Search Results (175)

We consider a basic joint communication and sensing setup comprising a transmitter, a receiver and a sensor. The transmitter sends a codeword to the receiver through a discrete memoryless channel, and the receiver is interested in decoding the transmitted codeword. At the same time, the sensor picks up a noisy version of the transmitted codeword through one of two possible discrete memoryless channels. The sensor knows the codeword and wishes to discriminate between the two possible channels, i.e., to identify the channel that has generated the output given the input. We study the trade-off between communication and sensing in the asymptotic regime, captured in terms of the channel coding rate against the two types of discrimination error exponents. We characterize the optimal trade-off between the rate and the exponents for general discrete memoryless channels with an input cost constraint. Full article

The isotropic Cauchy distribution is a member of the central $\alpha$-stable family that plays a role in the set of heavy-tailed distributions similar to that of the Gaussian density among finite second-moment laws. Given a sequence of n observations, we are interested in characterizing the performance of Likelihood Ratio Tests, where two hypotheses are plausible for the observed quantities: either isotropic Cauchy or isotropic Gaussian. Under various setups, we show that the probability of error of such detectors is not always exponentially decaying with n, with the leading term in the exponent shown to be logarithmic instead, and we determine the constants in that leading term. Perhaps surprisingly, the optimal Bayesian probabilities of error are found to exhibit different asymptotic behaviors. Full article

This study presents the analysis and comparison of Wi-Fi coverage modeling for a hotspot using deterministic and empirical propagation models developed by researchers from the Universidad de Las Américas in Quito, Ecuador. Signal intensity measurements were taken from both the hotspot and the repeater at various locations within the Checa parish using a Raspberry Pi and a Global Positioning System (GPS). To assess the accuracy of the models, heat maps were generated using Matlab (R2023A). The results showed that the adjusted model, comparing the received signal levels of the hotspot with the Stanford University Interim Propagation Model (SUI), exhibited a significant error margin, especially at distances below 60 m. However, starting at −70 dBm and beyond 60 m, the sampled data aligned better with the adjusted model. The discrepancy in the heatmaps was explained by the hotspot’s higher transmission power compared to the Wi-Fi repeater. Furthermore, the reception levels of the hotspot were low near the transmitter, which led to new measurements being taken with the Wi-Fi repeater (Raspberry Pi 3). With the new measurements, the adjusted model using logarithmic regression showed a better fit, particularly in the range from −40 dBm to −98 dBm, with a path loss exponent of 8.96. This demonstrated a significant improvement in prediction accuracy, particularly at short distances. The results emphasize the importance of using tools such as Matlab and reference models to optimize network planning, providing the Universidad de Las Américas with a valuable tool to generate heat maps in areas with characteristics similar to those of Checa in the context of their community outreach programs. This approach could be crucial for future research and optimization of community Wi-Fi networks in similar environments. Full article

This study uses Artificial Neural Networks (ANNs) and multiple linear regression (MLR) models to explore the relationship between gait dynamics and the metabolic cost. Six nonlinear metrics—Lyapunov Exponents based on Rosenstein’s algorithm (LyER), Detrended Fluctuation Analysis (DFA), the Approximate Entropy (ApEn), the correlation dimension (CD), the Sample Entropy (SpEn), and Lyapunov Exponents based on Wolf’s algorithm (LyEW)—were utilized to predict the metabolic cost during walking. Time series data from 10 subjects walking under 13 conditions, with and without hip exoskeletons, were analyzed. Six ANN models, each corresponding to a nonlinear metric, were trained using the Levenberg–Marquardt backpropagation algorithm and compared with MLR models. Performance was assessed based on the mean squared error (MSE) and correlation coefficients. ANN models outperformed MLR, with DFA and Lyapunov Exponent models showing higher R² values, indicating stronger predictive accuracy. The results suggest that gait’s nonlinear characteristics significantly impact the metabolic cost, and ANNs are more effective for analyzing these dynamics than MLR models. The study emphasizes the potential of focusing on specific nonlinear gait variables to enhance assistive device optimization, particularly for hip exoskeletons. These findings support the development of personalized interventions that improve walking efficiency and reduce metabolic demands, offering insights into the design of advanced assistive technologies. Full article

