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This paper presents an innovative approach utilizing in-memory computing (IMC) for the development and integration of AES (Advanced Encryption Standard) cipher technique. Our research aims to enhance cybersecurity measures for a wide range of applications for IoT, such as robotic self-driving and several uses contexts. Memristor (MR) design optimized for in-memory processing is introduced. Our work highlights the development of a 4-bit state memristor device tailored for various range of arithmetic functions in a hardware prototype of AES system. Additionally, we propose a pipeline AES design aimed at harnessing extensive parallelism and ensuring compatibility with MR devices. This approach enhances hardware performance by by managing larger data amounts, accelerating computational, and achieving greater precision demands. Compared to traditional AES hardware, AES-IMC demonstrates an approximate 30 % improvement in power with a comparable throughput rate. Compared with the latest AES-based NVM engines, AES-IMC achieves an impressive 62 % improvement in throughput at similar power dissipation levels. The IMC-developed design will protect against unintentional incidents involving unmanned devices, reducing the risks associated with hostile assaults such as hijacking and illegal control of robots. This helps to reduce the possible economic and financial losses caused by incidents, Comment: ATSIP2024 Conference , 6 pages

This paper introduces a perspective approach for simulating a memristive sensor tailored for low power biological analyte detection. The necessity for such innovation stems from the increasing demand for efficient biosensing technologies that can operate with minimal poxer consumption. Within this study, a numerical dynamic memristive model serves as a basis platform for implementing enhanced nanosensing method characterized by low cost and high sensitivity. Numerous simulations were conducted to validate the suitability of the dynamic memristive model's behaviour for emulating a chemical sensing approach. The simulated data is collected for deploying an AI application to ensure an advanced predictable biosensing intake function. All in all, this work paves the way for developing compact numerical models of memristive biosensors, addressing the pressing need for portable lox poxer consumption biosensing solutions., Comment: 6 pages

Future wireless communication systems must simultaneously address multiple challenges to ensure accurate data detection, deliver high Quality of Service (QoS), adding enable a high data transmission with low system design. Additionally, they need to reduce energy consumption and latency without increasing system complexity. Orthogonal Frequency Division Multiplexing (OFDM) is a commonly used waveform in 4G and 5G systems, it has limitations in handling significant delay and Doppler spread in high mobility scenarios. To overcome these weaknesses, a novel waveform named Orthogonal Time Frequency Space (OTFS) has been proposed, which aims to improve upon OFDM by closely matching signals to channel behavior. In this study, we propose a novel strategy that enables operators to dynamically select the best waveform based on estimated mobile user parameters. We use an Integrated Radar Sensing and Communication System (ISAC) to estimate delay and Doppler, as well as speed and range. This approach allows the base station to adapt to the mobile target, thereby enhancing the performance of wireless communication systems in high mobility and low complexity scenarios. Simulation results demonstrate the effectiveness of our proposed approach and show that it outperforms existing methods., Comment: 20 pages, 8 figures; 3 tables

The Future wireless communication systems face the challenging task of simultaneously providing high quality of service (QoS) and broadband data transmission, while also minimizing power consumption, latency, and system complexity. Although Orthogonal Frequency Division Multiplexing OFDM has been widely adopted in 4G and 5G systems, it struggles to cope with the significant delay and Doppler spread in high mobility scenarios. To address these challenges, a novel waveform called Orthogonal Time Frequency Space OTFS has emerged, aiming to outperform OFDM by closely aligning signals with the channel behaviour. In this paper, we propose a switching strategy that empowers operators to select the most appropriate waveform based on the estimated speed of the mobile user. This strategy enables the base station to dynamically choose the waveform that best suits the mobile users speed. Additionally, we suggest integrating an Integrated Sensing and Communication radar for accurate Doppler estimation, providing precise information to aid in waveform selection. By leveraging the switching strategy and harnessing the Doppler estimation capabilities of an ISAC radar, our proposed approach aims to enhance the performance of wireless communication systems in scenarios of high mobility. Considering the complexity of waveform processing, we introduce an optimized hybrid system that combines OTFS and OFDM, resulting in reduced complexity while still retaining performance benefits. This hybrid system presents a promising solution for improving the performance of wireless communication systems in scenarios with a high mobility. The simulation results validate the effectiveness of our approach, demonstrating its potential advantages for future wireless communication systems. The effectiveness of the proposed approach is validated by simulation results as it will be shown., Comment: 19 Page(pp:49-67);10 figures; 2 tables

