Researchers are interested in unmanned aerial vehicles (UAVs) because they have many uses in current 5G and future 6G networks and are safer than human-operated vehicles. Terahertz (THz)-band communications are a possible option in 6G for fast communication. THz wireless communication between UAVs is taken into consideration in this work. Different fading channels, which are significant influencing factors in THz communication channel modeling, are used to analyze the performance of UAV communications in THz networks. Consideration must be made to the structure, wireless channel parameters, and transmission characteristics when evaluating the performance of wireless technology. Nakagami-m, Rician, Weibull, and Rayleigh fading channels are all taken into consideration, along with log-normal fading. Moreover, an optimization algorithm for the THz channel is presented, which is meant to minimize transmission power by optimizing the trajectory of uplink and downlink transmissions between the UAV and users. The equations of the UAV locations and the transmission power optimization of each user are derived. Analytical formulations regarding capacity, outage probability, and bit error rate (BER) are generated when performance-influencing factors are taken into account. The analytical analysis is supported by the numerical results that are provided.

Typical assessments of balance impairment are subjective or require data from cumbersome and expensive force platforms. Researchers have utilized lower back (sacrum) accelerometers to enable more accessible, objective measurement of postural sway for use in balance assessment. However, new sensor patches are broadly being deployed on the chest for cardiac monitoring, opening a need to determine if measurements from these devices can similarly inform balance assessment. Our aim in this work is to validate postural sway measurements from a chest accelerometer. To establish concurrent validity, we considered data from 16 persons with multiple sclerosis (PwMS) asked to stand on a force platform while also wearing sensor patches on the sacrum and chest. We found five (five) of 15 postural sway features derived from chest (sacrum) were significantly correlated with force platform-derived features, which is in line with prior sacrum-derived findings. Clinical significance was established using a sample of 39 PwMS who performed eyes-open, eyes-closed, and tandem standing tasks. This cohort was stratified by fall status and completed several patient-reported measures (PRM) of balance and mobility impairment. We also compared sway features derived from a single 30-second period to those derived from a one-minute period with a sliding window to create subject-specific distributions of each postural sway feature (SSD method). We find traditional computation of sway features from the chest is sensitive to changes in PRMs and task differences. Distribution characteristics from the SSD method establish additional relationships with PRMs, detect differences in more tasks, and distinguish between fall status groups. Overall, the chest was found to be a valid location to monitor postural sway and we recommend utilizing the SSD method over single-observation analyses.

Radiation detection systems for security applications are often equipped with neutron sensors and required to promptly respond with an alarm if the neutron flux exceeds the background level. As the ambient neutron flux is essentially caused by cosmic radiation, this background could be evaluated by assessing the rate of muons crossing the gamma-ray detector which is comprised in such systems anyway. A study performed with a commercial Backpack Radiation Detection System (BRD) demonstrates the feasibility of this approach, supposed the gamma spectrometer is capable of discriminating muon signals from energetic events caused by the gamma-ray cascades following neutron captures. This requires a dynamic range exceeding the usual 3-4 MeV. The instrument used provided standard spectroscopy up to 10 MeV and facilitated charge-loss compensated spectroscopy up to 1 GeV, which allowed a separate assessment of normal gamma events (< 3 MeV), neutron-capture gamma events (3-7 MeV), and muon events (> 10 MeV). In this case, the rate of neutron-capture gamma events can be used as a neutron indicator on its own, while the muon rate signals the ambient neutron background level.

In this paper, a rectangular-shaped wideband dielectric resonator Graphene inspired electronic band gap (EBG) based complementary split ring resonator (CSRR) loaded miniaturized THz MIMO antenna is designed having a dimension of 56×56×3.6μm3 is designed on a Roggers RO3035 substrate with a relative permittivity of 3.6 and loss tangent 0.0015. This antenna works in the range of 6.0THz-12.5THz (70.76%) with a peak gain of 7.68 dBi. This composite DRA materials having Silicon (εr = 11.1) based cylindrical dielectric and cylindrical Graphene disk to be stacked on rectangular radiated antenna having height of 13.5µm and 0.2µm to improve the impedance bandwidth as well as gain of the antenna from 5.24dBi to 7.68dBi. Firstly single-element gold plated rectangular shaped antenna is transformed into a two-element MIMO antenna with mushroom-shaped EBG structure is deployed for improving the isolation between two antenna elements as well as to improve the front-to-back ratio (FBR). CSRR is loaded to obtained band notch characteristics for avoiding the interference between near wireless devices in the span of 10.6-10.8THz. Graphene disk having 3µm height put on Silicon (εr = 11.1) based cylindrical DRA provides high gain and improved the impedance bandwidth for achieving wide bandwidth from 6.0THz-12.5THz. The proposed MIMO Antenna performance has been judged by antenna parameters like gain, return loss, isolation between two antenna elements, diversity parameters like ECC, DG, TARC, Mean Effective Gain (MEG), Channel Capacity Loss (CCL) and found proposed DRA-MIMO antenna suitable for wide band nano/optical communication in 6G for IoT and bio-medical sensing application.

