Signal Strength Research Articles

OSC-GOR: Optimal Security-Aware Cluster-Based Hybrid Geographical and Opportunistic Routing for Vehicular ad hoc Networks

Vehicular ad hoc networks (VANETs) are crucial for infrastructure-less vehicular communication. The dynamic and complex nature of VANETs necessitates efficient routing strategies. This paper introduces an optimal security-aware cluster-based hybrid geographical and opportunistic routing (OSC-GOR) for VANETs. By utilizing the improved bald eagle optimization (IBEO) algorithm, we achieve optimal clustering that enhances energy efficiency. Cluster heads (CHs) are elected based on trust, calculated from metrics like energy consumption and signal strength. A Poplar optimization algorithm (POA) further refines these metrics. Subsequently, an optimal min–max recurrent neural network (OMM-RNN) determines the next forwarder nodes, facilitating data transfer in VANETs. Implemented using the NS-2 tool and SUMO traffic generator, OSC-GOR showcases optimized clustering and superior network efficiency. Comparative simulations reveal OSC-GOR’s advantages in energy use, latency, delivery ratio, goodput, and routing overhead, outperforming existing methods. Notably, OSC-GOR effectively counters VANET attacks such as GPS spoofing and Sybil threats.

Optical parameter design model of hemispherical laser retroreflector arrays for satellite laser ranging

A laser retroreflector array (LRA) with multiple retroreflectors is used to directionally reflect an incident laser beam and aims to enhance the received signal strength into laser ranging ground stations. A comprehensive mathematical equation of the received photons is established by modelling the LRA far field diffraction intensity (FFDI) and the satellite velocity aberration position. Based on this received photon model, a novel method on designing the dihedral angle offset and the aperture of retroreflectors is proposed, which maximizes the received photon under the minimal elevation angle of the ground station. Taking the hemispherical structure parameters of Chinese HY-2 satellite LRA and the specification parameters of the Shanghai Astronomical Observatory, the optimal aperture and the dihedral angle offset of the HY-2 laser retroreflectors are 17 mm and 1.6″, respectively. These two optimal optical parameters make the received photon under the elevation angle of 0° reach ∼0.9 count per laser shot. Until now, ∼851,400 successful observations have been successfully obtained by global ground stations since the first launch of HY-2 satellite series in 2011, which guarantees the high-precise orbit determination of HY-2 satellite series.

Integration of an IoT sensor with angle-of-arrival-based angle measurement in AGV navigation: A reliability study

Automated guided vehicle (AGV), which was initially designed for indoor operations in industry, has been increasingly applied in outdoor heavy-duty logistics tasks. In typical navigation tasks, such as the autonomous tracking of a designated object or a person, relative angle and relative distance between AGV and the target is required. To obtain the necessary information, various on-board sensors are extensively integrated. In this paper, the reliability of measuring the relative angle with the Angle-of-Arrival (AoA) method with two different Internet of Things (IoT) sensor sets from Texas Instrument (TI) and u-blox, according to Bluetooth 5.1 was investigated. The performance of IoT sensors was validated with angle accuracy parameters and received signal strength indicator (RSSI). The better IoT sensor was then integrated into the AGV navigation system, and the information gathered from IoT sensor enabled the AGV to turn toward the direction of the target. The process of AGV turning to the targeted direction based on IoT sensor information was respectively tested in the simulation and actual environment and evaluated by the disparity between the real relative angle and the rotation angle of the AGV. The results showed that this disparity was within ±5° in both simulated and actual environments, and methods for higher accuracy were proposed. In this way, the reliability and performance of Angle of Arrival (AoA) sensors in measuring the relative angle, which remains unexplored by other researchers, was systematically assessed contributing to extending the usability of AoA sensors in complex, real-world applications.

