Flexible LDPC Decoder Architectures

Awais, Muhammad; Condo, Carlo

doi:10.1155/2012/730835

Cited by 8 publications

(11 citation statements)

References 72 publications

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“…Urdu Opinion Lexicon (Chaoticity): A list of positive (2,607) and negative (4,728) Urdu opinion words (Awais, 2012).…”

Section: Resourcesmentioning

confidence: 99%

“…Using the aforementioned strategy, we acquired 1,044 (positive) and 1,002 (negative) Urdu opinion words. Additionally, we acquired 2,607 positive and 4,728 negative Urdu opinion words from a list proposed by Awais (2012). In this way, the size of the resulting lexicon (proposed) has increased to 3,651 (positive) and 5,730 (negative) words.…”

Section: Constructionmentioning

confidence: 99%

“…The aforementioned approaches for sentiment lexicon generation are widely used for creating sentiment to process English text. Several studies (Afraz, Muhammad, & Martinez-Enriquez, 2011;Awais, 2012;Badaro, Baly, Hajj, Habash, & El-Hajj, 2014;Bakliwal, Arora, & Varma, 2012;Dashtipour et al, 2016;Dehkharghani, Saygin, Yanikoglu, & Oflazer, 2016) have been conducted to perform sentiment analysis in languages other than English; till date, most of the research efforts made in the area of sentiment analysis deal with English text (Mukhtar et al, 2017). This is due to the fact that extraction and analysis of sentiments from text need a rich collection of lexical resources of that language.…”

mentioning

confidence: 99%

“…Previous work on Urdu sentiment lexicons (Afraz et al, 2011;Awais, 2012) is based on morphological syntactic, phonological, and orthographic aspects. However, their approach did not provide sufficient coverage of Urdu sentiment words along with their sentiment scores.…”

mentioning

confidence: 99%

“…The proposed technique is inspired by the previous work on developing sentiment lexicons (Afraz et al, 2011;Awais, 2012;Das & Bandyopadhyay, 2010;Ijaz & Hussain, 2007). The previous studies have integrated morphological concepts with corpus-based techniques by classifying the words according to their part of speech, concepts, and synsets using pre-annotated corpora.…”

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confidence: 99%

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Creating sentiment lexicon for sentiment analysis in Urdu: The case of a resource‐poor language

et al. 2019

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The sentiment analysis (SA) applications are becoming popular among the individuals and organizations for gathering and analysing user's sentiments about products, services, policies, and current affairs. Due to the availability of a wide range of English lexical resources, such as part‐of‐speech taggers, parsers, and polarity lexicons, development of sophisticated SA applications for the English language has attracted many researchers. Although there have been efforts for creating polarity lexicons in non‐English languages such as Urdu, they suffer from many deficiencies, such as lack of publically available sentiment lexicons with a proper scoring mechanism of opinion words and modifiers. In this work, we present a word‐level translation scheme for creating a first comprehensive Urdu polarity resource: “Urdu Lexicon” using a merger of existing resources: list of English opinion words, SentiWordNet, English–Urdu bilingual dictionary, and a collection of Urdu modifiers. We assign two polarity scores, positive and negative, to each Urdu opinion word. Moreover, modifiers are collected, classified, and tagged with proper polarity scores. We also perform an extrinsic evaluation in terms of subjectivity detection and sentiment classification, and the evaluation results show that the polarity scores assigned by this technique are more accurate than the baseline methods.

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“…Urdu Opinion Lexicon (Chaoticity): A list of positive (2,607) and negative (4,728) Urdu opinion words (Awais, 2012).…”

Section: Resourcesmentioning

confidence: 99%

Section: Constructionmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Creating sentiment lexicon for sentiment analysis in Urdu: The case of a resource‐poor language

et al. 2019

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A high‐throughput multimode low‐density parity‐check decoder for 5G New Radio

Pourjabar

Choi

2021

Circuit Theory & Apps

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This paper presents a partially parallel low-density parity-check (LDPC) decoder for the 5G New Radio (NR) standard. The design is using a multiblock parallel architecture with a flooding schedule. The decoder can support any code rates and code lengths up to the lifting size Z max = 96. To compensate for the dropped throughput associated with the smaller Z values, the design can double and quadruple its parallelism when lifting sizes Z ≤ 48 and Z ≤ 24 are selected, respectively. Therefore, the decoder can process up to eight frames and restore the throughput to the maximum. To facilitate the design's architecture, a new variable node for decoding the extended parity bits existing in the lower code rates is proposed. The FPGA implementation of the decoder results in a throughput of 2.1 Gbps decoding the 11/12 code rate. Additionally, the synthesized decoder using the 28-nm TSMC technology achieves a maximum clock frequency of 526 MHz and a throughput of 13.46 Gbps. The core decoder occupies 1.03 mm 2 , and the power consumption is 229 mW.

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Improving Network-on-Chip-based Turbo Decoder Architectures

Martina

Masera

2013

J Sign Process Syst

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In this work novel results concerning Network-on-Chip-based turbo decoder architectures are presented. Stemming from previous publications, this work concentrates first on improving the throughput by exploiting adaptive-bandwidthreduction techniques. This technique shows in the best case an improvement of more than 60 Mb/s. Moreover, it is known that double-binary turbo decoders require higher area than binary ones. This characteristic has the negative effect of increasing the data width of the network nodes. Thus, the second contribution of this work is to reduce the network complexity to support doublebinary codes, by exploiting bit-level and pseudo-floating-point representation of the extrinsic information. These two techniques allow for an area reduction of up to more than the 40% with a performance degradation of about 0.2 dB.

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Flexible LDPC Decoder Architectures

Cited by 8 publications

References 72 publications

Creating sentiment lexicon for sentiment analysis in Urdu: The case of a resource‐poor language

Creating sentiment lexicon for sentiment analysis in Urdu: The case of a resource‐poor language

A high‐throughput multimode low‐density parity‐check decoder for 5G New Radio

Improving Network-on-Chip-based Turbo Decoder Architectures

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