This paper proposes an adaptive-output-voltage regulation (AOVR) based solution to alleviate the dc-link undervoltage for grid-forming (GFM) inverters. First, it is shown that large disturbances may cause the dc-link undervoltage for GFM inverters threatening the safe operation of the system. To tackle this issue, an AOVR-based control strategy is proposed, where the main idea is to reduce the output voltage of the GFM inverter and also speed up the dynamic process of the power angle by intentionally enlarging the frequency deviation of the active power control loop. By doing so, the dc-link output energy will reduce to avoid possible undervoltage. The parameter design of the proposed method is based on the overmodulation constraints, and an auxiliary module is configured for the low-voltage-ride-through (LVRT) requirements. The applicability of the proposed solution is also checked when considering the overcurrent limitation. The effectiveness of the proposed control strategy is finally confirmed by the experimental tests.

This articles discusses the use of the Robotic Upright Stand Trainer (RobUST), a cable driven exoskeleton that actuates the trunk and pelvis independently, for postural balance control training. It examines the changes that occur in healthy patients after a perturbation based balance training session conducted in virtual reality. In addition, there are two testing conditions tested. In one condition subjects are given assistive support at the pelvis by RobUST to help resist perturbations and in the other, there is no assistive support. Data collected include electromyographic data from lower limb muscles, movement and variability of movement data.

In this letter, an ultra-broadband rectifier with expanded dynamic input power range (IPR) for both wireless power transfer (WPT) and radio frequency energy-harvesting (RFEH) is proposed and analyzed. Expanded dynamic IPR and broadband impedance matching are realized by utilizing a microstrip transmission line impedance compression circuit (MTLICC) with the topology of the paralleled voltage doubler. A proof-of-concept prototype shows a dynamic IPR of 8.5–23.5 dBm, a frequency range of 0.33–2.61 GHz (fractional bandwidth of 155%), PCE is greater than 50%, and a peak PEC of 79.7% (achieved at an input-power level of 19 dBm). The measurement results are in good agreement with the simulation. Covering most 3G/4G/5G/ISM frequency bands, this design is promising for RF power transmission and energy harvesting in the age of the Internet of Things systems.

Application of Kobayashi potentials (KP) is extended to the electromagnetic (EM) diffraction from the parallel-plate multiport.

The energy systems are evolving toward the comprehensive and massive integration of power electronics-based technologies, such as photovoltaic, wind turbines, and electric vehicles. All these emerging and promising power converters technologies must be tested to comply with the grid codes, especially during severe grid fault events, before their commercialization. Low-Voltage Ride-Through (LVRT) field tests are usually conducted to prove the ability of the converter to ride through faults. These field tests are expensive and require bulky equipment, while Power-Hardware-in-the-Loop (P-HIL) offers a more cost-effective and flexible alternative. However, the P-HIL must be stable and accurate to get reliable results. The accuracy of a P-HIL test is directly influenced by its interface algorithm. This paper proposes a frequency domain approach, based on the concept of singular values in Multi-Input Multi-Output systems, to study the P-HIL accuracy and stability in grid connected converter testing under asymmetrical line-to-line fault conditions. Accuracy and stability analysis have been performed analytically and validated by Matlab/Simulink simulations and by experimental P-HIL tests. This paper demonstrates that correct fault modeling leads to correct assessment of the reactive power injected under faults and of avoiding resonances.

This paper proposes a novel data-driven approach to designing orthonormal transform matrix codebooks for adaptive transform coding of any non-stationary vector processes which can be considered locally stationary. Our algorithm, which belongs to the class of block-coordinate descent algorithms, relies on simple probability models such as Gaussian or Laplacian for transform coefficients to directly minimize with respect to the orthonormal transform matrix the mean square error (MSE) of scalar quantization and entropy coding of transform coefficients. A difficulty commonly encountered in such minimization problems is imposing the orthonormality constraint on the matrix solution. We get around this difficulty by mapping the constrained problem in Euclidean space to an unconstrained problem on the Stiefel manifold and leveraging known algorithms for unconstrained optimization on manifolds. While the basic design algorithm directly applies to non-separable transforms, an extension to separable transforms is also proposed. We present experimental results showing that adaptive coding with our transform codebook designs outperform the commonly used discrete cosine transform (DCT) in many regions of natural images and motion-compensated prediction error images of video frames. According to these results, the proposed codebook designs slightly outperform those found by a common alternative approach, the sparsity-based optimization.

