Antenna design is crucial for the continuous development of RFID systems as antennas are indispensable and influence system performance. The need for efficient antennas while being small and lightweight has attracted engineers and researchers for the study and development of it. RFID antenna design is more sophisticated art as compared to the conventional ones. Generally, tag antenna will be small, mobile, passive and linearly polarised while reader antennas are fixed, bulk and are circularly polarized. As such, many proposals were made by the scientific community to try and alleviate the issue using different types of antennas, feeds, and substrates and their combinations. However, the wide range of solutions and their diversity make it hard to have a clear and fair overview of the different works. This paper surveys the most relevant and recent known state-of-theart antenna design for RFID applications. Along with basic rules, clues and guidelines about efficient antenna design; we show an example of dual-band circularly polarized reader antenna developed in our lab for practical applications.

Takt work represents a significant risk factor for the development of musculoskeletal complaints and diseases, especially in short-cycle processes. The increased risk results primarily from a permanent uniform load on the musculoskeletal system. Studies on motor variability suggest that an increase in load variation can have positive effects on reducing the risk. The research project “Integration of activity-specific load changes to reduce physical stress during takt work” aims to demonstrate the increase in load variation by introducing specific load changes during takt work as a possible means of preventing musculoskeletal disorders without causing negative effects on productivity. For this purpose, a pilot study was already carried out with ten subjects, which is presented in more detail in this paper. As foundation for the description of this study, the given paper first provides background on the applied theoretical concepts as well as the design of the overall research project. This is followed by the presentation of the experimental procedure and the results of the pilot study on cyclic assembly. Based on the stress profiles determined via surface electromyography the sequence of the analysed reference assembly process is reconfigured in order to integrate load changes. Future investigations within the research project are planned to compare both processes in terms of risk surrogate parameters for musculoskeletal disorders.

Self-sovereign identity is the latest digital identity paradigm that allows users, organizations, and things to manage identity in a decentralized fashion without any central authority controlling the process of issuing identities and verifying assertions. Following this paradigm, implementations have emerged in recent years, with some having different underlying technologies. These technological differences often create interoperability problems between software that interact with each other from different implementations. Although a common problem, there is no common understanding of self-sovereign identity interoperability. In the context of this tutorial, we create a definition of interoperability of self-sovereign identities to enable a common understanding. Moreover, due to the decentralized nature, interoperability of self-sovereign identities depends on multiple components, such as ones responsible for establishing trust or enabling secure communication between entities without centralized authorities. To understand those components and their dependencies, we also present a reference model that maps the required components and considerations that build up a self-sovereign identity implementation. The reference model helps address the question of how to achieve interoperability between different implementations.

Data come from two prospective multicentric clinical studies (ClinicalTrials.gov: NCT01309334 and NCT01134484).

Commit messages are a valuable resource in the comprehension of software evolution since they provide a record of changes such as feature additions and bug repairs. Unfortunately,  programmers often neglect to write good commit messages. Different techniques have been proposed to help programmers by automatically writing these messages. These techniques are effective at describing what changed, but are often verbose and lack context for understanding the rationale behind a change. In contrast, humans write messages that are short and summarize the high-level rationale. In  this paper, we adapt Neural Machine Translation  (NMT) to automatically “translate” diffs into commit  messages. We will train an NMT algorithm using a  corpus of diffs and human-written commit messages  from the top 1k GitHub projects. We will also design  a filter to help ensure that we only train the  algorithm on higher-quality commit messages. Our  evaluation uncovers a pattern in which the messages  we generate tend to be either very high or very low quality.

The mining sector generates a substantial quantity of stone waste and tailings, which constitutes an environmental risk. The most prevalent method for disposing of this industrial waste is dumping, which contributes to soil deterioration and water contamination while acquiring precious land. It can be recycled using a number of processes, such as the promising geopolymerization technique, which transforms waste into value. This study reviews current developments in the manufacturing of mine tailings-based geopolymer composites from industrial waste as a possible sustainable building material. This paper also gives in-depth studies on the characteristics and behaviors of mine tailings composites used in geopolymer manufacturing, including physical and mechanical properties. This review also identifies knowledge gaps that must be filled in order to advance mine tailings composites for geopolymers.

This paper presents two approaches to power circuit condition assessment. The first one covers a wide variety of machine learning classification algorithms where the input for the classification is a manually selected feature set. The second one utilizes deep learning classification based on the convolutional neural network. Both approaches revolve around the idea behind spectral kurtosis, one of which exploits its visual representation in the form of kurtogram. The first approach uses a spectral kurtosis curve as the base for feature extraction while the second approach uses a spectral kurtosis kurtogram as a single input into the convolutional neural network. The validation is performed on a large set of vibration signatures and compared to competing state-of-the-art algorithms. The results indicate promising features of the proposed approach.

