Visible light communication is envisaged as a promising enabling technology for sixth generation (6G) and beyond networks. It was introduced as a key enabler for reliable massive-scale connectivity, mainly thanks to its simple and low-cost implementation which require minor variations to the existing indoor lighting systems. The key features of VLC allow offloading data traffic from the current congested radio frequency (RF) spectrum in order to achieve effective short-range, high speed, and green communications. However, several challenges prevent the realization of the full potentials of VLC, namely the limited modulation bandwidth of light emitting diodes, the interference resulted from ambient light, the effects of optical diffuse reflection, the non-linearity of devices, and the random receiver orientation. Meanwhile, centralized machine learning (ML) techniques have exhibited great potentials in handling different challenges in communication systems. Specifically, it has been recently shown that ML algorithms exhibit superior capabilities in handling complicated network tasks, such as channel equalization, estimation and modeling, resources allocation, opportunistic spectrum access control, non-linearity compensation, performance monitoring, detection, decoding/encoding, and network optimization. Nevertheless, concerns relating to privacy and communication overhead when sharing raw data of the involved clients with a server constitute major bottlenecks in large-scale implementation of centralized ML techniques. This has motivated the emergence of a new distributed ML paradigm, namely federated learning (FL). This method can reduce the cost associated with transferring the raw data, and preserve clients privacy by training ML model locally and collaboratively at the clients side. Thus, the integration of FL in VLC networks can provide ubiquitous and reliable implementation of VLC systems. Based on this, for the first time in the open literature, we provide an overview about VLC technology and FL. Then, we introduce FL and its integration in VLC networks and provide an overview on the main design aspects. Finally, we highlight some interesting future research directions of FL that are envisioned to boost the performance of VLC systems.

Corrugated webs are used to increase the shear stability of steel webs of beam-like members and to eliminate the need of transverse stiffeners. Previously developed formulas for predicting the shear strength of trapezoidal corrugated steel webs, along with the corresponding theory, are summarized. An artificial neural network (ANN)-based model is proposed to estimate the shear strength of steel girders with a trapezoidal corrugated web, and under a concentrated load. 210 test results from previous published research were collected into a database according to relevant test specimen parameters in order to feed the simulated ANNs. Seven (geometrical and material) parameters were identified as input variables and the ultimate shear stress at failure was considered the output variable. The proposed ANN-based analytical model yielded maximum and mean relative errors of 0.0% for the 210 points from the database. Moreover, still based on those points, it was illustrated that the ANN-based model clearly outperforms the other existing analytical models, which yield mean errors larger than 13%.

Memristor is dubbed as the fourth fundamental electrical component which works primarily as a non-volatile memory element. Memristors can also be used to construct logic gates, and Memristor Ratioed Logic (MRL) is one of these structures. The higher area efficiency and CMOS architecture compatibility of MRL gates have lead researchers to pay attention to its use in digital logic architecture. In this work, binary MRL is integrated with Complementary Metal-Oxide Semiconductor(CMOS) logic elements to develop building blocks of an Arithmetic Logic Unit (ALU). The proposed 1-bit ALU is simulated using LTSpice, which allows the versatility of changing the parameters as per the model used. This work designs and analyses an optimized cascadable 1-bit ALU with with voltage level based binary logic state via simulation. The proposed circuit shows improvement in transistor count and delay over benchmark circuits.

This paper has the main purpose to give an introductory survey on the various systems that exploit magnetic fields for positioning. Such systems find applications in those scenarios, both indoors and outdoors, where Global Navigation Satellite Systems (GNSS) are not available or fail to provide information with the needed accuracy. While the main idea of using electromagnetic fields to provide position information dates back to the past century, new application–led research on this topic has emerged in recent years. Results have expanded the application range of Magnetic Positioning (MP) technologies and form now a domain of knowledge that enables realization of positioning systems applicable to indoor and outdoor environments. The paper provides the main characteristics of different positioning systems with focus on those solutions that are based on low–frequency magnetic fields. Some background theory is presented and positioning results from the literature are analyzed and compared.

