scholarly journals Improving Imaging Quality of Real-time Fourier Single-pixel Imaging via Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4190 ◽  
Author(s):  
Saad Rizvi ◽  
Jie Cao ◽  
Kaiyu Zhang ◽  
Qun Hao
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
High Quality ◽  
Imaging Quality ◽  
Convolutional Autoencoder ◽  
Image Set ◽  
Real Time Applications ◽  
The Cost ◽  
Single Pixel

Fourier single pixel imaging (FSPI) is well known for reconstructing high quality images but only at the cost of long imaging time. For real-time applications, FSPI relies on under-sampled reconstructions, failing to provide high quality images. In order to improve imaging quality of real-time FSPI, a fast image reconstruction framework based on deep learning (DL) is proposed. More specifically, a deep convolutional autoencoder network with symmetric skip connection architecture for real time 96 × 96 imaging at very low sampling rates (5–8%) is employed. The network is trained on a large image set and is able to reconstruct diverse images unseen during training. The promising experimental results show that the proposed FSPI coupled with DL (termed DL-FSPI) outperforms conventional FSPI in terms of image quality at very low sampling rates.

scholarly journals An Adaptive Bitrate Switching Algorithm for Speech Applications in Context of WebRTC

2021 ◽  
Vol 17 (4) ◽  
pp. 1-21
Author(s):  
Mohannad Alahmadi ◽  
Peter Pocta ◽  
Hugh Melvin
Keyword(s):  
Real Time ◽  
Data Exchange ◽  
Quality Of Experience ◽  
End User ◽  
High Quality ◽  
Content Type ◽  
Switching Algorithm ◽  
Wide Range ◽  
The Impact

Web Real-Time Communication (WebRTC) combines a set of standards and technologies to enable high-quality audio, video, and auxiliary data exchange in web browsers and mobile applications. It enables peer-to-peer multimedia sessions over IP networks without the need for additional plugins. The Opus codec, which is deployed as the default audio codec for speech and music streaming in WebRTC, supports a wide range of bitrates. This range of bitrates covers narrowband, wideband, and super-wideband up to fullband bandwidths. Users of IP-based telephony always demand high-quality audio. In addition to users’ expectation, their emotional state, content type, and many other psychological factors; network quality of service; and distortions introduced at the end terminals could determine their quality of experience. To measure the quality experienced by the end user for voice transmission service, the E-model standardized in the ITU-T Rec. G.107 (a narrowband version), ITU-T Rec. G.107.1 (a wideband version), and the most recent ITU-T Rec. G.107.2 extension for the super-wideband E-model can be used. In this work, we present a quality of experience model built on the E-model to measure the impact of coding and packet loss to assess the quality perceived by the end user in WebRTC speech applications. Based on the computed Mean Opinion Score, a real-time adaptive codec parameter switching mechanism is used to switch to the most optimum codec bitrate under the present network conditions. We present the evaluation results to show the effectiveness of the proposed approach when compared with the default codec configuration in WebRTC.

Data Storage in Cloud Based Real-Time Environments

2011 ◽  
pp. 236-258
Author(s):  
Sai Narasimhamurthy ◽  
Malcolm Muggeridge ◽  
Stefan Waldschmidt ◽  
Fabio Checconi ◽  
Tommaso Cucinotta
Keyword(s):  
Quality Of Service ◽  
Real Time ◽  
Data Storage ◽  
Storage System ◽  
Cloud Environment ◽  
Last Mile ◽  
Service Oriented ◽  
Real Time Applications ◽  
Direct Applicability

The service oriented infrastructures for real-time applications (“real-time clouds1”) pose certain unique challenges for the data storage subsystem, which indeed is the “last mile” for all data accesses. Data storage subsystems typically used in regular enterprise environments have many limitations which impedes direct applicability for such clouds, particularly in their ability to provide Quality of Service (QoS) for applications. Provision of QoS within storage is possible through a deeper understanding of the behaviour of the storage system under a variety of conditions dictated by the application and the network infrastructure. We intend to arrive at a QoS mechanism for data storage keeping in view the important parameters that come into play for the storage subsystem in a soft real-time cloud environment.

scholarly journals Progressive System: A Deep-Learning Framework for Real-Time Data in Industrial Production

