24 papers · ranked by Valyu relevance
Evelina Forno, Vittorio Fra, Riccardo Pignari, Enrico Macii + 1 more
Spiking Neural Networks (SNNs), known for their potential to enable low energy consumption and computational cost, can bring significant advantages to the realm of embedded machine learning for edge applications. However, input coming from standard digital sensors must be encoded into spike trains before it can be…
Mohammad Hosseini
—Today, with the growing demands of information storage and data transfer, data compression is becoming increasingly important. Data Compression is a technique which is used to decrease the size of data. This is very useful when some huge files have to be transferred over networks or being stored on a data storage…
Yibo Yang, Stephan Mandt, Lucas Theis
Neural compression is the application of neural networks and other machine learning methods to data compression. Recent advances in statistical machine learning have opened up new possibilities for data compression, allowing compression algorithms to be learned end-to-end from data using powerful generative models such…
Kun Tu, Dariusz Puchala, Jun Chen, Sadaf Salehkalaibar
In this paper, we address the problem of m-gram entropy variable-to-variable coding, extending the classical Huffman algorithm to the case of coding m-element (i.e., m-grams) sequences of symbols taken from the stream of input data for $m>1$. We propose a procedure to enable the determination of the frequencies of the…
Rimsha Fatima, Muhammad Hassan Khan, Muhammad Adeel Nisar, Rafał Doniec + 3 more
'Rafał Doniec' 'Muhammad Shahid Farid' 'Marcin Grzegorzek' 'Shyqyri Haxha'] This paper addresses the problem of feature encoding for gait analysis using multimodal time series sensory data. In recent years, the dramatic increase in the use of numerous sensors, e.g., inertial measurement unit (IMU), in our daily…
Robert Bamler
Entropy coding is the backbone data compression. Novel machine-learning based compression methods often use a new entropy coder called Asymmetric Numeral Systems (ANS) [Duda et al., 2015], which provides very close to optimal bitrates and simplifies [Townsend et al., 2019] advanced compression techniques such as…
Raúl Esteve Bosch, Jorge Rodríguez Ponce, Ander Simón Estévez, José María Benlloch Rodríguez + 5 more
'José María Benlloch Rodríguez' 'Vicente Herrero Bosch' 'José Francisco Toledo Alarcón' 'Giuseppe Ferri' 'Gianluca Barile' 'Alfiero Leoni'] NEXT collaboration detectors are based on energy measured by an array of photomultipliers (PMT) and topological event filtering based on an array of silicon photomultipliers…
Kees Schouhamer Immink, Jos H. Weber, Tuan Thanh Nguyen, Kui Cai + 2 more
The design of low-complexity and efficient constrained codes has been a major research item for many years. This paper reports on a versatile method named concatenated constrained codes for designing efficient fixed-length constrained codes with small complexity. A concatenated constrained code comprises two (or more)…
Amir Said
Entropy coding, compression, complexity This introduction to arithmetic coding is divided in two parts. The first explains how and why arithmetic coding works. We start presenting it in very general terms, so that its simplicity is not lost under layers of implementation details. Next, we show some of its basic…
Xiangzhen Zan, Ranze Xie, Xiangyu Yao, Peng Xu + 1 more
Thanks to its high density and long durability, synthetic DNA has been widely considered as a promising solution to the data explosion problem. However, due to the large amount of random base insertion-deletion-substitution (IDSs) errors from sequencing, reliable data recovery remains a critical challenge, which…
Luke Staniscia, Yun William Yu
Because of the rapid generation of data, the study of compression algorithms to reduce storage and transmission costs is important to bioinformaticians. Much of the focus has been on sequence data, including both genomes and protein amino acid sequences stored in FASTA files. Current standard practice is to use an…
Fajia Sun, Long Qian
DNA has been pursued as a compelling medium for digital data storage during the past decade. While large-scale data storage and random access have been achieved in artificial DNA, the synthesis cost keeps hindering DNA data storage from popularizing into daily life. In this study, we proposed a more efficient paradigm…