A simple active battery management system for lithium ion battery stacks is described, that is robust and scalable. The architecture is based on an isolation unit consisting of a small equal turns ratio high isolation transformer with two diodes. One isolation unit is connected to each series connected cell in the battery stack and enables both accurate cell voltage monitoring and active cell balancing. The output of the isolation unit is completely galvanically isolated and thus suitable for high voltage packs with stringent safely requirements such as automotive and industrial electrical vehicles. Detailed measurements results are presented and a complete prototype for a LiFePO4 16s2p 2kWh battery module is constructed and its performance measured.

$\ell_2$ and $\ell_1$ trend filtering are two of the most popular denoising algorithms that are widely used in science, engineering, and statistical signal and image processing applications. They are typically treated as separate entities, with the former as a linear time invariant (LTI) filter which is commonly used for smoothing the noisy data and detrending the time-series signals while the latter is a nonlinear filtering method suited for the estimation of piecewise-polynomial signals (\eg, piecewise-constant, piecewise-linear, piecewise-quadratic and \etc) observed in additive white Gaussian noise. In this article, we propose a Kalman filtering approach to design and implement $\ell_2$ and $\ell_1$ trend filtering % (QV and TV regularization) with the aim of teaching these two approaches and explaining their differences and similarities. Hopefully the framework presented in this article will provide a straightforward and unifying platform for understanding the basis of these two approaches. In addition, the material may be useful in lecture courses in signal and image processing, or indeed, it could be useful to introduce our colleagues in signal processing to the application of Kalman filtering in the design of $\ell_2$ and $\ell_1$ trend filtering.

This is a preprint of an article published in the Science of Computer Programming. The final peer-reviewed publication is available online at: https://doi.org/10.1016/j.scico.2023.102999 Code smells are structures in code that indicate the presence of maintainability issues. A significant problem with code smells is their ambiguity. They are challenging to define, and software engineers have a different understanding of what a code smell is and which code suffers from code smells. A solution to this problem could be an AI digital assistant that understands code smells and can detect (and perhaps resolve) them. However, it is challenging to develop such an assistant as there are few usable datasets of code smells on which to train and evaluate it. Furthermore, the existing datasets suffer from issues that mostly arise from an unsystematic approach used for their construction. Through this work, we address this issue by developing a procedure for the systematic manual annotation of code smells. We use this procedure to build a dataset of code smells. During this process, we refine the procedure and identify recommendations and pitfalls for its use. The primary contribution is the proposed annotation model and procedure and the annotators’ experience report. The dataset and supporting tool are secondary contributions of our study. Notably, our dataset includes open-source projects written in the C# programming language, while almost all manually annotated datasets contain projects written in Java.

Providing connectivity to around half of the world population living in rural or underprivileged areas is a tremendous challenge, but also a unique opportunity. Access to the internet would provide the population living in these areas a possibility to progress on the educational, health, environment, and business levels. In this paper, a survey of technologies for providing connectivity to rural areas, and that can help address this challenge, is provided. Although access/fronthaul and backhaul techniques are discussed in the paper, it is noted that the major limitation for providing connectivity to rural and underprivileged areas is the cost of backhaul deployment. In addition, energy requirements and cost efficiency of the studied technologies are analyzed. In fact, the challenges faced for deploying an electricity network, as a prerequisite for deploying communication networks, are huge in these areas and they are granted an important share of the discussions in this paper. Furthermore, typical application scenarios in rural areas are discussed, and several country-specific use cases are surveyed. The main initiatives by key international players aiming to provide rural connectivity are also described. Moreover, directions for future evolution of rural connectivity are outlined in the paper. Although there is no single solution that can solve all rural connectivity problems, building gradually on the current achievements in order to reach ubiquitous connectivity, while taking into account the particularities of each region and tailoring the solution accordingly, seems to be the most suitable path to follow.

