19 papers · ranked by Valyu relevance
Islam S. Badreldin, Karim G. Oweiss
Brain-machine interfaces rely on extracting motor control signals from brain activity in real time to actuate external devices such as robotic limbs. Whereas biomimetic approaches to neural decoding use motor imagery/observation signals, non-biomimetic approaches assign an arbirary transformation that maps neural…
Geraldo A. Barbosa
Polar encoding, described by Arikan in "IEEE Transactions on Information Theory, Vol. 55, No. 7, July 2009", was a milestone for telecommunications. A Polar code distributes information among high and low-capacity channels, showing the possibility of achieving perfect channel capacity. The high-capacity channels allow…
Matias Calderini, Jean-Philippe Thivierge
Decoding approaches provide a useful means of estimating the information contained in neuronal circuits. In this work, we analyze the expected classification error of a decoder based on Fisher linear discriminant analysis. We provide expressions that relate decoding error to the specific parameters of a population…
Nikhil Parthasarathy, Eleanor Batty, William Falcon, Thomas Rutten + 3 more
Decoding sensory stimuli from neural signals can be used to reveal how we sense our physical environment, and is valuable for the design of brain-machine interfaces. However, existing linear techniques for neural decoding may not fully reveal or exploit the fidelity of the neural signal. Here we develop a new…
Maqiang Zhao, Yuyu Yuan, Linbin Luo, Xinghui Li + 1 more
Absolute linear encoders have emerged as a core technical enabler in the fields of high-end manufacturing and precision displacement measurement, owing to their inherent advantages such as the elimination of the need for homing operations and the retention of position data even upon power failure. However, there…
Julia Lieb, Joachim Rosenthal
It is well known that there is a correspondence between convolutional codes and discrete-time linear systems over finite fields. In this paper, we employ the linear systems representation of a convolutional code to develop a decoding algorithm for convolutional codes over the erasure channel. In this kind of channel…
Pavel Rybin, Kirill Andreev, Victor Zyablov, Balazs Matuz + 2 more
'Alexey Frolov' 'Aaron Gulliver'] This paper deals with the specific construction of binary low-density parity-check (LDPC) codes. We derive lower bounds on the error exponents for these codes transmitted over the memoryless binary symmetric channel (BSC) for both the well-known maximum-likelihood (ML) and proposed…
J. Brendan Ritchie, David Michael Kaplan, Colin Klein
Since its introduction, multivariate pattern analysis (MVPA), or “neural decoding”, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the Decoder’s Dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence…
Syed Mohsin Abbas, Thibaud Tonnellier, Furkan Ercan, Warren J. Gross
—Guessing Random Additive Noise Decoding (GRAND) is a recently proposed universal decoding algorithm for linear error correcting codes. Since GRAND does not depend on the structure of the code, it can be used for any code encountered in contemporary communication standards or may even be used for random linear network…
Niklas Gassner, Julia Lieb, Abhinaba Mazumder, Michael Schaller
In this paper, we present a framework for generic decoding of convolutional codes, which allows us to do cryptanalysis of code-based systems that use convolutional codes as public keys. We then apply this framework to information set decoding, study success probabilities and give tools to choose variables. Finally, we…
Yan Yuan, Péter Scheepers, Lydia Tasiou, Yunus Can Gültekin + 2 more
Decoders Authors: ['Yan Yuan' 'Péter Scheepers' 'Lydia Tasiou' 'Yunus Can Gültekin' 'Federico Corradi' 'Alex Alvarado'] Abstract—This paper analyzes the design and competitiveness of four neural network (NN) architectures recently proposed as decoders for forward error correction (FEC) codes. We first consider the…
Julia Lieb, Joachim Rosenthal
In this paper, we employ the linear systems representation of a convolutional code to develop a decoding algorithm for convolutional codes over the erasure channel. We study the decoding problem using the state space description and this provides in a natural way additional information. With respect to previously known…
Jonathan Nguyen, Linfang Wang, Chester Hulse, Sahil Dani + 4 more
'Amaael Antonini' 'Todd Chauvin' 'D. Divsalar' 'Richard D. Wesel'] Abstract—The Consultative Committee for Space Data Systems (CCSDS) 141.11-O-1 Line Product Code (LPC) provides a rare opportunity to compare maximum-likelihood decoding and message passing. The LPC considered in this paper is intended to serve as the…
Valéria G. Pedrosa, Max H. M. Costa, Sangun Park
The index coding problem consists of a system with a server and multiple receivers with different side information and demand sets, connected by a noiseless broadcast channel. The server knows the side information available to the receivers. The objective is to design an encoding scheme that enables all receivers to…
Ilya Dumer
New soft- and hard decision decoding algorithms are presented for general Reed-Muller codes m r of length 2m and distance 2m−r . We use Plotkin (u, u + v) construction and decompose code m r onto subblocks u ∈ m−1 r and v ∈ n m−1 r−1 o . In decoding, we first try to find a subblock v from the better protected code and…
Il Memming Park, Jonathan W. Pillow
The efficient coding hypothesis, which proposes that neurons are optimized to maximize information about the environment, has provided a guiding theoretical framework for sensory and systems neuroscience. More recently, a theory known as the Bayesian Brain hypothesis has focused on the brain’s ability to integrate…
Erdal Arıkan, Najeeb ul Hassan, Michael Lentmaier, G. Montorsi + 1 more
The history of channel coding began hand in hand with Shannon's information theory [1]. Following on the pioneering work of Golay [2] and Hamming [3], the majority of linear codes developed in the early ages of coding theory were "error correction" codes in the sense that their aim is to correct errors made by the…
Tagir Akhmetshin, Arkadii Lin, Timur Madzhidov, Alexandre Varnek
Autoencoders represent a promising technique for the inverse quantitative structure-activity relationship (QSAR) task. However, undesirable bias, such as atom ordering, affects the neighbourhood behaviour of autoencoders’ latent space and, consequently, usage of the latent vectors as variables in machine-learning…
Authors not listed
The discovery of chemically novel or structurally anomalous metal-organic frameworks (MOFs) is essential for expanding reticular design space and enhancing dataset reliability. We present CHEM-AD (Chemically Unusual Metal–organic Frameworks via Autoencoder-based Detection), a label-free, CPU-efficient pipeline that…