Nondestructive differential eddy current testing for corrosion detection on coated aluminium alloys

PurposeThe objective of this study is to use non-destructive testing of corrosion on coated aluminium alloys using differential eddy current detection (DECD), with the aim of elucidating the relationship between the characteristics of corrosion defects and the detection signal.Design/methodology/approachPitting corrosion defects of varying geometrical dimensions were fabricated on the surface of aluminium alloy plates, and their impedance signals were detected using DECD to investigate the influence of defect diameter, depth, corrosion products and coating thickness on the detection signals. Furthermore, finite element analysis was used to ascertain the eddy current distributions and detection signals under different parameters.FindingsThe size of the defect is positively correlated with the strength of the detection signal, with the defect affecting the latter by modifying the distribution and magnitude of the eddy current. An increase in the diameter and depth of corrosion defects will enhance the eddy current detection (ECD) signal. The presence of corrosion products in the corrosion defects has no significant effect on the eddy current signal. The presence of a coating results in a decrease in the ECD signal, with the magnitude of this decrease increasing with the thickness of the coating.Originality/valueThe objective is to provide experimental and theoretical references for the design of eddy current non-destructive testing equipment and eddy current testing applications.

Research on Rate Adaptation of Underwater Optical Communication with Joint Control of Photoelectric Domain

As the communication distance changes, the received signal strength of an underwater optical communication system will change, and the range of its variation may not only exceed the dynamic range of the photoelectric detection device but also cause the reliability of communication to change due to the change in the received signal-to-noise ratio. In order to maintain better communication over a longer distance, this paper proposes a rate-adaptive method for underwater optical communication with joint control in the photoelectric domain. In the optical domain, the incident light’s power is adaptively adjusted by controlling the transmittance of the liquid crystal light valve to reduce saturation distortion. In the electrical domain, the constellation distribution is optimized according to the desired probability mass function, and the modulation order is adjusted in real time by estimating the received signal-to-noise ratio of the link. The simulation results show that under the forward error correction (FEC) threshold, the proposed method increases the dynamic range of the photomultiplier tube (PMT) by about 10 dB and expands the dynamic range of the system’s communication distance.

A novel artificial intelligence‐based antenna designing model for optimizing 5G mobile communication

AbstractAntenna designing involves the creation of devices to transmit or receive electromagnetic signals for various applications. It encompasses optimizing parameters such as size, shape, and frequency response to achieve desired performance characteristics, including signal strength, bandwidth, and efficiency, across diverse communication systems and frequencies. In this research, we intend to establish a novel artificial intelligence (AI)‐based antenna designing model for optimizing 5th generation (5G) mobile communication. We proposed Multi‐Objective Zebra Optimization (MOZO) to enhance 5G antenna design, aiming to minimize return loss () and maximize gain (G) concurrently. MOZO efficiently explores design space, offering Pareto‐optimal solutions that balance objectives. This approach ensures optimal antenna performance across the desired frequency range, advancing 5G communication efficiency. Our model enhances the functionality of a small, rectangular patch antenna designed for the 5G band, specifically operating at 30 GHz. Additionally, we conducted a comparative analysis between the performance of our optimized rectangular 5G antenna and several recently employed 5G‐millimeter wave (mmWave) antennas. This comparison sheds light on the efficacy and competitiveness of our optimized design within the 5G‐mmWave antenna landscape.

Adverse events associated with tezepelumab: a safety analysis of clinical trials and a pharmacovigilance system

ABSTRACT Background Tezepelumab is the first asthma biologic approved by the FDA that is not restricted by biomarker phenotypes. To date, there have been no studies reporting adverse events (AEs) associated with the real-world use of tezepelumab. Research design and methods This study included a comprehensive evaluation of AE reports related to tezepelumab since its approval (4th quarter of 2021 to 1st quarter of 2024) using the FAERS database, and compared with the currently reported clinical trial results (ClinicalTrials.gov). Results A total of 2153 reports of tezepelumab-related AEs were extracted. 256 preferred terms (PTs) of adverse reactions involving 27 system organ classes were identified. Significant AEs that were not reported on the drug label, such as ‘dyspnea,’ ‘body temperature,’ and ‘tongue pruritus,’ were reported. The median time to onset (TTO) of tezepelumab-related AEs was 35 days.The most frequent AEs in different sexes were ‘arthralgia’ and ‘dyspnea,’ with differences in signal strength ranking between the sexes. Conclusions This study represents the largest report to date on tezepelumab-related AEs, providing valuable insights into the potential side effects of tezepelumab. This work is crucial for the broader clinical application of this novel biologic and improving outcomes for patients with severe asthma.

