98,302 research outputs found
Increasing the Information Density of Storage Systems Using the Precision-Resolution Paradigm
Arguably, the most prominent constrained system in storage applications is the (d, k)-RLL (Run-Length Limited) system, where every binary sequence obeys the constraint that every two adjacent 1's are separated by at least d consecutive 0's and at most k consecutive 0's, namely, runs of 0's are length limited. The motivation for the RLL constraint arises mainly from the physical limitations of the read and write technologies in magnetic and optical storage systems.
We revisit the rationale for the RLL system and reevaluate its relationship to the physical media. As a result, we introduce a new paradigm that better matches the physical constraints. We call the new paradigm the Precision-Resolution (PR) system, where the write operation is limited by precision and the read operation is limited by resolution.
We compute the capacity of a general PR system and demonstrate that it provides a significant increase in the information density compared to the traditional RLL system (for identical physical limitations). For example, the capacity of the (2, 10)-RLL used in CD-ROMs and DVDs is approximately 0.5418, while our PR system provides the capacity of about 0.7725, resulting in a potential increase of about 40% in information density
Coding for Optimized Writing Rate in DNA Storage
A method for encoding information in DNA sequences is described. The method is based on the precisionresolution framework, and is aimed to work in conjunction with a recently suggested terminator-free template independent DNA synthesis method. The suggested method optimizes the amount of information bits per synthesis time unit, namely, the writing rate. Additionally, the encoding scheme studied here takes into account the existence of multiple copies of the DNA sequence, which are independently distorted. Finally, quantizers for various run-length distributions are designed
Coding for Optimized Writing Rate in DNA Storage
A method for encoding information in DNA sequences is described. The method
is based on the precision-resolution framework, and is aimed to work in
conjunction with a recently suggested terminator-free template independent DNA
synthesis method. The suggested method optimizes the amount of information bits
per synthesis time unit, namely, the writing rate. Additionally, the encoding
scheme studied here takes into account the existence of multiple copies of the
DNA sequence, which are independently distorted. Finally, quantizers for
various run-length distributions are designed.Comment: To appear in ISIT 202
Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)
Deep neural networks (DNN) have shown remarkable success in a variety of
machine learning applications. The capacity of these models (i.e., number of
parameters), endows them with expressive power and allows them to reach the
desired performance. In recent years, there is an increasing interest in
deploying DNNs to resource-constrained devices (i.e., mobile devices) with
limited energy, memory, and computational budget. To address this problem, we
propose Entropy-Constrained Trained Ternarization (EC2T), a general framework
to create sparse and ternary neural networks which are efficient in terms of
storage (e.g., at most two binary-masks and two full-precision values are
required to save a weight matrix) and computation (e.g., MAC operations are
reduced to a few accumulations plus two multiplications). This approach
consists of two steps. First, a super-network is created by scaling the
dimensions of a pre-trained model (i.e., its width and depth). Subsequently,
this super-network is simultaneously pruned (using an entropy constraint) and
quantized (that is, ternary values are assigned layer-wise) in a training
process, resulting in a sparse and ternary network representation. We validate
the proposed approach in CIFAR-10, CIFAR-100, and ImageNet datasets, showing
its effectiveness in image classification tasks.Comment: Proceedings of the CVPR'20 Joint Workshop on Efficient Deep Learning
in Computer Vision. Code is available at
https://github.com/d-becking/efficientCNN
Testing a new multivariate GNSS carrier phase attitude determination method for remote sensing platforms
GNSS (Global Navigation Satellite Systems)-based attitude determination is an important field of study, since it is a valuable technique for the orientation estimation of remote sensing platforms. To achieve highly accurate angular estimates, the precise GNSS carrier phase observables must be employed. However, in order to take full advantage of the high precision, the unknown integer ambiguities of the carrier phase observables need to be resolved. This contribution presents a GNSS carrier phase-based attitude determination method that determines the integer ambiguities and attitude in an integral manner, thereby fully exploiting the known body geometry of the multi-antennae configuration. It is shown that this integral approach aids the ambiguity resolution process tremendously and strongly improves the capacity of fixing the correct set of integer ambiguities.In this contribution, the challenging scenario of single-epoch, single-frequency attitude determination is addressed. This guarantees a total independence from carrier phase slips and losses of lock, and it also does not require any a priori motion model for the platform. The method presented is a multivariate constrained version of the popular LAMBDA method and it is tested on data collected during an airborne remote sensing campaign
- …