scholarly journals Combining serial and parallel decoding for turbo codes

2021 ◽  
Vol 24 (2) ◽  
pp. 896
Author(s):  
Mohammed AlMahamdy ◽  
Naser Al-Falahy
Keyword(s):  
Real Time ◽  
Turbo Codes ◽  
Performance Degradation ◽  
Error Rates ◽  
Combination Scheme ◽  
Parallel Decoding ◽  
Real Time Applications

Reducing the decoding latency of the turbo codes is important to real-time applications. Conventionally, the decoding of the turbo codes (TC) runs in serial fashion, which means only one of the constituent soft decoders runs at a time. Parallel decoding (PD) refers to running the soft decoders in parallel. Although it delivers the output faster (compared to the serial decoding (SD)), it affects the bit- and frame-error rates. This paper proposes a decoding procedure that combines both PD and SD. It bridges the two decoding modes to determine the best combination scheme to achieve the required level of performance at an acceptable decoding latency. Presented results show how this procedure can mitigate the performance degradation at a slight increase in the decoding latency.

Maintaining Consistency Over A Network in Real-Time Applications

1989 ◽  
Author(s):  
Insup Lee ◽  
Susan Davidson ◽  
Victor Wolfe
Keyword(s):  
Real Time ◽  
Real Time Applications

Peak-Power-Aware Energy Management for Periodic Real-Time Applications

2020 ◽  
Vol 39 (4) ◽  
pp. 779-788 ◽  
Author(s):  
Mohsen Ansari ◽  
Amir Yeganeh-Khaksar ◽  
Sepideh Safari ◽  
Alireza Ejlali
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Peak Power ◽  
Real Time Applications

scholarly journals Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning

2021 ◽  
Vol 35 (2) ◽  
pp. 251-264
Author(s):  
Dimitrios Boursinos ◽  
Xenofon Koutsoukos
Keyword(s):  
Neural Networks ◽  
Distance Learning ◽  
Real Time ◽  
Speaker Recognition ◽  
Deep Neural Networks ◽  
Cyber Physical Systems ◽  
Error Rates ◽  
Traffic Sign ◽  
Conformal Prediction ◽  
Physical Systems

AbstractMachine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

Effects of multilevel security on real-time applications

2002 ◽  
Author(s):  
R.K. Clark ◽  
I.B. Greenberg ◽  
P.K. Boucher ◽  
T.F. Lunt ◽  
P.G. Neumann ◽  
...  
Keyword(s):  
Real Time ◽  
Multilevel Security ◽  
Real Time Applications

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

Efficient placement of audio data on optical disks for real-time applications

1989 ◽  
Vol 32 (7) ◽  
pp. 862-871 ◽  
Author(s):  
Clement Yu ◽  
Wei Sun ◽  
Dina Bitton ◽  
Qi Yang ◽  
Richard Bruno ◽  
...  
Keyword(s):  
Real Time ◽  
Audio Data ◽  
Optical Disks ◽  
Real Time Applications
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