Arra Abhinay, Benjamin Rutschmann, Patrick L. Kohl · Apidologie · 2025
Abstract Honeybees communicate the locations of profitable resources using waggle dances. Researchers have used two manual dance decoding methods to infer the spatial foraging patterns of the bees. The ‘waggle run method’ infers foraging distance from waggle duration, representing the actual distance signal of the bees, and the ‘circuit method’ infers foraging distance from the duration of complete dance circuits, including both waggle and return phases. The circuit method enables decoding dances by real-time observation and is likely quicker, but it might give inaccurate distance estimates due to variations in the return phases. Here, we decoded 200 natural waggle dances from four Apis mellifera colonies using both methods to test how well inferences about foraging locations would match and compared the time required for dance decoding. We found a high linear correlation between foraging distances inferred from waggle duration and circuit duration ( R 2 , 0.793). However, the circuit method systematically overestimated foraging distances by about 86 m. Both methods provided similar results with respect to relative differences in spatial foraging patterns between the four test colonies. Decoding the dances using the circuit method was three to four times faster than the waggle run method (2 versus 7.3 min per dance). We conclude that the circuit method can be used for many research questions that require manual dance decoding to minimize time costs. However, when aiming to maximize the accuracy of inferred foraging locations, systematic errors of the circuit method should be accounted for, or the waggle run method should be used.
Cited by 1Open access
Zeyuan Ye (21353840), Haoran Li (424403), Liang Tian (499436) et al. · Figshare · 2025
(<b>A</b>) Details of the method for performing cross-decoding on one pair of Model 1–Model 2 using rotation-translation-scaling (RTS) matching. In step one, 2,000 input colors were randomly sampled. Model 1 and Model 2 were run for 1,000 trials each, with neural states at the end of the delay collected and denoted as and respectively. RTS matching finds the optimal scaling (s), rotation/reflection (R) and translation (t) to transform by minimizing the Frobenius distance between the transformed and . In step two, 500 trials were performed with input colors fixed to the common color. Model 1’s neural states at the end of delay () were collected and transformed into Model 2’s representation (). Model 2 ran through the post-delay epochs and finally generated output colors. (<b>B</b>) Same as <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013083#pcbi.1013083.g002" target="_blank">Fig 2B</a> in main text, but using RTS matching. (TIF)
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Huang, Jiun-Ting · eScholarship (California Digital Library) · 2023
Optimal detection is one of the most fundamental problems in communication and statistical signal processing. Nevertheless, in many applications, the optimal detection problem often reduces to an NP-complete, or even NP-hard, discrete optimization problem. This research project aims to develop randomized algorithms using Monte Carlo Markov chain methods to achieve near-optimal decision-making. In particular, this project focuses on two specific applications---signal detection in multiple-in-multiple-out systems and decoding of error correction codes. For the scenarios considered, we successfully proposed fast-converging Monte Carlo Markov chain algorithms that achieve near-optimality in reasonable iterations and outperform the previously best-known methods. These results open a new approach to optimal detection.
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Kelly N. Chuh, Anna R. Batt, Matthew R. Pratt · Cell chemical biology · 2016
Cited by 89Open access
Daphne Ippolito, Reno Kriz, João Sedoc et al. · 2019
While conditional language models have greatly improved in their ability to output high-quality natural language, many NLP applications benefit from being able to generate a diverse set of candidate sequences. Diverse decoding strategies aim to, within a givensized candidate list, cover as much of the space of high-quality outputs as possible, leading to improvements for tasks that re-rank and combine candidate outputs. Standard decoding methods, such as beam search, optimize for generating high likelihood sequences rather than diverse ones, though recent work has focused on increasing diversity in these methods. In this work, we perform an extensive survey of decoding-time strategies for generating diverse outputs from conditional language models. We also show how diversity can be improved without sacrificing quality by oversampling additional candidates, then filtering to the desired number.
Cited by 18Open access
Nachmani, Eliya, Marciano, Elad, Lugosch, Loren et al. · arXiv (Cornell University) · 2017
The problem of low complexity, close to optimal, channel decoding of linear codes with short to moderate block length is considered. It is shown that deep learning methods can be used to improve a standard belief propagation decoder, despite the large example space. Similar improvements are obtained for the min-sum algorithm. It is also shown that tying the parameters of the decoders across iterations, so as to form a recurrent neural network architecture, can be implemented with comparable results. The advantage is that significantly less parameters are required. We also introduce a recurrent neural decoder architecture based on the method of successive relaxation. Improvements over standard belief propagation are also observed on sparser Tanner graph representations of the codes. Furthermore, we demonstrate that the neural belief propagation decoder can be used to improve the performance, or alternatively reduce the computational complexity, of a close to optimal decoder of short BCH codes.