Advancements in Digital Forensics: A Quantitative Analysis of Cell Tower Triangulation Techniques

This study delves into the accuracy and reliability of Cell Tower Triangulation (CTT) in digital forensics, employing a diverse sample of mobile devices across various real-world scenarios—criminal investigations, missing persons, and corporate security breaches. By simulating these situations, the research aims to provide a comprehensive understanding of CTT’s effectiveness in urban and rural settings. Data was meticulously collected on signal strength, tower locations, and timestamps, and analyzed using statistical methods to assess CTT accuracy and reliability. Findings from descriptive statistics, comparative analyses, precision metrics, and correlation analyses reveal significant insights into CTT’s application in digital forensics. Despite certain limitations in precision, indicated by the variability in virtual triangle sizes, the study underscores the potential of CTT in supporting investigations while also highlighting the importance of ethical considerations and legal compliance. This research contributes to the advancement of digital forensics, offering a nuanced understanding of CTT’s capabilities and limitations.

Predicting 5G throughput with BAMMO, a boosted additive model for data with missing observations

Abstract To deliver on the promise of 5G, network providers and application developers need to understand the factors impacting millimetre wave (mmWave) 5G throughput. Missing data, however, pose significant challenges for modelling throughput. Even in controlled settings, signal strength data may be only intermittently observed when a device’s connection is weak, leading to missing predictor values in model training. In addition, users may choose not to share their data once the model is deployed, meaning that key predictors may be missing when we want to predict throughput for their devices. To address these challenges, we introduce boosted additive model for data with missing observation (BAMMO), a novel additive model estimator obtained via a componentwise boosting algorithm that naturally incorporates data with missing values in model fitting. We validate BAMMO’s approach to handling missing data by comparing it with competing methods on real 5G network data with a high proportion of missing values and in simulations, finding that it delivers more accurate predictions and takes less time to compute. To identify key predictors of mmWave 5G throughput, we develop a novel extension of sparsity oriented importance learning for BAMMO, giving us a measure of variable importance based on the entire boosting solution path rather than a single selected model.

XFEL SASE pulses can enhance time-dependent observables

Abstract X-ray free electron lasers (XFELs) have emerged as powerful sources of short and intense X-ray pulses. We propose a simple and robust procedure which takes advantage of the inherent stochasticity of self-amplified stimulated emission (SASE) pulses to enhance the time-resolution and signal strength in the recorded data. Notably, the proposed method is able to enhance the average signal without knowledge of the signal strength of individual shots. Simple metrics for the probe pulses are introduced, such as an effective pulse duration applicable to SASE pulses characterised in the time domain using {\it{e.g.}}\ an X-band transverse cavity (XTCAV). The approach is evaluated using simulated and real pulse data in the context of ultrafast electron dynamics in a molecule. Utilising H$_2$ as a model system, we demonstrate the efficacy of the method theoretically, successfully enhancing the predicted nonresonant ultrafast X-ray scattering signal associated with electron dynamics. The method presented is broadly applicable and offers a general strategy for enhancing time-dependent observables at XFELs.

