS parameters were considered jointly: (1) packet delivery ratio (PDR), (2) end-to-end delay, (3) and throughput. Specifically, the authors analyzed and compared the simulated performance of the SCTP and TFRC transport protocols for delivering multimedia streaming over MANETs. Two simulation scenarios were conducted to study the impact of traffic load and node speed (mobility) to their performance. Based on the simulation results, the authors found that the PDR and the end-to-end delay of TFRC are slightly better than those of SCTP in both scenarios. Additionally, the performance of SCTP is significantly better than TFRC in terms of throughput.

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