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Yaowarat Sirisathitkul, Chitnarong Sirisathitkul · AgriEngineering · 2025
Soil color serves as a critical indicator of its properties and conditions. It is shaped by a complex interplay of mineral and organic matter content, moisture levels, and other environmental variables. Additionally, human activities such as land-use changes and intensive agricultural practices can profoundly alter soil color. Soil color, driven by the presence of organic matter, plays a crucial role in understanding soil fertility. Its strong correlation with soil organic carbon content makes it a valuable parameter for assessing soil quality in agricultural practices. A variety of techniques have been developed to measure soil color, ranging from traditional Munsell color matching to modern color meters. Digital image colorimetry enables rapid on-site assessments of soil color, but environmental conditions such as soil water content and lighting conditions should be considered. Spectroscopic methods, particularly diffuse reflectance spectroscopy, pave the way for a more reliable and accurate composition analysis. Advances in remote sensing and computational methods are combined to explore the intricate relationships between soil color and environmental factors. Such an integrated approach not only enhances scalability but also leads to more insights and actionable strategies for environmental management and sustainable agriculture.
Cited by 10Open access
Matan Avitan, Moran Baruch, Nir Drucker et al. · 2025
Matan Avitan, Moran Baruch, Nir Drucker, Itamar Zimerman, Yoav Goldberg. Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics. 2025.
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Zheng Li · Frontiers in Systems Neuroscience · 2014
This article reviews advances in decoding methods for brain-machine interfaces (BMIs). Recent work has focused on practical considerations for future clinical deployment of prosthetics. This review is organized by open questions in the field such as what variables to decode, how to design neural tuning models, which neurons to select, how to design models of desired actions, how to learn decoder parameters during prosthetic operation, and how to adapt to changes in neural signals and neural tuning. The concluding discussion highlights the need to design and test decoders within the context of their expected use and the need to answer the question of how much control accuracy is good enough for a prosthetic.
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Tas, Nihal, Ucar, Sumeyra, Yilmaz Ozgur, Nihal · University of Calgary · 2020
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Muskaan Singh, Ravinder Kumar, Inderveer Chana · 2019
Deep Learning techniques have significant progress in the last few years. Different designs and methods of the model have experimented to enhance the performance in various domains of Natural Language Processing (NLP). Machine Translation is a domain of NLP. In this paper, we have reviewed different Encoding-Decoding methods for Neural Machine Translation(NMT). We also, compare these various methods and put forward a past, present and future of Neural Machine Translation.
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Murat Sever · AYBU AVESIS · 2015
Düşük-yoğunluklu eşlik-denetim (LDPC) kodları doğrusal blok kodları arasındadır. İlk olarak 1960 yılında R. Gallager tarafından keşfedilmelerine rağmen uzun yıllar boyunca unutulmuş, 1996 yılında yapılan bir çalışma ile bilim dünyasının yeniden ilgisini çekmişlerdir. Yüksek hata başarımına sahip olmaları nedeni ile günümüzde modern iletişim ve depolama sistemlerinde hata düzeltici kodlar olarak yaygın bir biçimde kullanılmaktadırlar. LDPC kod çözücülerin çalışma prensibi Gallager tarafından ortaya atılan kanı yayılımına (Belief Propagation) dayanmaktadır. LDPC kodlarının çözümünde performans iyileştirme maksadıyla çeşitli yöntemler bulunmuş ve pratikte de uygulanmaktadır. Bu yöntemlerden biri de kodların turbo çözümleme ile çözülmesidir. Önerilen yöntem ile kod çözümlemede daha güvenilir mesajların düğümler arası geçirilmesi sayesinde hızlı bir şekilde yakınsama sağlanmaktadır. Katmanlı mimaride çözümleme olarak da adlandırılan yöntem ile normal modda gereken yineleme sayısının yarısında aynı başarım seviyesine ulaşmak mümkündür. Tez çalışmamızda katmanlı mimaride LDPC çözümlemede performans arttırıcı yeni yöntemler sunulmuştur. Yeni yöntemler birçok değişik uzunlukta ve hızda LDPC kodları üzerinde denenmiştir. Yapılan benzetim çalışmaları sonucunda, önerilen yöntemler en iyi performans artırımını kısa ve hızlı LDPC kodları üzerinde sağladığı gözlenmiştir. Önerilen yöntemlerin seçilen LDPC kodları üzerinde denenmesine ilaveten, OpenMP kullanarak LDPC çözümleme işlemi paralelleştirilmiş, 8-çekirdekli gerçek DSP donanımı üzerinde yapılan testlerde tek çekirdeğe göre 6 kattan fazla hızlanma elde edildiği raporlanmıştır.
