It is universally accepted that Maxwell equations do not remain invariant under the Galilean transformation. This conflicts the principle of relativity which states that the physical law must remain invariant in the mathematical form in all inertial frames of reference. For this reason, the Lorentz transformation is invented, and the Galilean transformation is nowadays superseded by the Lorentz transformation. However, this paper challenges this widely held belief that the Maxwell equations are not invariant under the Galilean transformation. By applying the Galilean transformation to Lienard-Wiechert electromagnetic fields it is mathematically proven that the Maxwell equations indeed remain invariant under the Galilean transformation. In addition, the critical error in Lorentz’s proof of Galilean non-invariance of Maxwell equations is pointed out, and  it turns out that Lorentz’s conclusion that Maxwell equations are not invariant under the Galilean transformation is the result of a mathematical error.

The Web3 vision takes blockchain disintermediation to a next level by making it ubiquitous, encompassing not only payments and financial services but also digital identities, data and business models. Recently, Web3 has gained massive attention by major analysts such as Gartner, Forrester, Forbes Technology Council and the Harvard Business Review. Albeit the current enthusiasm about Web3, we are lost in a state of confusion about what Web3 actually is - or could be. The purpose of this paper is to mitigate the gap between the perceived usefulness of Web3 and its potential implementation. We take a descriptive design science approach. We provide informed arguments for a potential foundation of Web3 in terms of fundamental components,  architectural principles and a Web3 design space. We demonstrate the usefulness of the provided Web3 foundation by describing Alphabill, a platform that allows for universal asset tokenization and transfer as a global medium of exchange. The findings of this research enable policy makers, decision makers and information systems architects alike to make informed decisions about Web3 and its potential implementation as follows: (i) The Web3 can be characterized as the integration of digital rights exchange into the (application layer) internet protocols. (ii) The Web3 has the potential to revolutionize today’s information systems’s landscape by turning today’s information systems into deeply standardized views on a huge, single underlying information structure. The killer application of a well-founded Web3 is the Web3 itself – being the currently missing backbone (value-added middleware) for all of today’s and future enterprise applications and business-to-business communication. (iii) The scenario-based evaluation of the provided Web3 foundation reveals the described Alphabill platform as a Web3 enabling technology.

This article presents a STEM learning platform focusing on renewable energy generation, energy storage, and simultaneous transmission of energy and data. These enabling technologies are classified already by teenagers as authentic, multidisciplinary, sustainable and future-oriented, which helps to stimulate their interest. Linking electric energy generation/storage/consumption and information transfer is done through the innovative concept of talkative power conversion. Students are not only provided with meaningful background information on the topics under investigation, but they also learn how to implement these concepts on a mixed hardware/software platform, which increases their motivation and understanding of STEM topics. The learning platform elaborated in this article is suitable for pupils at high school as well as undergraduate and graduate students in electrical engineering and related fields, since the level of difficulty is adjustable. Experiments are exemplified at a level suitable for undergraduate students in electrical engineering and information technologies after two years of study.

This paper aims to cover and summarize research on the topic of data augmentation in cybersecurity. By exploring its use in various areas of the field, we hope to highlight the benefits of using data augmentation. To analyze a wide range of data, we opted for a systematic review.  This paper covers data augmentation in image, text, audio, and hybrid data sets as well as the different augmentation techniques.

Schizophrenia is a neurological disorder known to influence the motor region. Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique being investigated as a treatment for schizophrenia. Resting motor threshold (RMT) is the dosage parameter for TMS treatment protocols and is known to vary between participants with limited understanding of the drivers of this variance. Previous investigations have used functional magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and individual level neuroanatomy to explain RMT variability. Our previous investigation showed neuroanatomy influences RMT in both schizophrenia and healthy populations and this relationship was weakened by the presence of schizophrenia. In this study, 54 participants with schizophrenia-diagnosed, who were antipsychotic naive and 43 non-impaired controls underwent single pulse TMS, structural magnetic resonance imaging, and fMRI. An independent component analysis (ICA) was used to process fMRI data into 25 distinct channels where correlations were derived between channels. Linear and multiple regression models were used to evaluate first, the influence of these channel interactions on RMT followed by their influence when individual level neuroanatomy was also considered. We found that between-channel functional connectivity was altered in individuals with schizophrenia and that fMRI can contribute to prediction of RMT, but differently in both cohorts and to a lesser degree than individual level neuroanatomical measures. This suggests that functional connectivity influences TMS response and fMRI might help in dosage calculations of clinical TMS protocols.

A document by Md Al Amin . Click on the document to view its contents.

