An End-To-End IPTV Broadcast Service Network Architecture

2006 ◽  
Author(s):  
Bashar Bou-Diab ◽  
Bijan Raahemi
Keyword(s):  
Network Architecture ◽  
Service Network ◽  
End To End

scholarly journals A language model for Amdo Tibetan speech recognition

2021 ◽  
Vol 336 ◽  
pp. 06016
Author(s):  
Taiben Suan ◽  
Rangzhuoma Cai ◽  
Zhijie Cai ◽  
Ba Zu ◽  
Baojia Gong
Keyword(s):  
Speech Recognition ◽  
Network Architecture ◽  
Language Model ◽  
Acoustic Model ◽  
End To End

We built a language model which is based on Transformer network architecture, used attention mechanisms to dispensing with recurrence and convalutions entirely. Through the transliteration of Tibetan to International Phonetic Alphabets, the language model was trained using the syllables and phonemes of the Tibetan word as modeling units to predict corresponding Tibetan sentences according to the context semantics of IPA. And it combined with the acoustic model as the Tibetan speech recognition was compared with end-to-end Tibetan speech recognition.

scholarly journals Fuzzy System and Time Window Applied to Traffic Service Network Problems under a Multi-Demand Random Network

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 539 ◽  
Author(s):  
Chia-Ling Huang ◽  
Sin-Yuan Huang ◽  
Wei-Chang Yeh ◽  
Jinhai Wang
Keyword(s):  
Fuzzy System ◽  
Network Architecture ◽  
Time Window ◽  
Network Reliability ◽  
Economic Losses ◽  
Traffic Network ◽  
Service Network ◽  
The Past ◽  
Network Problems ◽  
Reliability Method

The transportation network promotes key human development links such as social production, population movement and resource exchange. As cities continue to expand, transportation networks become increasingly complex. A bad traffic network design will affect the quality of urban development and cause regional economic losses. How to plan transportation routes and allocate transportation resources is an important issue in today’s society. This study uses the network reliability method to solve traffic network problems. Network reliability refers to the probability of a successful connection between the source and sink nodes in the network. There are many systems in the world that use network architecture; therefore, network reliability is widely used in various practical problems and cases. In the past, some scholars have used network reliability to solve traffic service network problems. However, the processing of time is not detailed enough to fully express the real user’s time requirements and does not consider that the route traffic will affect the reliability of the entire network. This study improves on past network reliability methods by using a fuzzy system and a time window to construct a network model. Using the concept of fuzzy systems, according to past experience, data or expert predictions to define the degree of flow, time and reliability, can also determine the relationship between these factors. The time window can be adjusted according to the time limit in reality, reaching the limit of the complete expression time. In addition, the network reliability algorithm used in this study is a direct algorithm. Compared with the past indirect algorithms, the computation time is greatly reduced and complex problems can be solved more efficiently.

scholarly journals Data-to-Text Generation with Content Selection and Planning

2019 ◽  
Vol 33 ◽  
pp. 6908-6915 ◽  
Author(s):  
Ratish Puduppully ◽  
Li Dong ◽  
Mirella Lapata
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Network Models ◽  
Text Generation ◽  
Neural Network Models ◽  
Generation Task ◽  
Content Selection ◽  
End To End ◽  
Two Stages

Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to say and in what order. In this work, we present a neural network architecture which incorporates content selection and planning without sacrificing end-to-end training. We decompose the generation task into two stages. Given a corpus of data records (paired with descriptive documents), we first generate a content plan highlighting which information should be mentioned and in which order and then generate the document while taking the content plan into account. Automatic and human-based evaluation experiments show that our model1 outperforms strong baselines improving the state-of-the-art on the recently released RotoWIRE dataset.

Service-oriented 5G network architecture: an end-to-end software defining approach

2015 ◽  
Vol 29 (10) ◽  
pp. 1645-1657 ◽  
Author(s):  
Mao Yang ◽  
Yong Li ◽  
Bo Li ◽  
Depeng Jin ◽  
Sheng Chen
Keyword(s):  
Network Architecture ◽  
5G Network ◽  
Service Oriented ◽  
End To End

scholarly journals Security-Oriented Network Architecture

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Weiyu Jiang ◽  
Bingyang Liu ◽  
Chuang Wang ◽  
Xue Yang
Keyword(s):  
Network Architecture ◽  
Denial Of Service ◽  
Comprehensive Analysis ◽  
Distributed Denial Of Service ◽  
Arms Races ◽  
Network Attacks ◽  
Security Issues ◽  
Man In The Middle ◽  
Ip Spoofing ◽  
End To End

Internet benefits societies by constantly connecting devices and transmitting data across the world. However, due to the lack of architectural built-in security, the pervasive network attacks faced by the entire information technology are considered to be unending and inevitable. As Internet evolves, security issues are regularly fixed according to a patch-like strategy. Nevertheless, the patch-like strategy generally results in arms races and passive situations, leaving an endless lag in both existing and emerging attacking surface. In this paper, we present NAIS (Network Architecture with Intrinsic Security)—a network architecture towards trustworthiness and security. By solving stubborn security issues like IP spoofing, MITM (man-in-the-middle) attacks, and DDoS (distributed denial of service) attacks at architectural level, NAIS is envisioned to provide the most secure end-to-end communication in the network layer. This paper first presents a comprehensive analysis of network security at Internet range. Then, the system design of NAIS is elaborated with particular design philosophies and four security techniques. Such philosophies and techniques intertwine internally and contribute to a communication environment with authenticity, privacy, accountability, confidentiality, integrity, and availability. Finally, we evaluate the security functionalities on the packet forwarding performance, demonstrating that NAIS can efficiently provide security and trustworthiness in Internet end-to-end communication.

scholarly journals VIAE-Net: An End-to-End Altitude Estimation through Monocular Vision and Inertial Feature Fusion Neural Networks for UAV Autonomous Landing

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6302
Author(s):  
Xupei Zhang ◽  
Zhanzhuang He ◽  
Zhong Ma ◽  
Peng Jun ◽  
Kun Yang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Feature Fusion ◽  
Estimation Error ◽  
Estimation Method ◽  
Signal Interference ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
End To End ◽  
Altitude Estimation

Altitude estimation is one of the fundamental tasks of unmanned aerial vehicle (UAV) automatic navigation, where it aims to accurately and robustly estimate the relative altitude between the UAV and specific areas. However, most methods rely on auxiliary signal reception or expensive equipment, which are not always available, or applicable owing to signal interference, cost or power-consuming limitations in real application scenarios. In addition, fixed-wing UAVs have more complex kinematic models than vertical take-off and landing UAVs. Therefore, an altitude estimation method which can be robustly applied in a GPS denied environment for fixed-wing UAVs must be considered. In this paper, we present a method for high-precision altitude estimation that combines the vision information from a monocular camera and poses information from the inertial measurement unit (IMU) through a novel end-to-end deep neural network architecture. Our method has numerous advantages over existing approaches. First, we utilize the visual-inertial information and physics-based reasoning to build an ideal altitude model that provides general applicability and data efficiency for neural network learning. A further advantage is that we have designed a novel feature fusion module to simplify the tedious manual calibration and synchronization of the camera and IMU, which are required for the standard visual or visual-inertial methods to obtain the data association for altitude estimation modeling. Finally, the proposed method was evaluated, and validated using real flight data obtained during a fixed-wing UAV landing phase. The results show the average estimation error of our method is less than 3% of the actual altitude, which vastly improves the altitude estimation accuracy compared to other visual and visual-inertial based methods.

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