Multiple Observations for Secret-Key Binding with SRAM PUFs

We present a new Multiple-Observations (MO) helper data scheme for secret-key binding to an SRAM-PUF. This MO scheme binds a single key to multiple enrollment observations of the SRAM-PUF. Performance is improved in comparison to classic schemes which generate helper data based on a single enrollment observation. The performance increase can be explained by the fact that the reliabilities of the different SRAM cells are modeled (implicitly) in the helper data. We prove that the scheme achieves secret-key capacity for any number of enrollment observations, and, therefore, it is optimal. We evaluate performance of the scheme using Monte Carlo simulations, where an off-the-shelf LDPC code is used to implement the linear error-correcting code. Another scheme that models the reliabilities of the SRAM cells is the so-called Soft-Decision (SD) helper data scheme. The SD scheme considers the one-probabilities of the SRAM cells as an input, which in practice are not observable. We present a new strategy for the SD scheme that considers the binary SRAM-PUF observations as an input instead and show that the new strategy is optimal and achieves the same reconstruction performance as the MO scheme. Finally, we present a variation on the MO helper data scheme that updates the helper data sequentially after each successful reconstruction of the key. As a result, the error-correcting performance of the scheme is improved over time.

DEVELOPMENT OF A MODEL FOR ACCOUNTING THE MATERIAL VALUES OF THE WAREHOUSE DEPOT

The methodology for calculating the total area of the warehouse of the carriage depot and the optimal size of the stock of inventory items in the carriage depot of the Joint Stock Company "Uzpasstrans". Defined formulas for determining the costs of placing and receiving all orders, costs of storing stock for a certain period, total costs. A logical data scheme is proposed that reflects the main entities necessary to automate the process of determining the inventory of goods and materials in the warehouse of the carriage depot.

Multiple Observations for Secret-Key Binding with SRAM PUFs

We present a new Multiple-Observations (MO) helper data scheme for secret-key binding to an SRAM PUF. This MO scheme binds a single key to multiple enrollment observations of the SRAM PUF. Performance is improved in comparison to classic schemes which generate helper data based on a single enrollment observation. The performance increase can be explained by the fact that the reliabilities of the different SRAM cells are modeled (implicitly) in the helper data. We prove that the scheme achieves secret-key capacity for any number of enrollment observations, and, therefore it is optimal. We evaluate performance of the scheme using Monte Carlo simulations, where an off-the-shelf LDPC code is used to implement the linear error-correcting code. Another scheme that models the reliabilities of the SRAM cells is the so-called Soft-Decision (SD) helper data scheme. The SD scheme considers the one-probabilities of the SRAM cells as an input, which in practice are not observable. We present a new strategy for the SD scheme that considers the binary SRAM-PUF observations as an input instead and show that the new strategy is optimal and achieves the same reconstruction performance as the MO scheme. Finally, we present a variation on the MO helper data scheme that updates the helper data sequentially after each successful reconstruction of the key. As a result, the error-correcting performance of the scheme is improved over time.

Hybrid Architectures with Quantum Gravity Gradiometry and Satellite-to-Satellite Tracking for Spaceborne Mass Change Measurements - A Sensitivity and Performance Analysis

<p>Advances in atom interferometry have led to quantum gravity gradiometer instruments, which have further led to spaceborne mission concepts utilizing this technology to measure Earth’s gravity field and its time variations. The mass changes inferred from gravity change measurements lead to greater understanding of the dynamical Earth system, as demonstrated by GRACE and GRACE Follow-On missions.</p><p>We report the results from a sensitivity and performance assessment study with quantum gradiometers used in two configurations – first as a single-axis gradiometer with a GNSS receiver; and second in a novel hybrid configuration combining cross-track quantum gravity gradiometer and an inter-satellite tracking system. The relative advantages of the two configurations are assessed in terms of their susceptibility to system errors (such as tracking, pointing, or measurement errors), and to modeling errors due to aliasing from rapid time- variations of gravity (so-called “de-aliasing errors”). We evaluate and discuss the impact of de-aliasing errors on gravity fields resulting from the study. We conclude with a specification of the key measurement error thresholds for a notional hybrid gravity field mapping mission.</p><p>Part of the research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004).</p><p><span xml:lang="EN-US" data-scheme-color="@7F7F7F,0,18:50000" data-usefontface="false" data-contrast="none"><span>Acknowledgement: UTCSR effort was funded by JPL grant 1656926. Use of resources at the </span></span><span xml:lang="EN-US" data-scheme-color="@7F7F7F,0,18:50000" data-usefontface="false" data-contrast="none"><span>Texas Advanced Computing Center is gratefully acknowledged. </span></span></p>

AN ABSTRACT MODEL OF SEARCH INDEX QUERY IN THE RUSSIAN NATIONAL CORPUS

The paper discusses the so-called “bag problem,” which affects the search accuracy in the Russian National Corpus (RNC). Solving the problem requires a change of the search index data scheme used in RNC, which in its turn requires a significant refactoring of the RNC program code. The basis of such a refactoring is proposed to be an abstract model of the search index query, which allows us to separate the query formation from the query implementation. An experiment was carried out in which one of the RNC system program modules was decomposed, which confirmed sufficient expressiveness of the constructed model. Directions of further work are determined.

Artificial Neural Network for the Short-Term Prediction of Arctic Sea Ice Concentration

In this paper, we applied an artificial neural network (ANN) to the short-term prediction of the Arctic sea ice concentration (SIC). The prediction was performed using encoding and decoding processes, in which a gated recurrent unit encodes sequential sea ice data, and a feed-forward neural network model decodes the encoded input data. Because of the large volume of Arctic sea ice data, the ANN predicts the future SIC of each cell individually. The limitation of these singular predictions is that they do not use information from other cells. This results in low accuracy, particularly when there are drastic changes during melting and freezing seasons. To address this issue, we present a new data scheme including global and local SIC information, where the global information is represented by sea ice statistics. We trained ANNs using different data schemes and network architectures, and then compared their performances quantitatively and visually. The results show that, compared with a data scheme that uses only local sea ice information, the newly proposed scheme leads to a significant improvement in prediction accuracy.