This is a three part series paper, where the novel idea of representing flyback transformer losses as loss functions has been presented with scientific proof.Thirteen calculations for each transformer have been tabulated and used to derive mathematical loss function using hyperbolic secant spline interpolation which is subjected to Lyapunov method to predict core saturation and instability in the control loop. For scientific validity, 72 working transformers at thirty-six wattage’s which are being internationally used, have been examined to check, whether such proposition is valid or not.The definiteness of matrix,singular value decomposition,Lyapunov exponents are also tabulated. Then each of these transformers are simulated with non-linearies of Li-ion battery namely state of charge (SoC), self-discharge resistance, temperature-dependent leakage resistance,five time-constant dynamics, deterioration of battery performance over repeated charge and discharge cycles. The coefficients in the Jiles-Atherton model have been altered with fractional perturbations to obtain hysteresis curves.Various interesting comparisons in Bm,Bp,AC flux densities, effect on varying the coefficients in JA, residual errors, Lyapunov exponents, switching frequency vs MOSFET and transformer losses, reflected voltages, are tabulated and plotted.

Using intelligent reflecting surfaces (IRSs) to improve the coverage and the data rate of future wireless networks is a viable option. These surfaces are constituted of a significant number of passive and nearly passive components that interact with incident signals in a smart way, such as by reflecting them, to increase the wireless system’s performance as a result of which the notion of a smart radio environment comes to fruition. In this survey we supply a study review of IRS-assisted wireless communication starting with the principles of IRS which include the hardware architecture, the control mechanisms, and the discussions of previously held views about the channel model and pathloss, then the performance analysis considering different performance parameters, analytical approaches and metrics are presented to describe the IRS-assisted wireless network performance improvements. Despite its enormous promise, IRS confronts new hurdles in integrating into wireless networks efficiently due to its passive nature. Consequently, the channel estimation for, both full and nearly passive IRS and the IRS deployments are compared under various wireless communication models and for single and multi-users. Lastly, we propose the challenges and potential future study areas for the IRS aided wireless communication systems.

“This paper is a postprint of a paper submitted to and accepted for publication in IET Generation, Transmission & Distibution (GTD) and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.” Abstract: Grounding electrodes have an important role in electric power transmission and distribution systems. They are used to prevent excessive hazardous voltages between metallic structures and ground in the case of system faults or lightning surges. It is important that they provide a low impedance path for the current in to the ground. The electrical properties of soil, which vary substantially with geographical location and time of year, affect the process considerably along with the properties of the grounding electrode itself, such as its dimensions. In order to have an accurate estimation of the developed overvoltages and the backflashover rate of the transmission lines due to a lightning strike, one has to take into account the effect of the value of the soil electrical parameters, such as the electrical conductivity and dielectric constant. This paper investigates the high frequency behavior of the grounding electrodes by solving a full-wave electromagnetic problem using the Finite Element Method (FEM). The focus is on taking into account the effect of the variation of soil relative permittivity which has been neglected in the previous studies of the grounding systems. This allows an evaluation of the response of grounding systems due to seasonal changes and specifically change of the water content of the soil, which would cause its electrical properties to vary significantly. This study demonstrates the importance of considering the variation of relative permittivity of the soil especially in the modeling of electrodes buried in highly resistive soil.

Job shops are an important production environment for low-volume high-variety manufacturing. Its scheduling has recently been formulated as an Integer Linear Programming (ILP) problem to take advantages of popular Mixed-Integer Linear Programming (MILP) methods, e.g., branch-and-cut. When considering a large number of parts, MILP methods may experience difficulties. To address this, a critical but much overlooked issue is formulation tightening. The idea is that if problem constraints can be transformed to directly delineate the problem convex hull in the data preprocessing stage, then a solution can be obtained by using linear programming methods without much difficulty. The tightening process, however, is fundamentally challenging because of the existence of integer variables. In this paper, an innovative and systematic approach is established for the first time to tighten the formulations of individual parts, each with multiple operations, in the data preprocessing stage. It is a major advancement of our previous work on problems with binary and continuous variables to integer variables. The idea is to first link integer variables to binary variables by innovatively combining constraints so that the integer variables are uniquely determined by the binary variables. With binary and continuous variables only, it is proved that the vertices of the convex hull can be obtained based on vertices of the linear problem after relaxing binary requirements. These vertices are then converted to tight constraints for general use. This approach significantly improves our previous results on tightening individual operations. Numerical results demonstrate significant benefits on solution quality and computational efficiency. This approach also applies to other ILP problems with similar characteristics and fundamentally changes the way how such problems are formulated and solved.