This letter proposes a machine learning (ML)-based method for statistical device modeling from a data-oriented perspective. A mapping from the process variation to the selected variational neurons is derived by the proposed algorithm and backward propagation of variation method (BPV) to capture the non-homogeneous effects of model parameters. The same size as the nominal ML model is used, with fewer variation parameters rather than numerous physical parameters, in favor of circuit simulation speed. In addition, a secondary classification of the selected variational neurons is applied to model the correlation between n- and p-type devices. The ML-based statistical modeling framework has been well implemented and verified on the GAA devices and circuits, which indicates its great potential in modeling of emerging device technology.

This work entails Reinforcement Learning paradigm for Unmanned Aerial Vehicles providing network services to internet of things devices. The associated rate, energy efficiency and outage is primarily considered.

The commercial AC-DC transfer standards are widespread at leading calibration and measurement laboratories for DC and low-frequency metrology. Therefore, possibilities of various combinations of applying such instruments, particularly to verify the results of calibrating the precision current shunts or for optimizing the available facility, are of interest. The peculiarities of dual-using the AC-DC transfer standard are outlined, investigated, and discussed. The analytical and computer modeling, as well as the technical and theoretical analyses, are presented. Deriving the set of equations for the description of current distribution in the measurement technique for determining the AC-DC current transfer difference of the tested shunt is one of the work achievements. The measurement results obtained following the presented approach were verified using two voltage converters (Fluke 792A and PMJ TC). Some points of measurement uncertainty evaluation also are illuminated in the paper

This paper revisits the electric, magnetic, and toroidal dipolar moments in the metamaterial structure and presents the flatland design for generating a toroidal dipolar response for the electromagnetic plane wave at normal incidence. Based on the numerical analysis of the surface current, the electric field, the magnetic field, and the quantitative analysis of scattered power supported by the electromagnetic multipole theory, it is shown that the earlier design involving symmetric unit cells is not able to produce toroidal dipolar moment. This study resulted in a new design of a planar metasurface, which provides a toroidal response for a normal wave incidence. The scattered power calculated for the flatland metamaterials confirms the presence of toroidal moment in the proposed asymmetric structure. The scattered power due to the toroidal dipole in the proposed design is found to be at least 70 times and 8x108 times larger than the electric dipole and the magnetic dipole, respectively, in the z-direction.

In a power system environment, transmission & expansion planning (TNEP) is gaining attention and becoming an essential and computationally very challenging problem. Competent and robust optimization techniques are required to get the optimal solution technically and economically. Many computational techniques have been proposed to solve the TNEP problem earlier. Meta-heuristic techniques have gained a lot of importance in the last few years and successfully applied for TNEP. This paper aims to resolve transmission & expansion planning problems using hybrid algorithms. Two hybrid progressive algorithms based on the combination of PSO and GA methods are proposed and crossing over the PSO and GA have been implemented in this paper. The focus behind the proposed methods is to hybridize PSO and GA methods in a combination of parallel and series forms respectively. To validate the proposed hybrid algorithm and to test efficiency in comparison with other methods reported in the literature, it is tested on Garver’s-6 bus, IEEE-14 bus, and IEEE-24 bus test systems using MATLAB. The simulation and observations of the outcome demonstrate the effectiveness of the proposed hybrid algorithm’s time and ability to find the global optimum solution.

A series-fed traveling-wave uniform circular array of sequentially rotated slot antennas producing two orbital angular momentum (OAM) modes is presented. The antenna operates at 10190 MHz, is built using a single layer of substrate integrated waveguide technology, and can be readily expanded to create additional modes from a shared aperture. A unique simulation-based step-by-step design procedure is presented, taking practically all functional features of a series-fed array into account. A novel formulation for computing the radiation pattern of an array with variable-polarization elements is also offered. The antenna’s performance is evaluated using simulated, calculated, and measured data, with a significant correlation between the three sets. The first mode is a left-hand circularly polarized (LHCP) antenna, with measured impedance, gain, and axial ratio bandwidth of around 8.2, 5.9, and 3.9 percent, respectively. The second mode, which has a measured impedance bandwidth of roughly 7.9 percent, also features an LHCP radiation pattern around the broadside null. The simulated radiation efficiency for the first and second modes is around 48% and 30%, respectively. Furthermore, the orthogonality bandwidth for two identical antennas placed 28 cm apart in front of each other is around 4.5 percent. The antenna’s near-field radiation patterns are also studied.