This paper presents a novel methodology for modeling memristive biosensing within COMSOL Multiphysics, focusing on critical performance metrics such as antigen-antibody binding concentration and output resistive states. By studying the impact of increasing inlet concentrations, insights into binding concentration curve and output resistance variations are uncovered. The resultant simulation data effectively trains a support vector machine classifier (SVMC), achieving a remarkable accuracy rate of 97%. The incorporation of artificial intelligence (AI) through SVM demonstrates promising strides in advancing AI-based memristive biosensing modeling, potentially elevating their performance standards and applicability across diverse domains., Comment: BIOSENSORS 2024

The Lunar Gravitational-wave Antenna (LGWA) is a proposed array of next-generation inertial sensors to monitor the response of the Moon to gravitational waves (GWs). Given the size of the Moon and the expected noise produced by the lunar seismic background, the LGWA would be able to observe GWs from about 1 mHz to 1 Hz. This would make the LGWA the missing link between space-borne detectors like LISA with peak sensitivities around a few millihertz and proposed future terrestrial detectors like Einstein Telescope or Cosmic Explorer. In this article, we provide a first comprehensive analysis of the LGWA science case including its multi-messenger aspects and lunar science with LGWA data. We also describe the scientific analyses of the Moon required to plan the LGWA mission.

The interaction of a gravitational wave (GW) with an elastic body is usually described in terms of a GW "force" driving the oscillations of the body's normal modes. However, this description is only possible for GW frequencies for which the response of the elastic body is dominated by a few normal modes. At higher frequencies the normal modes blend into a quasi-continuum and a field-theoretical description, as pioneered by Dyson already in 1969, becomes necessary. However, since the metric perturbation $h_{\mu\nu}$ is an intrinsically relativistic object, a consistent coupling to GWs can only be obtained within a relativistic (and, in fact generally covariant) theory of elasticity. We develop such a formalism using the methods of modern effective field theories, and we use it to provide a derivation of the interaction of elastic bodies with GWs valid also in the high-frequency regime, providing a first-principle derivation of Dyson's result (and partially correcting it). We also stress that the field-theoretical results are obtained working in the TT frame, while the description in terms of a force driving the normal modes is only valid in the proper detector frame. We show how to transform the results between the two frames. Beside an intrinsic conceptual interest, these results are relevant to the computation of the sensitivity of the recently proposed Lunar Gravitational Wave Antenna., Comment: 63 pages

Data entry forms use completeness requirements to specify the fields that are required or optional to fill for collecting necessary information from different types of users. However, some required fields may not be applicable for certain types of users anymore. Nevertheless, they may still be incorrectly marked as required in the form; we call such fields obsolete required fields. Since obsolete required fields usually have not-null validation checks before submitting the form, users have to enter meaningless values in such fields in order to complete the form submission. These meaningless values threaten the quality of the filled data. To avoid users filling meaningless values, existing techniques usually rely on manually written rules to identify the obsolete required fields and relax their completeness requirements. However, these techniques are ineffective and costly. In this paper, we propose LACQUER, a learning-based automated approach for relaxing the completeness requirements of data entry forms. LACQUER builds Bayesian Network models to automatically learn conditions under which users had to fill meaningless values. To improve its learning ability, LACQUER identifies the cases where a required field is only applicable for a small group of users, and uses SMOTE, an oversampling technique, to generate more instances on such fields for effectively mining dependencies on them. Our experimental results show that LACQUER can accurately relax the completeness requirements of required fields in data entry forms with precision values ranging between 0.76 and 0.90 on different datasets. LACQUER can prevent users from filling 20% to 64% of meaningless values, with negative predictive values between 0.72 and 0.91. Furthermore, LACQUER is efficient; it takes at most 839 ms to predict the completeness requirement of an instance., Comment: Accepted for publication by ACM Transactions on Software Engineering and Methodology

In this article, the Heisenberg-Pauli-Weyl uncertainty principle and Donoho-Stark s uncertainty principle are obtained for the Poly-axially L 2 {\alpha} -multiplier operators