In this paper, fractional calculus is used to develop a generalized fractional dynamic model of an electrohydraulic system composed of a servo valve and a hydraulic cylinder, where a fractional position controller $P{I}^{\gamma }{D}^{\mu }$ is proposed for minimizing the performance index according to the integral of the time-weighted absolute error (ITAE). First, the general mathematical equations of the cylinder and servo valve are used to obtain the transfer functions in fractional order by applying Caputo’s definition and a Laplace transform. Then, through a block diagram of the closed-loop system without a controller, the fractional model is validated by comparing its performance concerning the integer-order electrohydraulic system model reported in the literature. Subsequently, a fractional PID controller is designed to control the cylinder position. This controller is included in the closed-loop system to determine the fractional exponents of the transfer functions of the servo valve, cylinder, and control, as well as to tune the controller gains, by using the ITAE objective function, with a comparison of the following: (1) the electrohydraulic system model in integer order and the controller in fractional order; (2) the electrohydraulic system model in fractional order and the controller in integer order; and (3) both the system model and the controller in fractional order. For each of the above alternatives, numerical simulations were carried out using MATLAB^®/Simulink^® R2023b and adding white noise as a perturbation. The results show that strategy (3), where electrohydraulic system and controller model are given in fractional order, develops the best performance because it generates the minimum value of ITAE. Full article

The climate impact of Arctic aerosols, like the Arctic Haze, and their origin are not fully understood. Therefore, long-term aerosol observations in the Arctic are performed. In this study, we present a homogenised data set from a sun and star photometer operated in the European Arctic, in Ny-Ålesund, Svalbard, of the 20 years from 2004–2023. Due to polar day and polar night, it is crucial to use observations of both instruments. Their data is evaluated in the same way and follows the cloud-screening procedure of AERONET. Additionally, an improved method for the calibration of the star photometer is presented. We found out, that autumn and winter are generally more polluted and have larger particles than summer. While the monthly median Aerosol Optical Depth (AOD) decreases in spring, the AOD increases significantly in autumn. A clear signal of large particles during the Arctic Haze can not be distinguished from large aerosols in winter. With autocorrelation analysis, we found that AOD events usually occur with a duration of several hours. We also compared AOD events with large-scale processes, like large-scale oscillation patterns, sea ice, weather conditions, or wildfires in the Northern Hemisphere but did not find one single cause that clearly determines the Arctic AOD. Therefore the observed optical depth is a superposition of different aerosol sources. Full article

The micromechanical properties (i.e., hardness, elastic modulus, and stress–strain curve) of AlCu films were determined by an instrumented indentation test in this work. For three AlCu films with different thicknesses (i.e., 1 µm, 1.5 µm, and 2 µm), the same critical ratio (h_max/t) of 0.15 and relative indentation depth range of 0.15–0.5 existed, within which the elastic modulus (i.e., 59 GPa) and nanoindentation hardness (i.e., 0.75 GPa, 0.64 GPa and 0.63 GPa for 1 µm, 1.5 µm and 2 µm films) without pile-up and substrate influence can be determined. The yield strength (i.e., 0.754 GPa, 0.549 GPa and 0.471 GPa for 1 µm, 1.5 µm and 2 µm films) and hardening exponent (i.e., 0.073, 0.131 and 0.150 for 1 µm, 1.5 µm and 2 µm films) of Al-(4 wt.%)Cu films for MEMS were successfully reported for the first time using a nanoindentation reverse method. In dimensional analysis, the ideal representative strain ε_r was determined to be 0.038. The errors of residual depth h_r between the simulations and the nanoindentation experiments was less than 5% when the stress–strain curve obtained by the nanoindentation reverse method was used for simulation. Full article

Short time series are fundamental in the foreign exchange market due to their ability to provide real-time information, allowing traders to react quickly to market movements, thus optimizing profits and mitigating risks. Economic transactions show a strong connection to foreign currencies, making exchange rate prediction challenging. In this study, the exchange rate estimation between the US dollar (USD) and the Chilean peso (CLP) for a short period, from 2 August 2021 to 31 August 2022, is modeled using the nonlinear Schrödinger equation (NLSE) and calculated with the fourth-order Runge–Kutta method, respectively. Additionally, the daily fluctuations of the current exchange rate are characterized using the Hurst exponent, H, and later used to generate short synthetic fluctuations to predict the USD–CLP exchange rate. The results show that the USD–CLP exchange rate can be estimated with an error of less than $5\%$, while when using short synthetic fluctuations, the exchange rate shows an error of less than $10\%$. Full article