Over the last 15 years, Software-Defined Networking (SDN) has gained widespread support from both research communities and the industry owing to its open nature and programmability. This paradigm empowers stakeholders, including researchers, practitioners, and developers, to craft innovative networking services using robust APIs and a global network view, free from reliance on vendor-dependent control planes. However, the flexible architecture of SDN has introduced a plethora of security challenges absent in traditional network environments. While many surveys have presented existing attacks, there has been a lack of systematization of attacks from a penetration perspective, which is crucial for understanding attacks and their root causes. This paper aims to analyze previous literature that has disclosed attack cases in SDN, scrutinizing their vulnerabilities, penetration routes, and root causes. Additionally, we provide an in-depth and comprehensive discussion of the underlying problems associated with these attacks and present defenses proposed by researchers to mitigate them, analyzing how the root causes are remedied. Through this study, we aim to illuminate existing security issues within the current SDN architecture, prompting a reevaluation of various security problems and providing a future research guideline for SDN security.

Large-scale processing of heterogeneous datasets in interdisciplinary research often requires time-consuming manual data curation. Ambiguity in the data layout and preprocessing conventions can easily compromise reproducibility and scientific discovery, and even when detected, it requires time and effort to be corrected by domain experts. Poor data curation can also interrupt processing jobs on large computing clusters, causing frustration and delays. We introduce DataCurator , a portable software package that verifies arbitrarily complex datasets of mixed formats, working equally well on clusters as on local systems. Human-readable TOML recipes are converted into executable machine-verifiable templates, enabling users to easily verify datasets using custom rules without writing code. Recipes can be used to transform and validate data, for pre- or post-processing, selection of data subsets, sampling, and aggregation, such as summary statistics. Processing pipelines no longer need to be burdened by laborious data validation, with data curation and validation replaced by human and machine verifiable recipes specifying rules and actions. Multithreaded execution ensures scalability on clusters, and existing Julia, R, and Python libraries can be reused. DataCurator enables efficient remote workflows, offering integration with Slack and the ability to transfer curated data to clusters using OwnCloud and SCP. Code available at: https://github.com/bencardoen/DataCurator.jl.

This publication proposes a parametric data model and a gradient-based maximum likelihood estimator suitable for the description of delay-dispersive responses of multiple dynamic UWB-radar targets. The target responses are estimated jointly with the global target parameters range and velocity. The large relative bandwidth of UWB has consequences for model-based parameter estimation. On the one hand, the Doppler effect leads to a dispersive response in the Doppler spectrum and to a coupling of the target parameters which both need to be considered during modeling and estimation. On the other hand, the shape of an extended target results in a dispersive response in range which can be resolved by the radar resolution. We consider this extended response as a parameter of interest, e.g., for the purpose of target recognition. Hence, we propose an efficient description and estimation of it by an FIR structure only imposing a restriction on the target’s dispersiveness in range. We evaluate the approach on simulations, compare it to state of the art solutions and provide a validation on measurement data. © 2023 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works .

Local energy communities and electricity markets have emerged as possibilities for interaction among prosumers. A substantial effort has been invested into creating efficient pricing mechanisms for various market arrangements, all of which take into consideration distinct characteristics of local electricity trading. However, since they are all evaluated in terms of various systems and market conditions, it is challenging to directly compare the mechanisms. In this research, three well-established pricing mechanisms from the literature are systematically compared and evaluated under identical settings on their influence on welfare distribution across various market participant groups, privacy protection, transparency and complexity level. According to the findings, the supply-demand ratio pricing system leads to the lowest costs for consumers and is also the most privacy compliant and transparent. Furthermore, prosumers obtain the highest cost-savings through the consensus alternating direction method of multipliers pricing mechanism, whereas the equilibrium pricing mechanism performs best regarding economic fairness. The aim of this article is to provide insight into the performance of different pricing mechanisms to energy regulators and local electricity market facilitators. The comparative analysis should aid in making informed decisions on the implementation of local electricity markets.