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 649
Author(s):  
Yifeng Liu ◽  
Wei Zhang ◽  
Wenhao Du
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Large Scale ◽  
Quality Data ◽  
Time Data ◽  
High Quality ◽  
Real Time System ◽  
High Quality Data ◽  
Learning Framework ◽  
Data Accumulation

Deep learning based on a large number of high-quality data plays an important role in many industries. However, deep learning is hard to directly embed in the real-time system, because the data accumulation of the system depends on real-time acquisitions. However, the analysis tasks of such systems need to be carried out in real time, which makes it impossible to complete the analysis tasks by accumulating data for a long time. In order to solve the problems of high-quality data accumulation, high timeliness of the data analysis, and difficulty in embedding deep-learning algorithms directly in real-time systems, this paper proposes a new progressive deep-learning framework and conducts experiments on image recognition. The experimental results show that the proposed framework is effective and performs well and can reach a conclusion similar to the deep-learning framework based on large-scale data.

scholarly journals Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 982 ◽  
Author(s):  
Hyo Lee ◽  
Ihsan Ullah ◽  
Weiguo Wan ◽  
Yongbin Gao ◽  
Zhijun Fang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Large Scale ◽  
Recognition Rate ◽  
Experimental Results ◽  
Learning Approach ◽  
Deep Model ◽  
Proposed Model ◽  
Real Time Applications ◽  
Model Recognition

Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications.

scholarly journals Evolution of orbit and clock quality for real-time multi-GNSS solutions

GPS Solutions ◽  
2020 ◽  
Vol 24 (4) ◽  
Author(s):  
Kamil Kazmierski ◽  
Radoslaw Zajdel ◽  
Krzysztof Sośnica
Keyword(s):  
Real Time ◽  
Atomic Clock ◽  
Orbital Plane ◽  
Poor Quality ◽  
Satellite Orbits ◽  
High Quality ◽  
Changes Over Time ◽  
Over Time ◽  
Orbit Characteristics

Abstract High-quality satellite orbits and clocks are necessary for multi-GNSS precise point positioning and timing. In undifferenced GNSS solutions, the quality of orbit and clock products significantly influences the resulting position accuracy; therefore, for precise positioning in real time, the corrections for orbits and clocks are generated and distributed to users. In this research, we assess the quality and the availability of real-time CNES orbits and clocks for GPS, GLONASS, Galileo, and BeiDou-2 separated by satellite blocks and types, as well as the product quality changes over time. We calculate the signal-in-space ranging error (SISRE) as the main orbit and clock quality indicator. Moreover, we employ independent orbit validation based on satellite laser ranging. We found that the most accurate orbits are currently available for GPS. However, Galileo utmost stable atomic clocks compensate for systematic errors in Galileo orbits. As a result, the SISRE for Galileo is lower than that for GPS, equaling 1.6 and 2.3 cm for Galileo and GPS, respectively. The GLONASS satellites, despite the high quality of their orbits, are characterized by poor quality of clocks, and together with BeiDou-2 in medium and geosynchronous inclined orbits, are characterized by SISRE of 4–6 cm. BeiDou-2 in geostationary orbits is characterized by large orbital errors and the lowest availability of real-time orbit and clock corrections due to a large number of satellite maneuvers. The quality of GNSS orbit and clock corrections changes over time and depends on satellite type, block, orbit characteristics, onboard atomic clock, and the sun elevation above the orbital plane.

Design of High Accuracy Robot Welding Machine for a Cone Bottom

2014 ◽  
Vol 496-500 ◽  
pp. 1289-1292
Author(s):  
De Huan Tang ◽  
De Yang Luo
Keyword(s):  
Real Time ◽  
Tracking System ◽  
High Accuracy ◽  
Welding Robot ◽  
Robotic Welding ◽  
High Quality ◽  
The Real ◽  
Laser Tracking ◽  
Welding Machine

This paper designed a special welding machine for an aluminum cone bottom workpiece. This machine contains highly accurate positioner system, laser tracking system, and robotic welding devices. It is used to weld the transverse seams and the longitudinal seams of the workpiece. The interaction of welding robot with positioner and the real-time seam correcting can ensure high quality of welding.

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