The Einstein Telescope (ET), the European project for a third-generation gravitational-wave detector, has a reference configuration based on a triangular shape consisting of three nested detectors with 10 km arms, where in each arm there is a `xylophone' configuration made of an interferometer tuned toward high frequencies, and an interferometer tuned toward low frequencies and working at cryogenic temperature. Here, we examine the scientific perspectives under possible variations of this reference design. We perform a detailed evaluation of the science case for a single triangular geometry observatory, and we compare it with the results obtained for a network of two L-shaped detectors (either parallel or misaligned) located in Europe, considering different choices of arm-length for both the triangle and the 2L geometries. We also study how the science output changes in the absence of the low-frequency instrument, both for the triangle and the 2L configurations. We examine a broad class of simple `metrics' that quantify the science output, related to compact binary coalescences, multi-messenger astronomy and stochastic backgrounds, and we then examine the impact of different detector designs on a more specific set of scientific objectives., Comment: 197 pages, 73 figures. v2: corrections in the part on the sensitivity to stochastic backgrounds. Accepted in JCAP

In this article, we introduce and study three numerical methods for the Dirichlet Monge Amp\`ere equation in two dimensions. The approaches consist in considering new equivalent problems. The latter are discretized by a wide stencil finite difference discretization and monotone schemes are obtained. Hence, we apply the Barles-Souganidis theory to prove the convergence of the schemes and the Damped Newtons method is used to compute the solutions of the schemes. Finally, some numerical results are illustrated., Comment: 14pages, 7figures

We address the problem of mitigating damage to a power grid following a storm by managing a vehicle that has to be routed while simultaneously performing two tasks: learning about damage from the grid (which requires direct observation) and repairing damage that it observes. The learning process is assisted by calls from customers notifying the utility that they have lost power (``lights-out calls''). However, when a tree falls and damages a line, it triggers the first upstream circuit breaker, which results in power outages for everyone on the grid below the circuit breaker. We present a dynamic routing model that captures observable state variables such as the location of the truck and the state of the grid on segments the truck has visited, and beliefs about outages on segments that have not been visited. Trucks are routed over a physical transportation network, but the pattern of outages is governed by the structure of the power grid. We introduce a form of Monte Carlo tree search based on information relaxation that we call {\it optimistic MCTS} which improves its application to problems with larger action spaces. We show that the method significantly outperforms standard escalation heuristics used in industry.}, Comment: 44 pages, 7 figures. arXiv admin note: substantial text overlap with arXiv:1605.05711

This paper contains a detailed study of the properties of a simple model attempting to explain dark energy as originated from quantum fluctuations of a light spectator scalar field in inflation. In [1] we recently outlined how Starobinsky's stochastic formalism can be used to study the spatial correlations imprinted on dark energy by its quantum origin in this model and we studied their possible role in relieving the Hubble tension. Here we provide a more comprehensive derivation of the results in [1] and we refine some of our estimates, comparing to the approximate results obtained previously. Among the main results, we analyze the non-coincident correlators predicted by a full field theoretical treatment and their relation with those computed within the stochastic formalism. We find that in the region where stochastic theory predicts significant sub-Hubble correlators it is in disagreement with field theoretical predictions. However, agreement can be restored by introducing a reduced speed of sound for the scalar field. We also discuss an alternative approach to the problem of studying correlators within the stochastic formalism based directly on the evolution of probability distributions. We find that the two approaches give the same answer for 2-point functions of the field, but not for 4-point functions relevant to density correlators and we discuss the behaviour of the two methods with respect to Wick's theorem., Comment: v2: version submitted to Phys. Rev. D

The origin of dark energy driving the accelerated expansion of the universe is still mysterious. We explore the possibility that dark energy fluctuates, resulting in spatial correlations. Due to these fluctuations, the Hubble rate itself becomes a fluctuating quantity. We discuss the effect this has on measurements of type Ia supernovae, which are used to constrain the luminosity distance. We show that the luminosity distance is affected by spatial correlations in several ways. First, the luminosity distance becomes dressed by the fluctuations, thereby differing from standard $\Lambda$CDM. Second, angular correlations become visible in the two-point correlation function of the luminosity distance. To investigate the latter we construct the angular power spectrum of luminosity distance fluctuations. We then perform a forecast for two supernova surveys, the ongoing Dark Energy Survey (DES) and the upcoming Legacy Survey of Space and Time (LSST), and compare this effect with relativistic lensing effects from perturbed $\Lambda$CDM. We find that the signal can rise above the lensing effects and that LSST could test this effect for a large part of the parameter space. As an example, a specific realisation of such a scenario is that quantum fluctuations of some field in the early universe imprint spatial correlations with a predictable form in the dark energy density today. In this case, the Hubble rate fluctuates due to the intrinsic quantum nature of the dark energy density field. We study whether the signal of this specific model would be measurable, and conclude that testing this model with LSST would be challenging. However, taking into account a speed of sound $c_s<1$ of the dark energy fluid can make this model observable., Comment: 38 pages, 9 figures