Feature selection (FS) represents an essential step for many machine learning-based predictive maintenance (PdM) applications, including various industrial processes, components, and monitoring tasks. The selected features not only serve as inputs to the learning models, but also can influence further decisions and analysis, e.g., sensor selection, understandability of the PdM system. Hence, before deploying the PdM system, it is crucial to examine the reproducibility and robustness of the selected features under variations in the input data. This is particularly critical for real-world datasets with a low sample-to-dimension ratio (SDR). However, to the best of our knowledge, stability of the FS methods under data variations has not been considered yet in the field of PdM. This paper addresses this issue with an application to tool condition monitoring in milling, where classifiers based on support vector machine and random forest were employed. We used a 5-fold cross-validation to evaluate three popular filter-based FS methods, namely Fisher score, minimum redundancy maximum relevance (mRMR), and ReliefF, in terms of both stability and macro-F1. Further, for each method, we investigated the impact of the homogeneous FS ensemble on both performance indicators. To gain broad insights, we used four (2:2) milling datasets obtained from our experiments and NASA’s repository, which differ in the operating conditions, sensors, SDR, number of classes, etc. For each dataset, the study was conducted for two individual sensors and their fusion. Among the conclusions: 1) Different FS methods can yield comparable macro-F1, yet considerably different FS stability values. 2) Fisher score (single and/or ensemble) is the superior in most of the cases. 3) mRMR’s stability is overall the lowest, the most variable over different settings (e.g., sensor(s), subset cardinality), and the one that benefits the most from the ensemble. This paper was accepted for publication in Sensors. The peer-reviewed publication can be found in: https://www.mdpi.com/1424-8220/23/9/4461

Abstract- For the miniaturization of the structures of semiconductor device fabrication, high uniformity of side-flatness and edge roll-off of 300 mm wafers are required. In this study, the formation of light point defects (LPDs) on silicon (Si) wafer surface due to an edge gripper handling system was investigated. The relationships between the generation of LPDs with respect to flatness, edge profile, and edge roll-off of Si wafers were analyzed. It was found that the variation of traditionfacet parameters and near-edge geometry metric, such as edge site frontsurface-referenced least squares/range (ESFQR), have no impact on the formation of surface LPDs. By contrast, the performance of Z-height double derivative (ZDD), allowed an accurate prediction of formation of surface LPDs. Additionly, for a 300mm silicon wafer, the surface LPDs occurred with frontside ZDD obtained at a radius of 149.2 mm, ranging above -954 nm/mm2 . The surface was LPDs free when ZDD was below -1235 nm/mm2 . Surface LPD formation occurred randomly and was not predictable when ZDD ranged from -954 nm/mm2 to -1235 nm/mm2 . The result indicates that the LPDs caused by wafer handling is proportional to the performance of ZDD at the edge roll-off area of silicon wafer, this is consistent with the requirement of edge roll-off considering wafer geometry.

Multidimensional channel sounding measures the geometrical structure of mobile radio propagation. The parameters of a multipath data model in terms of directions, time-of-flight and Doppler shift are estimated from observations in frequency, time and space. A maximum likelihood estimation framework allows joint high-resolution in all dimensions. The prerequisite for this is an appropriate parametric data model that represents the multipath propagation correctly. At the same time, a device data model is necessary that typically results from calibration measurements. The used model should be as simple as possible since its structure has a considerable effect on the estimation effort. For instance, the inherent effort in parameter search is reduced if the influence of the parameters is kept orthogonal. Therefore, the data model is characterized by several approximations. The most important is the “narrowband assumption” which assumes a low relative bandwidth and also avoids considering any frequency response in magnitude and phase. We extend the well-known multidimensional \gls{rimax} parameter estimation framework by including proper frequency responses. The advantage reveals most clearly with high bandwidth in the mmWave and sub-THz range. It allows for a more realistic modeling of antenna arrays. It breaks with the usual narrowband model and allows a better modeling of mutual coupling and time delay effects. If the interacting object extends over several delay bins (hence an extended target in radar terminology) we propose a model that assigns a short delay spread, respectively a frequency response to the propagation path that associates itto the respective object. We verify the validity of the device model by numerical experiments on simulated and measured antenna data and compare it to a state-of-the-art method. Additionally, we use synthetic data based on raytracing results and measurements both ranging from \SI{27}{\giga\hertz} up to \SI{33}{\giga\hertz} with known ground truth information and show that the proposed estimator not based on the narrowband assumption delivers better performance for higher relative bandwidths than the conventional \gls{rimax} implementation.

This paper presents a monolithic, cost-effective, label free, low power, robust 3x2 fully differential ISFET pixel array aimed at pH sensing. Each pixel is based on a differential readout architecture which eliminates need for additional non-ideality compensation circuitry. Additionally, the design offers the capability to correct for residual drift and trapped charge offset through substrate-based voltage adjustment during calibration. The design also provides a unique ability for selecting pH sensitivity according to the desired mode of operation which has not been demonstrated previously. We present experimental results obtained from the pixel array fabricated in an unmodified AMS 0.35μm CMOS process using a 3.3V power supply. Each pixel demonstrates a differential sensitivity of 61mV/pH when operated in high sensitivity mode and 25mV/pH when operated in high non-ideality rejection mode over the entire pH range, with the ability to remove the already small drift of 0.3 mV/min and any offset mismatch by adjusting the substrate voltage