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Ni̇hal Taş, Sümeyra Uçar, Nihal Yılmaz Özgür · Contributions to Discrete Mathematics · 2020
We develop a new coding and decoding method using the generalized Pell $(p,i)$-numbers for $p=1$. The relations among the code matrix elements, error detection, and correction have been established for coding theory when $p=i=1$. We give two new blocking algorithms using Pell numbers and generalized Pell $(p,i)$-numbers for $p=1$.
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BERTHET, Antoine Olivier, COHEN, Gérard · OpenGrey (Institut de l'Information Scientifique et Technique) · 2001
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V. A. Pantyushin, O.V. Mayorova · Zemleustrojstvo, kadastr i monitoring zemel' (Land management, cadastre and land monitoring) · 2022
The concept "image boundaries" is proposed to be considered not only as an object of decryption, but also as a direct decryption feature of land plots. The article deals with the theoretical and practical issues of automation of decoding agricultural land based on the selection of their boundaries in digital images. Analysis of ways and methods of decoding in order to select boundaries allows us to conclude about the effectiveness of using image segmentation in the processes of preprocessing and automatic selection of boundaries in a digital image. It is proposed to use the well-known operators of spatial differentiation with subsequent thresholding for image segmentation, such as Roberts, Sobel, and Laplace operators. The visual analysis of the results of digital image processing shows that the most expedient and appropriate way is to use the Laplace operator to solve the problem of identifying land use boundaries. The experimental studies show that the number of selected contours in the image will depend on the measure of the threshold value, which allows the elements to be defined as border points.
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Kenzo Sakiyama, Raphael Montanari, Roseval Donisete Malaquias et al. · Lecture notes in computer science · 2023
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Iago Henrique de Oliveira, Abner Cardoso Rodrigues · Frontiers in Neuroscience · 2023
Electroencephalography (EEG) is a technique that can be used in non-invasive brain-machine interface (BMI) systems to register brain electrical activity. The EEG signals are non-linear and non-stationary, making the decoding procedure a complex task. Deep learning techniques have been successfully applied in several research fields, often improving the results compared with traditional approaches. Therefore, it is believed that these techniques can also improve the process of decoding brain signals in BMI systems. In this work, we present the implementation of two deep learning-based decoders and we compared the results with other state of art deep learning methods. The first decoder uses long short-term memory (LSTM) recurrent neural network and the second, entitled EEGNet-LSTM, combines a well-known neural decoder based on convolutional neural networks, called EEGNet, with some LSTM layers. The decoders have been tested using data set 2a from BCI Competition IV, and the results showed that the EEGNet-LSTM decoder has been approximately 23% better than the competition-winning decoder. A Wilcoxon <i>t</i>-test showed a significant difference between the two decoders (<i>Z</i> = 2.524, <i>p</i> = 0.012). The LSTM-based decoder has been approximately 9% higher than the best decoder from the same competition. However, there was no significant difference (<i>Z</i> = 1.540, <i>p</i> = 0.123). In order to verify the replication of the EEGNet-LSTM decoder on another data, we performed a test with PhysioNet's Physiobank EEG Motor Movement/Imagery dataset. The EEGNet-LSTM presented a higher performance (0.85 accuracy) than the EEGNet (0.82 accuracy). The results of this work can be important for the development of new research, as well as EEG-based BMI systems, which can benefit from the high precision of neural decoders.
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Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2002
Efficient complexity reduction methods for short-frame iterative decoding by Kei Chun-Ling thesis 2002 xiii, 91 leaves : ill. ; 30 cm Turbo coding has already become a prominent error correction technique for wireless communications, especially after its adaptation in the…Read more ›
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Dongfeng Zhao, Daoben Li · 2008
In this paper, two decoding methods are proposed for coded cooperation using LDPC codes. A simple upper bound based on the union bound is introduced and the performance of these two methods is evaluated and compared by simulations.
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Jaeyeon Jung, Hyungwoo Lee, Jinwoo Jeong et al. · The Journal of Korean Institute of Communications and Information Sciences · 2026
본 논문에서는 현대전에서 운용되는 DJI사의 상용 무인기와 조종기 신호의 복호화 방법을 기술한다. 적군 무인기 대응에 활용될 수 있는 정보를 신호 복호화를 통해 확보할 수 있다는 점에서 본 연구는 중요한 의의를 가진다. 연구 대상 신호의 프로토콜인 Ocusync는 LTE-like한 전송 구조를 가지므로, 3GPP LTE 표준을 기반으로 복호화 절차를 기술하였다. 제안된 복호화 방법의 성능은 신호 대 잡음비(Signal-to-Noise Ratio, SNR) 0~30 dB 구간에서 Cyclic Redundancy Check(CRC)를 활용하여 검증 하였으며, 이를 통해 제안 방법의 실효성을 입증하였다.
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