Martin Seifrid, Stanley Lo, Dylan Choi, Gary Tom + 12 more
Martin Seifrid 1 , Stanley Lo 2 , Dylan G. Choi 3 , Gary Tom 2 , My Linh Le 3 , Kunyu Li 3 , Rahul Sankar 3 , Hoai-Thanh Vuong 3 , Hiba Wakidi 3 , Ahra Yi 3 , Ziyue Zhu 3 , Nora Schopp 3 , Aaron Peng 3 , Benjamin Luginbuhl 3 , Thuc-Quyen Nguyen 3 , Alán Aspuru-Guzik 2
Ibrahim Nawaz, Parv Agarwal, Thomas Heinis
DNA storage is a developing field that uses DNA to archive digital data owing to its superior information density and stability. Although DNA storage has been performed on a significant scale, challenges arise from the synthesis and sequencing of data-encoded oligonucleotides. Synthesis of DNA introduces significant…
Gašper Beguš, Alan Zhou, T. Christina Zhao
Comparing artificial neural networks with outputs of neuroimaging techniques has recently seen substantial advances in (computer) vision and text-based language models. Here, we propose a framework to compare biological and artificial neural computations of spoken language representations and propose several new…
Ahmadreza Sezavar, Catarina Brites, João Ascenso
—Event cameras are a cutting-edge type of visual sensors that capture data by detecting brightness changes at the pixel level asynchronously. These cameras offer numerous benefits over conventional cameras, including high temporal resolution, wide dynamic range, low latency, and lower power consumption. However, the…
Rıza Özçelik, Laura van Weesep, Sarah de Ruiter, Francesca Grisoni
In this work, we introduce peptidy -- a lightweight Python library that facilitates converting peptides (expressed as aminoacid sequences) to numerical representations suited to machine learning. peptidy is free from external dependencies, integrates seamlessly into modern Python environments, and supports a range of…
Mahmoud Magdy, Khalid M. Hosny, Neveen I. Ghali, Said Ghoniemy
Recently, there has been a rapid growth in the utilization of medical images in telemedicine applications. The authors in this paper presented a detailed discussion of different types of medical images and the attacks that may affect medical image transmission. This survey paper summarizes existing medical data…
Nima Maleki, Hamid Karimi-Rouzbahani
Sensory neural coding, the brain’s process of transforming inputs into informative patterns of neural activity, generates complex and multiplexed neural codes which are hard to interpret. Although decoding methods have facilitated the interpretation of these codes, the specific features of neural activity that…
Alexandr Kuznetsov, Alexander Onikiychuk, Olga Peshkova, Tomasz Gancarczyk + 3 more
'Tomasz Gancarczyk' 'Kornel Warwas' 'Ruslana Ziubina' 'Evangelos Kranakis'] Direct spread spectrum technology is traditionally used in radio communication systems with multiple access, for example, in CDMA standards, in global satellite navigation systems, in Wi-Fi network wireless protocols, etc. It ensures high…
Sven-Jannik Wöhnert, Kai Hendrik Wöhnert, Eldar Almamedov, Carsten Frank + 1 more
Associating meta information with images is common since the early days of the photography. This ranges from date, time and place where an image was taken up to semantics such as "Grandma with Peter at Christmas 1992" written on the back of the image. Nowadays, most images are taken with a digital device that…
Efe Çiftci, Emre Sümer, Sedat Akleylek
Digital steganography is the science of establishing hidden communication on electronics; the aim is to transmit a secret message to a particular recipient using unsuspicious carriers such as digital images, documents, and audio files with the help of specific hiding methods. This article proposes a novel steganography…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
HANIYEH ABDOLLAHZADEH, Tonya Peeples, Mohammad Shahcheraghi
DNA-based nanomaterials have shown great potential in numerous applications, thanks to their unique properties including DNA's various molecular interactions, programmability, and versatility with biological modules. Meanwhile, the DNA origami platforms have shown promise in the creation of drug carriers. This…