Vehicular ad hoc networks (VANETs), which are the backbone of intelligent transportation systems (ITSs), facilitate critical data exchanges between vehicles. This necessitates secure transmission, which requires guarantees of message availability, integrity, source authenticity, and user privacy. Moreover, the traceability of network participants is essential as it deters malicious actors and allows lawful authorities to identify message senders for accountability. This introduces a challenge: balancing privacy with traceability. Conditional privacy-preserving authentication (CPPA) schemes are designed to mitigate this conflict. CPPA schemes utilize cryptographic protocols, including certificate-based schemes, group signatures, identity-based schemes, and certificateless schemes. Due to the critical time constraints in VANETs, efficient batch verification techniques are crucial. Combining certificateless schemes with batch verification leads to certificateless aggregate signature (CLAS) schemes. In this paper, cryptanalysis of Xiong’s CLAS scheme revealed its vulnerabilities to partial key replacement and identity replacement attacks, alongside mathematical errors in the batch verification process. Our proposed CLAS scheme remedies these issues by incorporating an identity authentication module that leverages chameleon hashing within elliptic curve cryptography (CHAM-CLAS). The signature and verification modules are also redesigned to address the identified vulnerabilities in Xiong’s scheme. Additionally, we implemented the small exponents test within the batch verification module to achieve Type III security. While this enhances security, it introduces a slight performance trade-off. Our scheme has been subjected to formal security and performance analyses to ensure robustness. Full article

The mean stress effect remains a critical aspect in multiaxial fatigue analysis. This work presents a new criterion that, based on the classical Findley criterion, applies a material-dependent exponent to the mean normal stress term and includes the ultimate tensile stress as a fitting parameter. This way of considering the non-linear effect of the mean stress, with a material-dependent rather than a fixed exponent, is totally innovative among the multiaxial fatigue criteria found in the literature. In order to verify its accuracy, the new criterion has been checked against an extended version of the Papuga database of multiaxial experimental tests with 485 results, and compared with the criteria by Findley, Robert, and Papuga. The new criterion provides outstanding results for pure uniaxial cases, with multiaxial performance similar to the Robert criterion with a smaller range of error and a conservative trend, even surpassing the popular Papuga method in several relevant loading scenarios. These features enhance the applicability and versatility of the criterion for its use in the fatigue design of structural components. Full article

The discrete element method (DEM) has been extensively utilized to investigate the mechanical properties of granules, particularly their microscopic behavior, overcoming limitations in field tests such as cost, time consumption, and soil condition restrictions. To ensure the development of reliable DEM simulations, proper contact model selection and parameter calibration are essential. In this research, a DEM parameter calibration method that could represent the nonlinear relationship between clayey soil pressure and sinkage at different moisture contents was proposed. Firstly, the sinking modulus K and the soil deformation exponent n were identified to reflect the nonlinear pressure–sinkage relationship. Then, sensitive DEM parameters on the soli pressure–sinkage relationship were investigated and calibrated, and the effect of moisture content on them was explored. Finally, the transfer of soil internal stress during subsidence was analyzed using the constructed discrete element model. The average error of the sinking modulus K and the soil deformation exponent n between the DEM and the experimental result at four moisture contents were 4.7% and 4.9%, respectively. The relative error of soil internal stress between simulation and experiment was 6.7%, 4.4%, and 9.7% at depths of 50 mm, 100 mm, and 150 mm, respectively. The soil particle trajectory, soil internal stress distribution, and variations during plate pressure–sinkage progress were analyzed by the constructed DEM model. The results demonstrated good agreement with theoretical models and experimental findings. The proposed clayey soil DEM modeling process that considers the pressure–sinkage nonlinear relationship at different moisture contents can be applied in machine-soil research. Full article