GOAL: Accounting for gait individuality is important to positive outcomes with wearable robots, but tuning multi-activity models is time-consuming and not viable in a clinic. Generalizations can be made to predict gait individuality in unobserved conditions. METHODS: Kinematic individuality—how one person’s joint angles differ from the group—is quantified for every subject, joint, ambulation mode (walking, running, stair ascent, and stair descent), and intramodal task (speed, incline) in an open-source able-bodied dataset. Four N-way ANOVAs test how prediction methods affect the fit to experimental data between and within ambulation modes. We test whether walking individuality carries across modes, or whether a modal prediction is more effective against average kinematics. RESULTS: Kinematic individualization improves fit across joint and task if we consider each mode separately. Across all modes, tasks, and joints, individualization improved the fit in 81% of trials, improving the fit on average by 4.3º across the gait cycle. This was statistically significant at all joints for walking and running, and half the joints for stair ascent/descent. CONCLUSIONS: Kinematic individualization tends to improve fit across all joints and can be easily predicted by observing only one task within an ambulation mode.

Nowadays, additive manufacturing (AM) has been increasingly developed and applied in many fields. As a rapid development of AM, similar terms are appeared, such as 3D printing, freeform fabrication, rapid tooling, rapid manufacturing, direct digital manufacturing, layer manufacturing, additive process, 4D printing, 3D bioprinting, and 3D biofabrication. This paper reports and analyses the development of these terms. Moreover, it tries to give the readers a comprehensive understanding of these terms and their development.

This paper addresses the mobility problem in massive multiple-input multiple-output systems, which leads to significant performance losses in the practical deployment of the fifth generation mobile communication networks. We propose a novel channel prediction method based on multi-dimensional matrix pencil (MDMP), which estimates the path parameters by exploiting the angular-frequency-domain and angular-time-domain structures of the wideband channel. The MDMP method also entails a novel path pairing scheme to pair the delay and Doppler, based on the super-resolution property of the angle estimation. Our method is able to deal with the realistic constraint of time-varying path delays introduced by user movements, which has not been considered so far in the literature. We prove theoretically that in the scenario with time-varying path delays, the prediction error converges to zero with the increasing number of the base station (BS) antennas, providing that only two arbitrary channel samples are known. We also derive a lower-bound of the number of the BS antennas to achieve a satisfactory performance.Simulation results under the industrial channel model of 3GPP demonstrate that our proposed MDMP method approaches the performance of the stationary scenario even when the users’ velocity reaches 120 km/h and the latency of the channel state information is as large as 16 ms.

This study examines how legal frameworks should set a clear definition for Artificial Intelligence’s (AI) role in creative work through the perceptions of the younger generation on AI-generated music. Using a survey composed of 6 components (Perceived Quality, Perceived Imaginativeness, Spatial Presence, Empathy, and Musician’s Competence), this study sampled the technology and art related intended majors sampling groups to determine whether the computational music passes the Turing test. After listening to two samples with hidden artist identities (Human vs AI), the participants (n = 35) were asked to evaluate the samples presented. Then, artist identities are revealed and participants were asked to make changes to their initial responses. This study found that participants have similar appraisals towards human-composed music and AI generated music while there is a significant difference between the initial attitudes towards AI for the two sampling groups. For participants who changed their responses, there are no significant changes in attitude but an overall negative shift in responses is observed. This study concluded that the possibility for the younger generation to recognize AI as actual artists and thus supporting AI IP rights is low. Further implications of the findings and future directions for research are discussed.

Along with the National Institute of Standards and Technology (NIST) post-quantum cryptography (PQC) standardization process, lightweight PQC-related research and development have also gained substantial attention from the research community recently. Ring-Binary-Learning-with-Errors (RBLWE), a lightweight variant of Ring-LWE, which uses binary errors to replace the regular Gaussian distributed errors to achieve smaller complexity, has great potential to built such lightweight PQC scheme for emerging Internet-of-Things (IoT) and edge computing applications.The parameter settings of the RBLWE-based encryption scheme, however, are much smaller than the typical Ring-LWE one and then how to reduce its computational complexity becomes an interesting research topic.Following this direction, in this paper, we propose a Karatsuba Initiated Novel Accelerator (KINA) for efficient implementation of RBLWE-based PQC. Overall, we have made several coherent interdependent stages of efforts to carry out the proposed work: (i) we have presented the mathematical derivation process to propose a new Karatsuba Algorithm (KA)-based  polynomial multiplication, the main algorithmic operation of the RBLWE-based scheme; (ii) we have then effectively mapped the proposed algorithm into a desired hardware accelerator with the help of a number of optimization techniques; (iv) we have also provided detailed complexity analysis and implementation comparison to demonstrate the superior performance of the proposed RBLWE-based PQC accelerator. Overall, the proposed RBLWE-based PQC accelerator involves two unique features: (i) this is the first report about Karatsuba initiated RBLWE-based PQC accelerator; (ii) the proposed RBLWE-based PQC accelerator offers flexible processing speed and can be fit in various high-performance applications. The proposed KINA is highly efficient and has the potential to be included in the implementation recommendation choices for RBLWE-based scheme deploying in lightweight applications.