Two popular application domains of low-power single-switch induction heating systems are cooking and off-line sealing of plastic/glass containers. Sealing operation could be using cap or capless, the latter is preferred because of ease of inspection of sealing quality. For near continuous-duty operation of the converter, due to the superior power loss and thermal characteristics, two SiC MOSFETs connected in parallel are preferred for the switch. The design of gate drive circuit (GDC) for these devices is complicated. The desired gate voltage is not only highly asymmetrical, due to small gate threshold voltage, it is sensitive to ringing in gate signal, the range of negative bias is also constrained. The duty-cycle of the high-frequency gate signal for the switch is small. This article proposed a novel GDC where, using a single-polarity control power supply, a pulse-transformer is used to feed desired asymmetrical gate voltage to several isolated devices. To avoid spurious turn-on, this research further proposed a novel transformer for feeding bi-polar asymmetrical gate voltage with requisite signal and power-noise isolation to effectively use of respective Kelvin source terminal. The proposed idea has been completely validated by driving two SiC MOSFETs in a power converter for high-speed off-line induction sealing applications.

Counterfactual statements, which describe events that did not or cannot take place, are beneficial to numerous NLP applications. Hence, we consider the problem of counterfactual detection (CFD) and seek to enhance the CFD models. Previous models are reliant on clue phrases to predict counterfactuality, so they suffer from significant performance drop when clue phrase hints do not exist during testing. Moreover, these models’ prediction also biases towards non-counterfactual over the counterfactual class. To address these issues, we propose to integrate neural topic model into the CFD model to capture the global semantics of the input statement. We continue to causally intervene the hidden representations of the CFD model to balance the effect of the class labels. Extensive experiments show that our approach outperforms previous state-of-the-art CFD and bias-resolving methods in both the CFD and other bias-sensitive tasks.

This article addresses two architectural challenges associated with managing and orchestrating open radio access network (O-RAN) slice subnets within the service management and orchestration (SMO) Framework. The first challenge involves unifying the components and interfaces of the O-RAN Alliance, Third Generation Partnership Project (3GPP), and European Telecommunications Standards Institute (ETSI) to enhance the capabilities of the SMO Framework regarding the management and orchestration (M&O) of O-RAN slice subnets and their required resources. The objective of this unification is to eliminate ambiguity in the interaction, interworking, and integration of various standards within a unified SMO Framework and to enable it to leverage the latest specifications. The second challenge entails facilitating standard-compliant interoperability among the components of the three modules by proposing two logical functions and their interfaces within the SMO Framework. This interoperability is intended to enable the components and interfaces of the O-RAN architecture to influence the decisions made by the 3GPP and ETSI components and interfaces concerning O-RAN slice subnets. It will also allow the 3GPP and ETSI components and interfaces to improve their capabilities by utilizing the features and services of the O-RAN architecture.

In the above paper, Ahn et al. proposed a small-size four-port in-phase and out-of-phase hybrid coupler. However, the design equations proposed in [1, eq.(9)] are incorrect. In this letter, the incorrectness of the design equations has been demonstrated through a numerical example. Also, corrected design equations are provided in this letter for small-size couplers with unequal port impedances.

We hereby present a novel interference mitigation strategy specifically designed to enhance the quality of service that a typical terrestrial user equipment (UE) would experience after the occurrence of a calamity. In particular, we devise a novel stochastic geometry framework where the functioning ground base stations are modeled as an inhomogeneous Poisson point process, and promote proper silencing as an effective solution to improve both coverage and reliability (which is usually overlooked in emergency scenarios); in particular, the latter is evaluated by means of the signal-to-interference-plus-noise ratio (SINR) meta distribution performance metric. The derived downlink performances assume Rayleigh fading conditions for all wireless links. The numerical results show insightful trends in terms of both average coverage probability (which is optimized by choosing the best area for applying the silencing strategy) and SINR meta distribution, depending on: distance of the UE from the disaster epicenter (henceforth intended as the center of the area where the BS can be damaged), disaster radius (also referring to the latter area), and quality of resilience of the terrestrial network. The aim of this paper is therefore to prove the effectiveness of proper silencing in emergency scenarios, at least from the coverage and reliability perspectives.

This work considers the design and performance analysis of multirate non-orthogonal multiple access (MR-NOMA) with arbitrary rates and various modulation orders, where closed-form bit error rate (BER) expressions are derived for the joint-multiuser maximum likelihood sequence detector (JMLSD) and low-complexity optimal successive interference cancellation (SIC) receiver. The derived expressions are used to optimize the power assignment (PA) at the base station to minimize the BER while strictly satisfying certain BER requirements. The presented results show that MR-NOMA can increase the trade-off freedom between spectral efficiency and robustness to errors due to its ability to control the energy by varying the power and symbol duration, simultaneously, while single-rate non-orthogonal multiple access (SR-NOMA) can only control the power only. The mathematical derivations are validated via Monte Carlo simulation.