Continuous measurement of red wood ant (Formica rufa) outdoor behaviour using passive acoustic monitoring

Ants serve as ecosystem engineers that maintain important ecological processes within forests. Given their ecological importance, it is a clear scientific shortcoming that we lack non-invasive methods to survey their behaviour inside common opaque habitats such as mounds, litter, and soil. In this study, we assess if acoustic signals from red wood ant (Formica rufa) mounds are useful to infer temporal changes in ant activity within forested ecosystems. We found that acoustic indices used previously as a proxy for soil fauna in soil ecological studies (Acoustic Complexity Index, Bioacoustic Index) can indeed separate sounds generated by the ant's daily routines (biophony) from other forest sounds. Yet, we also show that these indices are problematic proxies for soil diversity as they increase not only due to an increased number of species but also due to an increased number of the same species. Acoustic measures that incorporated the strength of acoustic signals, Average Power Density (APD) and Peak Power Density (PPD) also increased with increasing ant abundance and constituted the conceptually best proxy for ant activity. For example, the PPD could i) track diurnal changes in Formica rufa activity with a high temporal resolution (minutes) and ii) detect altered behavioural responses to temperature changes. We conclude that microphones detecting biophony can provide high-resolution information about in situ ant behaviours in forested ecosystems. Thus, passive acoustics monitoring offers a promising avenue as a non-invasive monitoring tool for soil macrofauna studies.

A fingerprint dictionary processing approach in indoor localization system based on wi-fi

Indoor localization using Wi-Fi fingerprinting based on Received Signal Strength (RSS) has gained widespread attention due to its immunity to external factors and ability to penetrate obstacles. The localization process involves an offline phase for building a radio map and an online phase for matching location queries. Existing matching algorithms often prioritize enhancing online phase accuracy, overlooking the importance of offline data preprocessing, which can negatively impact overall performance. This study introduces a novel approach called Fingerprint Dictionary Preprocessing (FDP) that employs Convolutional Dictionary Learning (CDL) to process radio map data. CDL learns a set of kernels capturing site characteristics, representing RSS values from Access Points (APs) in a sparse manner. The proposed FDP system compresses data through feature learning, reducing storage requirements for data transmission. In the online phase, CDL is utilized for assisting matching fingerprints against the learned dictionary, accurately locating users. The contributions of the FDP system presenting a cost-effective and practical solution for indoor localization, addressing the challenges associated with large data collection and multi-dimensional data requirements, making it a promising approach for real-world applications. We conducted experiments in two real indoor environments, and the results indicated that the proposed FDP system, whether applied to the original radio map or the preprocessed fingerprint database, led to improved localization accuracy and reduced localization time.

Up, Up, and Away: Winds and Dynamical Structure as a Function of Altitude in the Ultrahot Jupiter WASP-76b

Due to the unprecedented signal strengths offered by the newest high-resolution spectrographs on 10 m class telescopes, exploring the 3D nature of exoplanets is possible with an unprecedented level of precision. In this paper, we present a new technique to probe the vertical structure of exoplanetary winds and dynamics using ensembles of planet absorption lines of varying opacity, and apply it to the well-studied ultrahot Jupiter WASP-76b. We then compare these results to state-of-the-art global circulation models (GCMs) with varying magnetic drag prescriptions. We find that the known asymmetric velocity shift in Fe i absorption during transit persists at all altitudes, and observe tentative trends for stronger blueshifts and more narrow line profiles deeper in the atmosphere. By comparing three different model prescriptions (a hydrodynamical model with no drag, a magnetic drag model, and a uniform drag model) we are able to rule out the uniform drag model due to inconsistencies with observed trends in the data. We find that the magnetic model is slightly favored over the the hydrodynamic model, and note that this 3-Gauss kinematic magnetohydrodynamical GCM is also favored when compared to low-resolution data. Future generation high-resolution spectrographs on extremely large telescopes will greatly increase signals and make methods like these possible with higher precision and for a wider range of objects.

Self-Powered, Flexible, Wireless and Intelligent Human Health Management System Based on Natural Recyclable Materials.