Postprint accepted on 21 May 2020 for publication in IEEE 92nd Vehicular Technology Conference. (c)2020 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.

Accepted for full oral presentation at the 16th Conference on Artificial General Intelligence, taking place in Stockholm, 2023. We integrate foundational theories of meaning with a mathematical formalism of artificial general intelligence (AGI) to offer a comprehensive mechanistic explanation of meaning, communication, and symbol emergence. This synthesis holds significance for both AGI and broader debates concerning the nature of language, as it unifies pragmatics, logical truth conditional semantics, Peircean semiotics, and a computable model of enactive cognition, addressing phenomena that have traditionally evaded mechanistic explanation. By examining the conditions under which a machine can generate meaningful utterances or comprehend human meaning, we establish that the current generation of language models do not possess the same understanding of meaning as humans nor intend any meaning that we might attribute to their responses. To address this, we propose simulating human feelings and optimising models to construct weak representations. Our findings shed light on the relationship between meaning and intelligence, and how we can build machines that comprehend and intend meaning.

In this paper, we propose quantum circuits to enable dynamic assertions for classical values, entanglement, and superposition. This enables a dynamic debugging primitive, driven by a programmer’s understanding of the correct behavior of the quantum program. We show that besides generating assertion errors, the assertion logic may also force the qubits under test to be into the desired state. Besides debugging, our proposed assertion logic can also be used in noisy intermediate scale quantum (NISQ) systems to filter out erroneous results, as demonstrated on a 20-qubit IBM Q quantum computer. Our proposed assertion circuits have been implemented as functions in the open-source Qiskit tool.

Foldable robotics is accepted as one of the leading technologies in the soft robotics field. Integrating the sensing components, including hinge angle proprioception, into the robot with a single fabrication method is a part of the field’s ultimate goal. Here we present a cheap single-step method for angle sensing integration into the hinges, with an accurate and reproducible performance. We use silver nanoparticle inkjet printing on the flexible structural layer (PET) of the foldable robot (i.e. Delta robot), using an office-type printer. Silver printed sensors were studied for slight bending applications; however, we report their behavior under a 1 mm minimum radius of curvature, an advanced range both for silver strain sensors and any printed hinge position sensors. Among the three patterns studied, one gave a mean absolute dynamic hysteresis error below 1 degree. Reproducibility of a printed angle sensor behavior is reported for the first time, with three prototypes of each pattern (2degree standard deviation). Printed sensor feedback is tested with proportional control for the first time, via set-point and tracking tasks. On-off control law is also implemented and errors below 1 degree are achieved. Proportional control performances are compared with encoder feedback control and the difference between the realized trajectories are found to be under 1 mm in the task plane.

A document by Muhammad Asad Riaz . Click on the document to view its contents.

Next generation wireless networks are expected to provide much higher data throughput and reliable connections for a far larger number of wireless service subscribers and machine-type nodes, which result in increasingly stringent requirements of spectral efficiency (SE) and energy efficiency (EE). Orthogonal frequency-division multiplexing with index modulation (OFDM-IM) stands out as a promising solution to satisfy the SE requirement with a reasonable increase in system complexity. However, the EE of OFDM-IM is still required to be enhanced. Moreover, diversity gain is hardly harvested in OFDM-IM systems, which hinders further reliability enhancement. In this regard, relay assisted OFDM-IM, as a promising joint paradigm to achieve both high SE and EE, was proposed and has been studied since last year. The objectives of this article are to summarize the recent achievements of this joint paradigm, articulate its pros and cons, and reveal the corresponding challenges and future work.