Distributed Rateless Codes with Unequal Error Protection Property for Space Information Networks

Jiao, Jian; Yang, Yi; Feng, Bowen; Wu, Shaohua; Li, Yonghui; Zhang, Qinyu

doi:10.3390/e19010038

Cited by 16 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an additional feature, a direct enumeration of the contained chunks can be stored per packet. This allows the encoder to operate in an unequal error protection mode [ 22 , 23 ]. Since the determination of prioritized packets makes a simple reconstruction of the encoded chunks using a seed impossible, an explicit enumeration of the chunk numbers used per packet is required here.…”

Section: Methodsmentioning

confidence: 99%

NOREC4DNA: using near-optimal rateless erasure codes for DNA storage

Schwarz

Freisleben

2021

BMC Bioinformatics

View full text Add to dashboard Cite

Background DNA is a promising storage medium for high-density long-term digital data storage. Since DNA synthesis and sequencing are still relatively expensive tasks, the coding methods used to store digital data in DNA should correct errors and avoid unstable or error-prone DNA sequences. Near-optimal rateless erasure codes, also called fountain codes, are particularly interesting codes to realize high-capacity and low-error DNA storage systems, as shown by Erlich and Zielinski in their approach based on the Luby transform (LT) code. Since LT is the most basic fountain code, there is a large untapped potential for improvement in using near-optimal erasure codes for DNA storage. Results We present NOREC4DNA, a software framework to use, test, compare, and improve near-optimal rateless erasure codes (NORECs) for DNA storage systems. These codes can effectively be used to store digital information in DNA and cope with the restrictions of the DNA medium. Additionally, they can adapt to possible variable lengths of DNA strands and have nearly zero overhead. We describe the design and implementation of NOREC4DNA. Furthermore, we present experimental results demonstrating that NOREC4DNA can flexibly be used to evaluate the use of NORECs in DNA storage systems. In particular, we show that NORECs that apparently have not yet been used for DNA storage, such as Raptor and Online codes, can achieve significant improvements over LT codes that were used in previous work. NOREC4DNA is available on https://github.com/umr-ds/NOREC4DNA. Conclusion NOREC4DNA is a flexible and extensible software framework for using, evaluating, and comparing NORECs for DNA storage systems.

show abstract

Section: Methodsmentioning

confidence: 99%

NOREC4DNA: using near-optimal rateless erasure codes for DNA storage

Schwarz

Freisleben

2021

BMC Bioinformatics

View full text Add to dashboard Cite

show abstract

“…As a new type of CNN, ResNet introduces the residual module into the deep neural network and abandons the redundant network structure. It addresses the problems of vanishing gradient and network degradation of traditional CNN 23,27 . As shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and PLS‐DA

Wang

Liu

et al. 2021

J Sci Food Agric

View full text Add to dashboard Cite

BACKGROUND: How to quickly identify poisonous mushrooms is a worldwide problem, because poisonous mushrooms and edible mushrooms have very similar appearances. Even some edible mushrooms must be processed further before they can be eaten. In addition, mushrooms from different geographical origins contain different levels of heavy metals. Eating frequent mushrooms with excessive heavy metal content can also cause food poisoning. This information is very important and needs to be informed to consumers in advance. Through the demand for the safety of porcini mushrooms in the Yunnan area we propose a hierarchical identification system based on Fourier-transform near-infrared (FT-NIR) spectroscopy to evaluate the edible safety of porcini species. RESULTS: We found that deep learning is the most effective means to identify the edible safety of porcini, and the recognition accuracy was 100%, by comparing two pattern recognition tools, deep learning and partial least square discriminant analysis (PLS-DA). Although the accuracy of the PLS-DA test set is 96.10%, the poisonous porcini is not allowed to be wrongly judged. In addition, the cadmium (Cd) content of Leccinum rugosiceps in the Midu area exceeded the standard. Deep learning can trace Le. rugosiceps geographic origin with an accuracy of 100%.CONCLUSION: The overall results show that deep learning methods based on FT-NIR can identify porcini that is at risk of being eaten. This has useful application prospects in food safety.

show abstract

“…The accumulated error of the antenna pointing error will cause the satellite elevation error, which may deteriorate the signal-to-noise ratio (SNR), decrease link margin [34] and bit error rate (BER) [35], and so forth. To the best of our knowledge, this is the first work on GEO satellite perturbation that reveals the effect of satellite elevation error for SIN backbone satellite relaying.…”

Section: Background and Motivationmentioning

confidence: 99%

Performance Analysis of Space Information Networks with Backbone Satellite Relaying for Vehicular Networks

Jiao

Gao

et al. 2017

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

Space Information Network (SIN) with backbone satellites relaying for vehicular network (VN) communications is regarded as an effective strategy to provide diverse vehicular services in a seamless, efficient, and cost-effective manner in rural areas and highways. In this paper, we investigate the performance of SIN return channel cooperative communications via an amplify-andforward (AF) backbone satellite relaying for VN communications, where we assume that both of the source-destination and relaydestination links undergo Shadowed-Rician fading and the source-relay link follows Rician fading, respectively. In this SIN-assisted VN communication scenario, we first obtain the approximate statistical distributions of the equivalent end-to-end signal-to-noise ratio (SNR) of the system. Then, we derive the closed-form expressions to efficiently evaluate the average symbol error rate (ASER) of the system. Furthermore, the ASER expressions are taking into account the effect of satellite perturbation of the backbone relaying satellite, which reveal the accumulated error of the antenna pointing error. Finally, simulation results are provided to verify the accuracy of our theoretical analysis and show the impact of various parameters on the system performance.

show abstract

Distributed Rateless Codes with Unequal Error Protection Property for Space Information Networks

Cited by 16 publications

References 27 publications

NOREC4DNA: using near-optimal rateless erasure codes for DNA storage

NOREC4DNA: using near-optimal rateless erasure codes for DNA storage

Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and PLS‐DA

Performance Analysis of Space Information Networks with Backbone Satellite Relaying for Vehicular Networks

Contact Info

Product

Resources

About