Combining wearable sensors with modern technologies such as internet of things and big data to monitor or intervene in obesity-induced chronic diseases, such as obstructive sleep apnea, type II diabetes, cardiovascular diseases, and Alzheimer's disease, is of great significance to the self-health management of human beings. This study designed a loofah-conducting graphite four friction layer enhanced triboelectric nanogenerator (LG-TENG) and developed a health management system for human motion recognition and early warning of sleep breathing abnormalities. By uniformly spraying and depositing conductive graphite on the surface of the loofah and the elastic film cross-interlocking bending structure design, the signal strength of the LG-TENG has been improved by 390%. The stable output signal is still maintained after 1500 s of continuous operation at a frequency of 2 Hz. LG-TENG can realize accurate motion analysis by muscle contraction state. Combining different deep learning models resulted in 98.1% accuracy in recognizing seven categories of displacement speeds for an individual and 96.46% accuracy in recognizing seven categories of displacement speeds for three individuals. In addition, the sleep breathing monitoring early warning system was developed by integrating Bluetooth wireless transmission and upper computer analysis technology. This system aims to analyze and provide real-time warnings for sleep-breathing abnormalities. This research promotes an innovation of TENG technology based on the advantages of natural materials, recyclability and low cost. It offers new ideas for self-health management and scientific exercise for obese people, showing a broad application prospect.

Downregulation of PIK3IP1/TrIP on T cells is controlled by TCR signal strength, PKC and metalloprotease-mediated cleavage.

The protein known as PI3K-interacting protein (PIK3IP1), or transmembrane inhibitor of PI3K (TrIP), is highly expressed by T cells and can modulate PI3K activity in these cells. Several studies have also revealed that TrIP is rapidly downregulated following T cell activation. However, it is unclear as to how this downregulation is controlled. Using a novel monoclonal antibody that robustly stains cell-surface TrIP, we demonstrate that TrIP is lost from the surface of activated T cells in a manner dependent on the strength of signaling through the T cell receptor (TCR) and specific downstream signaling pathways, in particular classical PKC isoforms. TrIP expression returns by 24 hours after stimulation, suggesting that it may play a role in resetting TCR signaling at later time points. We also provide evidence that ADAM family proteases are required for both constitutive and stimulation-induced downregulation of TrIP in T cells. Finally, by expressing truncated forms of TrIP in cells, we identify the region in the extracellular stalk domain of TrIP that is targeted for proteolytic cleavage.

OPTICAL BLUR AFFECTS DIFFERENTLY ON AND OFF VISUAL PATHWAYS.

Optical blur affects ON and OFF pathways differently.Blur expands ON but shrinks OFF receptive fields, weakening OFF more than ON signals.Magnification from positive blur reduces OFF more than ON signal strength and latency.ON-OFF response balance can signal retinal image quality and guide eye growth. Pons et al. demonstrate that optical blur affects differently the receptive field properties and responses from ON and OFF visual pathways, revealing a new relation between ON/OFF pathway balance and blurred visual perception. The results highlight a new role of ON and OFF pathways in optimizing retinal image quality that can have implications for the regulation of eye growth.

Indoor Scene Intelligent Identification System Based on Spatial Topological Relationship Constraints

Abstract. Indoor scene identification can provide prior environmental information for indoor positioning applications, achieving positioning enhancement. Due to the similarity of wireless signals in adjacent spaces, it can lead to incorrect identification in neighbouring scenario units. To address this problem, this paper first employs a Naive Bayes classifier to train and classify wireless signal strength data from different scenes, constructing a scene identification algorithm. Secondly, a topological relationship adjacency matrix and adjacency list tailored for indoor positioning are constructed, imposing spatial topological constraints to assist scene identification. Finally, an indoor scene intelligent identification system is developed and implemented. The experimental results indicate that the scene identification method based on spatial topological relationship constraints can effectively improve identification accuracy. The overall accuracy for 8 scenario units is 98.2%, and the identification accuracy is increased by 1.1% compared with the original method.