In the detection process of the internal defects of large oil-immersed transformers, due to the huge size of large transformers and metal-enclosed structures, the positional localization of miniature inspection robots inside the transformer faces great difficulties. To address this problem, this paper proposes a three-dimensional positional localization method based on adaptive denoising and the SCOT weighting function with the addition of the exponent β (SCOT-β) generalized cross-correlation for L-type ultrasonic arrays of transformer internal inspection robots. Aiming at the strong noise interference in the field, the original signal is decomposed by an improved Empirical Mode Decomposition (EMD) method, and the optimal center frequency and bandwidth of each mode are adaptively searched. By extracting the modes in the frequency band of the positional localization signal, suppressing the modes in the noise frequency band, and reconstructing the Intrinsic Mode Function (IMF) of the independently selected superior modal components, a signal with a high signal-to-noise ratio is obtained. In addition, for the traditional mutual correlation algorithm with a large delay estimation error at a low signal-to-noise ratio, this paper adopts an improved generalized joint weighting function, SCOT-β, which improves the anti-jamming ability of the generalized mutual correlation method at a low signal-to-noise ratio by adding an exponential function to the denominator term of the SCOT weighting function’s generalized cross-correlation. Finally, the accurate positional localization of the transformer internal inspection robot is realized based on the quadratic L-array and search-based maximum likelihood estimation method. Simulation and experimental results show the following: the improved EMD denoising method better improves the signal-to-noise ratio of the positional localization signal with a lower distortion rate; in the transformer test tank, which is 120 cm in length, 100 cm in width, and 100 cm in height, based on the positional localization method in this paper, the average relative positional localization error of the transformer internal inspection robot in three-dimensional space is 2.27%, and the maximum positional localization error is less than 2 cm, which meets the requirements of engineering positional localization. Full article

The Precision Time Protocol (PTP) plays a pivotal role in achieving precise frequency and time synchronization in computer networks. However, network delays and jitter in real systems introduce uncertainties that can compromise synchronization accuracy. Three clock skew estimators designed for the PTP scenario were obtained in our earlier work, complemented by closed-form approximations for the Mean Squared Error (MSE) under the generalized fractional Gaussian noise (gfGn) model, incorporating the Hurst exponent parameter (H) and the a parameter. These expressions offer crucial insights for network designers, aiding in the strategic selection and implementation of clock skew estimators. However, substantial computational resources are required to fit each expression to the gfGn model parameters (H and a) from the MSE perspective requirement. This paper introduces new closed-form estimates that approximate the MSE tailored to match gfGn scenarios that have a lower computational burden compared to the literature-known expressions and that are easily adaptable from the computational burden point of view to different pairs of H and a parameters. Thus, the system requires less substantial computational resources and might be more cost-effective. Full article

A central challenge in hypothesis testing (HT) lies in determining the optimal balance between Type I (false positive) and Type II (non-detection or false negative) error probabilities. Analyzing these errors’ exponential rate of convergence, known as error exponents, provides crucial insights into system performance. Error exponents offer a lens through which we can understand how operational restrictions, such as resource constraints and impairments in communications, affect the accuracy of distributed inference in networked systems. This survey presents a comprehensive review of key results in HT, from the foundational Stein’s Lemma to recent advancements in distributed HT, all unified through the framework of error exponents. We explore asymptotic and non-asymptotic results, highlighting their implications for designing robust and efficient networked systems, such as event detection through lossy wireless sensor monitoring networks, collective perception-based object detection in vehicular environments, and clock synchronization in distributed environments, among others. We show that understanding the role of error exponents provides a valuable tool for optimizing decision-making and improving the reliability of networked systems. Full article

Quantitative inversion of GPR data opens the door to precise characterization of underground environments. However, in order to make the inverse scattering problem solution easier from a computational viewpoint, simplifying assumptions are often applied, i.e., two-dimensional approximations or the consideration of idealized field probes and electromagnetic sources. These assumptions usually produce modeling errors, which can degrade the dielectric reconstruction results considerably. In this article, a processing step based on long short-term memory cells is proposed for the first time to correct the modeling error in a multiantenna GPR setting. In particular, time-domain GPR data are fed into a neural network trained with couples of finite-difference time-domain simulations, where a set of sample targets are simulated in both realistic and idealized configurations. Once trained, the neural network outputs an approximation of multiantenna GPR data as they are collected by an ideal two-dimensional measurement setup. The inversion of the processed data is then accomplished by means of a regularizing Newton-based nonlinear scheme with variable exponent Lebesgue space formulation. A numerical study has been conducted to assess the capabilities of the proposed inversion methodology. The results indicate the possibility of effectively compensating for modeling error in the considered test cases. Full article