Fintech refers to the innovative use of technology in the provision of financial services and products. Several factors contributed to the exponential growth of fintech in India, including an increase in internet penetration, adoption of smartphone, favourable government legislations, and a dynamic economy. The agenda behind this study is to create a thorough grasp of the variables affecting fintech adoption in India. A study backed by Partial Least Square Structural Equation Modelling (PLS-SEM1) explored how user trust (UT), perceived benefit (PB), perceived risk (PR), and intention to adopt fintech (IAF) are related by analysing primary data collected from a representative sample of students pursuing higher education. As revealed by the results, IAF is significantly influenced by PB, PR, and UT. The level of trust between users moderate the relationship between PR and IAF. In order to foster fintech adoption, foster innovation, and address potential challenges, targeted policies and regulations need to be developed.

The integration of Artificial Intelligence (AI) into the workplace requires commitment not only from the leadership of managers but, equally important, from the engineers responsible for its implementation. This paper presents a survey on perceptions concerning the utilization of AI tools in engineering environments. It was focused on engineers and students in the areas of electricity, electronics and computing. The questionnaire covered demographic information, Artificial Intelligence knowledge level, preferred tools, primary applications, perceived impact, and attitudes toward labor substitution. With the endorsement of the IEEE Uruguay Section and IEEE Region 9, the survey was distributed via email to a diverse group of potential participants, in more than 20 countries, including all IEEE members in Region 9 .

In this research, we provide a co-design strategy for WNCS that simultaneously optimizes network and control parameters. The goal is to optimize power use on the wireless communication layer by establishing a variable inter-packet gap (IPG) or by transforming the control strategy from reliable control to energy-efficient control with moderate reliability. Control performance is optimized for moderate to high packet loss using an RL-based technique that guarantees optimal resource allocation. The design of a multi-objective issue that takes into account both dependability and resource efficiency makes this possible. The developed approach is then applied to the multi-objective optimization problem. The suggested co-design RL-based control approach shows the ability to reduce transmission power by an average of 5% to 10% even when there are losses in the network. In addition, it effectively preserves or enhances control performance.

Within the realm of flexible manufacturing, fixture layout planning allows manufacturers to rapidly deploy optimal fixturing plans that can reduce surface deformation that leads to crack propagation in components during manufacturing tasks. The role of fixture layout planning has evolved from being performed by experienced engineers to computational methods due to the number of possible configurations for components. Current optimisation methods commonly fall into sub-optimal positions due to the existence of local optima, with data-driven machine learning techniques relying on costly to collect labelled training data. In this paper, we present a framework for multi-agent reinforcement learning with team decision theory to find optimal fixturing plans for manufacturing tasks. We demonstrate our approach on two representative aerospace components with complex geometries across a set of drilling tasks, illustrating the capabilities of our method; we will compare this against state of the art methods to showcase our method's improvement at finding optimal fixturing plans with 3 times the improvement in deformation control within tolerance bounds.

Lattice-based cryptography is considered the most promising candidate for post-quantum public key cryptography such as key encapsulation and digital signature schemes. Accurate and efficient sampling of matrix / vector elements and polynomial coefficients from specified discrete probability distributions is crucial to the security and efficiency of lattice-based cryptography protocols. In this work, a design methodology is proposed to implement these sampling operations using currently available quantum computing hardware. Quantum circuits for sampling from uniform, trinary, binomial and discrete Gaussian distributions are presented. Implementation results obtained from simulation as well as measured from real cloud-based quantum hardware are also presented and analyzed. Although the proposed circuits require only few qubits, they implement practical distribution parameters used by various lattice-based protocols. This clearly demonstrates the immediate relevance of such quantum circuits in the context of currently available small-scale quantum computers, and they have the potential to enhance post-quantum cryptography implementations on quantum-enabled cloud environments.

When developing a technology based on single-photon avalanche diodes (SPADs), the SPAD characterization is mandatory to debug, optimize and monitor the microfabrication process. This is especially true for the development of SPAD arrays 3D integrated with CMOS readout electronics, where SPAD testing is required to qualify the process, independently from the final CMOS readout circuit. This work reports on a characterization and monitoring platform dedicated to SPAD testing at die and wafer level, in the context of a 3D SPAD technology development. The platform relies on a dedicated integrated circuit made in a standard CMOS technology and used in different configurations from a prototype printed circuit board (die-level testing) to active probe cards (wafer-level mapping). The platform gives full access to SPAD characteristics in Geiger mode such as the dark noise, photon detection efficiency and timing resolution. The integrated circuit and its configuration are described in detail as well as results obtained on different SPAD test structures. In particular, the dark count rate mapping demonstrates the benefits of testing SPADs at wafer level at the R&D stage.