A fast and stable calibration method for VLP system without any geometric measurement

Abstract. Visible light positioning (VLP) is one of the most promising technologies for providing high-precision, low-cost indoor positioning and navigation services. However, the traditional calibration methods of VLP are complex and unfriendly to users, which hinders the large-scale commercial deployment of VLP systems. In this letter, a fast and stable calibration method for Lambert model in the received signal strength (RSS)-based VLP system is proposed, which dispenses with geometric measurement and greatly simplifies the calibration procedures. The proposed method calibrates the Lambert model through two steps, firstly using a ratio method to calibrate the Lambert order, and then estimating the constant term. The actual processes only require moving a robot equipped with a photo-detector (PD) along a rectangular trajectory once, and then the program will automatically estimate the required parameters by analyzing the RSS during this period. Experimental results show a good stability of the calibrated parameters, as well as a excellent distance measuring accuracy within 12 cm. And the entire processes, including signal collection and processing, only take a total of no more than four minutes.

RSPO/LGR signaling regulates proliferation of adult hippocampal neural stem cells.

In the dentate gyrus of the adult hippocampus, neurogenesis from neural stem cells (NSCs) is regulated by Wnt signals from the local microenvironment. The Wnt/β-catenin pathway is active in NSCs, where it regulates proliferation and fate commitment, and subsequently its activity is strongly attenuated. The mechanisms controlling Wnt activity are poorly understood. In stem cells from adult peripheral tissues, secreted R-spondin proteins (RSPO1-4) interact with LGR4-6 receptors and control Wnt signaling strength. Here, we found that RSPO1-3 and LGR4-6 are expressed in the adult dentate gyrus and in cultured NSCs isolated from the adult mouse hippocampus. LGR4-5 expression decreased in cultured NSCs upon differentiation, concomitantly with the reported decrease in Wnt activity. Treatment with RSPO1-3 increased NSC proliferation and the expression of Cyclin D1, but did not induce the expression of Axin2 or RNF43, two well-described Wnt target genes. However, RSPOs enhanced the effect of Wnt3a on Axin2 and RNF43 expression, as well as on Wnt/β-catenin reporter activity, indicating that they can potentiate Wnt activity in NSCs. Moreover, RSPO1-3 were found to be expressed by cultured dentate gyrus astrocytes, a crucial component of the neurogenic niche. In co-culture experiments, the astrocyte-induced proliferation of NSCs was prevented by RSPO2 knockdown in astrocytes and LGR5 knockdown in hippocampal NSCs. Additionally, RSPO2 knockdown in the adult mouse dentate gyrus reduced proliferation of neural stem and progenitor cells in vivo. Altogether, our results indicate that RSPO/LGR signaling is present in the dentate gyrus and plays a crucial role in regulating neural precursor cell proliferation.

A weighted hybrid indoor positioning method based on path loss exponent estimation

With the rapid development of the Internet of Things (IoT), location-based services (LBS) have gained significant attention due to their widespread applications in daily life. This paper addresses the indoor target positioning problem in wireless sensor networks (WSNs). A weighted constrained linear least squares algorithm based on path loss exponent estimation (PLE-WCLLS) with received signal strength (RSS) and angle of arrival (AoA) hybrid measurements is proposed. To address the challenges of unknown transmission power and path loss exponent (PLE), the proposed method employs a linear least squares (LLS) estimation approach based on the ranging maximum likelihood (ML) estimation model to estimate both parameters. Subsequently, a confidence weight adjustment strategy is designed to reduce positioning errors. To handle the highly non-convex and nonlinear nature of the RSS/AoA hybrid optimization model, a linearization method based on Taylor series expansion is presented. Accurate target position estimation is achieved by solving a constrained quadratic programming problem. The effectiveness of the proposed algorithm is validated through numerical simulations and experimental evaluation in a real indoor environment. Compared to traditional positioning methods, the PLE-WCLLS algorithm improves positioning accuracy by 13.2%, and it performs exceptionally well even in scenarios with fewer sensor nodes. This gives it broad application prospects in areas such as IoT device management, personnel tracking in smart buildings, and asset localization in industrial automation.